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Field and modelling studies of the effects of

herbage allowance and maize grain feeding

on animal performance

in beef cattle finishing systems

A thesis in partial fulf i l lment

of the requ irements for the degree of

Doctor of Philosophy

I n Ani mal Science

At Massey University, Palmerston North,

New Zealand

Claudio Fabian Machado

2004

ABSTRACT

Machado, C.F. 2004. Field and modell ing studies of the effects of herbage allowance and

maize grain feeding on animal performance in beef cattle f in ishing systems. PhD Thesis, Massey University, Palmerston North, New Zealand.

iii

The objetive of the work described i n th is thesis was to develop a mathematical

model designed as a tool for research intended to improve the efficiency of

f in ish ing systems for 1-2 year old beef cattle u nder intensive grazing mangement

on sown pastures i n Argent ina. The work involved a) three experiments in

Argentina carried out to define the effects of herbage al lowance and maize grain

supplementat ion on herbage i ntake and animal performance, b) one experiment in

Argent ina fol lowing a prel iminary study in New Zealand of seasonal variat ion i n the

com posit ion and nutritive valu e of intensively managed beef pastures, and c) an

exercise to develop a model of beef cattle production i ncorporat ing modu les

deal ing with aspects of pasture production and uti isat ion , herbage intake and

anim al performance. The resu lts from the series of short-term grazing studies

showed consistency in the comparison of the effects of increas ing herbage

al lowance and supplementatio n on herbage intake and an imal LWG (Chapter 4) . A

method combin ing the use of n -alkane and 13C method proved to be accurate for

quantitative estimates of herbage and maize grain i ntake, and al lowed estimates of

a substantial variation in individual maize grain intake (between 31 to 4 1 % CV)

when an imals are supplemented i n groups. The substitution rate (SR) measured in

these studies varied l ittle across experiments or level of gra in at a herbage DM

al lowance of 2.5 % LW d-1 (0 .36 and 0.38 kg herbage DM per kg grain DM for

Chapters 3 and 4 respectively). Increasi ng level of herbage DM allowance

increased quadratical ly the SR from 0 .38 to 0.83 and 0 .87 kg herbage DM per kg

grain DM . The n-alkane method was effective i n providing estimates of diet

digestib i l ity. D ifferent methods for estimating diet composition , such as m icro­

h istological evaluation of faeces, differences i n nutrient and component selection

indexes and n-alkanes were u sed in the in itial grazing trial ( Chapter 2) but they

were not considered to be rel iable and they were too laborious for continued use

under field conditions.

The outcome of the studies on seasonal variation i n herbage qual ity in it ial ly was

iv

usefu l in establ ish ing a database of the range of values observed , and in

demonstrating their relative robustness, at least u nder conditions of good pasture

management. In these studies, herbage nutritive value did not seem to be a l im it ing

factor for growing beef cattle, at least in terms of the min imum observed content of

metabo l isable energy ( 1 0.8 MJ ME kg OM) or crude protein (17.3 % OM) .

Add itionally, sig nificant re lationships were establ ished between morpholog ical and

maturity estimates and herbage nutrit ional variables in a pasture under grazing

condit ions. These relationships showed promise for future use i n the pred iction of

herbage nutritive value, but require further work.

The model developed ("8eefSim") , represents the main biological dynam ic

processes of the target system of this thesis, together with additional management

decision and financial estimates. I t was shown that the model presents adequate

flexibi l ity and can be interrogated in terms of its response to different management

conditions, scenarios and ti meframes. Pasture management and grai n feeding

were co ntrolled in an i nteractive management module responding to deviations in

pasture conditions and animal l iveweight from pre-determi ned targets . Two key

outcomes of the mode l , l iveweight gain and herbage intake were accurately

predicted when compared against experimental i n formation under different levels

of herbage allowance and maize feeding. System com parisons developed with the

model showed agreement with the l iterature , and maize grain feeding associated

with the monitori ng procedure demonstrated an effective use of grain in the

system . The model provides a good biological basis for a hol istic appraisal of the

effects of "process technologies" such as grain feeding in beef cattle fi n ish ing

systems, and wil l be developed further.

Key-words: herbage al lowance; maize grai n feeding ; beef catt le ; rotational

grazing; herbage qual ity; model l ing

v

Dedicated to Maria (my wife), Jova and Manuel (my parents)

vii

PREFACE

This thesis was developed as part of the research activities of the author at the

Facu ltad de Ciencias Veterinarias , Universidad Nacional del Centre de la Provincia

de Buenos Ai res, FCV-UNCPBA-Argentina. The t hesis was developed u nder a

"sandwich system", where research preparation and the final reporting, i .e . the fi rst

and the last part of the PhD activit ies, was developed at Massey Un iversity , and

most of research was carried out in the candidate's home country (Argentina) .

Extra arrangements s uch as funding assistantship, organizat ion of fami ly i ssues

and maintainance of other work responsibi l it ies , were overcome and the way that

the research was structured made it possible to focus in an Argentinean pastoral

research problem , with the formal supervis ion from New Zealand expertise i n the

area.

The topic was identified from author's interaction with a group of beef cattle farmers

and agricultural consultants . An early prior study was developed on a com mercial

farm to i nvestigate tactical alternatives of maize g rain supplementation in beef

cattle f in ish ing systems during summer1 . Beyond its stimu lat ing resu lts and

cooperative experience, the most chal lenging and frustrat ing th ing was the

l im itations for extending the resu lts in terms of the whole system perspective. What

would happen if supplements were used earl ier in the production cycle, or in

d ifferent su mmer conditions, with different stocking rates, or combin ing d ifferent

animal types . . . . ? With some of these "what- if" questions in m ind and with the

support of New Zealand expertise, this thesis was planned to address to a l im ited

extent of these identified issues. An u nderstanding of changes in herbage intake

and animal performance when maize grain was fed and herbage quality were

identified early in the programme and incorporated into model l ing as tool for

information synthesis and addit ional i nsights.

The experimental part of th is thesis was mostly developed on farms, and that

shaped the selection of research tech niques applied to the plant/an i mal interface.

The author started as a novice in model l ing , and further motivation for model l ing

train i ng came from h is teaching experience . The author firmly believes that the

1 Machado et al. (2001), translated and presented in Annex A for explanation, but not as a part of the thesis.

viii

efforts i n the s imu lator development such as presented i n th is thesis , with adequate

adaptation and improvement, should contribute to "experiential learning" and

"systems th inking" development for agricu ltural students and professionals.

ix

ACKNOWLEDGEMENTS

Fi rst , I would l ike to show my gratitude to my chief supervisor Associative Professor

Stephen Morris . Steve guided me with patience, a constructive crit ical eye and

perseverance, never accepting less than my best efforts. Steve generously

arranged extra fu nding to cover any constraints to advance the project. The speed

that he retu rned my drafts was highly valued. Thanks you very much, Steve.

I wish to acknowledge to my co-supervisors, Prof. John Hodgson and Prof. Nestor

Auza. I was part icularly fortunate to have as an advisor to P rofessor Hodgson , who

is now "reti red" and could therefore provide a high level of dedication to the

completion of th is thesis . H is explanat ions of the basics of g razing made th is thesis

easier. Prof. Nestor Auza has provided valuable support for the completion of the

thesis, the gu idance of Nestor came continuously s ince my student days. Thank

you very much John and Nestor for you r support.

I also acknowledge the support of the fund ing support of my own univers ity and the

New Zealand M i n istry of Fore ign Affairs and Trade for provid ing the economic help

for l iving expenses and travel during this study. The funding provided by AN PCyT

(National Agency of Science and Technology) for some of the experimental costs ,

P ICT 080977 1 , was highly appreciated .

I n th is venture , I have had the immense good fortune of receiving ski lful and

enthusiastic help for the development of grazing experiments in places and

condit ions where i n it ially control f ield trials seemed u nfeasible. The help of

Fernando DiCroce , Facundo Gonzalez , Jose Oviedo and Daniel D iCroce at the

beg inni ng , and Horacio Berger and Mariano Copes at the end of the program me

are h igh ly recogn ised.

The challenge of model development could be much more difficult without the

valuable help received from Ryan Sheriff who helped me solve different

programming and mathematical issues. Gaston Mart in i , Horacio Berger and

Eduardo Ponssa in Argentina also helped in th is task. All the contributions are

h igh ly appreciated .

x

I want to show my g ratitude to Drs. M ike Wade, Monica Agnusdei G raciela

Canzian i , Fernando Mi lano, Mariana Recabarren and Maria Bakker, who helped

me in the completion of th is thesis .

To Drs . P . More l , D. McCal l , S. Woodward , L . Barioni and M . Upsdel l (developer of

Flex i , used in some statistical procedures) for providing me knowledgeable advise.

Thanks for your help.

The "sandwich" system has g iven me the opportu nity to meet fr iends twice i n New

Zealand. From the Argentinean group, I want to record my appreciat ion to the Russ

fam i ly ( Emy, Alana, Marcela and Tato) and Alvaro , Ceci l ia y Poppi for so many

enjoyable moments and friendsh ip . Also Alvaro provided me with h igh ly

appreciated techn ical advise. I would l ike to thank Serg io Garcia (Yani ) now in

Austral ia, a f ine scientist, for his useful ins ights, many interesting conversations ,

but always to Yani the fr iend, for the permanent encouragement and enthusiasm in

the complet ion of th is thesis. Valeria, Juan Cruz and Ol ivia were part of the good

company we have had in our f irst stay at Massey, the same l ike Cesar Pol i and

Family. I had the privi lege to share an off ice, the gym, some barbecues and good

talk with Johne Alawneh . My appreciations for the support and fr iendship from h im

and h is fami ly , Leena and the smal l Sarah. Recently, we incorporated two new

roommates , Tuluta Fis ihoi and Gonzalo Tunon. Thanks for al l the help and good

talks. I want to show my gratitude to al l that made our stay enjoyable, El isa and

Matias , Natalia y Alexis , Rene, Hector and respectives fami l ies and also the new

group of student and famil ies arrived th is year.

To Dorothy, Col in Holmes and Ruth Hodgson, their k indness and friendsh ip is

h ighly recogn ised.

To all the Staff of the Science and Technology office of my U niversity , the support

and and fr iendship dur ing last years i s deeply acknowledged .

To my friends from Tandi l , Guly , Luciano, Rodolfo , Roberto , Alejandro y Nestor, al l

people from ATA (Ath letism association of Tandi l ) , and those l iv ing in Pr ingles,

xi

Carlos Bernal , Daniel Amores and respective fami l ies.

A Jova y Manuel (mis padres) , por el amor recibido y ejemplo constante y

si lencioso hacia el esfuerzo y a la superaci6n . A m i hermana M6nica y sobrinos

( Manuela, Martrn y Mariano) , a toda mi fami l ia di recta y polltica, con el carino y

aliento recibido en este tiempo fuera del pars.

I would l ike to give particular thanks to Maria ( my wife) , for support ing me

throughout the l ife we have been sharing and for seeing so clearly i nto the areas of

l ife where I am bl ind. Our chi ldren Cami la y Ju l ian were born during this study, and

also she managed to complete a postgraduate diploma i n the same period. My

appreciations to Camila y Ju l ian for remember me everyday that over any

d ifficu lties , the world is ful l of joy and hope, all is about perspectives .

Contents

Abstract

Preface

Acknowledgements

Introduction

Part I: Grazing experiments

Chapter 1 A review of the effects of energy supplementation and sward characteristics on animal performance in pastoral beef cattle fin ish ing systems

Chapter 2

Chapter 3

Chapter 4

Herbage intake, d iet composition , grazing behaviour and performance of Angus heifers on two herbage al lowances during late spring

Effect of maize grain and herbage al lowance on i ntake, animal performance and g razing behaviour of heifers in winter

Effects of herbage allowance and maize grain on herbage and g rain i ntake, and performance of Angus steers i n late spring

Part 11: Herbage qual ity

Chapter 5 Seasonal variation in the quality of an alfalfa-based pasture and its relationship with morphological and maturity estimates

Part I l l: Model l ing

Chapter 6

Chapter 7

Overview

Annexes

Annex A

Annex B

Modell ing studies of g rain feed ing i n a grazing-based Beef cattle fin ish ing operation:

1 . Development of a "beef cattle f inishing unit" simu lator

Modell ing studies of g rain feed ing in a grazing-based beef cattle f in ishing operation:

2. Model evaluation and system simulations

Maize supplementation and feedlot as an alternative for summer steer finishi ng systems

Seasonal changes of herbage qual ity with in a New Zealand beef cattle finishing pasture

xiii

i i i

vi i

IX

1 13 1 5

45

63

87

111 1 1 3

133 1 35

1 95

217 227 229

243

xiv

Index of figures

Chapter 1. Figure 1 . Relationship between herbage allowance and liveweight gain of 20

steers during Autumn / Winter and Spri ng / Summer periods (Reid 1 986),

Figure 2 . Relationship between level of maize g rain supplementation ( i n kg 24 d'1 ) and extra liveweight gain over the unsupplemented group ( in kg d'1 ) in grazing beef cattle,

Figure 3 . The effect of herbage mass and herbage allowance on herbage 28 intake (drawn from Wales et a l . 1 999),

C hapter 2. Figure 1 . Effect of herbage allowance on grazing behaviour of Angus heifers 53

throug hout the day,

C hapter 3. Figure 1 . I ndividual maize grain intakes as recorded within each treatment 73

replicate,

Figure 2 . Effect o f herbage allowance and maize supplementation on grazing 75 behaviour of heifers throughout the day,

C hapter 4.

Figure 1 . Relationship between herbage allowance (X) in kg d'1 and ind ividual liveweight gain (Y) in kg d' 1 ,

94

Figu re 2. Relationship between herbage allowance (X) in kg d'1 and individual herbage intake (Y) in kg d'1 .

96

Figure 3 . Relationship between herbage al lowance (X) in kg d'1 and individual substitution rate (Y) in kg herbage kg maize g rain' 1 ,

97

Figure 4 . Relationship between herbage allowance (X) in kg d'1 and 1 0 1 ind ividual l iveweight gain (Y) , condensing i nformation between Chapters 2, 3 and 4.

Figu re 5 . Relationship between herbage al lowance (X) in kg d'1 and individual herbage intake (Y) in kg d'1 , condensing information

1 02

between Chapters 2, 3 and 4 .

Figure 6 . Relationship between i ndividual herbage intake (X) i n kg d'1 and liveweight gain (Y) in kg d'1 , condensing information between

1 02

Chapters 3 and present study,

Chapter 5.

Figure 1 . Seasonal change in herbage metabolisable energy (a) and crude 1 20 protein (b) i n pre-g razing samples during the sampling period,

Chapter 6.

Figure 1 .

Figure 2 .

Figure 3 .

F igure 4 .

F igure 5 .

F igure 6 .

Figure 7 .

F igure 8 .

Figure 9 .

Figure 1 0 .

Figure 1 1 .

F igure 1 2.

Figure 1 3.

F igure 1 4.

F igure 1 5.

Cybernetic representation of the biophysical , decision and economic modu les in BeefSim.

Extend model l ing structure in the BeefSim model.

Structure of the pasture submodel used in the simu lator.

Response surface of relative intake (0-1 ) of a 1 00 kg LW animal to increasing levels of leaf herbage masses and leaf herbage allowances as predicted by Eqn.20. Leaf allowance is expressed here as % LW.

Response surface of relative intake (0- 1 ) of a 450 kg LW animal to i ncreasing levels of leaf herbage masses and leaf herbage allowances as predicted by Eqn.20.

A general outl ine of how different constraints to intake are estimated and integrated in the "beef catt le finishing unit" simu lator.

Diet composition at increasing level of leaf allowance (a) with a sward composition in fractions of 0 .41 , 0 .29 and 0.29 of leaf, stem and dead respectively (as estimated by Eqns. 26-28) .

Effect of leaf fraction in the herbage DM ( Eqn.26) and leaf al lowances (0< <1 , 1 =unrestricted) on the fraction of leaf in diet DM .

Simu lation o f Herbage mass composition a ) pre-grazing and b) post-grazing in a series of strips of herbage g razed daily.

a) Paddock currently under g razing and number of allocated strips and b) herbage mass and number of the paddock with the highest herbage mass in a regrazi ng sequence of 30 days with 1 2 paddocks.

Main view of the relationship between the decision and the biophysical module.

Extended dialog of the manager block.

Extended dialog of economics block.

Tracking of adjustments made to grain supplementation by the manager block in association with deviation between actual liveweight and targets liveweight ( Eqn . 50) .

Dialog of the evolutionary optimizer block (front tab).

xv

1 39

1 40

1 44

1 49

1 49

1 50

1 52

1 53

1 55

1 59

1 66

1 66

1 70

1 7 1

1 7 1

xvi

Chapter 7.

Figure 1 .

Figure 2.

Figure 3.

Figure 4 .

Figure 5 .

Figure 6 .

Figu re 7 .

Figure 8 .

Annex A.

Figure 1 .

Annex B.

Figure 1 .

Figure 2.

Figure 3.

Regression of observed l iveweight gain on predicted l iveweight gain from the model (Chapter 6) of beef bred animals g raz ing at different herbages allowances and maize supplementation levels (Chapter 3 and 4).

Regression of observed herbage i ntake on pred icted herbage intake from a model (Chapter 6) of beef bred animals g razing at different herbages allowances and maize supplementation levels (Chapter 3 and 4) .

Herbage allowance adjustment as a function of indicated pasture cover deviation from target.

Monthly target ani mal liveweights in the "baseline" system . Liveweights at the start o f the corresponding month.

Effect of stocking rate and use of strateg ic feedi ng of maize grain on the ratio between herbage eaten (ton ha-1 yr- 1 ) and Lherbage accumulation rate (ton ha-1 yr- 1 ) as and indicator of grazing efficiency in the s imu lated alternatives.

Effect of the stocking rate (X) on total l iveweight gain (Y) of Angus steers obtained from the model and from the literature.

Effect of stocking rate and use of strategic feed ing of maize grain on gross margin (GM) and LW production (Prod . ) in the s imu lated alternatives.

Accumulated probabil ity that animals exceed a given l iveweight on 1 December. Constructed from the simu lation of 1 00 production cycles using stochastic herbage accumu lation years (Table 3) .

Average animal l iveweights (±standard errors) of the treatments throughout the trial.

Seasonal change in herbage metabo l isable energy (a) and crude protein (b) during the sampli ng period (November 1 998-March 2002) .

Energy-based predicted liveweight gai ns of a 350-kg LW Friesian bull fed at different levels of herbage intake dur ing the year based on Freer et al. ( 1 997)

Total smoothing herbage metabolisable energy (a) and crude protein (b) during the sampling period (November 1 998-March 2002) .

1 98

1 99

203

205

207

208

2 1 0

2 1 2

237

249

250

2 5 1

Chapter 1. Table 1 .

Table 2.

Chapter 2. Table 1 .

Table 2.

Table 3 .

Table 4.

Chapter 3. Table 1 .

Table 2 .

Table 3.

Chapter 4.

Table 1 .

Table 2 .

Table 3.

Chapter 5.

Table 1 .

Table 2.

Index of tables

Effect of level of maize grain supplementation under grazing conditions on liveweight gain of finishing beef catt le.

Effect of increasing leaf al lowance on herbage intake.

Chemical composition means, concentration of dominant n-alkanes and herbage mass in a pre-grazed pasture at two herbage allowances. Effect of herbage allowance on final liveweight, fasted l iveweight loss, liveweight gain, herbage intake and in vivo d iet digestib i l ity of Angus heifers.

Effect of herbage allowance on the indicated grazing behaviour characteristics of Angus heifers.

Comparison of means of d ifferent pairs of component selection indexes (dead vs. green and clover vs . grass) pooled between treatments.

Chemical composition, concentration of dominant n-alkane and concentration of 1 3C in the uti l ised feeds.

Effects of two herbage al lowances and three supplement levels in heifers on different parameters associated with animal performance.

Effect of herbage allowance (LHA and M HA) and maize supplementation (LHA-M1 and LHA-M2) on grazing behaviour of heifers.

Chemical composition, concentration of dominant n-alkanes and concentration of 1 3C in the forages util ised.

Fi nal liveweight, liveweight gain and fasted l iveweight loss of Angus steers fed at three herbage allowances and two maize g rain levels during late spring .

Sward and an imal parameters of Angus steers fed at three herbage allowances and two maize grain levels during late spring .

Means of rainfal l , daily temperature and hydric balance as recorded over last 1 8 years and during the trial.

Seasonal variation of nutritional variables in an alfalfa-based pasture.

xvii

22

29

52

52

54

54

70

71

74

93

93

95

1 1 9

1 1 9

xviii

Table 3 .

Table 4.

Table 5.

Table 6 .

Chapter 6.

Table 1 .

Table 2 .

Table 3 .

Chapter 7. Table 1 .

Table 2.

Table 3.

Table 4.

Table 5.

Table 6 .

Annex A.

Table 1 .

Table 2 .

Table 3 .

Seasonal variation in contents o f metabolisable energy and crude protein in different sward components .

Seasonal variation of morphological and maturity variables in an alfalfa-based pasture.

Regression equations of herbage quality variables and morphological and maturity variables in an alfalfa-based pasture.

Summary of canonical correlation analysis of the whole sample set of herbage nutritional variables and morphological variables and two options of expressi ng maturity variables in an alfalfa-based pasture.

Alphabetical order of model definitions .

Constants used in the pasture sub-model .

Model constants.

Initial parameters from Chapter 3 and 4 considered for the simu lations.

Regress ion analysis of observed (Y) values (Chapter 3 and 4) and predicted (X) values for liveweight gain and herbage intake using the model developed in Chapter 6.

Mean and standard deviations of monthly herbage accumulation rate (kg ha-1 d-1 ) for an alfalfa-grasses pasture in the southeast of Buenos Aires province (Tres arroyos, n=6) , and pasture cover target assumed in the s imu lations.

Resu lt of setting the model as the referenced beef cattle f inishing farmlet for development of a "baseline" system.

Effect of stocking rate and use of strateg ic feeding of maize grain in a simulated farmlet (10 ha) with Angus steers starting on the 1 March with 1 75 kg LW.

Assigned values for the economic parameters of the model.

The percentage of d ry matter digestibi l ity, crude protein , neutral detergent fiber and acid detergent fiber of the dietary components.

Dry matter digestibi lity, neutral detergent fiber and crude protein of the herbage during the trial.

Total l iveweight gain and shrunk l iveweight losses of the different treatments.

1 20

1 2 1

1 22

1 23

1 72

1 76

1 77

1 98

1 99

201

204

207

2 1 0

233

235

236

Annex B.

Table 1 .

Table 2.

Overall correlation analysis of the indicated parameters in pre­g razing herbage samples (n=74).

Annual mean and standard deviation of the overall dataset (n=74) and seasonal variation of means of the different herbage quality parameters in pre-g razing herbage samples.

xix

248

249

Introduction

2 c. F. Machado

1. Context of the study

Grazing-based f in ishing systems for beef cattle are traditional on Argentinean

farms . The feed resources u sed are very h eterogeneous, covering natu ral

grasslands, tropical and temperate perennial pastures and forage crops , with

mult iple combinations of animal breeds and duration of the finishing cycle ( Rearte

1 998). G rain supplementation is frequently used in beef systems (Santin i &

El izalde 1 993), with a fluctuating level of use depending on the ratio between g rain

and meat price at the t ime. The use of ferti l iser on pasture is common on ly at

seeding time (Rearte 1 998). These conditions have led to a significant research

effort on a nu mber of issues, on the effects of grain supplementation on rumen

metabolism and digestion sites (Elizalde et al . 1 994 ; El izalde et al . 1 996), dietary

nitrogen use ( Recabarren et al . 2002), animal performance (Giraudo et a l . 1 984;

Latimori et a l . 2000) and meat qual ity (Garcia & Casal 1 993; Garcia et al . 1 999).

Recently , economic changes in Argentina including increased fixed expenses have

led to intensification as a way of increasing productivity but decreasing expenses

per u nit of product ( Rearte 1 998). High turnover of cattle improves profitabil ity , and

predictability of performance, t imel iness of supply all of which are increasingly

important ( Rearte 1 999), because of the need to f ree land for cropping and to avoid

competition with new weaners entering the system in the autum n or delay the

finishing of older animals during summer (Arosteg uy 1 984).

In this more intensive context, util isation of g rain and maize silage has increased,

including the use of simple feedlots linked to pastoral systems ( El izalde 1 994).

However, instead of adding value to the ope ration , use of supplements can

sometimes become a weakness to pastoral system s because of under-util isation of

grass, usually the cheapest feed resource.

2. Scope of the study

The scope and the approach of this thesis are focused on beef catt le fin ish ing

farmers in the southeast of Buenos Aires province. I n these beef cattle systems,

weaner animals are fin ished i n less than 1 2 months at 1 5-20 months of age.

Animal performance duri ng summer is extremely important to avoid overlap of feed

Introduction 3

requirements of f inish ing animals with that of weaners entering the system in the

autum n . One of ·the problems identified i n the system is inadequate response to

strategic g rain supplementation during a dry summer (grass shortage and decline

of herbage quality).

P rel iminary studies highl ighted the need for more detai led information on the

interaction between concentrate use and sward condition , to improve the

predictabi l ity of grain feeding responses (Machado et al . 200 1 , p resented in Annex

A of this thesis). Additional ly , discussion with farmers and consultants raised

awareness that an adequate "system" quantification of the technique is not a

straig htforward process. The animal response to g rain feeding and ratio between

g rain and meat price at the time are major factors influencing choice of

supplementary feeding strategy. This represents a correct (although h igh ly

conservative) evaluation , and both di rect and indirect effects of the u se of

supplements at different time scales u nder grazing conditions need to be

accou nted for (Holmes & Matthews 200 1 ). G rain feeding is likely to have important

benefits fu rther than simply feeding animals adequately: it may increase system

flexibility, al lowing i ntensive use of pastures in spring by maintaining a h igh

stocking rate during winter ( Rearte & Pieroni 200 1 ), or increase the reliabil ity of

f inish ing animals during summer (Machado et al . 200 1 ).

The scope of this thesis includes research on som e basic biological factors , and

evaluation of the outcomes within a systems context. There is agreement that

when studying the relationships between sward characteristics, supplement use

and animal performance, the plant-animal interface is the key process to

u nderstanding the behaviour of grazing systems ( Forbes 1 988; Hodgson 1 990 ;

"I i us & Hodgson 1 996). I n Argentina, i nvestigation of the plant-animal interface has

been based on whole year studies (Kloster et al . 2000 ; Kloster et al . 2003) and

h as rel ied on prediction of herbage intake based on relatively sim ple sward

measurements ( Frame 1 98 1 ) or more short-term detailed behaviou ral studies

(Cangiano & Gomez 1 985 ; Cangiano et a l . 2002; Utsumi et a l . 2002). However,

the relationship between herbage on offer, herbage intake and animal performance

h as not been extensively studied . The logic of herbage al lowance-intake responses

h as proved to be important in the management of g razing systems in different parts

4 c. F. Machado

of the world (Baker et al . 1 98 1 ; Nicol & Nicol l 1 987; Oougherty et al . 1 989 ;

Redmon et al . 1 995 ; Wales et al . 1 999). Hence, the primary objective of th is study

was to provide basic i nformation on herbage al lowance-intake funct ions when

supplementary maize g ra in is fed, and the effect on animal performance i n beef

cattle fin ish ing systems.

Methodological difficu lties for accurate and rel iable measurement of phenomena

occurring at the plant-an i mal i nterface are well recogn ised (Oumont & lason 2000).

The use of n-alkanes for i ndividual i ntake measurements (Mayes et al . 1 986) has

been successful in beef cattle grazing systems around the world ( Real i n i et a l .

1 999; French et al . 200 1 ; Moshtaghi N ia & Wittenenberg 2002), but there was no

experience with this method in Argent ina when this thesis was designed.

Nevertheless , the n -alkane method presents some difficu lty when animals are

supplemented as a group , as grains typical ly have a low level of n-alkanes. An

iterative alkane& 1 3C tech nique (Garcia et al . 2000), has been shown to estim ate

accurately the level of OM intake of h erbage and maize s i lage in graz ing dairy

cows supplemented as a g roup, without the need of addi ng other external m arkers.

Maize feeding in different forms (cracked dry g rain , wet cracked grain or chopped

whole plant s i lage) is frequently used in Argentinean beef cattle production ( Rearte

1 999), and a goal of the present thesis was to test th is methodology in such

systems.

Herbage qual ity is another important feature of the plant-animal i nterface and

therefore animal performance (Pearson 1 997). Herbage qual ity changes

seasonal ly and dynamical ly when physiological changes take place in the plants,

and grazing or harvesti ng conditions interact with those changes ( Nelson & Moser

1 994; Saul et al . 1 999). Whereas in stal l-fed animal studies, the i nteraction

between forage quality , level of i ntake and g rain feeding can be studied d i rectly

(Matejovsky & Sanson 1 995), its investigat ion u nder g razing condit ions (as the

control led variable) clearly presents d i fficult ies. Some studies are oriented to

describe seasonal variat ion of herbage q uality under a particu lar management (e .g .

Litherland et al . (2002)) or to describe the changes in herbage qual ity i n g razing

experiments, where other variables are controlled (Stockdale 1 999). In the case of

Argent ina, the huge range of pastu res and their variable managements m akes it

Introduction 5

d ifficu lt to choose which pastures to study . Furthermore , in order to obtain

information on the seasonal change i n herbage qual ity i n a beef cattle pasture

managed i ntensively i ncluding the potential u nderlying relationsh ips between

nutrit ional variables and morphological-maturity variables were also examined.

Therefore, a second objective was to study the seasonal change of herbage qual ity

i n i ntensive beef catt le f in ish ing systems.

In order to have some contro l , most of the analytical i nformat ion about g razing

systems is obtained at scales of space and t ime that are smal ler than the scales at

which it i s desired to apply the knowledge ( Demment et al . 1 995) . Furthermore , to

research variables that have important whole system impl icat ions (such as grain

feed ing or herbage qual ity change) , is not a straightforward process , and the

selection of methods to be used in the studies plays a key role in the outcomes

(McCall et al. 1 994) .

I n the development o f i ntensive grazed-based beef cattle production i n Argent ina,

attention has concentrated on the monitoring of farm lets i ncluding level of

fert i l isat ion and supplementation (Garcia et a l . 1 998) , cattle genotypes (Mezzadra

et a l . 1 992; Romera et al . 1 998 ; Latimori et al . 2000) , and strategies of g razing

m anagement (Kloster et al . 2000; Kloster et al . 2003) . However , th is approach is

costly and t ime restricted , which constra ins the number of alternative systems that

can be tested (Garcia 2000) . An alternative for a system approach is the use of

model l i ng as a too l for i nformation synthesis and system understanding . There is

wide agreement about the advantages of model l ing studies for research in the

bio logy of graz ing systems (Wright & Dent 1 969; McCall et al . 1 994 ; Herrero et al.

1998) . However, the lack of adequate local i nformation about the plant-an imal

i nterface constrains the effectiveness of th is approach (Dove 1 996) . This was the

main reason to combine field experi mentatio n and model l ing in t h is thesis , as the

overall benefits of associating f ield research with model l ing have been wel l

demonstrated (McKin ion 1 980 ; Walker et a l . 1 989 ; Garcia 2000 ) .

3. Objectives

The major objectives i n th is thesis are therefore as fol lows :

6 c. F. Machado

To provide information on herbage allowance-intake functions and the effects on

animal performance when maize grain is supplemented, in beef cattle finishing

systems.

To investigate the seasonal change of herbage quality in a beef cattle finishing

pasture

To develop a "finishing beef cattle unit" simulator driven by stocking rate and

herbage allowance, as a tool for information synthesis and research on the

biological and economic implications of grazing management and grain feeding in

intensive beef cattle systems.

5. Thesis Structure

Part I (G razing experimentat ion) contains four chapters . I n Chapter 1 , publ ished

in format ion is reviewed about the effects of sward characteristics and grain

supplementat ion on beef cattle fin ish ing systems. I n Chapter 2 , the effect of

herbage al lowance on herbage intake and an imal performance is studied in a pi lot

tria l , o riented to explore and to adjust methodologies for studyi ng the plant-an imal

i nterface. In Chapter 3 and 4 , repl icated g razing tr ia ls including levels of maize

grain supplementation and measurement of herbage and g rain i ntake, animal

perfo rmance and behaviou r are described . Particularly , the funct ional response to

increasing level of maize grain fed is studied in Chapter 3 , and the functional

response to increasing herbage al lowance is investigated in Chapter 4 .

Part I I (Herbage qual ity) i ncludes one chapter. Chapter 5 reports on a 28-month

monitoring study of an Argent inean beef fin ish ing pasture , i ncluding a detai led

sward descript ion to identify potential descriptors of herbage quality. A prel im inary

study of q uality in a New Zealand beef production pastu re is also described in

Annex B .

Part I I I ( Model l ing studies) contains two chapters . Chapter 6 covers the

development of a "fi n ish ing beef cattle un it" dynamic model , where relevant

Introduction 7

knowledge about g razing-based beef cattle f in ish ing systems is synthesised, with

special emphasis on the sward characteristics assessed u nder field

experimentation . I n Chapter 7, the pred ictive value of the model is tested against

resu lts from field experiments in th is thesis and from the l iterature, and the used to

i nvestigate some management options i n an i ntensive beef cattle fin ish ing system.

An overview of the main f indings of the thesis , h ighl ight ing the i r relevance and

presentat ion of conclusions is presented at the end of the thesis .

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Introduction 9

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supplementat ion. Journal of Animal Science 73:2 1 56-2 1 63.

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Introduction 1 1

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12 C. F. Machado

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graz ing dairy cows affect mi lk responses obtained from concentrates

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in herbage mass, allowance, and level of s upplement on nutrient intake and

m i lk product ion of dairy cows in spring and summer. Australian Journal of

Experimental Agriculture 39: 1 1 9- 1 30.

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g razing systems. Australian Journal of Agricultural Economics 1 3: 1 44- 1 53 .

Part I

Grazing experintents

Chapter 1

A review of the effects of energy su pplementat ion

and sward characteristics on an i mal performance

i n pastoral beef catt le fi n is h i n g systems

1 6 Part I - Chapter 1

Abstract

Evidence of the effects of energy supplementation and sward characteristics on

an imal performance i n pastoral beef cattle f in ish i ng systems is reviewed . Emphasis

i s on the relationship between animal response, and changes occurring at the

p lant-an imal interface . Energy supplementat ion and sward characteristics h ave

di rect and indi rect effects on the animal and on the system, but most

experimentat ion is animal oriented which somet imes makes integrat ion difficult in a

systems context . Uneven descriptions of the sward o r its expression i n d ifferent

u nits emphasised the need for standard isation for comparative purposes. A more

i ntegrative appraisal of short-term field experiments with in a system approach is

requ i red, and the development of models as tools for synthesis of informat ion could

be usefu l .

Key-words: Review; herbage allowance ; grain ; supplementation ; beef; l iveweight

gain

1. Introduction

Recent signals in world markets emphasise the importance of healthy and safe

foods from sustainable production systems. In th is context , countr ies with

establ ished trad itions of pastoral beef production systems have been looking for

ways to improve system efficiency and sustainabil ity with environmental ly fr iendly

techno logies. Additional ly , the interest in pastu re-based beef cattle f in ish i ng

systems has i ncreased at the expense of pen-fed fin ish ing systems , in o rder to

al leviate many of the welfare, social and environmental problems associated with

feed lots (Gerrish et al. 1 996) .

Th is situation has led to renewed research interest in the combinat ion of herbage

and g ra in feeding under g razing condit ions. I t has been demonstrated that system

performance may be highly i nsens itive to contrasts i n g razing management , but the

desire to control grass supply on a dai ly basis has renewed interest in rotational

g razi ng systems ( Leaver 2000) , with the emphasis on herbage allowance,

i ncorporating more detai led descript ions of sward condit ions ( Dougherty et al .

A review of energy supplementation and sward conditions in grazing beef finishing systems 1 7

1 989; K im et al . 1 995 ; Okaj ima et a l . 1 996) .

The effects of grazing system and levels of herbage allowance on herbage i ntake ,

animal performance and herbage uti l i sation have been studied in beef cattle

f in ish ing systems in Argentina (Kloster et al . 2000) i ncluding maize feed ing

(Mart inez Ferrer et al . 2002) . However the field evidence for Argentinean

condit ions is l im ited and that constra ins the development of models ( Dove 1 996)

which could be useful when exploring the benefits of grain feedi ng under g razing

conditions.

Herbage is usual ly the lowest cost feed for beef product ion . Decis ions about

whether to feed g ra in to grazing animals depend on the expected returns

compared to the added feed expenses. Estimation of the latter are qu ite

straightforward , but the former depends on where and how the potential b iological

benefits of the supplementation are assessed ; as they can be both di rect and

indi rect effects , both on the ani mal and the system (Holmes & Matth ews 200 1 ;

Rearte & Pieroni 200 1 ) . Associated with the d iff iculty of an adequate quantification

of these indi rect benefits, it is not surpris ing that the most frequent economic basis

for decid ing grain feeding is the grain :beef price rat io at the moment (Aiken 2002).

Although sufficient in some cases, th is valuation constrains decisions about g rain

feeding more than would a hol istic appraisal of the technique, based on a better

u nderstanding of the biolog ical i mpl ications.

The objective of the research described i n th is thesis was to create sets of field

data relevant to these questions , and to use them to evaluate a model of a beef

cattle fi n ish ing system based on g razed pasture and grain supplementation . This

was used then as a basis for testing alternative strategies of pasture management

and supplement use. This Chapter provides a review of the avai lable l iteratu re on

these topics. However, each chapter of th is thesis contains a review of the

pert inent l iterature , therefore the objective of th is brief l iterature review is to

develop a framework of how the thesis was planned, deal ing with the effects of

herbage al lowance , sward conditions and g rain feeding on animal performance

and herbage intake and impl icat ions to the whole system. Attent ion is concentrated

on grazi ng beef cattle systems, but reference to dairy , sheep or i ndoor experiments

1 8 Part I - Chapter 1

is made when th is helps to i l l ustrate important issues, and focus where possible is

on South American data. For conven ience , the review deals fi rst with effects on

animal performance, then concentrates on nutrient i ntake from pasture and

supplement , and final ly covers some of the system implicat ions of grain

supplementation under grazing condit ions. The review is not exhaustive but is

desig ned to i l lustrate important principles .

2. Production responses

2. 1 . Effect of herbage allowance

Herbage al lowance has been used as the experimental variable in d ifferent

systems and countries. A two-year repl icated study was carried out in Argentina

where two herbage OM al lowances (2.8 and 3.8 % LW d-1 )2 and two intervals of

rotat ional grazing systems (2 and 7 days) were assigned us ing Brit ish bred steers

grazi ng an alfalfa-grass pasture (Kloster et al. 2000) . I ndividual LWG increased

s ign i ficantly with the level of herbage allowance from 0 .5 1 to 0.59 kg LW d-1 .

The inclusion of more than two levels of herbage al lowance provides a better

measure of the shape of the relat ionship between al lowance and animal

performance. Redmon et al . ( 1 995) grazed beef steers between 267 to 3 1 3 kg LW

on winter wheat, and allocated these steers to a range of herbage OM allowances

between 5 .5 to 64 % LW d-1 . The LWG presented a curv i l inear relash ionship to OM

herbage al lowance (HA) fitted as :

LWG=-0.059 + 0 . 1 2 HA -0 .003 HA2, R2=0 .59 and MSE=0.073.

Th is resulted in a LWG plateau of 1 .3 kg d-1 when HA was 23 % LW d-1 . S im i larly ,

i n a study where three herbage allowances (6 , 1 2 and 1 8 kg OM d-1 ) of a pastu re

domi nated by perennial ryegrass and th ree levels of supplements were allocated to

567±32 kg LW crossbred steers ( French et al . 200 1 b) , LWG i ncreased from 0 . 1 4 to

0.53 and 0 .75 kg d-1 at HA of 6 , 1 2 and 1 8 kg OM d-1 , respectively.

However, l i near effects of herbage al lowance on LWG have also been reported .

2 Herbage and grain on offer, animal performance, and intakes are al l expressed in this thesis as per head basis, unless otherwise stated. 3 Different forms of expressing accuracy and error on literature equations are presented literally in this review.

A review of energy supplementation and sward conditions in grazing beef finishing systems 19

When steer calves were grazed at 3 .0 , 4 .5 , 6 .0 or 7.5 kg pasture OM 1 00 kg LW-1

d-1 i n successive 28-d cycles of g razing d u ring summer (Marsh 1 977) , total LWG's

were 0.29, 0 .43, 0 .52 and 0 .63 kg LW d-1 for 3 .0 , 4 .5 , 6.0 or 7 .5 kg pastu re OM 1 00

kg LW-1 d-1 , respectively. This was explained l i nearly by herbage al lowance as :

LWG = 0.081 + 0 .074±0 .0076 HA, n=40 and r=0.9 1 .

Simi larly, when herbage OM al lowances of 3 .3 , 4.3 and 5 .3 kg 1 00 kg LW-1 were

imposed over th ree years, LWG's were s ignif icantly affected (P<0 .05) with overall

values of 0 .39 , 0 .52 and 0 .62 kg LWG d-1 ( Bartholomew et al. 1 98 1 ) . The lack of

any i ndicat ion of a decl i ning response at the highest herbage al lowance , is

i ndicative that the range of HA imposed were too narrow to encompass the point of

inflect ion (Marsh 1 977) , but it is worth noting that only Redmon et al. ( 1 995) tested

th is point exhaustively. Implicat ions of h igh HA to the system , are ment ioned later

in sect ion 4 .

There is evidence in the l i terature that the relat ionship between HA and LWG is

seasonally affected (Figure 1 ) , although i n th is comparison the effects of season

and animal age are confou nded (Reid 1 986) . When animals of a s im i lar l iveweight

and breed were used in two comparisons between spring and autumn (Marsh

1 975) , daily l iveweight gains in spring were h igher ( 1 .09 and 1 .37 kg d-1 ) than from

autumn condit ions (0 .98 and 0 .71 kg d-1 ) .

Changes i n sward composition at the same al lowance has been shown to affect

animal performance ( Hodgson 1 984). The effect of herbage al lowances between

2 .0 to 3 .0 kg OM d-1 on performance of weaned lambs was assessed in a trial

( repeated over two years) carried out d u ring late spring/early summer (Butler &

Hoogendoorn 1 987) . As the swards were dissected into components, different

relationships between sward conditions and animal performance were explored .

Liveweight gains were not explained by O M herbage al lowance, and green OM

al lowance only i nf luenced LWG in one year. G reen leaf OM al lowance was the best

s ing le predictor, but a combination of leaf al lowance and dead content ( DC) was

the best , resu lt ing in the fol lowing l inear relationship :

LWG = 47 + 0 .09 leaf al lowance - 599 DC, R2 = 0.79.

20

1 .2

1 .0 .

:0 0.8

0) 0.6 .:x

CJ 0.4 S ...J

0.2

0.0

_0.2°

·0

-0.4

• • •

• . . ..

.". . - ; - - ; - - - - ; ! .. ..

• •

• Autumn I Winter

• Spring / Summer

8.0 1 0.0 1 2.0

Herbage allow ance (kg OM 1 00 kg LW1 d·1 )

Part I - Chapter 1

Figure 1 . Relationship between herbage al lowance and l iveweight gain of steers dur ing Autumn / Winter and Spring / Summer periods ( Reid 1 986) .

For the fitted asymptotic curves: LWG Autumn / Winter = 0 .48-1 .37 0.65HA, R2

= 0.95. LWG Spring / Summer = 1 .09-2.82 0.45 HA, R2

= 0.94.

2.2. Effect of adding grain under grazing

2.2. 1 . Liveweight gain response

There is i n the l iterature a huge diversity of supplementatio n tr ials, i nvolving

alternative production systems, different types and levels of g rain use and varying

duration of the trial (Horn & McCol lum 1 987; Paterson et al . 1 994) , which makes it

difficult, i f not impossible, to establ ish generalizations in the pattern of animal

response when grain i s fed. Table 1 gives the most relevant examples of results of

maize grain feeding u nder grazing condit ions. However, even in more constrained

situations, a h igh variation of experim ental conditions and anim al responses are

observed and no clear conclusions can be drawn. However the expected animal

response to g rain feeding is h ig h ly dependant on the level of performance by

unsupplemented animals (Conway 1 968; Reardon 1 975; Steen 1 994). When the

data in Table 1 is expressed in terms of extra LWG over the u nsupplemented

group, a s ignif icant overall relationsh ip is obtained (Figure 2 ) , resu lt ing in the

fol lowing equat ion :

Extra LWG= 0 .08±0.04 + 0.09±0 . 0 1 Maize g rai n ; n=42, R2= 0 .46 , P<0.001 and

A review of energy supplementation and sward conditions in grazing beef finishing systems 2 1

RMSE = 0 . 1 5 (Maize supplementat ion ranged from 0 .45 to 6 . 0 kg d-1 ) .

The effect o f g rain feeding o n the performance of g razing animals i s l ikely to be

associated with the extra energy for the animal (Al lden 1 98 1 ; Horn & McCol lum

1 987) , and/or the better efficiency of uti l i zation of the energy coming from the grain

for l iveweight gain (NRC 2000) . However, the increased animal performance

cannot s imply be explained in terms of extra energy in all cases . Grigsby et al .

( 1 99 1 ) , with a low level maize-based supplement (Table 1 ) , obtained a conversion

rate of 2.2 (supplement to extra LWG ratio) , and com mented that an increased

synthesis of microbial protein was the most l i kely explanation of i ncreased animal

performance. A reduction of nitrogen losses from the rumen by feeding an

increasing level of maize grain has been observed (E l izalde et al . 1 999b) and also

a decrease of blood u rea level (Karnezos et al . 1 994) .

22 Part I - Chapter 1

Table 1 . Effect of level of maize grain supplementation under g razing conditions on l iveweight gain of finish ing beef cattle.

Animal SLW Supp. Pasture PO Season LWG Other Reference Class (kg} (kg d·'} comments

Crosbred 21 3 0 .0 Leucaena H W 0.73 (Petty et a l . 1 998) heifers

0 .5 Pangola 0 .78 rotational grass (i rrigated)

1 .0 0 .99 Dry season 1 .5 1 . 1 2 0 .85

Crosbred 408 0.0 Ryegrass M S -0.04 (Boom & Sheath steers 1 999)

2.0 White 0.44 rotational clover

4.0 0.55

450 0.0 H W 0.93 2.0 1 .09 4.0 1 .25

Crosbred 41 4 0.0 Ryegrass M S 0 . 1 6 1 0% (Boom & Sheath steers soybean 1 998)

meal 2.0 White 0.46 Rep. 1

clover rotational 4.0 0.62 6.0 0 .71

41 1 0.0 M S -0.05 Rep 2 2 .0 0 .37 rotational 4 .0 0.6 6 .0 0.89

Crosbred 460 0.0 Ryegrass H W 0.88 1 0% steers soy bean

meal Rep 1

1 .0 White 1 . 1 6 rotational clover

2.0 1 .34 4.0 1 .21

460 0 .0 H W 0.9 Rep 2 1 .0 1 rotational 2 .0 1 .26 4 .0 1 .48

Crosbred 278 0.0 Ryegrass W 0.9 Continuous ( Rude et al . 2002) steers stocked

1 . 1 Sp. 0.9 SLW= starting animal liveweight, PO=pasture qual ity, LWG= an imal l iveweight g ain , H=high pasture quality, M=moderate pasture qual ity, W=winter, Sp.=spring, S=summer and A=autumn.

A review of energy supplementation and sward conditions in grazing beef finishing systems 23

Table 1 . (Cont.)

Animal SLW Supp. Pasture Pasture Season LWG Other Reference Class (kg) (kg d- quality comments

1 } Crosbred 295 Russian Low A 0.62 Continuous (Adams 1 985) steers stocked

297 0 .96 Wild 0.62 ryegrass

282 0.95 0.82 Crosbred 301 0 .0 Ryegrass High Sp. 0.99 Continuous (Grigsby et al . steers and stocked 1 991 ) heifers Crosbred 076 S 1 .56 Trial 1 steers and heifers

270 0 .0 Ryegrass High Sp. 1 .08 Group fed 0 .51 S 1 .25 Trial 2

Crosbred 267 0.0 Bermuda High-Low S 0.83 Continuous (Ai ken 2002) steers Grass stocked

268 0 .45 0.98 1 .35 1 . 1 5 2.25 1 . 1 6

Hereford 260 0.0 Alfalfa- High S 0.6 Rotational (Lake et al . 1 974) steers grasses

0 .23 A 0.65 0.45 0.74 0 .91 0.72 1 .82 0.83 Trial 1

Hereford 228 0.0 Alfalfa- 0.65 steers grasses

0 .45 0.66 Trial 2 0.81 0.68 1 .36 0.75 1 .82 0.88 2.27 0.85 2 .72 0.85

British bred 301 0 .0 Alfalfa- Moderate- S 0.62 Rotational (Machado et al. steers grasses High 200 1 )

2 .5 A 0.72 5 .5 0.98

SLW= starting animal l iveweight, LWG= animal liveweight gain, W=winter, Sp.=spring , S=summer and A=autumn.

2.2.2 . Other effects

Addit ional effects of grain feeding can be d i rect or i ndirect . A di rect aspect of clear

com mercial i nterest is the effect of grain feeding on carcass characteristics .

I ncreases in dressing percentage with increasing grain level fed have been found

in lambs ( Karnezos et al . 1 994) and steers ( Reardon 1 975; Taylor & Gulbransen

1 990) . S imi larly, in carcass studies a h igher marbl ing score i n heifers (Jeffery et al .

24 Part I - Chapter 1

1 996) and a higher carcass conformation score i n steers has been observed

(French et a l . 200 1 b ) .

§- 1 .0 o i5J e 0.8

c � :s 0.6

Q) > Cl 0 6 0.4 '

� ....l 0.2 � x ,. •

ill 0 . 0 •

0.0 1 .0 2.0 3.0 4.0 5.0 6.0 7.0

Maize grain (kg d'1)

Figure 2. Relationship between level of maize g rain supplementation ( in kg d'1 ) and extra l iveweight gain over the unsupplemented group ( in kg d'1 ) i n g razing beef catt le .

Data are from the experiments of Perry et al . ( 1 972) ; Lake et a l . ( 1 974) ; Gi raudo et a l . ( 1 984) ; Adams ( 1 985) ; Grigsby et a l . ( 1 991 ) ; Petty et al . ( 1 998) ; Boom & Sheath ( 1 998) ; El izalde et a l . ( 1 998) ; Boom & Sheath ( 1 999 ) ; Machado et a l . (200 1 ) ; Rude et a l . (2002) ; Aiken (2002) . Maize supplementat ion ranged between 0 .45 to 6 .0 kg d'1 .

An important system impl ication of g rain feeding u nder g razing is the residual effect

on animal performance i n subsequent conditions , either g razing on ly o r i n a

feedlot. This has impo rtant production-economic i mpl ications when combin ing and

designing alternative productive systems and is discussed in section 4 .2 .3 .

2.3. Combination of sward conditions and supplementation

Studies oriented to research the effects of the combination between herbage

allowance and grain feeding in beef cattle systems are scarce . I n a combinat ion of

two summer trials carried out with beef catt le , Reardon ( 1 975) offered al lowances

between 7 to 27 kg OM d-1 with herbage mass of 3000 to 3900 kg OM ha-1 and

feeding up 5 kg OM maize silage. A curvi l inear response of carcass weig ht gain

(CWG) to herbage al lowance was obtained from 0 . 1 kg d-1 up to a plateau at 0 .5 kg

CWG d-1 , and responses to si lage was strongly related to weight gain prior to

supplement addition . When carcass-weight gains were 0 .2 or 0 .4 kg d-1 , 1 .0 kg of

supplement improved carcass weight gain by 0 . 1 and 0 .04 kg d-1 , respectively

(Reardon 1 975). I n a factorial trial that included th ree levels of herbage al lowance

(6, 1 2 and 1 8 kg OM d-1 , m easured to 4 cm above g round level) and three levels of

A review of energy supplementation and sward conditions in grazing beef finishing systems 25

barley-based concentrate (0 , 2 . 5 and 5 .0 kg d-1 ) fed to crossbred steers (567 kg

LW d-1 ) g raz ing a pasture dominated by perenn ial ryegrass (French et al . 2001 b ) ;

i ncreasing both HA and supplement increased LWG (range between 0 . 1 4 to 1 . 1 4

kg LW d-1 , P<O .05) .

Us ing sward surface height (Steen 1 994 ; Steen & Ki lpatrick 1 998) or stocking rate

(Conway 1 968) as an indicator of pasture on offer produces results i n agreement

with those obtai ned using herbage al lowance. I n the case of Steen & Kilpatrick

( 1 998 ) , beef-bred bul ls were continuously stocked du ring sum mer-autumn on a

ryegrass pasture from 5.5 to 1 1 months of age to m aintain sward surface height of

6 .5 and 1 0 cm with or without 1 .6 kg d-1 of prote in-corrected barley.

Supplementation i ncreased performance at the lowest sward height (when animals

were losing weight supplements alleviated the losses ) , but no improvement was

found at a 1 0 cm sward height . I n a 3-year study of bul l beef systems, Steen ( 1 994)

contin uously stocked bu l ls of 5 to 1 0 months of age in summer on ryegrass

pastures to maintain sward surface heights of 7, 9 and 1 1 cm with or without 1 . 5 kg

d- 1 of protein-corrected barley . Reducing sward height from 1 1 cm to 7 cm , reduced

LWG by 0 .27 kg d·1 in u nsupplemented swards dur ing years 2 and 3 of the

experiment, but th is was reduced to 0 . 1 7 kg d·1 when concentrates were g iven.

There was l ittle effect of supplementation on animals maintained at either 9 or 1 1

cm sward heights .

Conway ( 1 968) carried out a factorial trial repeated over three years i nvolvi ng two

stock ing rates ( 1 .0 and 1 .4 an imals ha-1 ) and feed ing 0 .0 or 1 .8 kg d· 1 of barley and

beet pulp to Hereford x Angus steers of an average 353 kg LW. There were

d ifferences between years in LWG , but the author concluded that feed ing

supplement on pasture would g ive signif icant animal responses only when the

present graz ing practice is constrain ing LWG in the absence of supplements.

Response to grain feeding is not only affected by herbage on offer , but also by

herbage qual ity. Boom & Sheath ( 1 998) obtained differences in LWG between

t reatment repl icates where the amou nt of herbage on offer and grain fed were

s im i lar , but with differences in sward conditions (54 and 35 % of g reen leaf for

repl icates 1 and 2 respectively) , with associated changes in digestibi l ity and protein

26 Part I - Chapter 1

content.

3. Effect on the plant/animal interface

It is accepted that changes occurring at the plant-animal interface are the key

drivers to understand and predict changes in an imal performance under d ifferent

sward condit ions (Forbes 1 988; I I l i us & Hodgson 1 996; Hodgson et al. 1 999)

Changes in herbage allowance and sward condit ions i nf luence animal performance

th rough their effect on herbage intake and qual ity of the diet ( Hodgson 1 984) .

These two aspects are not l ikely to be independent, but for s impl icity , evidence

from the l iterature is presented here separately.

3. 1 . Herbage allowance and sward conditions

3 . 1 . 1 . Effect on herbage intake

There is general agreement in the l iterature that herbage intake shows asymptotic

or quadratic patterns relative to herbage allowance as in the case of LWG .

However, the l iterature value of herbage allowance where herbage intake starts to

decl i ne is h igh ly variable, and comparisons must be made with care because of

d ifferences in the expression of herbage al lowance , uneven descript ion of the

sward in terms of composition and qual ity, and the use of d ifferent pastures.

Herbage intake usually approaches a maximum when herbage OM al lowance

reaches a value around 9 to 1 2 % LW d-1 in perenn ial ryegrass-white clover

pastures ( Hodgson 1 984) . H igher values have been observed using other feed

sources. When the association between HA and OM i ntake (kg 1 00 kg OM LW-1 d-1 ) in beef bred steers g razing winter wheat at a HA range between 5 .5 and 64 %

LW d-1 was studied ( Redmon et al . 1 995) , the results fitted sign ificantly to a

quad ratic function , where :

OM OM i ntake = 1 .3 + 0. 1 2 HA -0 .003 HA2, R2=0.52 , M SE=0.07, with a plateau

ach ieved (2 .57 kg 1 00 kg LW-1 d-1 ) when dry matter HA was 2 1 . 1 % LW d-1 .

Oougherty et al . ( 1 992) used different short-term observations (2-hour grazing of

A review of energy supplementation and sward conditions in grazing beef finishing systems 27

tethered animals) f itted together to establ ish a generalized relat ionship between

herbage OM intake rate and herbage al lowance (>5 cm grou nd level ) in a lfalfa

pastu re. Herbage intake rate (maxim u m of 0 . 5 kg 1 00 kg OM LW'1 h'1 ) declined

markedly when HA was lower than twice the herbage OM intake. These authors

commented that the apparent controversy with the conclusions of Hodgson ( 1 984)

are l i kely to be explained by d ifferences in satiety mechanisms operat ing over

different time frames, differences in the leaf content at the t ime, and by differences

in how herbage al lowance was expressed (>5 cm or above ground level ) .

Posit ive effects of herbage mass on herbage intake have also been observed .

When dairy cows were allocated to a combinat ion of herbage al lowances and

herbage masses in an irrigated ryegrass-dominated pastu re (Wales et al . 1 999) ,

herbage intake ( H I ) i n kg OM d·1 was explained by the fol lowing equation ( Figure

3 ) :

H I = 3 .0 + 0 . 1 3±0 .0 1 9 HA + 1 .29±0 .0 1 9 HM; R2=0.94 , residual 88=0.56, CV=4.3%,

P<0.05.

I n a complementary study with i n the same trial reported by Wales et al . ( 1 999 ) , the

defol iat ion pattern was further studied (Tharmaraj et al . 2003) . Herbage intake was

signif icantly explained by variations in sward surface height (r=0 .55 , P<0.0 1 ) , area

offered to each cow ( r=0.25, P<0 .0 1 ) and the pre-grazing bulk density ( r=-0 . 26 ,

P<0 .0 1 ) to yield a combined equation with an R2=0.77 and a residual 80 of 0 .0 1 6

( p<0 .01 ) .

Negative effects of herbage mass o n herbage intake have also been reported. I n a

study with Friesian steers (323 kg LW) dur ing late spring-su mmer (Reardon 1 977)

grazing a range of herbage al lowances (HA from 9.9 to 42.3 kg OM d·1 ) and pre­

grazi ng herbage masses (HM from 1 820 to 5280 kg OM ha·1 ) , herbage i ntake ( H I ,

kg O M d·1 ) was expressed by the following equat ion :

H I=7.03 + 0 .0945 HA - 0.000542 HM; r=0 .55 , residual 80=1 .6 1 .

This apparent controversy of the positive o r negative effect of herbage mass is

l i kely to be explained by interact ions between the quantity and qual ity of the

herbage. There is g rowing evidence , mostly from dairy product ion studies, that leaf

28 Part I - Chapter 1

allowance (LA) is a better measure of nutritional status than total OM al lowance

(Table 2). When the daily herbage i ntake was predicted from herbage al lowance ,

green al lowance and leaf al lowance , R2 i ncreased from 0 . 1 1 to 0 .37 and 0 .84 for

the three variables, respectively (Hoogendoorn 1 986) . There is now a need for

standardisation of how LA is expressed for comparative purposes.

Q) .:.::. ro :::' c -o .0; � � o .D Cl

... .:.::. Q) -I

1 6.0

1 5.0

1 4.0

1 3.0 0

1 2.0

1 1 .0

1 0.0 25 30 35

Herbage allow ance

(kg DM do1 )

40 45

5 .0

Herbage mass

(ton ha·1 )

Fig ure 3. The effect of herbage mass and herbage allowance on herbage intake (drawn from Wales et al . 1 999) .

3 . 1 .2. Effect of herbage allowance and sward conditions o n diet composition

and quality

The abi l ity of the animal to harvest a nutrit ionally enhanced proport ion of sward

components decl ines as herbage allowance decreases (Geenty & Sykes 1 982;

Dougherty et al . 1 989) , which is l ikely to lead to a reduction of the nutritive value of

the d iet. However, th is will usually have a smal ler impact in relative terms on

animal performance than i ts effect on daily herbage intake ( Hodgson 1 984) .

A review of energy supplementation and sward conditions in grazing beef finishing systems 29

Table 2. Effect of increasing leaf allowance on herbage intake.

Leaf al lowance Leaf Herbage Herbage Pasture Production Reference allowance Intake i ntake units type System units

8 . 1 kg OM d" 1 3 .9 kg OM d" Perennial Dairy (Kim et al . 1 3 .5 ground 1 5.2 ryegrass 1 995) 1 9.3 level 1 5.4

1 2 .0 kg DM d·l 1 1 .9 kg D M d·l Perennial Dairy (Hoogendo 1 3 .3 ground 1 3 .0 ryegrass orn 1 986) 1 5 . 1 level 1 3 .9 & white 1 5.2 1 2 .2 clover-1 5. 1 1 1 .2 based 1 6 .0 1 1 .4 1 6 .0 1 3.4 1 6 .2 1 1 .9 1 7 .4 1 6 . 1 4 .3 7 .9

26.5 2 1 .3 6.7 7 . 1

1 4 .7 1 3 . 5 1 8 .4 1 5.0 1 2.0 kg OM d·l > 1 0 .7 kg OM d·l Perennial Dairy (Delagarde 1 8 .0 5cm ground 1 1 .8 ryegrass et a l . 2000) 24.0 level 1 3 .8

+ Control 1 2.0 kg DM d·l > 1 4 . 1 kg O M d·l Perennial Dairy (Parga et Control 1 8 .0 8cm g round 1 4.8 ryegrass al . 2000)

Leafy 1 2.0 level 1 4.7 Leafy 1 8 .0 1 4 .5

# 4.6 kg DM d·l > 5.6 kg D M d·l Perennial Beef Estimated 9 . 1 4cm ground 9 .4 ryegrass- from

1 3 .7 level 1 2 .5 based (French et a l . 200 1 b)

+ = type of sward, # = only unsupplemented treatments presented .

Redmon et al . ( 1 995) found a quadratic relat ionship between herbage al lowance

and the in vitro OM digestibi l ity ( IVOMO) of samples obtained from oesophageal

fistu lates g razed at a range of herbage al lowances, and the fol lowing equation was

fitted:

I VOM O ::: 62 .2 + 1 .08 HA -0 .022 HA2, R2=0.64, MSE=5. 1 , with a LWG plateau

achieved (75.3 %) when HA was 24 .3 % LW herbage OM d-1 .

S imi larly, Jamieson and Hodgson ( 1 979) observed a depression in the intake of

digestible nutrients by 20-23% at low HA, when steer calves were g razed at a

range of HA between 3 and 9 % LW d-1 . S im ilarly, when cattle g razed at three

herbage al lowances (6 , 1 2 and 1 8 kg d-1 ) and three concentrates levels (0 , 2 .5 and

5 .0 kg d-1 ) were fed in autumn (French et al . 200 1 b) , in vivo OM diet digestibi l ity of

the u nsupplemented g roups was 68.4 , 82.9 and 85.3 % for the herbage al lowances

30 Part I - Chapter 1

of 6, 1 2 and 1 8 kg d-1 , respectively .

3.2. Effect of adding grain on herbage intake and grazing behaviour

Usually when grain is fed to grazing animals , herbage intake is depressed. This

replacement is cal led substitution rate (SR), and is expressed as the OM u nit of

change in herbage per unit increase in OM concentrate intake (Horn & McCol lum

1 987; SCA 1 990) . When g rains are fed , SR can range fro m 0 ( i .e . no effect on

herbage intake) to above 1 (Oixon & Stockdale 1 999). The amou nt of

supplemented grain seems to play an i mportant ro le . A low level of grain ( less than

0 .25-0 .30 % LW d-1 ) can be fed without causing a detrimental effect on herbage

intake , particu larly on h igh quality pastures (Griebenow et a l . 1 997) .

The suggested explanations for the substitution rate phenomenon include those

associated with rumen and whole tract digestion , metabolic regu lation or voluntary

herbage intake (Oixon & Stockdale 1 999) . Although usually a "carbohydrate effect"

on herbage fiber digestion is quoted ( Mou ld et al . 1 983; Horn & McCol lum 1 987) ,

substitution rate has been observed without effect on fiber digestion in an indoor

study using fresh alfalfa, where maize g rain levels of 0, 0 .6 and 1 .2 % LW d-1 were

fed to steers ( El izalde et al . 1 999a) .

The effect of level of g rain fed on substitution rate has raised some controversy.

Whereas a curvi l inear response of SR to the level of intake has been observed in

indoor ( 8erge & Oulphy 1 985) and in g razing studies ( Robaina et al . 1 998) , a l inear

effect has also been reported in indoor studies ( El izalde et al . 1 999a) and u nder

grazing cond itions (Opatpatanakit et al . 1 993) . This point deserves more

investigat ion . On the other hand , substitution rates reported in the l iteratu re are

related to the current level of herbage intake u nder unsupplemented conditions.

There are several equations to predict SR using the u nsupplemented herbage

intake as independent variable both in stal l -fed and grazing animals ( Meijs &

Hoeskstra 1 984; Oixon & Stockdale 1 999 ; El izalde et al . 1 999a) .

A direct effect of a relationship between increasing herbage al lowance (l ikely

associated to its effect on herbage i ntake) and higher substitution rates has been

demonstrated in dai ry cows (Meijs & Hoeskstra 1 984) and in sheep (Carson et al.

A review of energy supplementation and sward conditions in grazing beef finishing systems 3 1

2003) . I n agreement, a recent comprehensive study where cont inental bred steers

(567 kg LW) assigned in autumn to n ine g razing treatments of th ree herbage

al lowances (6 , 1 2 and 1 8 kg d-1 ) and three concentrates levels (0 .0 , 2 .5 and 5 .0 kg

d-\ concentrate feeding did not affect herbage intake at low HA, however it

decreased herbage intake at medium and high HA ( French et a l . 200 1 b). I n

summary, addi ng g rain to the diet o f g razing animals increases the complexity of

the al ready complex mechanisms contro l l i ng herbage intake (Fo rbes 1 988) . I t also

reinforces the importance of a better understanding of the control of

unsupplemented herbage intakes, to predict potential responses to

supplementation strategies.

Substitution rate at a given level of concentrate is positively correlated with

herbage quality (Horn & McCol lum 1 987 ; Matejovsky & Sanson 1 995; Caton &

Dhuyvetter 1 997) . This is also an important issue for control and understand ing of

the resu lts from grazing studies, where herbage qual ity changes dynamically and

animal selectivity can enhance the nutritive value of the sward . With all these

sources of change, it is not surprising that the methodology avai lable to measure

individual g ra in and herbage intake under g razi ng conditions usual ly constrains a

deeper understanding of how substitut ion rate is contro l led and affected (Dove

1 996) .

Another di rect effect of feeding g rain under g razing is t he reduction o f g razing t ime

(Al lden 1 98 1 ; Giraudo et al . 1 984; Leaver 1 986; Mayne 1 988; Krysl & Hess

1 993; Caton & Dhuyvetter 1 997) . However, the magnitude of the effect is h igh ly

variable, est imated at between 3-30 min and 20-23 min kg OM SUpp.-1 by Leaver

( 1 986) and Mayne ( 1 988) , respectively. This behavior has been l inked to the

induction of satiety-regulat ing mechanisms (Dixon & Stockdale 1 999) . As grazing

time affects energy expenditure, the independent monitoring of g razing behaviour

and herbage and grain intake may provide a meaningful i nsight about the response

to feeding g ra in u nder grazing condit ions (Krysl & Hess 1 993) .

When an imals are offered supplement as a group, the coeffic ient of variation for

individual intake of grain can be substantial (Bowman & Sowel l 1 997) . Boom &

Sheath ( 1 998) estimated that 20% of their experimental animals were non-feeders

32 Part I - Chapter 1

when increasi ng levels of maize g rain were fed to crossbred steers during two tr ials

carried out during sum mer and wi nter. As in the case of s ubstitut ion rate, the

avai labil ity of adequate methodological procedu res i ncluding individual i ntakes of

grain are needed for a better understanding of the possible practical i mplicat ions of

the resu lts .

4. Implications of gra in feeding and sward characteristics to the whole

system

Field studies on the effect of herbage characteristics and grain feed ing on animal

performance are usually developed at smal ler scales of time and space than those

considered with in a whole system approach. Additional ly , most of the

exper imentation is an imal oriented, but it is important to ach ieve a clear

compromise between animal and sward requ i rements in order to maintain whole

systems viabi l ity (Hodgson 1 984) . For example, although h igher herbage

allowances can i ncrease animal performance , it can also increase res idual sward

height and th is may resu lt in a deterioration of sward qual i ty i n mid- and late­

season ( Hoogendoorn 1 986) .

G iven the natural variabi l ity of herbage g rowth between months and years , o n e key

issue to ach ieve the above mentioned system viabi l ity, is the necessity to develop

a flexible g razing practice. This has been achieved by integrat ion of grazi ng and

conservat ion ( Baker 1 988) , by feedi ng energy supplements ( Rearte & P ieroni

2001 ) or combin ing animals with d ifferent feed requirements ( French et al. 200 1 a;

Kloster et al . 2003 ) .

The reduct ion o f forage i ntake when supplements are fed is not necessari ly

undesirable, depending on the situation , especial ly when the objective is to extend

the use of existing herbage on the farm (Horn & McCol lum 1 987) . However, the

capital ization of the extra pasture left i n the paddock is h ighly dependent on how

th is pasture is uti l i zed, therefore the objectives for sett ing a level of

supplementation m ust be clearly u nderstood . On the other hand, the advantages of

supplementation to the system are very evident when grazing intensity is adjusted

( Hodgson & Tayler 1 972 ; Reardon 1 975) .

A review of energy supplementation and sward conditions in grazing beef finishing systems 33

Another i mportant system impl icat ion of grain feed ing under g razing condit ions is

the residual effect on animal performance . I n the case of a subsequent feedlot

phase, it seems that there is some differential res idual response associated with

the level of supplementation at pastu re . Low to moderate levels of grain feeding

u nder graz ing (up 2 .8 kg d-1 i n these studies) have had a neutral or positive effect

on subsequent feedlot performance (Steen 1 994 ; Owensby et al . 1 995; Steen &

Ki lpatrick 1 998 ; El izalde et a l . 1 998) . However, a negative effect ( r=-0.96) of

previous g razing supplementation on performance during the feed lot phase has

been observed ( Perry et al . 1 972) i n a 4-year repl icated study during spring­

summ er ( 1 35 days in average) with Hereford steers fed with four levels (0, 33, 66

and 1 00 % of the required amount to fu l ly feed the animals) of a concentrate based

on g round maize ear.

When supplemented animals have been f in ished on pastures after the pastu re

shortfal l period, compensatory growth by steers fed pasture only diets eroded all

the LWG advantage of supplemented animals (Jeffery et al. 1 996; Boom & Sheath

1 998; Boom & Sheath 1 999) . For th is reason, Jeffery et a l . ( 1 996) suggested that

in order to ach ieve maximum econom ic returns from grain feeding , animals should

be sold dur ing or at the end of the supplementat ion period rather than carrying

them over to the next season . However, th is suggest ion could become less

important when supplementat ion is oriented to i ncrease stocking rate rather than to

increase i nd ividual animal LWG .

A hol istic evaluation of the economic benefits of grai n supplementation requires an

understanding of the biological process i nvolved , not only in the animal but also for

all system components. This review has h igh l ighted wel l -docu mented evidence on

th is aspect, but also identified aspects that requ i re further research . Economic

appraisal of different levels of intensification has been done through different

approaches. These i nclude the use of experimental or biophysically s imulated

performance data di rectly in an economic budget, or alternatively us ing the

biological data to develop response functions that are then used in an economic

model (Wachenheim et al . 2000) . For studying grain feeding to g razed beef cattle

systems, a whole-farm model has p roved to be an adequate tool (Ru iz et al . 2000 ;

34 Part I - Chapter 1

Peri l lat et al . 2004). U nder Argentinean conditions , Ru iz et al . (2000) concluded

that farmers' decis ions adjusted better to a min i mum cost model , keepi ng a lower

stocking rate and level of supplementation , rather than an optim isation model .

5. Impl ications and conclusions

Grain supplementation and management of sward characteristics are impo rtant

drivers of animal and system performance, and key components to i ncorporate

f lexibi l ity to the grazing management of beef cattle fin ish i ng systems. It is important

to establ ish some indications of the pattern of the relationship between herbage

allowance , grain feeding and an imal performance over a wide range of situat ions .

The fact that the effects are direct and ind i rect, affect ing both the an imal and the

whole system , emphasises that the selected research methodologies m ust al low

the integration of information at a systems scale. Recent development of

tech niques for i ndividual i ntake estimation of maize grain in g roup-fed and free­

ranging animals (Garcia et al . 2000) provides an i mportant advance in this sense.

Additional ly , effort is requi red for the development of models as tools for synthesis

of i nformation and for a more integrative appraisal of short-term field exper iments,

usual ly animal oriented , with in a systems approach .

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Chapter 2

Herbage intake, d iet composition , g razi ng

behaviour and performance of Angus heifers on

two herbage al lowances d uring late spring

46 Part I - Chapter 2

Abstract

Eighteen beef heifers (253±4 .8 kg LW) were allocated during late spring (26

October - 21 Decem ber) to either a low herbage OM al lowance ( LHA) or moderate

herbage OM al lowance (M HA) of 2 . 5 and 5 .0 % LW d-1 respectively. Heifers were

strip-grazed daily for 57 days (1 5 pre-experimental and 42 experimental days) on a

red clover-perenn ial ryegrass pasture, with and average pre-g razing herbage mass

of 2800 kg OM ha-1 . I ntakes were assessed by slow release n-alkanes capsules

(CapteC®) , and pre- and post-graz ing herbage mass differences using a r ising

plate meter. In two 24-h periods, individual ani mals were observed at 1 0 m in .

intervals to record their grazing behaviour. M icro-histological evaluation of faeces

and differences in nutrient and component selection indexes were used as

i ndicators of diet characteristics. Heifers grazing the moderate herbage allowance

achieved higher (p<0.01 ) l iveweight gai n (0 .79 kg d- 1 ) and f inal l ive-weig ht (293 kg

LW) than LHA heifers (0 .58 kg d-1 and 275 kg LW, respectively) . Moderate

al lowance heifers had a sign ificantly h igher (p<0 .05) herbage i ntake when

calculated from both methods (6 .5 and 7 .6 kg OM d-1 at MHA and 3.5 and 6 .3 kg

OM d-1 for intakes esti mated from pre- and post-grazing herbage mass differences

and using n-alkanes, respectively). The difference method underest imated herbage

intake com pared with the n-alkane method by 2 .7 and 1 . 1 kg OM d-1 for LHA and

M HA, respectively) . Average post-grazing herbage mass were of 1 1 80 and 1 320

kg OM ha-1 for LHA and MHA, respectively. Low allowance heifers g razed longer

( P<0.05 ) , mai nly dur ing the night (P<0 .00 1 ) , and had higher bite rates and bites

per day (p<0.05) , wh ile the MHA he ifers had a h igher bite weight (p<0.05) . No

differences between allowance treatments were found in diet digestib i l ity ,

harvesting efficiency, micro-histological evaluation of faeces, nutrient and

component selection indices .

Key-words: n-alkanes ; herbage al lowance ; herbage- intake ; l ivewe ight gain , Angus

heifers

1 . Introduction

In pastu re-based animal production systems, a major chal lenge is manipulat ing the

Herbage intake and grazing behaviour of heifer on two allowances during late spring 47

seasonal forage production cycle to meet animal feed requ i rements. Some animal

production enterprises come closer than others to match i ng the seasonal pattern of

pastu re production , but in a l l cases there is a certain degree of imbalance

(Hodgson 1 990) . Feed intake is the main determinant of the animal response, so

alternative management options ( i ncluding supplementation) should be based on

an u nderstanding of animal feed i ntake (Forbes 1 988) . I t is general ly accepted that

there i s an asymptotic relationsh ip between herbage al lowance, and i ntake or

ani mal l iveweight gain (Chapter 1 ) . However, differences in experimental design

and methods of estimating herbage intake have generated i nconclus ive or

contradictory results. I ndirect techn iques to estimate i ndividual herbage intake i n

free g razi ng animals such as n -alkane markers (Mayes e t a l . 1 986) have overcome

some of the l imitat ions of other internal markers (Parker et al . 1 990) . Control led

release n-alkane capsules have been used to measure i ntake i n beef cattle

(Real i n i et al . 1 999) , but have not been evaluated under Argentinean condit ions.

Herbage al lowance and its impact on herbage i ntake have been studied in g roups

of an imals in Argenti na beef cattle (Gandara et al . 1 987; Kloster et al . 2000) and

dairy production systems (Romero et al. 1 995; Gagl iostro et al . 1 996) using the

pre- and post-grazing difference method (Frame 1 98 1 ) . Estimation of herbage

intake with n-alkanes has been shown to be precise (Mayes et al . 1 986) , however

the m ethod i s costly and t ime l i m ited . For th i s reason , i t i s i mportant to combine

and adjust both alternatives to est imate feed i ntake.

The objective of th is experiment was to establ ish rout ines and evaluate tech niques

under Argentinean condit ions to study herbage intake (us ing n-alkanes and the

pre- and post-grazing difference method) , grazing behaviou r and indicators of diet

composit ion to complement other measurements used throughout the thesis .

Specifical ly , the i mpact of either low or moderate herbage al lowance on i ndividual

anim al herbage intakes, l iveweight gain and g razing behaviour of Angus heifers

were measu red .

2. Material and Methods

Eighteen 1 5-month-old Angus heifers weigh ing 253±4 .8 kg LW were obtained from

48 Part I - Chapter 2

a commercial farm, where they had been weaned the previous fal l , and grazed on

a range pasture pr ior to the start of the trial . T reatments were unrepl icated , and

animals were allocated to either a low ( LHA) or moderate herbage OM al lowance

(MHA) of 2 .5 and 5 .0 kg OM 1 00 kg animal LW-1 d-1 , respectively ( i .e . 9 ani mals

per treatment) . The trial was carried out at the Agricultu ral School "Jose Ramon

Santamarina" Tandi l , Argentina (37.2 ° S 59. 2 ° W) during late spring. Animals in

both treatments grazed a pastu re contain ing perenn ial ryegrass (Lolium perenne)

and red clover (Trifolium pratense). Fifty measurements per treatment us ing a

ris ing plate meter (Earle & McGowan 1 979) were taken each day, after which daily

herbage OM allowances were al located . Herbage residuals were also estimated

using the ris ing plate meter.

Herbage intake was calculated from differences between pre- and post-grazing

herbage masses ( Frame 1 98 1 ) . Animals were strip-grazed and moved daily (at

1 :00 pm) to a fresh strip with no back grazing of experimental areas . A 1 5-d pre­

experimental period was used and experimental measurements were then taken

over a 42-d period. Animals were de-wormed with ivermect in ( Ivomec™) 48 h prior

to allocation to treatments and on day 30 of the study. All heifers were treated with

a progesterone-based compound (M . P. HTM , Burnet lab . ) with the objective of

avoiding oestrus and hence alterat ions in animal grazing behaviour.

2. 1 . Animal measurements

Animals were weighed unfasted (with in 1 h of removal from herbage) fortn ightly

and after a 1 5-h fast at the start and end of the tr ial . Measurements of i ngestive

behaviour were carried out on days 30 and 37 of the trial . All animals ( identified

with a phosphorescent nu mber painted on both sides for night observation) were

observed once every 1 0 min. and their activit ies were recorded as g razing ,

ruminating or idl ing over a 24-h period (Hodgson 1 982). Bit ing rates were recorded

for individual animals at their peak intake (morn ing and afternoon) , by measuring

the t ime to make 20 prehension bites (Hodgson 1 982) , taking the average of th ree

measurements, and then using th is information to compute the daily number of

bites .

On day 1 4 al l animals were inserted with capsules (Captec® 1 50-300 kg LW)

Herbage intake and grazing behaviour of heifer on two allowances during late spring 49

contain ing dotriacontane (C32) and hexatriacontane (C36) with a constant release

rate for each n-alkane of approximately 200 mg d-1 to estimate i ndividual herbage

intakes . Routines for sampl ing and estimation of ind ividual i ntake and digestib i l ity

are described elsewhere (Mayes et al. 1 99 1 ) . The capsu les have an active period

of 20 days , and two independent periods of intake measurement were recorded

from days 7 to 1 2 and days 1 5 to 1 9 post-appl icat ion. During each period , faecal

samples were col lected dai ly from individual an i mals by morning or even ing

observation (2-h periods) of animals defecating and i m mediately refrigerated

(Morr is et a l . 1 993) . Samples were pooled with in an imal on a wet-basis for each

period (36 samples ) . Sub-samples were dried in an oven at 60 °C for 48 h and

were conserved for micro h istolog ical analysis of faeces (see below) . The rest of

the pooled samples were freeze-dried then grou nd in a mi l l ( 1 mm) and analyzed

for n-alkanes composition by gas chromatography (Mayes et al. 1 986) . Three

an imals in the second period (two from LHA and one from MHA) were discarded

for intake and digestibi l ity estimation because of extremely low C32 and C36

concentrat ions. I n order to check capsule release rate , two capsu les from the same

batch used in the main experiment were placed in the rumen of a fistulated animal

that g razed s im i lar herbage in a nearby paddock. At days 0 , 1 , 3 , 7, 1 0 , 1 5 , 20 and

2 1 the length of residual n-alkane content was measured with a ru ler. A regression

analysis was used to estimate capsule release rate by converting length to weight .

Duri ng each day that i ndividual i ntakes were estimated , two exclosure cages were

placed in each strip to be grazed. Th i rty hand-plucked herbage samples per cage

were taken post-graz ing to represent the ani mals d iet by visual comparison with

the g razed sector. Another sample was col lected for clover and ryegrass n-alkanes

analys is . Herbage samples were pooled by sampl ing period and treatment (one for

each 5-day period) as described by Garcia et al. (2000) . Samples were sealed i n

polythene bags and taken t o t h e laboratory in a n icebox. Herbage samples were

freeze-dried , g round ( 1 mm screen) and divided into two sub-samples. The f irst was

analysed for n-alkane composit ion and the second was analysed for d ry matter in

vitro digestibi l ity ( Dig%) (Ti l ley & Terry 1 962) , crude protein (CP%) by M icro

Kjeldhal (A.O.A.C. 1 960) , neutral detergent fiber ( N D F%) (Goering & Van So est

1 970) and non-structu ral carbohydrates ( NSC%) by the anthrone method (Pichard

& Alcalde 1 990) .

50 Part I - Chapter 2

The following variables were est imated from grazing behaviour , i ndividual herbage

intake, individual digestibi l ity and animal l ive-weight : bite weight (g OM bite-1 ) , dai ly

energy intake (MJ ME d-1 ) and harvesting efficiency (HE) as g OM eaten kg LW -1

min grazing-1 (Krysl & Hess 1 993) .

The characteristics of the diet were measu red us ing micro h istological analysis of

faeces and pre- and post-grazing assessment of morpholog ical and nutrit ional

parameters of the sward . Simi larly, diet composit ion was tested with n-alkanes

us ing the software package ( EatWhat) described by Dove and Moore ( 1 995 ) , wh ich

is based on a least squares procedure. For these analyses, alkane faecal

recoveries were assumed from the l iterature , but no feasible solutions were fou nd .

M icro-h istological analysis involved a separation of pooled herbage samples i nto

ryegrass and red clover. The dried samples of faeces col lected from each animal

were ground through a 20-mesh ( 1 m m apertu re) screen using a Wiley mi l l kn ife

grinder to ensure uniformity of fragment size. Additional ly, from the dried herbage

sub-samples, nine dry matter clover : ryegrass mixes were prepared ( 1 0 :90 , 20 :80,

30 :70, 40:60, 50 :50, 60:40, 70 :30, 80 :20 and 90 : 1 0) and coded for a b l ind

microscope reading. The herbage mixes were digested (Ti l ley & Terry 1 962) and

the undigested residuals were dried and stored . The digested mixes and faecal

materials were washed with ordinary bleach (sod ium hypochlorite 0.3%) to remove

pig ments , and then water-washed over a 50-mesh screen to remove the bleach

and smaller non-diagnostic particles. Five s l ides were prepared for each sample

(animals with in treatments and each species mix combination ) with

gelatine:g lycerin (1 :7) , and 1 00 microscope f ields per treatment (20 per sl ide) were

observed us ing a 1 00x microscope. Calibrat ions of the coded and digested

proportions were recorded , and compared with the n ine orig inal mixes by l i near

regression analysis . Botanical composition of ryegrass and red clover fragm ents

were recorded as density of the total identifiable fragments in 1 00 microscope

fields (Sparks & Malechek 1 968) .

On days 30 and 37, five homogeneous areas per treatment of approximately 1 m2

were selected and tagged. Pre- and post-grazing quadrats (0 . 1 m2) were cut from

them at g round level . Samples were sealed in polythene bags, washed in the

Herbage intake and grazing behaviour of heifer on two allowances during late spring 5 1

laboratory to el iminate soil and faeces contaminat ion and sorted into species,

g reen-dead proportion , lamina plus clover leaf ( L) and non- leaf fract ion (NL) with in

the green component. Total OM was estimated separately by oven-drying at 60 CC

for 48 h . On day 37, a s ite adjacent to each pre- and post-g razing quadrat

representing herbage of sim ilar characteristics , was also sampled and refrigerated

on site. These samples were freeze-dried and ground in a mi l l (1 mm screen) and

analysed for Oig%, CP%, N O F% and NSC % as previously described.

Nutrient contents of the herbage selected by the animals were expressed us ing the

fol lowi ng formula (Stockdale 1 999) :

NSI = (kg OM nutrient ha- 1 (Pre) - kg OM nutrient ha-1 (Post)) Eqn. 1

(kg OM ha-1 (Pre) - kg OM ha-1 ( Post) )

Where NSI is nutrient selection index. Sim i larly , by using the difference in botanical

composit ion and adapting the previous formula, each component was expressed

as:

CSI = (kg OM component ha-1 (Pre) - kg OM compo nent ha·1 (Post)) Eqn. 2

(kg OM ha-1 (Pre) - kg OM ha-1 ( Post) )

Where CSI is the component selection i ndex. Experimental variables were

analyzed using ANOVA as a completely random design . Final l iveweight was

tested also with in itial l iveweight as covariate. As neither period nor treatment

effects were different for component selection indexes, a group analys is across

treatments was developed between two pairs of indexes, g reen vs. dead

components and clover vs. g rass components. Regression analyses were

performed between each component selection index and nutrient selection index.

All analyses used the SAS statist ical analysis system (SAS/STAT 2001 ) .

3. Results

3. 1 . Animal performance and dry matter intake

Herbage morphological composition was simi lar between treatments, with average

52 Part I - Chapter 2

values for dead material (as fract ion of total sample) of 0 .39±0 .06 (0 .6 1 of g reen

m aterial ) , and as fraction of green material , clover represented 0 .72±0.08 (0 .28 of

g rasses) and leaf contents (clover leaf plus grass leaf) was 0 .3 1 ±0.02 (0.69 of

g rass stem and sheath and legume stem fract ions) . Chemical composit io n ,

concentration of dominant n-alkanes and average pre-grazi ng herbage mass are

shown i n Table 1 .

Table 1 . Chemical composition means, concentration of dominant n-alkanes and herbage mass in a pre-grazed pasture at two herbage allowances.

Dig CP NOF NSC C29 C3, C33 Herbage mass

% OM mg 1 00 g OM·' kg OM ha-'

LHA 69 1 7 36 9 299 .9 20 1 .4 1 8.6 2940 MHA 66 1 6 38 9 350.6 1 72.3 1 4. 1 2660 LHA= herbage OM allowance of 2 .5 % LW d-l, MHA= herbage OM al lowance of 5 .0 % LW d-l, Dig = % OM in vitro digestib i l ity, CP= crude protein , N O F = neutral detergent fiber, NSC = non-structural carbohydrates and C29 , C3, and C33 alkanes of n carbons.

Animals on the moderate herbage allowance achieved a h igher l ive weight gai n ,

and final l ive-weight when in itial l ive-weight was added to the model as a covariate

(Table 2 ) . Heifers grazing the moderate herbage al lowance had h igher intakes

when assessed with both n-alkanes and the pre- and post-grazing difference

method. However, the difference method values were only 55 and 85% of the

herbage intake estimated from n-alkane concentration for treatment LHA and M HA

respectively (Table 2) . The capsule release rate was l i near (391 ±1 mg d-1 ) s im i lar

to the amount reported by the man ufacturers (400 mg d-\ Lower herbage

residuals in the lower al lowance resu lted in a h igher degree of herbage uti l isat ion

(52%) by the LHA an imals than M HA animals (36%) . In vivo diet digestib i l ity was

not different between treatments.

Table 2. Effect of herbage al lowance on final liveweight, fasted l iveweight loss, liveweight gain , herbage intake and in vivo diet digestib i l ity of Angus heifers .

LHA MHA #PSE Final LW (kg) 275 293 8 .2 Fasted LW loss 1 5 h (% unfasted LW) 0 .07 0 .07 0 .006 Full LWG (kg dO' ) 0 .58 0 .79 0 .05 1 5 h fasted LWG (kg d O' ) 0 .49 0 .76 0.07 n-alkanes herbage intake (kg OM dO' ) 6.30 7.6 0 .37 Post-grazing herbage mass (kg OM ha-' ) 1 1 80 1 320 53. 1 Pre- and post-grazing herbage intake (kg OM dO' ) 3 .5 6 .5 0 .22

S ig. **

NS * * * *

*

NS **

NS �iet digestibi l ity (n-alkanes) 73 73 1 .5 LHA= herbage OM al lowance of 2 .5 % LW d-', MHA= herbage OM al lowance of 5 .0 % LW d-l, #PSE= pooled standard error, Sig.=significance level, NS = no significant d ifference, * P<0 .05 and ** P<0.0 1 .

Herbage intake and grazing behaviour of heifer on two allowances during late spring 53

3.2. Grazing behaviour

Animals g razing the moderate herbage allowance grazed longer and rested less

than animals grazing the lower al lowance , with no differences detected i n t ime

allotted to ru minating (Table 3) . Most of this difference in g razing time occu rred

during the night. Duri ng daylight hours, LHA heifers tended (p<0. 1 ) to graze more

(Table 3) , part icularly during afternoon after a new pastu re strip was al located

( Figure 1 ) . Bit ing rate and bites per day were higher in LHA heifers, whi le MHA

heifers had h igher bite weights. Harvesting eff iciencies, esti mated from herbage

intake calculated from n-alkane concentration , were simi lar between treatments

(Table 3 ) .

a) �-------------

� r-------��----� 50

:=- 40 :c c: 'E - 30 :i!" :� ;:; <x: 20

10

6 7 8 9 10 1 1 1 2 1 3 1 4 1 5 1 6 1 7 18 1 9 20 21 22 23 0 1 2 3 4 5

Time

b) 60

50

40 :c c: I 30 "" :� ;:; <x: 20

10

6 7 B 9 10 1 1 12 13 1 4 1 5 1 6 1 7 18 19 20 2 1 22 23 0 1 2 3 4 5

Time

Figure 1 . Effect of herbage allowance on grazing be haviour of Angus heifers throughout the day.

LHA= herbage allowance of 2.5 % LW d-' (a), MHA= herbage allowance of 5.0 % LW d-' (b) . Arrows indicate time when animals were sh ifted to a new g razing strip (1 :00 pm).

3.3. Diet characteristics

The n ine prepared clover:ryegrass percentage mixes were predicted accurately by

microscope read ings (clover % = 1 .04x + 0 .35 ; R2=0.97 and ryegrass % = 1 .02x -

1 .29; R2=0.93, where x is the microscope reading) . The microscope readi ngs of

faeces indicated no differences i n species contribution to herbage i ntake between

the two treatments. There were also no sign ificant differences for any component

selection index or nutrient selection i ndex. When paired indexes (dead vs. green

and g rass vs. clover) were compared across treatments (Table 4), the grass and

dead components were eaten less than clover and green, respectively. Values

54 Part I - Chapter 2

closer to zero indicate that the corresponding component of a pai r was eaten less

than the herbage intake estimated from pre- post-grazing differences (denominator

of Eqns. 1 and 2). Green and clover components were close to 1 .0 , i ndicating that

they were consumed proportionally to the herbage i ntake estimated from pre- post­

g razing difference. From the l i near regressions between the different component

select ion indexes and nutrient selection indexes , only dry matter digestibi l ity was

significantly explained by the leaf index (digest ibi l ity select ion i ndex = 0 .52 leaf

select ion index + 0 .5 1 ; R2= 0.79) . No feasible solutions were fou nd when test ing n­

alkanes for d iet composit ion (results not shown) .

Table 3. Effect of herbage allowance on the indicated grazing behaviour characteristics of Angus heifers.

Whole day: Grazing time (min) Ruminat ing time (m in) Idl ing t ime (min) Biting rate (bites min·1 ) Bite number (bites d·1) Bite weight (mg bite · ) HE @ (g OM eaten kg LW - 1 min grazing - 1 )

Daylight period (6 :00 pm-8:00 pm) Grazing time (min) Ruminating t ime (min) Id l ing t ime (min) Morning period (6 :00 am -1 2 :00 am)

Grazing t ime (min) Ruminating t ime (min) Id l ing time (min)

Afternoon period (1 :00 pm-8 :00 pm) Grazing t ime (min) Ruminating t ime (min) Id l ing time (min)

Night period (9 :00 pm - 5 :00 am)

LHA

421 379 6 1 4 44.9

1 8871 362

0.056

293 208 398

75 1 24 221

2 1 8 84 1 78

MHA

468 386 560

32.0 1 51 87

587 0 .058

265 238 396

66 1 1 7 238

1 99 1 22 1 59

#PSE

1 2. 1 1 3 .3 20.2

2.4 752.4

38 . 1 0 .0032

1 0 . 1 9.3

1 2.4

4.9 7 . 1 9 .6

6 .9 5 . 1 6 .4

Grazing time (min) 1 27 1 98 1 0 .2 Ruminating t ime (min) 1 77 1 59 8 .9

Sig.

NS **

** ** NS

+

NS

NS NS NS

+

Id l ing time (min) 2 1 0 1 58 1 0 .7 +

LHA= herbage OM allowance of 2 .5 % LW d-1, MHA= herbage OM allowance of 5.0 % LW d-1, #PSE= pooled standard error, Sig.=significance level , HE @ = herbage effic iency, NS = no significant difference ( P>0. 1 ) , + P<0. 1 , .. P<0.05, ** P<0.0 1 , *** P<0.001 .

Table 4. Comparison of means of different pai rs of component selection i ndexes (dead vs. g reen and clover vs. g rass) pooled between treatments.

#PSE Sig.

Dead Green

-0.02 1 .02 0. 1 7 **

Clover Grass

0 .91 0 .08 0 . 1 7 **

#PSE= pooled standard error, Sig.=significance level and ** P<0.0 1 .

Herbage intake and grazing behaviour of heifer on two allowances during late spring 55

4. Discussion

The overal l heifer l iveweight gains of this study were h igh if compared with fully fed

animals either at feedlot or u nder grazing conditions. Feedlot heifers fed a h igh

g rain-based d iet ad libitum, ach ieved 1 kg d-1 (Machado et a l . 1 997) . U nder winter

grazing condit ions , when Holstein-Friesian heifers of two different genetic l i nes

(heavy and l ight) were fed ad libitum herbage al lowances (of arou nd 1 2 % of LW)

of a h igh qual ity pastu re, l ive-weight gains of 0 .92 kg d-1 were obtained (Garcia­

Mun iz 1 998) . I n the present tria l , heifers were dosed with a progesterone-based

com pound to avoid g razing behaviour disturbance and th is may have i ncreased

LWG . I mprovement in LWG of around 5% could be expected from us ing this type

of compound ( Duckett & Andrae 200 1 ) .

Herbage intake was h igher at t h e moderate herbage al lowance , associated with

h igher final l iveweight gains. Di rect relationsh ips between herbage al lowance,

herbage intake and animal l iveweight gain are well establ ished in the l iterature

(Hodgson 1 984; Stockdale 1 985) . One of the objectives of th is trial was the

com parison of two methods of estim ating herbage i ntake . Although separate

cal ibrations of the r is ing plate meter for pre- and post-g razing were performed in

the present trial , the pre- and post-grazi ng difference method underest imated

intake (by 2 .7 and 1 . 1 kg OM d-1 for LHA and M HA, respectively) when intake using

the n-alkanes m ethod was taken as a reference (Table 2) . The theoretical i ntakes

derived from recorded l ive weight gains and herbage d igestibi l it ies were 4 .7 and

5 .5 kg OM d-1 for LHA and M HA animals respectively (AFRC 1 993) , representi ng

74 and 72% of the herbage i ntakes est imated us ing the n-alkanes. As both

treatments were unreplicated , th is prevented the development of any formal

statistical test to compare both method of estimate the herbage i ntake. Robaina et

al . ( 1 998) compared both methods and reported an u nder-estimation with pre- and

post-grazing difference method, however, because of herbage t reading , it seems

more l i kely that the r is ing plate meter over-estimates herbage i ntake ( Reeves et al.

1 996 ; Garcia 2000) . Possible reasons for u nder-estimation may be associated with

changes between pre- and post-grazing sward structure in a more m atu re sward ,

wh ich leads to a h igher resistance of residual dry matter to the ris i ng plate meter.

The estimation of herbage i ntake of late spring-summer pastu res us ing sward

56 Part I - Chapter 2

sampl i ng techn iques is often d ifficult when pasture is clumpy and may conta in a

mixture of dry stem material and g reen herbage ( Robaina et al . 1 998) . The pre­

and post-graz ing difference method u nderestimated herbage i ntake differential ly i n

the two treatments, possibly as a consequence of modificat ions i n structure and

mass of the herbage residuals as the same post-grazing equat ion was used for

both treatments .

The lower herbage C33 content i n M HA herbage as shown in Table 1 , contributed

to the difference between intake methods, because the herbage (and faeces) C33 i s

used to estimate herbage intake. The d ifference between herbage C33 i n LHA and

M HA swards may be associated with a s l ightly h igher clover content (although non

s ignificant) in the sward grazed by LHA heifers, as the C33 concentration for

ryegrass and c lover were 8 and 34 mg 1 00 g OM-1 , respectively ( resu lts not

shown) .

Heifers graz ing the low herbage al lowance grazed for 47 min less t ime than heifers

grazing the moderate herbage allowance. At a low herbage allowance animals

usually graze longer in an attempt to compensate for the reduced herbage on offer,

but reduction in dai ly g razing t ime at very low herbage al lowance has been

reported u nder rotat ional grazing (Jam ieson & Hodgson 1 979) . Oougherty et a l .

( 1 988) observed that grazing time of beef animals increased when herbage OM

allowances fell from 2 .5 to 1 .5 % LW-1 , but dropped abruptly with a further

reduction in al lowance from 1 .5 to 1 .0 % LW-1 .

Additional d ifferences were found when g razing t ime was analysed separately for

morning and afternoon activities. Heifers grazi ng the low herbage al lowance

allotted 52 % of thei r dai ly grazing t ime during the afternoon ( 1 :00 pm-8:00 pm)

coi nciding with the shift to a new str ip, wh ich agrees with other authors (Jamieson

& Hodgson 1 979) . Thi s behaviour has been associated with an u nwi l l i ngness to eat

prior to strip sh ift ing caused by low residuals in low al lowance paddocks ( Forbes &

Hodgson 1 985) . However, herbage residuals in Jamieson and Hodgson ( 1 979) and

in the present trial , were 1 643 kg OM ha-1 and 1 7 1 7 kg OM ha-1 respectively, so

other factors might be present. The low g reen herbage residual in both tr ials (28

and 46%, respect ively) and the fou l ing and trampl ing caused by the h igh

Herbage intake and grazing behaviour of heifer on two allowances during late spring 57

concentration of the heifers (22 m2 d-1 he ife(1 ) may have contributed to th is g razing

behaviour. Animals grazing the low herbage al lowance had h igher biti ng rate and a

lower bite weight , consistent with the l iterature where the animal tries to

compensate for a lower bite mass by i ncreasing biti ng rate ( Hodgson & Brookes

1 999) . There was no difference in harvesting eff iciencies (expressed as g DM

eaten kg LW-1 m in grazing-1 ) between heifers grazing either low or moderate

herbage al lowances. Although LHA heifers had a s ignificantly lower dry matter

intake , they also had a lower l iveweight and grazed for a shorter time.

Heifers showed clear preference for green material over dead components, which

is in agreement with Cohen and Garden ( 1 983) . S imi larly, a preference for clover

over grass was detected by the i ndex comparison across treatments. The micro­

h istological analysis techn ique was sensitive, but it did not detect a clover

preference from the treatments as shown in the component selection indexes

(Table 4 ) . Differences might be related more to canopy structu re and intensity of

g razing down than to selectivity. Component selection and nutrient selection

indexes are mechanistic in thei r approach , but they have a h igh standard error as

they accumulate the variation of fou r independent measurements (see equations 1

and 2) .

5. Conclusions

The performance of the Angus heifers g razing the low herbage al lowance was

unexpectly h igh (although sign if icantly lower than at the moderate al lowance)

considering they g razed half of the herbage allowance. The r is ing plate meter was

an acceptable tool for allocating herbage, but when u sed for estimating herbage

intake for g roups of animals , i t should be used careful ly when pre- and/or post­

g razing sward structu res differ between treatments.

The techn iques used for obtain ing information about d iet characteristics were not

rel iable enough , so the efforts involved may not be justified when the objective of

the research is to estimate herbage intake. The trial al lowed the testing of

methodologies u nder local Argentinean conditions to allow progress with more

complex experimental designs focu sed on herbage allowance, herbage i ntake and

58 Part I - Chapter 2

an imal performance using grain supplementation . This wil l be the subject of

chapters 3 and 4.

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la oferta inicial de forraje sobre la ganancia diaria de peso vivo de vacunos

en crecimiento . Revista Argentina De Producci6n Animal 7:54 1 -555.

Garcia-M un iz J. G. 1 998: Studies of Holstein-Fr iesian cattle bred for heavy or l ight

mature l ive weight. PhD Thesis. Massey University, New Zealand :269 p .

Garcia S . C . 2000: System , com ponent, and model l ing studies of pastu re-based

dairy systems i n which the cows calve at different t imes of the year. PhD

Thesis. Massey University, New Zealand: 203 p.

Garc ia S . C . ; Holmes C . W.; Hodgson J . ; MacDonald K. A . 2000 : The combination

of the n-alkanes and 1 3C techn iques to estimate ind ividual dry matter intakes

of pasture and maize s i lage by grazing dairy cows. Journal of Agricultural

Science, Cambridge 135: 47-55.

Goering H . K. ; Van Soest P . 1 970: Forage fiber analysis (apparatus reagents ,

procedures and some appl ications) . Agriculture Handbook Nr 379, USDA.

Washington, DC. 20 p.

Griebenow R. L . ; Martz F. A. ; M orrow R. E. 1 997: Feed and forage : Forage-based

beef f in ish ing systems: a review. Journal of Production Agriculture 1 0: 84-

9 1 .

Hodgson J. 1 982 : I ngestive behaviour. In : Leaver, J. Ed. Herbage Intake

Handbook. Brit ish Grassland Society. Hurley, Un ited Kindom. 1 1 3- 1 39.

60 Part I - Chapter 2

Hodgson J . 1 984: Sward condit ions, herbage al lowance and animal production .

Proceedings o f the New Zealand Society o f Animal Production 44: 99- 1 04.

Hodgson J . 1 990: Grazing Management: Science into Practice. Longman

Handbooks in agricu lture , Longman Scientific and Techn ical co-publ ished

with John Wiley. New York: 203 p .

Hodgson J . ; Brookes I . M . 1 999: Nutrit ion of graz ing animals. In: White, J . &

Hodgson , J . Eds. New Zealand Pasture and Crop Science. Oxford

University press . 1 1 7- 1 32 .

Jamieson W. S . ; Hodgson J . 1 979 : The effect of daily herbage al lowance and

sward characteristics upon the ingestive behavior and herbage i ntake of

calves under strip-grazing management. Grass and Forage Science 34:26 1 -

271 .

Kloster A. M . ; Latimori N . J . ; Amigone M . A. 2000 : Evaluaci6n de dos sistemas de

pastoreo rotativo a dos n iveles de asignaci6n de forraje en u na pastura de

alfalfa y gram fneas. Revista Argentina Oe Producci6n Animal 20: 1 87- 1 98.

Krysl L . J . ; Hess B . W. 1 993 : I nfluence of supplementation on behavior of g razi ng

cattle. Journal of Animal Science 71 :2546-2555.

Machado C. ; Matoso H . ; Auza N. 1 997 : A note of productive perfomance of feedlot

heifer-calves fed on two daily feeding frequencies. Journal of Animal and

Feed Sciences 6:8 1 -85.

Mayes R . ; Lamb C. ; Colgrove P . 1 986 : The use of dosed and herbage n-alkanes

as markers for the determi nation of herbage i ntake. Journal of Agricultural

Science, Cambridge 1 07: 1 6 1 - 1 70.

Mayes R. W. ; Dove H . ; Lamb C. S.; El l is K. J . 1 99 1 : Evaluation of an i ntraruminal

device for the control led release of an alkane marker for intake estimat ion i n

sheep. Proceedings of the 42nd Annual Meeting of the European

Association of A nimal Production 1 :457-46 1 .

Morris S . T . ; H i rschberg S. W. ; M ichel A. ; Parker W. J . ; McCutcheon S . N. 1 993 :

Herbage intake , l iveweight gain of bul l and steers cont inuously stocked at

Herbage intake and grazing behaviour of heifer on two allowances during late spring 61

fixed sward he ights duri ng autumn and spring . Grass and Forage Science

48: 1 09- 1 1 7.

Parker W. J . ; Morris S. T . ; G arrick D. ; Vincent G . ; McCutcheon S. 1 990:

I ntraru m inal ch romiu m controlled release capsules for measuri ng h erbage

i ntake : a review. Proceedings of the New Zealand Society of Animal

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In: Ruiz , M . E. and Ruiz , A . Eds. Nutrici6n De Rumiantes: Gufa metodol6gica

de Investigaci6n. I ICA. San Jose De Costa Rica :3-20 . :3-20.

Real in i C. ; Hodgson J . ; Morris S . T. 1 999: Effect of sward surface height on

herbage intake and performance of f in ish ing beef cattle. New Zealand

Journal of Agricultural Research 42: 1 55-1 54.

Reeves M . ; Fulkerson W.; Kellaway R . ; Dove H . 1 996: A comparison of three

techn iques to determine the herbage intake of dairy cows graz ing kikuyu

( Pennisetum clandestinum) pasture. Australian Journal of Experimental

Agriculture 36 : 23-30 .

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g rain feeding by grazing dairy cows. Australian Journal of Experimental

Agriculture 38:54 1 -549.

Romero L. A . ; Comeron E. A . ; Bruno O. A. ; D iaz M. C . 1 995 : Efecto del n ivel de

asignaci6n de pasturas de alfalfa sobre la respuesta de vacas lecheras. 1 .

Consumo y comportamiento ingestivo . Revista Argentina De Producci6n

Animal 15 :623-626.

SAS/STAT. 200 1 : Release. 8 SAS. Inst. Inc. , Gary, NG.

Sparks D . R . ; Malechek J. C . 1 968 : Estimat ing percentage dry weight i n d iets using

a m icroscopic technique. Journal of Range Management 2 1 :267-268.

Stockdale R. C . 1 985 : I nf luence of some sward characteristics on the con sumption

of i rr igated pastures grazed by lactat ing dai ry cows . Grass and Forage

Science 40:3 1 -39.

62 Part I - Chapter 2

Stockdale R. C . 1 999 : The nutrit ive characteristics of herbage consumed by

g razing dairy cows affect mi lk responses obtained from concentrates

supplementation . Australian Journal of Experimental Agriculture 39:379-387.

Ti l ley J. M. A. ; Terry R. A. 1 962: two-stage technique for the in vitro digestion of

forage crops. Journal of the British Grassland Society 1 8: 1 04 - 1 1 1 .

Chapter 3

Effect of maize g rain and herbage al lowance on

i ntake, a n i mal performance and g razing behavio u r

o f heifers i n wi nter

64 Part /- Chapter 3

Abstract

Thi rty-four 1 3-month old Angus x Hereford heifers (227±8 kg LW) were strip-grazed

dai ly for 64 days during winter ( 1 3 August to 1 4 September) in groups of fou r or

five on a pasture conta in ing predominantly Festuca arundinacea and Trifolium

repens. Repl icated treatments were : Low herbage OM al lowance without m aize

g ra in (LHA: 2.5 % LW d-1 ) , moderate herbage OM allowance without maize g rai n

(MHA: 5 % LW d·1 ) and at the low herbage al lowance , two levels of OM maize

g rain : 0 .5 and 1 .0 % LW d-1 for LHA-M1 and LHA-M2, respectively. Soybean m eal

was fed to all treatments to reach a diet crude protein level above 1 5 %. Herbage

and grain intakes were assessed by slow release n-alkanes and 1 3C , and from

pre- and post-grazing estimations of herbage mass. Two 24-h periods of g razing

behaviour were recorded . The herbage intake in treatment M HA (6.5 and 4.6 kg

OM d-1 from pre- and post-grazing est imations of herbage mass and n-alkanes &

1 3C, respectively) was s ignificantly h igher ( p<0.05) than LHA (4.8 and 3 .3 kg O M

d-1 from pre- and post-grazing estimations of herbage mass and n-alkanes & 1 3C ,

respectively) using both m ethods for intake estimation , and also decreased l i nearly

with maize feeding level when the n-alkanes and 1 3C was used. The LWG of M H A

was s ignif icantly h igher ( P<0 .05) than L H A (0.65 and 0 . 2 5 kg d-1 , respectively) and

i ncreased l inearly (p<0.0 1 ) with level of maize fed. Substitution rates (SR) did not

change with maize feed ing level (average of 0 .36 kg herbage OM kg maize OM-\

The MHA and LHA treatm ents had a s im i lar graz ing t im e (average of 373 .5 m in . d-1 ) and in vivo digestibi l ity of herbage eaten (0 .68, as proport ion of total OM) . As

maize grain feeding level was increased, id l ing t ime, bite weight and harvesting

rate (g OM eaten kg LW -1 min . spent g razing -1 ) increased. Maize feed ing

decreased grazing time, and also tended to decrease the coefficients of variatio n of

LWG . Animals on MHA had higher fasting l iveweight losses than LHA, with no

effect of level of maize g ra in fed . Estimates of herbage intake using the alkane &

1 3C techn ique were in good agreement with the pred ict ion of energy requ i rement

for the attained animal performance , and al lowed (without the need of add ing other

markers) defin it ion of an i mportant variabi l ity in m aize i ntake with in groups of

animals.

Keywords : n-alkane; herbage al lowance; intake ; maize , l iveweight gain , heifers

Increasing levels of maize grain on intake and performance of heifer during winter 65

1 . Introduction

Di rect uti l ization of forage by grazing is the main component of Argentinean beef

cattle systems , however in recent years more s i lage , hay and concentrate have

been used ( Rearte 1 998) . Grain feeding in g razing systems acts as a buffer to

smooth out the inherent risk of variation i n herbage mass and quality, with

additional benefits of system flexibi l ity and intensive use of pastures through

increasing the stocking rate (Rearte & Pieron i 200 1 ) . However, it is important to

encourage g raziers to get maximum benefit from grazed g rass , and to keep

supplementary feed costs low and hence l imit overall expenses.

When grain is fed , typically there is substitut ion of grain for herbage expressed in

kg DM herbage kg DM grain-1 (Dixon & Stockdale 1 999) . Some controversy

regarding the effect of level of supplement on the substitution rate (SR) has been

observed in the l iteratu re . Substitut ion rate was not affected by the level of

supplements when steers were fed maize gra in and fresh alfalfa i ndoors ( El i zalde

et al. 1 999) or grain was fed to grazing animals ( Boom & Sheath 1 998) . However,

in some experiments SR i ncreased with the level of supplements , both in i ndoor

experiments (Petchey & Broadbent 1 980) and in g razing trials (Robaina et al .

1 998) . Because SR has clear implications to herbage uti l i sation and therefore to

economic performance, a better understanding of it wi l l lead to a more effective use

of grain feeding.

Accurate information about i ndividual herbage and g rain i ntake i n grazi ng animals

is scarce ( Dove 1 996) , partly because of the technical diff icu lties of making

accurate measurements of the different herbage and grain components when fed

together. Herbage i ntake of grain supplemented animals has been estimated us ing

n -alkanes with corrections for the concentrate eaten and its n-alkane composition

(Mayes et al. 1 986) . Alternatively, if the concentrate is sufficiently d ifferent in

alkane composit ion and the n-alkane recovery rate is known , individual concentrate

i ntake can be estimated ( Dove & Mayes 1 996) . However, grains typical ly have a

low level of n-alkanes , which affects the use of th is method. To overcome th is

difficulty , i n most of the beef cattle studies measuring herbage i ntake us ing the n­

alkane method and feeding grains , the supplement has been fed individual ly

66 Part /- Chapter 3

( French et a l . 200 1 a ,b) . Although the research advantages of feeding the

supplement in th is way are evident, the natural express ion of an imal variat ion when

g roup fed (as in most Argentinean beef cattle systems) is not real ised. Variat ion

exists in individual an imal intake when animals are fed concentrates as a g roup ,

and th is behavioural phenomenon can explain in some c ircumstances the

contradictory effects of gra in supplementation (Bowman & Sowell 1 997) .

To measure individual grain intakes, markers have been added to the concentrates

such as Ytterb ium (Curtis et al . 1 994) . Alternatively, beeswax may be used to

i ncrease the odd-numbered carbon chain concentrat ions but with a distinctive

pattern ( Moshtagh i Nia & Wittenenberg 2002) , to enable the use of the n-alkane

method of est imating intake. However, this adds an extra operation , which is

d ifficult to implement when the supplement is si lage, and also has an inherent r isk

of feed deterioration. The iterative alkane & 1 3C techn ique (Garcia et al . 2000)

based on contrasts in the balance of 1 3C isotopes in the herbage and the

concentrate has been shown to accurately est imate the level of OM intake of

herbage and maize si lage in graz ing dairy cows supplemented as a group. Th is

tech nique is a combination of the n-alkane technique and the technique of Jones et

al. ( 1 979 ) , whereby the contribution of temperate pastu res and maize through their

C3 and C4 contents is assessed , but it has not been previously used in beef cattle

systems.

Dry and wet maize g rain are com mon supplements in com mercial Argentinean

beef systems, but individual herbage intake when maize is fed has not been

studied, and th is i nformation could be relevant for grazing practices. Therefore , the

main aim of this study was to measure the effects of level of wet maize g ra in fed

and herbage al lowance on performance , intake and g razi ng behaviour of Angus

heifers g razing a perennial beef fin ish ing pasture in winter.

2. Materials and Methods

2. 1 . Experimental design

Thi rty-four 1 3-month old heifers (227±8 kg LW) were allocated to fou r repl icated

Increasing levels of maize grain on intake and performance of heifer during winter 67

t reatments in winter : Low herbage OM allowance without maize gra in (LHA: 2.5 %

LW d-1 ) , moderate herbage OM al lowance without maize g rain (MHA: 5 % LW d·1 )

and at the low herbage al lowance , two levels of maize g rain : Low HA+0.5 % LW d-1

OM of maize grai n ( LHA-M1 ) and Low HA+ 1 .0 % LW d-1 OM of maize g rain (LHA­

M2) . Each treatment was replicated twice with 4 to 5 animals in each replicate

g roup. Herbage al lowance was expressed as herbage OM mass to ground level , as

a percentage of animal l ive weight ( Hodgson 1 984) . The trial was carried out on a

commercial farm at Gardey, Tandi l - Argentina (37 0 1 6' S 54 0 1 9.9 ' W) , using a

un iform pasture containing predominantly tal l fescue (Festuca arundinacea) and

wh ite clover ( Trifolium repens) , rested in advance for eight weeks and receiving no

ferti l iser. Animals were strip-grazed and moved dai ly (at 8 :00 am) to a fresh strip

with no back-grazi ng of experimental areas. The experiment lasted for 64 days ( 1 5

pre-experimental and 49 experi mental days) from 1 3 August to 1 4 September.

Wet cracked maize g rain was used and i t was stored in a plastic s i lage bag (50

ton ) , from where the amount needed was extracted daily. Prote in m eal (soybean

meal) was fed to all groups as herbage crude protei n was lower than the

recom mended prote in content in the diet for young growing cattle ( i .e . 1 5- 1 8 % of

crude protein in OM , Hodgson & Brookes 1 999) . Soybean meal was used to

ach ieve a min i m u m crude protein level of 1 5 % OM i n the diet, including g razed

h erbage.

2.2. Measurements

Fifty measurements of pre- and post-grazing herbage mass were taken dai ly i n

each repl icate u s i ng a calibrated ris ing plate meter and a dou ble sampl ing

procedure ( Earle & McGowan 1 979) . From the pre-grazing measu rement , dai ly

herbage allowances were allocated by adjust ing the grazing area. Ani mal group

herbage i ntake was calcu lated from the difference between pre- and post-grazing

herbage masses ( Frame 1 98 1 ) .

Animals were de-wormed with ivermectin ( Ivomec™) 48 h prior to al location to

treatments and on day 30 of the study. All heifers were treated with a

progesterone-based compound ( M . P .HTM , Burnet lab . ) with the objective of

avoid ing oestrus and hence alterations in animal grazing behaviour. Concentrates

were fed at 1 :00 p m , with heifers g razing the LHA and M HA treatments receiving

68 Part /- Chapter 3

only soybean, whi le the LHA-M 1 and LHA-M2 treatments were fed a mixture of

soybean and wet maize g rain . A trough space of 80 cm per animal was assigned,

and residual concentrates left after feed ing ( if any) were weighed and sampled for

OM esti mat ion (oven-dried at 60 QC for 48 h u nti l constant weight) . Animal g roup

concentrate DM i ntake was est imated from pre- and post-feeding measu rements.

Animals were weighed unfasted (with in 1 hour of removal from grazing) fortnightly

and after a 1 5-h fast at the end of the trial . Fortnightly an imal l ive weig hts were

used to adjust herbage and concentrate al lowances. Measurements of g razing

behaviour were carried out on days 25 and 33 of the tr ia l . Al l i ndividual animals

( identif ied w ith a phosphorescent number painted on both s ides for n ight

observat ion) were observed once every 1 0 min. and the ir activities were recorded

as graz ing , ruminat ing or idl ing over a 24-h period (Hodgson 1 982) . Bit ing rates

were recorded for i ndividual animals at their peak grazi ng t imes (morn ing and

afternoon) , by measuri ng the t ime to make 20 prehension bites (Hodgson 1 982) ,

averaging th ree measurements for each ani mal , and then us ing t h is i nformation to

compute the daily number of bites .

On day 1 4 al l animals were dosed with n-alkane capsules (Captec® 1 50-300 kg

LW) contain i ng dotriacontane (C32) and hexatr iacontane (C3S) with a constant

release rate for each n-alkane of 200 mg d-1 . Routines for sampl ing and estimation

of i ndividual i ntake and digestibi l ity were carried out as described by Dove & Mayes

( 1 991 ) . The capsu les had an active period of 20 days and two i ndependent

measurements of intake and behaviour measurement were made at days 7 to 1 2

and 1 5 to 1 9 post-application . Dur ing each period of i ntake record ing , faecal

samples were col lected daily during morning or evening observation (2-h periods)

of ani mals defecat ing (Morris et al. 1 993) . Samples were freeze-stored and

compou nded with in each an i mal over the 5 -day period of col lection . After

defreezing , they were ground through a mi l l ( 1 mm) and analysed for n-alkane

composit ion by gas ch romatography (Mayes et al . 1 986) . The 1 3C was estimated

(only for the period 1 5- 1 9 days) from organic matter content by m ass spectrometry

(F innigan Mat DeltaS, Laboratory of isotopes , INGE IS-UBA).

I ndividual herbage and maize i ntakes were estim ated by us ing the iterative alkane

Increasing levels of maize grain on intake and performance of heifer during winter 69

& 1 3C method (Garcia et al . 2000) . As soybean meal was added , concentrate

i ntake was est imated using the 81 3Cconcentrate (weighted average of 81 3Cmaize and

81 3CsOYbean from the known proportion fed in the troughs, 65 and 77 % maize for

LHA-M 1 and LHA-M2 respectively) , assuming no select ion between them. This

assumption seemed valid from field observat ions, as the cracked wet maize grain

m ixed wel l with the soybean meal. I ndividual maize i ntake was estimated from

i ndividual concentrate intake and maize proport ion in the mixture. In the case of

those treatments supplemented with soybean meal only , d ifferences between

81 3CsOYbean and 81 3Cherbage (Table 1 ) , although both were C3 foods, al lowed the use

of the alkanes & 1 3C techn ique. I n u nrepl icated supplementation trials the grazing

only group acts as the control for estimating the substitution rate. In the current

trial , to estim ate i nd ividual substitut ion rates associated with m aize i ntake , an

average "basal" herbage i ntake was calculated from animals al located to both

repl icates of t reatment LHA, and individual differences from th is basal level were

calculated for supplemented animals . From these herbage intake differences (kg)

and maize intakes (kg ) , individual substitution rates were estimated .

The fol lowing variables were estimated from grazing behaviour observations ,

i ndividual herbage intake, individual d iet digest ibi l i ty and animal l iveweight : bite

weight (g OM bite-\ daily energy intake (MJ M E d-1 ) , and harvesti ng rate (HR) as g

OM eaten kg LW - 1 min . spent grazing -1 (Krysl & Hess 1 993) .

2.3. Herbage sampling and chemical analysis

During each intake measurement period, th i rty hand-plucked herbage samples

representing the d iet for each repl icated plot were obtained daily. Herbage samples

were pooled with i n each repl icate by sampl ing period (Le one per repl icate for 5

days) as described by Garcia et al . (2000) . Samples were sealed i n polythene bags

and taken to the laboratory in an icebox . Part of the sample was freeze-dried ,

ground ( 1 mm screen) and divided into two sub-samples. The f i rst was analysed for

n -alkane com posit ion , and the second analyzed for dry matter in vitro digestibi l ity

( Dig %) (Ti l ley & Terry 1 962) and crude protein (CP %) by Micro Kjeldhal . To

determ ine the organic matter (OM) fraction the dry matter was subsequently ashed

i n a m uffle furnace at 550 CQ. The second part of the pooled sample was divided

into two. The fi rst was completely sorted by hand into tal l fescue and wh ite clover,

70 Part /- Chapter 3

and then into thei r component parts , i . e . lamina, sheath & stem and dead (tal l

fescue) and leaflet, petiole & sto lon and dead in white clover. These components

were dr ied in an oven unt i l constant weight (60 °C for 48 h) and expressed as

fract ion of dry matter. The rest of the sample was also oven dr ied and preserved

for 1 3C determi nation .

2. 4. Statistical analysis

Measurements of pastu re were analysed under a completely random design . A

repeated measures design was used to analyse the behaviou ral variables , us ing

treatment replicates (grazing plots) as experimental un its i n both des igns . I n the

repeated measures design , the P ROC M IXED procedure and ESTI MATE and

CONTRAST statements for compari ng m eans were used (L ittel l et al . 1 998) . Pre­

planned contrasts included l inear and quadratic contrasts for maize level for the

groups fed at low herbage allowance ( LHA, LHA-M 1 and LHA-M2) ' The othe r

animal variables were analysed us ing P ROC G LM i n a randomized design with

animals as experimental un its , and with pre-planned contrasts as stated above .

Following a s ign ificant F-test (p<0 .05) , least squares means were separated us ing

least s ign ificant differences (Steel & Torrie 1 980). All analyses were performed

using SAS/STAT (200 1 ) .

3. Results

3. 1 . Herbage and concentrate intake and animal performance

Chemical composition , concentrat ion of dominant n-alkanes and (51 3C in herbage

and supplement are presented in Table 1 . The pasture was dominated by F.

arundinacea (0 .96±0 .0 1 fraction of total OM) , and the whole canopy morphological

composit ion was made up 0 .46±0 .03, 0 . 35±0.05 and 0. 1 9±0.03 fractions of total

OM of leaf, ste m and dead components, respectively.

Increasing levels of maize grain on intake and performance of heifer during winter 71

Table 1 . Chemical composition, concentration of dominant n-alkane and concentration of 1 3C in the uti l ised feeds.

Forage DM CP Dig OM C29 C3, C33 � % mg 1 00 9 DM"

Herbage 35.2 1 3 .6 6 1 .2 87.3 251 .5 499.2 1 1 .8 Wet maize grain 73 .7 9.3 85.3 96.5 1 . 1 0 .8 0.6 Soybean meal 88.0 41 .3 92.2 92.3 3 . 1 2 .7 2.2

-28.2 - 1 1 . 1 -25.0

DM= dry matter, CP= crude protein , Dig= in vitro DM Digestibi lity, OM= organic matter, C29=nonacosane, C3, = hentriacontane, C33=tritriacontane, 81 3C= abundance of 1 3C relative to a carbonate standard .

Table 2. Effects of two herbage allowances and three supplement levels i n heifers on d ifferent parameters associated with animal performance.

LHA LHA-M, LHA-M2 MHA #PSE Sig. MLinear

Pre-grazing mass (kg ha" ) Post-grazing mass (kg ha" )

c DM disappearance Herbage intake (kg DM d·1 ) Maize i ntake (kg DM d·1 ) Total intake (kg DM d·1 ) Substitution rate (kg DM herbage kg D M maize grain· 1 )

n-alkane & 81 3C Herbage intake (kg DM d·1 ) Maize i ntake (kg DM d·1 ) Total intake (kg DM d" ) Maize in diet (% LW d' ) Diet digestibi l ity Substitution rate (kg DM herbage kg

5330 1 230 a

4930 1 370 a

4 .7 a

1 .33 a

6 .7 b

0 . 1 3

2 .9 a

1 . 1 5 a

4 .5 b

0 .5a

0 .69 a

0 .39

5420 4940 1 670b 2460c

4.2 a

2.76 b

7.7c

0.24

2.6 a

2.54 b

5.8 d 1 .00 b

0.76 b

0 .33 0.68 a

1 78 1 06

0 .24 0 .03 0 .25 0 . 1 2

0 . 1 7 0 .25 0 .24

0.002 0.01 0 .24

DM maize grain·1 ) M E intake (MJ M E d'1 ) 37 .5 a 46.9 b 67 . 1 c 51 .5 b 2 .29 Final LW (k� ) 235 a 249 ab 272 b 256ab 8 .38

NS NS ** * *

** NS **

NS

**

***

• ***

NS

*** •

LWG (kg d' ) 0 .25 a 0 .62 b 1 .0 1 c 0 .65b 0 .08 Fasted LW loss (% full LW) 4.8 a 4 .2 a 4 .7 a 6.6 b 0 .60 • NS

LHA= 2 .5 % LW d" herbage OM allowance, LHA-M1 = LHA plus 0 .5 % LW D M maize grain d" , LHA-M2 = LHA plus 1 .0 % LW maize grain d·1 , M HA= 5.0 % LW d·1 herbage al lowance, #PSE= pooled standard error, S ig .= sign ificance level , MLinear = l inear contrast of maize level effect, cDM d isappearance= pre-post-grazing dry matter d ifference, different letters withi n a row indicate s ignificant differences (p<0 .05) , NS = no significant d ifference ( p>0. 1 ) , -=P<0.05, **=P<0.01 and ***=P<0.0 1 .

Herbage intake and total OM intake estimated by the pre- and post-grazing

difference method were sign ificantly h igher ( p<O.05) i n MHA than in LHA (Table 2) .

Herbage and total OM i ntake estimated from the n-alkane tech nique fol lowed

s im i lar patterns, but n-alkane esti mations were lower in all treatments than

difference values, and there was a l i near decl inat ion of herbage i ntake (p<O .05) as

level of maize fed increased . Addit ional ly , total OM intake estimated us ing the n­

alkane method was s ign ificantly h igher ( p<O.0 1 ) on LHA than LHA-M 1 .

The precision of the two methods used for assessing M E i ntake versus the

72 Part /- Chapter 3

theoretical metabol isable energy requ i rements for the level of performance

attained , was tested. The treatment means of ME eaten estimated alternatively

from the alkane & 1 3C method ( M E-alk) and from the pre- and post-g raz i ng

difference method and feed qualities ( M E-diff) , were regressed on predicted M E

requ i rements (AFRC 1 993) calculated from animal l iveweights and LWG (M E-req)

taken as independent data (Mayer & Butler 1 993) . I n both cases the reg ressions

fitted significantly ( p<0.0 1 ) for M E-alk (R2=0.98 , RMSE=1 .9) and M E-diff (R2=0.99,

RMSE=1 .35) , and the intercepts were not s ignificantly d ifferent from zero ( p>0.05) .

When they were forced through the orig i n both were s ignificant at P<0 .0 1 ( M E-alk

R2=0.99, RMSE= 1 .7 ; M E-diff R2=0.99, RMSE=1 .83) , but only the s lope of M E-alk

(0.99±0 .01 ) was not different from un ity ( p>0 .05) , which leads to M E-alk= M E-req

( p>0 .05) . The M E-diff had a slope coeff ic ient of 0 .72±0 .0 1 , which was sign ificantly

lower than 1 .0 ( p<0.0 1 ) .

In vivo digestib i l ity of the whole d iet increased l inearly with i ncreasing maize levels

fed , while there was no significant d ifference ( p>0.05) between LHA and M HA.

The combined effect of total intake and whole diet digest ibi l ity produced a h igher

ME i ntake in MHA than LHA. Simi larly , ME i ntake increased l i nearly as the level of

maize grain fed was increased (Table 2 ) .

I ndividual substitution rates (kg OM herbage kg OM maize-1 , soybean meal fed

excluded) were not affected by maize supplementat ion level ( p>0 . 1 ) , averagi ng

0 .36 between LHA-M1 and LHA-M2 when i ntakes were estimated using the alkane

& 1 3C method (Table 2). Pre-grazing herbage mass was s im i lar between

treatments but, as a consequence of d ifferent rates of sward depletion , post­

g razing residuals were h igher for M HA than LHA, and increased l inearly as the

level of maize fed i ncreased (Table 2) . The amount of soybean meal fed (estim ated

from n-alkane and C 1 3 method) was s ignificantly higher ( p<0.05) in LHA-M2 (0 .7

kg OM d-1 ) than the rest of the treatments (0 .37 , 0 .40, 0 .45 kg OM d-1 for LHA, LHA­

M 1 and MHA, respect ively) . This gave a crude protein level i n the diet of 1 6.7 , 1 4 .9 ,

1 5. 1 and 1 6 .0 % OM for LHA, LHA-M 1 , LHA-M2 and MHA, respectively . I ndividual

substitution rates i ncluding soybean m eal were 0.29 and 0 .25 (±0 . 1 8 PSE) kg O M

herbage kg OM supplemenr1 for LHA-M 1 and LHA-M2 , respectively.

Increasing levels of maize grain on intake and performance of heifer during winter 73

� .£: 3 .0 � o Cl � Q) 2 .0

� ell C c ·ffi 0, 1 .0 Q) N ·ffi

� 0 .0

LHA-M1 LHA-M1 LHA-M2

Treatment replicates

LHA-M2

Figure 1 . I ndividual maize g rain i ntakes as recorded within each treatment repl icate.

Dotted l ines ind icate mean values for each replicate. LHA-M1 = 2.5 % LW d·1 herbage DM allowance plus 0 .5 % LW D M maize grain d·1 , LHA-M2 = 2.5 % LW d·1 herbage DM allowance plus 1 .0 % LW maize grain d·1 .

Mean i ndividual maize OM intakes were 1 . 1 5 ±0 .2 and 2 .54 ±0 . 1 2 kg d·1 for LHA­

M 1 and LHA-M2 respectively (Figure 1 ) . F inal l iveweight and l iveweight gains were

h igher i n M HA than in LHA, and i ncreased l i nearly ( P<0. 0 1 ) with the level of maize

grain fed (Table 2 ) . The coefficient of variation (CV) of LWG tended to decrease

with the level of maize fed, with values of 73, 37 and 30 % for LHA, LHA-M 1 and

LHA-M2, respectively. The lowest CV i n LWG was in MHA (2 1 %) . With in each

supplemented treatment, i ndividual LWG was associated l i nearly ( P<0.05) to

individual maize intake (R2 of 0 .77 and 0 .6 1 for LHA-M1 and LHA-M2, respectively) .

Animals on M HA had h igher fast ing weight losses than those on LHA, and the level

of maize g rain did not affect the fast ing weight losses. None of the quadratic trends

for analyzed variables in this study reached significance (P>0.05 ) .

3.2. Animal behaviour

Animals g razing treatments MHA and LHA spent a s imi lar t ime ( p>0 .05) g razing

and eating soybean meal , and had a s imi lar bit ing rate (Table 3 ) . Animals in M HA

rum inated more and spent less t ime idl ing, and g razed longer during the night (+40

m in ) . At LHA, animals grazed longer duri ng dayl ight hou rs (+4 1 min ) , principally

after g roups were shifted in the morning to a new g razing str ip ( Figure 2) . Maize

supplementat ion decreased the dai ly grazi ng time l i nearly ( -5 7 min . kg maize-1 ) ,

and i ncreased idl ing t ime and the time spent eating the supplem ent l i nearly. M aize

74 Part /- Chapter 3

supplementat ion decreased ( l inearly) the nu mber of bites per day. Bite weight and

harvesting rate (g OM eaten kg LW ·1 m i n . spent grazing .1 ) were h igher in M HA

than i n LHA, and were increased ( l i nearly) by maize feed ing level .

Table 3 . Effect o f herbage al lowance (LHA and M HA) and maize supplementation (LHA-M1 and LHA-M2) on grazing behaviour of heifers.

Whole day: Grazing time (min . ) Ruminating time (min . ) Id l ing time (min) Supp. Time (min) Bit ing rate (bites min01 ) Bite number (bites d01 ) Bite weight (mg bites01 ) H E (g DM eaten kg LWo1 min .

graz ing01 ) Dayl ight period (7 :00 am-5 :59 pm)

Grazing time (min) Ruminating t ime (min) Id l ing time (min) Supp. Time (min)

Morning period (7 :00 pm -0:59 pm)

Grazing t ime (min . ) Ruminating time (min) Idl ing t ime (min)

Afternoon period (0:59 pm -5 :59 pm)

Grazing time (min) Ruminating t ime (min) Id l ing time (min) Supp. Time (min)

Night period (6:00 pm - 6:59

LHA

374c 341 a

71 4 a

1 1 a

51 1 9074b

1 62 a

0.037a

294 c 78 a

277 ab

1 1 a

1 25 b

52 1 72 b

1 1 a

LHA­M 1

281 b

3 1 3 a

827b

1 9 b

48 1 3488b

203 b

0 .039 ab

226a

323 a

863 b

28 c 43

971 8a

258 d 0.045 b

M HA

373c 41 6 b

638a

1 3 a

51 1 9023c

238 c 0.053 c

253bC

1 27 c 267 a

1 3 a

#PSE

1 4.5 1 5.3 24.6

1 .2 5 .2

1 356 7 .5

0.003

1 3 .8 5.6

1 5.6 1 . 1

1 0 .9 3.7 9 .7

8 .8 1 2 .9

7 .9 2 .7

am) Grazing time (min) 81 b 58 ab 31 a 1 21 c 9 .6 Ruminating time (min) 262b 221 ab 2 1 4a 289 c 1 3 .6 Idl ing time (min) 437 b 501 c 535c 370 a 1 5.5

LHA= 2.5 % LW dO' herbage allowance, LHA-M 1 = LHA plus 0.5 % LW maize grain dO', LHA-M2 = LHA plus 1 .0 % LW maize grain d·1 , MHA= 5.0 % LW do1 herbage allowance,

Sig

** ** **

NS ** ** **

**

**

* *

** NS

*

** ** **

M­Linear

** NS

* *

NS * * * *

**

**

** NS

* NS NS

* * + •

#PSE= pooled standard error, Sig .= significance level, MLinear = l inear contrast of maize level effect, different letters with in a row indicate significant differences (P<0.05), NS = no significant d i fference (P>0. 1 ) , += P<0. 1 , * = P<0.05, **=P<O.01 .

Increasing levels of maize grain on intake and performance of heifer during winter 75

50

40

c:

I 30 >­'5 .� ..: 20

1 0

50

__ 40 :.:: c:

I 30 '" :� � 20

10

7 8 9 1 0 1 1 1 2 1 3 14 1 5 1 6 1 7 1 8 1 9 20 21 222300 1 2 3 4 5 6 Time

� _____

Da_

y ____ �1IIIIIIIIIIIIII

7 8 9 10 1 1 1 2 1 3 1 4 15 1 6 1 7 18 1 9 20 21 222300 1 2 3 4 5 6

Time

60 "t""---r-r-r-

50

40

c:

I 30 '" :� � 20

1 0

50

:- 40 :.:: c:

I 30 '" :� � 20

1 0

7 8 9 1 0 1 1 12 1 3 1 4 1 5 1 6 1 7 1 8 1 9 20 21 22 23 00 1 2 3 4 5 6 Time

Day Night

7 8 9 10 1 1 12 1 3 1 4 1 5 1 6 1 7 1 8 1 9 20 2 1 22 23 00 1 2 3 4 5 6 Time

Figure 2. Effect of herbage allowance and maize supplementation on grazing behaviour of heifers throughout the day.

Treatments details are explained in material and methods. White arrows indicate time when animals were shifted to a new strip (8 :00 am) , and white circles indicate time when supplements were fed (1 :00 pm). Each bar represent a mean of two replicates of 4-5 animals measured on two different days .

4. Discussion

Refusals of maize g ra in and soybean meal were neglig ib le . The quantity of maize

OM fed was lower than original ly planned (0 .6 and 1 .2 % LW d-1 ) , but d ifferences

were maintained at a factor of 2 (0 .5 and 1 .0 % of LW for LHA-M 1 and LHA-M2

respectively) , and reached a level that did not jeopardize the objectives of the trial .

This i s part ly explained by the fact that grain al location was adjusted every 1 5 days

when the animals were weighed. Diets were not isonitrogenous , but in all

treatments dietary n itrogen content exceeded values considered adequate for this

an imal class (Hodgson & Srookes 1 999).

Maize feeding reduced herbage intake l i nearly and con sequently increased post-

76 Part /- Chapter 3

grazing residuals, which agrees with other reports (Horn & McCol lum 1 98 7 ; M ayne

1 988; Robaina et al . 1 998) . If herbage i ntake expressed as kg kg LW-1 d-1 i nstead

of kg d-1 is used for SR estimat ions to avoid any b ias from the different l iveweights

( i .e . u nsupplemented an imals were l ighter) , substitut ion rates of 0 .29 and 0 .38

(±0. 1 9 PSE) were obtai ned for LHA-M 1 and LHA-M2, respectively. These values

differ s l ightly from those estimated using the absolute herbage intakes (Table 2 ) ,

but these differences cou ld increase in longer s upplementation trials where the LW

of control and supplemented animals differ more than in the present tria l .

Substitution rates d id not change significantly across the different maize levels for

both methods of intake estimation (Table 2 ) . Add ing the soybean meal eaten in the

ind ividual SR est imat ion did not significantly affect the results ( p>0.05) , with a

sl ightly lower SR (average of 0 .27 kg OM herbage kg OM maize p lus soybean

mear1 ) than when only maize g ra in was used (Table 2). The lack of effect of level

of supplementat ion on SR agrees with the find ings of Opatpatanakit et a l . ( 1 993 ) ,

who fed 0 , 4 and 8 kg d-1 o f ro l led barley to dairy cows , and El izalde et a l . ( 1 999)

who fed steers fresh alfalfa indoors together with cracked maize at 0, 0 . 6 and 1 .2

% LW d-1 . However i n terms of absolute values, the substitution rate obtained in

the current study (average of 0.36 kg OM herbage kg OM maize-1 ) is low compared

with the value of 0 .63 obtained by Opatpatanakit et al. ( 1 993) and the 0 .69 fou nd

by El izalde et al . ( 1 999) . Differences between these trials are l i ke ly to be

associated with the lower herbage allowance (2 .5% LW above ground level d-1 ) i n

the current study. A di rect positive relat ionsh ip between herbage al lowance and

substitut ion rate has been demonstrated for dairy cows (Meijs & Hoeskstra 1 984;

Grainger & Mattews 1 989) and in beef cattle ( French et a l . 2001 b) .

The pre- and post-grazing difference method cons istently overestimated herbage

intake when the n-alkane & 1 3C method was taken as a reference (Table 2 ) , a

resu lt which is i n ag reement with previous find ings (Reeves et al . 1 996; Garcia

2000) . These authors m entioned herbage treading losses as one of the l ikely

causes for overestimation of intake from herbage measurements. The n-alkane &

1 3C intake method was considered a better indicator of i ntake than the pre- and

post-graz ing difference m ethod. The M E eaten (MJ ME d-1 ) estimated by n-alkane

& 1 3C was in very close agreement with the M E requ i rement predicted (AFRC

1 993) from recorded LWG and animal l iveweight during the study , whereas sward-

Increasing levels of maize grain on intake and performance of heifer during winter 77

based estimates were consistently 37% higher than that estimated from M E

requ i rements predicted .

A di rect effect of maize supplementation was to reduce g raz ing t ime and i ncrease

idl i ng t ime, which i s i n agreement with most research (Al lden 1 98 1 ; Leaver 1 986 ;

Gekara et al . 2002 ) . The decl i ne i n grazing t ime of 57 m i n kg DM maize-1 i n th is

study is much h igher than the 7 .5 min kg DM SUpp.-1 estimated from a l i near

adjustment after Gekara et al . (2002 ) , and the 3-30 and the 20-23 m in . kg DM

SUpp.-1 found by Leaver (1 986) and Mayne ( 1 988) , respectively. Reasons for such

a h igh depression in this tr ial are not clear. The l i near decl i ne in grazing t ime was

evident at all t imes of the day, but effects were more m arked during dayl ight hours

and part icularly in the afternoon (after supplement feed ing ) , with s lopes of -37 and

-2 1 m i n . kg DM maize-1 respectively (Table 3 ) . This behaviour could be associated

with the i nduct ion of a satiety-regulat ing mechanism as suggested by Baile &

McLaugh l in ( 1 987) , hence decreas ing the drive for g razing although there was

herbage avai lable for the animals .

Digestib i l ity of total d iet increased l i nearly with i ncreasing maize level fed , which is

i n agreement with the experiment 2 of Robaina et al . ( 1 998 ) , who fed dairy cows 0

or 6 . 7 kg of a barley-based supplement at two herbages al lowances. S ince grain

was more digestible than herbage (Table 1 ) , th is response was expected ( Dixon &

Stockdale 1 999).

L iveweight gains i ncreased l i nearly with increasing level of maize fed , with an

incre ment above u nsupplemented control of 0.3 kg LWG d-1 kg DM maize-1 for both

LHA-M 1 and LHA-M2 . This is s im i lar to the mean of 0 .22 kg LWG d-1 kg DM maize-1

found by Boom & S heath ( 1 998 ) , when 1 to 2 kg of maize was fed to steers dur ing

summer and winter , and the 0 .23 kg LWG d-1 kg DM m aize-1 estimated from Aiken

(2002) , and h igher than the 0. 1 kg LWG d-1 kg DM maize-1 estimated from Lake et

a l . ( 1 974) . Although more complex mechanisms involvi ng s ites of d igestion and

rumen energy and protein asynch ronies may be operat ing ( Horn & McCol lum 1 987;

Dixon & Stockdale 1 999) , it is l i kely that the degree of improvement in the various

tr ia ls i s mostly related to how the animals were performing without g rain feed ing

( Reardon 1 975) . A productive response to g rain feed ing i n th is trial is l i kely to be

78 Part /- Chapter 3

associated with at least fou r factors. Firstly, the decl ine in g razing t ime may lead to

a decrease i n energy expenditure (Caton & O huyvetter 1 997) that is potential ly

related to l iveweight gain . The decl i ne in grazing t ime is also associated with a less

than proportional decl ine in herbage intake , meaning that the efficiency of

harvesting the forage , expressed in g OM eaten kg LW -1

min . spent g razing -1

(Krysl & Hess 1 993) , s ign i f icantly i ncreased (Table 3) . Secondly, there was a l inear

increase in total M E eaten . Thi rdly , the ME provided by the g rain may increase the

overall efficiency of energy use (NRC 2000) . Fourthly , heifers in the present tr ial

were dosed with a progesterone anabolic to avoid g razing behaviour disturbance

and this may have i ncreased LWG . Improvement in LWG of around 5% could be

expected from using th is compound (Ouckett & Andrae 200 1 ) .

Treatment MHA resu lted i n the h ighest herbage i ntake , which led to an extra LWG

of 0.23 kg LWG d-1 kg OM extra h erbage-1 . However, MHA was used in th is study

as a posit ive contro l , rather than a real option for the farmer in a season where

forage scarcity is frequent. The reduction of forage intake (and increase of herbage

residuals) when supplements are fed is not necessari ly u ndesirable depending on

the situation, i . e . when the objective is to extend the use o f exist ing herbage i n the

paddocks (Horn & McCol lum 1 987) . The quantitative effect of extra residual

herbage mass on herbage production during autumn and winter has been

estimated by Matthew et al . ( 1 995) u nder New Zealand condit ions as 2 kg OM h a-1

d-1 for every 1 00 kg OM ha-1 i ncrease in herbage res idual , over the range 1 000-

2000 kg OM ha-1 .

I ndividual maize intake u nder g roup fed condit ions resulted i n coefficients of

variat ion of 36.4 % in LHA-M1 and 24.8 % in LHA-M2 . The recorded variat ion i s i n

agreement with the 36% CV reported b y Garcia et al . (2000) for dairy cows . The

variabi l ity of individual i ntake of supplements with in each treatment was l i nearly

associated to f inal LWG (average of R2=0.69, P<0.05) . The improved feeding

condit ions result ing from maize supplementat ion or by increasing herbage

al lowance to a level of 5% LW, tended to decrease the variabil ity in animal LWG .

A CV i n LWG of up to 86% duri ng winter in i ntensive beef systems has been

reported (Cosgrove et al . 2003) . Current changes in market characteristics where

the value of high carcass grad ing and t imel iness of supply are i ncreasing ,

Increasing levels of maize grain on intake and performance of heifer during winter 79

emphasize the importance of consistency and predictabi l ity of performance

(McRae 2003) . Winter grain feeding allows the m aintenance of a h igh stocking

rate, that contributes to a h igh herbage uti l ization during the spring period (Rearte

& Pieroni 200 1 ) , and the advantages of reducing LWG variat ion by grain feeding

should be taken i nto account for whole system evalu ation .

I ncreasing the level of concentrates fed has caused lower shrunk l iveweight losses

in steers (Machado et al . 200 1 ) and lambs (Karnezos et al . 1 994) . However no

effects were fou nd in the present trial . Animals grazi ng LHA had lower shrunk l ive

weight losses than animals in MHA. This is l i kely to be associated to the

differences in herbage intakes (Table 2) , as higher forage i ntake increases digesta

volume (Luginbuhl et al . 1 994) .

5. Conclusions and impl ications

I ncluding maize gra in up to a level of 1 .0 % LW per day (OM) in animals grazing a

low herbage OM al lowance (2 .5 % LW d-1 ) , resulted in consistent substitution rates

(0 .36 kg OM herbage kg OM maize-1 ) and a l i near i ncrease in LWG . When animals

g razed a moderate herbage OM allowance (5 .0 % LW d-1 ) , animal performance

was equ ivalent to the feeding of low-level maize OM supplementation (0 .5 % LW d-1 ) on a low herbage allowance. Prediction of ME i ntake from the alkane & 1 3C

techn ique gave s im ilar resu lts to the theoretical energy requ i rements for the animal

performance ach ieved, and al lowed (without the need of adding others markers)

identif ication of an important with in-group variabi l ity in maize i ntake, wh ich

associated positively with LWG . Feeding maize a lso tended to decrease LWG

variabi l ity. The resu lts support evidence from the l iteratu re that supplements fed to

g razing animals usually depress herbage i ntake and grazing time, i ncrease an imal

performance and decrease LWG variabi l ity.

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Parente M . ; Auza N. 200 1 : Suplementaci6n d e grano de mafz e n pastoreo y

a corral como alternativas de terminaci6n de novi l los en el verano . A vances

En Producci6n Animal 26: 1 39- 1 46.

Matthew C. ; Hodgson J . ; Matthews P . N . 1 995 : G rowth of pastures principles and

thei r appl ication . Dairyfarming Annual, Massey University 4 7: 1 22- 1 29.

Mayer D. G . ; Butler D . G . 1 993 : Statistical val idation . Ecological Modelling 68:2 1 -

32.

Mayes R . ; Lamb C . ; Colgrove P. 1 986 : The use of dosed and herbage n-alkanes

as markers for the determinat ion of herbage intake. Journal of Agricultural

Science, Cambridge 1 07: 1 6 1 - 1 70 .

Mayne C. S . 1 988 : Effects of supplementat ion on the performance of both growing

and lactat ing cattle at pasture. In: Frame, J . Ed. Efficient Beef Production

from Grass. Occasional Sympos ium. No.25 , Brit ish G rassland Society,

Berkshire , U K. 55-7 1 .

McRae A. F . 2003 : Historical and practical aspects of profitabi l i ty i n commercial

beef production systems. Proceedings of the New Zealand Grassland

Association 65:29-34.

Meijs J . A. C . ; Hoeskstra J . A. 1 984: Concentrate supplementation of grazi ng dairy

cows . 1 . Effects of concentrate i ntake and herbage al lowance o n herbage

84 Part /- Chapter 3

i ntake. Grass and Forage Science 39:59-66.

Morris S. T.; Parker W. J . ; Grant D . A. 1 993: Herbage i ntake , l iveweight gain , and

grazing behavior of Friesian , P iedmontese x Friesian , and Belgian Blue x

Friesian bul ls . New Zealand Journal of Agricultural Research 36:231 -236.

Moshtagh i Nia S. A.; Wittenenberg K. M. 2002: Evaluat ion of n -alkanes as markers

for estimation of dry matter i ntake and digest ib i l ity i n steers consuming al l ­

forage or forage-concentrate diets . Canadian Journal of Animal Science

82:4 1 9-425.

N RC . 2000 : Nutrient requ i rements of beef cattle. National Resarch Council.

National Academy Press. Washington, D. C.

Opatpatanakit Y . ; Kellaway R . C . ; Lean I . J . 1 993: Substitution effects of feed ing

rolled barley g rain to g razing dairy cows . Animal Feed Science and

Technology 4:25-38.

Petchey A. M . ; Broadbent P. J. 1 980 : The performance of fatten ing cattle offered

barley and grass si lage in various proport ions either as discrete feeds or as

a complete diet. Animal Production 3 1 :251 -257.

Reardon T. F. 1 975 : Summer feeding of beef cattle. Proceedings of the Ruakura

Farmers ' Conference 27: 1 4- 1 8 .

Rearte D . 1 998: Beef catt le production and meat qual ity on graz ing system i n

temperate regions. Revista Argentina De Producci6n Animal 18: 1 29- 1 42 .

Rearte D . ; Pieroni G . A. 200 1 : Supplementation of temperate pastures .

Proceedings of the XV International Grassland Congress:679-691 .

Reeves M . ; Fulkerson W. ; Kel laway R . ; Dove H . 1 996: A comparison of th ree

techniques to determ ine the herbage intake of dairy cows grazing kikuyu

( Pennisetum clandestinum) pasture. Australian Journal of Experimental

Agriculture 36: 23-30.

Robaina A. C . ; G rainger C . ; Moate P . ; Taylor J . ; Steward J . 1 998 : Responses to

gra in feeding by grazing dairy cows. Australian Journal of Experimental

Increasing levels of maize grain on intake and performance of heifer during winter 85

Agriculture 38:54 1 -549.

SAS/STAT. 200 1 : Release. 8 SAS. Inst. Inc. , Cary, NC.

Steel R . G . D . ; Torrie J . H . 1 980: Principles and Procedures of Statistics: A

Biometrical Approach . . 2nd Ed. McGraw-Hi l l Publ ishing Co . , New York. 631 p.

Til ley J. M. A. ; Terry R. A. 1 962: two-stage technique for the in vitro digestion of

forage crops. Journal of the British Grassland Society 1 8: 1 04- 1 1 1 .

Chapter 4

Effects of herbage al lowance and maize g ra in on

herbage a nd g ra i n intake, and performance of

Angus steers i n late spri ng

88 Part 11 - Chapter 4

Abstract

Fifty-fou r Angu s steers (28 1 ±22kg LW) were al located during late spring (2

November to 29 December) to s ix repl icated treatments in g roups of fou r or five

animals in a 3x2 factorial design in a fescue-white clover pasture for 57 days.

There were three herbage OM al lowances , LHA (2 .5% LW d-1 ) , MHA (S% LW d-1 )

and HHA (7 .S% LW d-1 ) , and two levels of group-fed OM maize g rain Mo (0 % LW

d-1 ) and M1 (O .S % LW d-1 ) . Animals were weighed unfasted fortn ight ly and after a

1 S-h fast at the end of the tria l . Estimates of herbage and maize intake were

obtained us ing the difference between feed offered and feed residuals and with a

method combin i ng n-alkane capsu les (Captec®) and 1 3C . Herbage residuals

increased l i nearly (p<0 .0 1 ) when herbage al lowance increased (from 1 240 to 1 7 1 0

kg OM ha-1 for LHA to HHA, respectively) and when maize was fed ( P<O.OS) .

Herbage and total i ntake and M E eaten estimated from n-alkane & 1 3C method

increased (p<0.0 1 ) with increas ing herbage al lowance. In vivo dry matter

d igestibi l ity of the total d iet was h igher ( P<O.OS) when maize g rain was fed (71 and

77 % for contro l and maize supplemnted treatments, respectively) , and i ncreased

l inearly accord ing to the herbage al lowance level . Maize grain intake h ad an overall

mean of 1 .2 kg OM d-1 , with a coefficient of variation (CV) between i ndividual

animals of 41 %. The substitution rate (SR) was quadratical ly i ncreased by

increased levels of herbage al lowance from 0.38 to 0.87 when the alkane & 1 3C

method was u sed. Differences between methods used to est imate SR were

detected . The predicted ME requ i rements from observed an imal l iveweights and

LWG (M E-req) , were s imi lar ( p<O.OS) to M E intake est imated from in vivo

digestibi l ity and OM intake obtained from the alkane & 1 3C method. Final

l iveweight and l iveweight gains i nc reased quadratical ly (p<0.05) with the level of

herbage al lowance (from 268 kg LW and 0 . 1 6 kg d-1 at LHA to 29 1 kg LW and 0 .69

kg d-1 at H HA, respectively) and maize feeding level i ncreased (P<O.OS) LWG by

0.09 kg d-1 . The fasting l iveweight losses i ncreased s ignificantly ( p<O.OS) with the

level of herbage al lowance , with no s ignificant effect of maize feed ing . The

coefficient of variation of LWG decl ined with the level of herbage al lowance, with

values of 1 07 , 34 and 23% for LHA, M HA and H HA, respectively . These f indings

were compared with the results of Chapters 2 and 3 , and the i m pl ications of these

general izat ions discussed.

Effects of increasing herbage allowance with and without maize grain 89

Keywords: n-alkanes ; herbage allowance; feed-intake; maize; l iveweight gain ;

Angus steer

1 . Introduction

Di rect ut i l izat ion of the sward by g razi ng is usually the cheapest option for feeding

rum inants. The g reater the control the l ivestock producer exerts over forage

production , consumption and match ing animal requ i rements to seasonal forage

production cycles , the better are the chances that the operat ion will be profitable

(Forbes 1 988) . In those systems where g rain may be fed profitably in g razi ng

conditions , a productive and efficient use of th is resou rce requires a better

u nderstanding of i nteractions occu rring at the plant-animal interface ( Dove 1 996) .

Herbage al lowance has been used successfu l ly i n rotational grazing systems as an

experimental variable to control an imal performance (Reardon 1 977 ; Jamieson &

Hodgson 1 979 ; Baker et al . 1 98 1 ; Reid 1 986) . Animal performance usual ly

increases at a steadily decreasi ng rate towards a maximum value as herbage

al lowance (HA) increases, reflecting the effect of HA on herbage i ntake (Hodgson

1 984) . Recently, the interest in this variable has been renewed, i ncorporating more

detailed sward descriptions ( Dougherty et al . 1 989; Ki m et a l . 1 995 ; Okaj ima et al .

1 996) and estimating herbage intake with n-alkanes ( French et a l . 200 1 ) or oth er

external markers ( Redmon et al . 1 995 ; Delagarde et al . 2000) .

The effects of grazing system and levels of herbage al lowance on h erbage i ntake ,

an imal performance and herbage ut i l isation have been studied i n beef cattle

f in ish ing systems in Argentina (K loster et al . 2000) . I n such g razi ng-based beef

cattle f in ish i ng systems, grain is frequently used ( Rearte 1 998; M artinez Ferrer et

a l . 2002) , so it is important to establ ish some indicat ions of the pattern and logic of

the relat ionsh ip between herbage al lowance , g rain feeding and animal

performance over a wide range of levels . The l imited availabi l ity of such information

usual ly constrains the development of models (Dove 1 996) , which are usefu l when

exploring the benefits of alternative g razing systems. Short-term trials were

developed to evaluate two levels of herbage al lowance (Chapter 2) and repeated

including three levels of maize g ra in fed (Chapter 3 ) . Substitution rate did not

90 Part 11 - Chapter 4

change with the leve l of grain fed at a herbage allowance of 2.5% LW d-1 (average

of 0 .36 kg herbage OM kg maize OM-\ and a large variat ion in the level of

i ndividual grain i ntake (31 % CV) when fed as a g roup was demonstrated (Chapter

3 ) . This chapter reports the resu lts of a further study on the effect of th ree levels of

herbage allowance in late spring on herbage intake and animal performance of

Angus steers group-fed with and without maize g rain .

2. Material and Methods

2. 1. Treatments

Fifty-fou r Angus steers (28 1 ±22 kg LW) were al located to six repl icated treatments

in a 3x2 factorial design in a fescue-white clover pasture for 57 days in late spring .

There were three herbages OM al lowances , LHA (2 .5% LW d-1 ) , M HA (5% LW d-1 )

and HHA (7 .5% LW d-1 ) , and two levels of g roup-fed OM maize grain , Mo (0 % LW

d-1 ) and M1 (0 .5 % LW d-1 ) . Each treatment was replicated twice with four to five

animals in each replicate group and herbage al lowance was expressed as herbage

OM mass to g round level as percentage of animal l iveweight (Hodgson 1 984). The

trial was carried out on a commercial farm at Chi l lar, Azu l (37Q 1 9 '5 59Q 59'W)

du ring late spring (2 November to 29 December) with 1 5 pre-exper imental and 42

experimental days. A un iform pasture was used contain ing predominantly tal l

fescue ( Festuca arundinacea) and white clover ( Trifolium repens) , which was

rested in advance for five weeks and received no fert i l iser. The paddock was

partially flooded during rest, which delayed the start of the study, so the sward was

topped at 20 cm height before the experimental period (25 cm before topping) .

An imals were strip-grazed and moved daily (at 1 2 :00 am) to a fresh strip with no

back-grazing of experimental areas . Cracked maize grain was used and it was

stored in 45-kg bags , from where the amount needed was extracted daily.

2.2. Measurements

Thirty measurements of pre- and post-grazing herbage mass were taken daily in

each replicate using a calibrated ris ing plate meter and a double sampl ing

procedure (Earle & McGowan 1 979) . From the pre-graz ing est imations of herbage

mass, daily herbage allowances were al located by adjusting the g razi ng area.

G roup herbage intake was calculated from the difference between pre- and post-

Effects of increasing herbage allowance with and without maize grain 9 1

graz ing estimations of herbage m ass ( Frame 1 98 1 ) .

Anim al s were de-wormed with ivermect in ( Ivomec TM) 4 8 h prior to allocat ion to

treatments and at day 30 of the study. Maize was fed to the s upplemented g roups

at 1 :00 pm. A trough space of 80 cm per an imal was assigned, and residual

concentrates left after feed ing ( if any) were weighed and sampled for DM

estim at ion (oven-dried at 60 QC for 48 h u nt i l constant weight) . G roup maize DM

i ntake was estimated as the difference between amount offered and refusals .

Anim als were weighed u nfasted (with i n 1 h of removal from grazing) fortn ightly and

after a 1 5- h fast at the end of the tr ial . Fortn ightly an imal l ive weights were used to

adju st herbage and concentrate allowances. On day 40 n-alkane capsules

(Captec® 300-650 kg LW) contain i ng dotriacontane (C32) and hexatriacontane

(C36) with a constant release rate for each n-alkane of 400 mg d-1 were

adm i nistered to all animals. Rout ines for sampl ing and est imation of individual

herbage i ntake and digestibi l i ty were carried out as described by Dove and M ayes

( 1 99 1 ) . Faeces col lections for i ntake est imation were made between days 48 and

52 (8- 1 2 days post-applicat ion) . Faecal sam ples were col lected daily duri ng

morn ing and even ing observat ion (2-h periods) of an imals defecat ing ( Morr is et al .

1 993) . Samples were freeze-stored and compounded with i n each animal over the

period of col lect ion. After defreezi ng , they were g round th rough a mil l (1 m m ) and

analysed for n-alkane composit ion by gas chromatography (Mayes et al . 1 986) .

The 1 3C was estimated from o rganic matter content by mass spectrometry

(F inn igan Mat DeltaS, Laboratory of i sotopes, I N G E I S-UBA) .

I ndividual herbage and maize i ntakes were est imated by us ing the iterative alkane

& 1 3C method (Garcia et al. 2000 ) . In unrepl icated s upplementat ion tr ials the

graz ing o nly group acts as the control for est imat ing the substitut ion rate. I n the

current tr ial , to estimate i ndividual substitution rates associated with maize i n take ,

an average "basal" herbage i n take was calculated from an imals al located to both

repl icates of the "grazing only" treatments at each level of herbage al lowance, and

i ndividual d ifferences from these basal levels were calcu lated for supplemented

anim als . From these differences and maize i ntakes i ndividual substitution rates

were estim ated .

92 Part 11 - Chapter 4

During the intake m easurement period , th i rty hand-plucked herbage samples

representing the diet for each repl icated plot were obtained daily. Herbage samples

were pooled with i n each repl icate by sampl ing period as described by Garcia et al .

(2000) . Samples were sealed in polythene bags and taken to the laboratory in an

icebox. Part of the sample was freeze-dried, g round (1 mm screen) and divided i nto

two sub-samples. The fi rst was analysed for n-alkane composit ion , and the second

analyzed for dry matter in vitro digestibi l ity ( Dig %) (Ti l ley & Terry 1 962) and crude

protein (CP %) by M icro Kjeldhal (A.O.A.C. 1 960) . To determine the organic m atter

(OM) fraction the dry matter was subsequently ashed in a muffle fu rnace at 550 CQ.

The second part of the pooled sample was divided i nto two . The f i rst was

completely sorted by h and into tal l fescue and wh ite clover , and then i nto the ir

component parts , i .e . lamina, sheath p lus stem and dead (tall fescue) and leaflet ,

petiole plus stolon and dead in wh ite clover. These components were dried in an

oven unti l constant weig ht (60 °C for 48 h) and expressed as fraction of dry m atter.

The rest of the sample was also oven dried and preserved for 1 3C determination .

2.3. Statistical analysis

A factorial design was used to analyse sward measurements, us ing treatment

repl icates (grazing plots) as experimental u nits . Pre-planned contrasts included

l i near and quadratic contrasts for herbage al lowance level . The other variables

were analysed with the animal as experimental un it using PROC G LM as Yijk= 1J +

HAi + M j + HAi X M j + paddock(HAi x Mj) + eijk . Where Yijk is the response variable

being model led ( individual herbage , maize and total i ntakes , SR, LWG , ME eaten

daily and in vivo digestibi l ity of the whole diet) , HAi is the effect of herbage

al lowance, Mj is the effect of maize supplementation , HAi x Mj is the effect of the

interact ion between herbage allowance and maize supplementation and

Paddock(HAi x Mj) represents the error term effect of the repl icated paddock. eijk i s

the model residual . T h e pre-planned contrasts were as stated above. Fol lowing a

s ignificant F-test ( P<0.05) , least squares means were separated us ing least

sign ificant d ifferences (Steel & Torrie 1 980) . All analyses were performed us ing

SAS/STAT (200 1 ) .

Effects of increasing herbage allowance with and without maize grain 93

3. Resu lts

Chemical composition , concentration of dominant n-alkanes and 81 3C i n herbage

and grain fed are presented i n Table 1 . No interactions were found between

herbage al lowance and maize suppleme ntation effects i n any of the studied

variables, so main effects are presented separately. The sward was composed (as

fractions and standard deviations of the whole canopy) , by 0. 29±0 .09 of grass

stem , O . 1 9±0 .05 of grass lamina, 0 . 1 0±0.OB of clove r leaflet, 0 . 1 4±0.04 of clover

stem&stolon and 0 .2B±0 . 1 1 of dead materia l .

Table 1 . Chemical composition , concentration of dominant n-alkanes and concentration o f 1 3C in the forages uti l ised.

Forage OM CP Dig OM C29 C3, C33 8'3C % mg 1 00 g OM·' %0

Herbage 33.6 1 5 .6 68.2 89.6 1 56 .2 333 . 1 75.2 -27.9 Maize grain 88.7 9 .7 83.2 96.5 1 . 1 0 .8 0 .6 - 1 1 . 1 DM=dry matter, CP= crude protein, Dig=in vitro OM Digestibi l ity, OM= organic matter, C29=nonacosane, C3, = hentriacontane, C33= titriacontane and 81 3C=abu ndance of 1 3C relative to a carbo nate standard .

Final l iveweight and l ivewe ight gains in LHA were lower ( p<0.05) than in MHA and

H HA, and increased quadratically (p<0.05) with the level of herbage al lowance.

Level of maize fed i ncreased sig nificantly ( P<0.05) LWG by 0.09 kg d-1 (Table 2).

A quadratic function adjusted significantly to predict LWG from herbage al lowance

( Figure 1 ) . The fasting liveweight losses increased significantly (p<O.05) with the

level of herbage al lowance , with no significant effect of maize feeding level (Table

2) . The coefficient of variat ion of LWG declined with the level of herbage

al lowance , with values of 1 07 , 34 and 23% fo r LHA, M HA and HHA, respectively.

Table 2. Final liveweight, l iveweight gain and fasted liveweight loss of Angus steers fed at three herbage allowances and two maize grain levels during late spri ng .

LHA MHA HHA PSE­HA

Mo M, PSE- HA M

M

Final LW (kg) 268a 2876 291 6 1 . 62 280 a 284 6 1 .3 * * LWG (kg d·' ) 0 . 1 6 a 0 .6 1 b O.69 b 0.04 0 .44 a 0.53 b 0 .03 *-

Fasted-Loss (% fu l l LW) 6.0a 7.0 ab 7.2 b 0.003 6.3 6 .9 0 .3 *- NS LHA= 2.5 % LW d·" LHA=5.0 % LW d·" HHA=7.5 % LW d·" Mo = 0% d·' maize grain , M , = 0 .5% d· ' maize g rain , HA= herbage OM allowance effect, M= OM Maize effect, PSE-HA and PSE-M= pooled standard errors of HA and M effects,

HA-Quadratic

NS

HA-Quadratic= contrast for quadratic effects o f herbage OM allowance level , different letters in a row within effects ( HA and M) indicate significant differences (p<0 .05) , NS= non sig nificant differences (p>0. 1 ) , *=P<0.05 and **=P<0 .0 1 .

94

0.80 0.70 0.60 :

�0.50 Ol 60.40 , � 0.30

0.20 . 0 . 10 0.00

0.0 5.0 1 0.0 1 5.0 Herbage allow ance (kg d ' )

. . . . .

20.0

Part 11 - Chapter 4

25.0

Figure 1 . Relationship between herbage OM al lowance (X) i n kg d-1 and individual liveweight gain (Y) in kg d-1 •

For the fitted quadratic curve, Y= 0 .21 ±0. 1 0 + 0 .03±0.01 7X - 0 .0009±0.0006X2; R2= 0 .51 , P<0.00 1 , Residual SS = 0 . 1 4 and n==26.

Pre-grazing herbage mass was simi lar between treatments (Table 3) , and residuals

increased when herbage al lowance increased ( P<0.0 1 ) , and when maize was fed

( p<0.05) . When pre- and post-grazing measurements were used to estim ate

herbage and total i ntake (Table 3) , both variables increased l inearly ( P<0.0 1 ) as

herbage al lowance i ncreased, with s ignificant d ifferences ( P<0.0 1 ) between LHA,

M HA and H HA. Using th is method, substitut ion rate was not s ign ificantly affected

( p>O.05) by herbage al lowance.

H erbage , total i ntake and M E eaten i ncreased (p<0.0 1 ) with increasing herbage

al lowance, when the n-alkane & 1 3C method was u sed (Table 3 ) , f itt ing

s ignificantly ( p<O.00 1 ) to a quadratic curve (F igure 2 ) . The treatment LHA was

s ignificantly lower (P<0.05) than MHA and HHA in the three previously m entioned

variables. In vivo dry matter digestibil ity of the total diet was h igher ( P<0.05) when

maize grain was fed , and i ncreased l i nearly according to the herbage al lowance

level . Maize grain intake had a mean of 1 .2 kg DM d-1 , with a coefficient of variatio n

(CV) between i ndividual animals of 4 1 % . With in each supplemented treat ment ,

i nd ividual LWG was predicted l inearly ( p<0.05) from maize i ntake (R2 of 0 .42, 0 .47

and 0.58 for LHA-M , M HA-M and H HA-M , respectively) , but no s ignif icant

relationship could be establ ished with h erbage i ntake or substitution rate.

Effects of increasing herbage allowance with and without maize grain 95

Table 3. Sward and animal parameters of Angus steers fed at three herbage allowances and two maize grain levels during late spring .

Pre-grazing herbage mass

LHA MHA HHA PSE-HA

(kg ha" ) 2350 2490 2480 76 2420 Post-grazing herbage mass (kg ha" ) 1 240a 1 640 ab 1 7 1 0 b

c D M disappearance Herbage DM intake (k� d" ) Total DM intake (kg d' ) Maize intake ((kg d" ) Substitution rate (kg DM herbage kg DM maize grain' , ) n-alkane & 8' 3C

Herbage DM intake (k� d" ) Total DM intake (kg d' ) Diet digestib i l ity (%) Energy intake (MJ ME d" ) Maize DM intake (kg d" ) Maize DM intake (% LW d" ) Substitution rate

3 .3 a

3 .9 a

1 .3 0 .54

3 .6 a

4.2 a

72 a

45.7 a

1 .0 0 .42

5.7 b

6.4 b

1 .5 0.82

4.6 b

5.2 b

75 ab

60.1 b

1 .2 0.47

7.6 c

8.5 c

1 .4 0.38

4.7 b

5.3 b

76 b

61 . 1 b

1 .3 0 .49

0 .71 0 .71 0. 1 3 0 .53

0 .26 0 .28

1 .2 3 .5

0. 1 9 0.06

(kg D M herbage kg DM 0 .38 a 0 .83 ab 0 .87b 0 . 1 6 maize grain" )

5.9 5.9

4.8 b

4.8 71 a

52. 1

M, PSE- HA M HA­

2470

5.2 6.6 1 .4

0 .58

3 .9 a

5 . 1 77 b

59 .2 1 .2

0.46

M Linear

65 NS NS NS

39 .. *

0 .58 ** NS * * 0 .58 * * N S **

0.2 NS -- NS 0.30 NS -- N S

0 .21 * * * * 0.23 * * NS

0 .9 * * ..

2.9 ** + ** 0 . 1 NS -- NS

0 .04 NS -- NS

0 .70 0 .09 • •

LHA= 2 .5 % LW d" , MHA=5.0 % LW d" , HHA=7.5 % LW d" , Mo = 0 % d" maize grain , M , = 0.5 % d" maize grain, HA= herbage DM allowance effect, M= Maize DM effect, PSE-HA and PSE-M= pooled standard error of HA and M effects, HALinear = contrast for l i near effect of herbage DM allowance level , d ifferent letters in a row within effects (HA and M) indicate sign ificant differences (p<0.05) , cDM disappearance= pre- and post­graz ing dry matter difference, NS= non significant differences ( p>0. 1 ) , +=P<0 . 1 , *=P<0.05 and **=P<0.0 1 .

Supplemented animals had an overal l SR of 0 .70 kg herbage kg maize grain-1

(Table 3) . The l inear contrast for SR associated with increased levels of herbage

al lowance was significant at P<0.05 but a quadratic term was not s ignificant.

However, a separate regression analysis based on individual values expressed in

kg OM d-1 fitted to a quadratic function (Figure 3 ) .

96

8.00

7 .00

� 6.00 'b � 5.00 Q) .:.:. . � 4.00 · Q) g> 3.00 .0 Qj :r: 2.00 !

1 .00

0 .00 .

0.0 5.0

• • •

• • •• • •

. # ------

1 0.0 1 5.0 Herbage allow ance (kg d ' )

• • •

•

20.0

Part 11 - Chapter 4

25.0

Figure 2. Relationship between herbage al lowance (X) in kg DM d-1 and individual herbage intake (Y) in kg DM d-1 •

For the fitted quadratic curve, Y= 1 .4±0.73 + 0.41 ±0 . 1 2 X - 0.01 ±0 .004 X2; R2= 0.47, P<0.00 1 , Residual SS = 0.56 and n=25.

E stim ates of herbage i ntake in LHA were s imi lar from sward m easurements and

the a lkane & 1 3C method, but pasture estimates exceeded alkane estimation at

h igher herbage allowances (23 and 63 % higher in M HA and H HA) . I n the case of

m aize grain intake, both methods were simi lar with an overall h igher estimat ion of

7% when the method of pre- and post-feeding difference was u sed . The ach ieved

level of maize OM feeding (overal l mean of 0 .46 % LW d-1 ) was s l ightly lower than

o rig inal ly planned (0.6 % LW d-1 ) .

The accuracy and precision of the the alkane & 1 3C m ethod (M E-alk) and the pre­

and post-graz ing difference method with feed digestibi l it ies (M E-diff) were tested

again st metabol isable energy requirements for the l evel of performance attained

(M E-req) with the method described in Chapter 3 (Mayer & Butler 1 993). L inear

regressions fitted significantly for M E-alk ( R2=0.88 , RMSE=3.3 P<0.0 1 ) and M E-diff

( R2=0. 62 , RMSE= 1 5 .3 P<0. 1 ) , and the i ntercepts were not s ignificantly different

from zero ( p>0.05). Regressions forced through the origin were significant at

P<0 .0 1 (M E-alk R2=0.99, R MSE=4.4 and M E-diff R2=0.96 , RMSE= 1 0.6) , but on ly

the slope of M E-ark ( 1 .09±0 .03) was not different from u nity (p>0.05) , which l eads

to M E-alk:: M E-req (p<0.05) . The M E-d iff had a slope coeff icient of 0 .73±0 . 01 ,

wh ich was significantly lower than 1 .0 (P<0.05) .

Effects of increasing herbage allowance with and without maize grain

Ol N 1.20 ·cu E 1.00 Ol .::s:-Ol 0.80 Cl ro .Q 0.60 ..... Ol ..c: :::- 0040 Ol .i; .::s:- ro

- ..... 0.20 Ol Ol

-ro .....

0.00 C 0 ·0 .20 '+J ::l

� ·0040 -

Cl) .0 ::l

·0.60 if)

0 0 5.0. •

10.0

• •

15.0

• •

20.0

Herbage allow ance (kg d-1 )

25.0

9 7

Figure 3 . Relationship between herbage allowance (X) i n kg OM d-1 and individual substitution rate (Y) in kg herbage kg OM maize grain-1 .

For the fitted quadratic Y= -0 .39±0.37 + 0 . 1 5±0.06X - 0.004±0.002X2, R2= 0.39, P<0.0 1 , Residual

SS = 2.03 and n=22.

There was also sign ificant quadratic relationship between herbage i ntake (X) in kg

OM d-1 and l iveweight gain (Y) kg d-1 :

(Y= -0.078±0 .29 + 0 . 1 9±0. 1 2X - 0 .01 2±0 .0 1 2X2 ; R2= 0 . 5 1 , P<0.0 1 , Residual SS =

0 . 1 5 and n=25) .

4 . Discussion

One of the objectives of the thesis was to provide information on herbage

al lowance-intake functions and the effects on animal performance when maize

g rain is supplemented in beef cattle f in ishing systems. A series of experiments

were designed to adjust locally the methodologies and obtain pre l iminary

information (Chapter 2) , to study the effects of increasing levels of maize g rain

(Chapter 3) and the study of increasing level of herbage al lowance (present study) .

The discussion developed here involves evaluation of the observed resu lts i n th is

chapter , but also a comparative assessment with Chapters 2 and 3 , exploring the

possibi l ities of general ized relationsh ips with in the studies relating herbage

al lowance to LWG and herbage intake . Additional ly , brief comments are made

about the g razing methodologies used .

98 Part 11 - Chapter 4

4. 1 . Present study

The resu lts i ndicated an asymptotic relationship between herbage al lowance and

l iveweight gain (Table 2 and Figu re 1 ) . This is i n agreement with other studies

(Reid 1 986 ; Redmon et al . 1 995) . The maximum LWG achieved (0.69 kg LWG d-

1 ) at 7.5 % LW d-1 is s imi lar to the 0.63 kg d-1 observed during summer in steer

calves at the same al lowance (Marsh 1 977) but lower than the results of Reid

( 1 986) during spring with Angus steers of simi lar weights, who observed an

asymptote of 1 .09 kg LWG d-1 at a herbage allowance h igher than 6.5 % LW d-1 .

I ncreas ing herbage al lowance s ignificantly i ncreased herbage intake est imated by

both methods (Table 3), and i ncreased herbage residuals which is in agreement

with the l iterature (Hodgson 1 984; Reid 1 986). Simi larly, associated to the

observed substitution rates, g rain feeding increased the herbage residuals

s ignificantly, as reported by Meijs & Hoekstra ( 1 984 ) , G rainger & Mattews ( 1 989)

and in Chapter 3.

The better estimation of herbage intake by the alkane & 1 3C method than from

sward measurements is i n agreement with the observations of Garcia et al . (2000)

and the resu lts of Chapter 3 . Est imates of herbage intake from sward

measurements exceeded alkane estimations at h igher herbage al lowances (Table

3) , simi larly to the observation in Chapter 3 , but not in Chapter 2. Flooding of the

experimental area caused a delay in grazing of 1 4 days , which affected sward

condition so it was necessary to top the sward to l imit heading . Some reproductive

stems were observed to be trodden into the soil su rface (part icularly at h igher

herbage allowances) , which were l ikely to be undetected by the measurement of

herbage residuals using the ris ing plate meter . This situat ion was described by

Robaina et al. ( 1 998) , who observed that the estimation of herbage intake on late

spring-summer pastures using sward sampl ing techniques is often difficu lt when

pasture is clumpy and may contain a mixture of dry stem material and g reen

herbage . The alkane & 1 3C procedu re is clearly the method of choice in these

studies, and appeared to g ive rel iable estimates of both herbage and maize

consumption.

Effects of increasing herbage allowance with and without maize grain 99

The effect of i ncreasi ng herbage allowance on LWG is l i kely to be associated with

at least two main factors , i ncreased diet quality and i ncreased herbage i ntake .

Qual ity of the diet expressed as in vivo OM digestibi l ity increased as herbage

al lowance increased (Table 2) , which is in agreement with other authors measuring

in vivo OM digestib i l ity (French et al . 200 1 ) and in vitro OM digest ibi l ity from

oesophageal fistulates (Jamieson & Hodgson 1 979; Redmon et al . 1 995) . The

functional response of in vivo OM digestibi l ity was affected clearly by herbage OM

al lowance in this study. However, between 2 .5 and 5 .0 % LW d-1 (LHA and MHA

respectively) values were not d ifferent , in coi ncidence with the results i n Chapter 2

and 3 at the same levels of al lowance. This i ndicates the value of a wider range of

allowances as was used i n th is study . The observed level of crude protein i n the

pastu re ( 1 5 .6 % CP) could appear marginal when some part is replaced by maize

g rain (9.8 % CP) i n terms of the min imum recommended level of 1 5 % for young

cattle (Hodgson & Brookes 1 999) . However, it is l i kely that selective g razing (as

i ndicated by a difference of 9 % in the OM digestib i l ity of the sward and the diet

selected from it) i n unsupplemented treatments (Tables 1 and 3 ) , would have also

enhanced the protein content of the diet.

The pattern of LWG observed here at increased herbage allowance (Figure 1 ) was

clearly associated with the intake estimated with the alkane & 1 3C method ( Figure

2) , which became in th is case the most relevant driver in terms of LWG response.

The asymptotic value of herbage i ntake (and LWG) was reached at lower

al lowance than the 9 to 1 2 % LW d-1 suggested by Hodgson ( 1 984) . Animals were

not l ikely to express thei r potential LWG (above 1 kg d·1 in feed lot conditions,

Machado et al . 1 997) , nevertheless they had an asymptotic pattern of herbage

intake and LWG at a herbage allowance of above 5 % LW. If it is assumed that

herbage qual ity was not a constraint to LWG, sward structure was l ike ly to p lay a

role i n the restricted intake and performance, as i ndicated by Pearson ( 1 997) .

I ncreasing the herbage al lowance increased the substitution rate ( Figure 3) , in

accord with previous studies (Meijs & Hoeskstra 1 984; French et a l . 200 1 ) . The

substitution rate at a herbage al lowance of 2 .5 % LW (0.38 kg OM herbage kg OM

maize grain-\ was the same as that observed in Chapter 3 (0 .39 kg OM herbage

1 00 Part 11 - Chapter 4

kg OM maize grain-1 ) at the same herbage allowance and level of supplementat ion_

Although maize feeding depressed herbage intake (overall SR of 0 .70 kg herbage

kg maize g rain- 1 ) , it also increased LWG significantly by 0 .09 kg d-1 due to h igher

total OM i ntake and the h igher ME content of the maize . The conversion of 0 . 1 3 kg

LWG kg maize grain -1 is close to the overall values of 0 . 1 7 kg LWG kg maize grai n -

1 observed by Boom and Sheath ( 1 998) and the 0 . 1 2 kg LWG kg supplemenr1

observed by Boom and Sheath ( 1 999) , but lower than the 0 .3 kg LWG kg maize

grain-1 observed in Chapter 3. The importance of the actual an imal performance

u nder unsupplemented conditions to predict potential response was emphasised i n

Chapter 1 . Unsupplemented animals in the present study were gain ing 0 .44 kg d-1

(average over a l l herbages al lowances) , whereas i n Chapter 3 the control group

had a LWG of 0.25 kg d-1 . Maize feeding improved ( P<0.0 1 ) in vivo diet digestibi l ity

by 8 %, in concordance with other studies that fed starch g rains (French et aL

200 1 ) , which is a common f inding when the digestibil ity of the grain is h igher than

that of the herbage (Table 1 ) , as stated by Oixon & Stockdale ( 1 999) .

4.2. Generalised comparison of grazing experiments

i n Figu res 4 and 5 the observations of LWG and herbage intake of

unsupplemented animals at different herbages al lowances studied in Chapter 2, 3

and the present study are presented. Although experimental condit ions differed

(season , animal sex and herbage quality) , in both figu res a general trend could be

observed and fitted significantly ( p<0 .0 1 ) to quadratic funct ions_ However ,

observations from Chapter 2 behaved as outl iers at pool i ng , and final ly were not

included in the combined regression equations shown in Figures 4 and 5. Al l

experiments grazed simple legume-grass pastures , however, in the case of the

study reported i n Chapter 2, pasture was dominated by clover (0 . 72±0.02, as

fraction of green component) , whereas the clover content of the sward in Chapter 3

and the p resent chapter were 0 .33 and 0 .03 as fraction of g reen component,

respectively. A higher ingestibi l ity of legumes in comparison with grasses has been

observed ( Freer & Jones 1 984) , which is l ikely to expla in partial ly the h igher i ntake

and consequently the higher LWG observed in Chapter 2 .

Effects of increasing herbage allowance with and without maize grain 1 0 1

1 . 00

0.90 x x )jK

0.80 X

� � • 0.70 X

:0 0.60 x Cl :::s 0.50 <.? 3 0.40 ...J

0.30 X • Present study

0.20 • Chapter 3

• X Chapter 2 0. 1 0 i 0.00

0.0 5.0 1 0 .0 1 5.0 20.0 25.0

Herbage allow ance (kg d" )

Figure 4. Relationship between herbage allowance (X) i n kg d" and individ ual liveweig ht gain (Y), condensing informatio n between Chapters 2, 3 and 4.

For the fitted quadratic curve �i ncluding observations from Chapters 3 and 4) Y= -0.1 09±0.001 -0.08±0.0006X - 0.002±0 . 1 02X , R2= 0.58, P<0.00 1 , Residual SS = 0.56 and n=42.

The relationship between herbage i ntake and LWG from this study (spring) and

from Chapter 3 (winter) , were relatively consistent ( Figure 4 and 5 ) . This was

unexpected as season can have a strong i nfluence on the relationship between

herbage allowance and LWG (Reid 1 986) . However, th is f inding could be

associated with the herbage nutritive values in the studies, which were moderate

and s imi lar as shown i n Table 1 (Chapter 3) and i n Table 1 (th is Chapter) .

In Figure 6 the relationship between observations of LWG and herbage i ntake for

unsupplemented animals in Chapter 2, 3 and the present study are presented.

Chapter 2 information was consistent to the general pattern , therefore was

incl uded in the equation fitt ing. This s ign ificant relationship agrees with the

suggestio n of Hodgson ( 1 984) that the effect of herbage allowance on LWG mostly

reflects the effect of HA on herbage intake.

1 02

9.00 -

8.00 -

7.00 x '0 Ol 6 . 00 � � ID

5.00 -.::t:. re .�

4 .00 -ID Ol re 3.00 � .0

ID I 2.00 -

1 .00 -

0.00

0 .0 5.0

x

x x x

1 0.0

x x

x

• . #

x

x x

• ••

1 5 .0

• x • •

• •

• • • • .. Present study

• Chapter 3 x Chapter 2

20.0

Herbage allow ance (kg d-l )

Part 11 - Chapter 4

25.0

Figure 5. Relationship between herbage al lowance (X) i n kg OM d-l and i nd ividual herbage intake (Y) in kg OM d-l , condensing information between Chapters 2, 3 and 4.

For the fitted quadratic curve including observations from Chapters 3 and 4, Y= - 1 .37±0.73 + 0 .42±0 . 1 2X - 0 .01 1 ±0 .005X2, R2= 0.47, P<0.001 , Residual SS = 28 and n=42.

0.80 -

0.70 -

0.60 •

'U 0.50

Ol • .::t:.

0.40 C) � 0.30 -....J

0.20 -• •

0. 1 0 •

0.00 - -- -

2.0 3.0

· x •

•

•

•

4.0 5.0

x

x

• x

x

• Present Study

• Chapter 3

X Chapter 2

6.0 7.0

Herbage intake (kg d-l )

x

x

8.0

Figure 6. Relationship between i ndividual herbage intake (X) in kg OM d-l and liveweight gai n (Y) in kg OM d-l , condensing i nformation between C hapters 3 and present study.

For the fitted quadratic curve ( including observations from Chapters 2, 3 and 4) Y= -0.27±0 .22 + 0.25±0.09X - 0 . 1 6±0.008X2, R2= 0.46, P<0.00 1 , Residual SS = 1 .03 and n=55.

Caution is necessary in making SR comparisons, as est imates of substitution rates

were affected by the method used for its calculation (Table 3 ) , which is in

Effects of increasing herbage allowance with and without maize grain 1 03

agreement with Robaina et al . ( 1 998) and the findings of Chapter 3 . Additional ly , I f

herbage intake is expressed as kg OM kg LW-1 d-1 instead of kg O M d-1 and is used

for S R est imat ions to avoid any bias from the different l iveweights ( i .e .

u nsupplemented animals were l ighter) , substitution rates of OAO, 0 .63 and 1 .0 1

(±0 . 2 PSE) were obtained for LHA-M , M HA-M and HHA- M , respectively. Simi larly

to the observat ions in Chapter 3, substitution rate values expressed per kg LW

d iffer s l ight ly from those estimated us ing the absolute h erbage i ntakes (Table 3) ,

but it would be expected that these d ifferences could i ncrease i n longer duration

supplementat ion tr ials .

Maize g rain intake i n the present study had an overal l coefficient of variation (CV)

of 41 % between individual animals , which is in agreement with the 36% CV

reported by Garcia et al . (2000) , and s l ight ly higher than the mean value observed

(3 1 %) i n C hapter 3 . Boom & Sheath ( 1 998) reported from i ndividual visual

observation of beef steers eating maize g rain at levels between 2 and 6 kg d-1 , that

on any day 20 % of the animals were not consuming g rain . Although the actual

range in maize i ntake was h igh between animals in this study (between 0 .56 and

2 . 1 5 kg OM d-1 ) and in Chapter 3 (between OA6 and 1 .8 1 and between 1 .67 and

3 .28 kg OM d-1 in LHA-M1 and LHA-M2 , respectively) , al l an imals were eating some

maize . However, considering that the estimates of intake of maize (and also

herbage i ntake) represent a pooled five-day period, it is l i kely the variabil i ty i n

maize intake between animals within days would be even higher than the values

quoted above. S im i larly, the same constraint of t ime scale affects the significant

l inear relationships between i ndividual maize grain intake and LWG with in each

treatment, in th is study and in Chapter 3 (average R2=0.59) . With th is caution , and

considering that there is no techn ique available for estimation of the daily i ndividual

grain i ntake in free-grazing animals s upplemented in g roups , the association with

animal performance may h ave practical i mp lications, given that the h igh variabil ity

in animal LWG in response to g rain feed ing is well recognised ( Horn & McCol lum

1 987) .

4.3. Grazing methodologies used in the studies

There is a broad range of methodological approaches to study herb ivore foraging,

but their rel iab il i ty and l imitations of exper imental resu lts in terms of management

1 04 Part 11 - Chapter 4

purposes h ave been highl ighted ( Du mont & lason 2000) . The n-alkane method

used in th is thesis has proven to be an effective alternative for i ndividual herbage

intake estim ation , but it had not been tested previously in Argent ina when th is

project was designed. The use of control led- release devices (CRD, Captec®)

inserted into the rumen of the animals was preferred in th is thesis, since it

min im ised i nterference with normal animal behaviour and management. One of the

key issues about the CRD is the assumption of a relatively constant release rate of

dotriacontane (C32) and hexatriacontane (C36) after a stabi l ization period of seven

days. Specific studies to test the accuracy of CRD or com pare it with other

methods, has shown that th is device provides a satisfactory means of delivering an

accurate, dai ly dose of n-alkanes ( Dove et al . 2002; Hendricksen et al . 2003 ;

Molina et a l . 2004) . Though a local cal ibration with total faecal col lection when

possible is advisable , some studies were based in the release rate provided by

CRD manufactu rers ( Real in i et al . 1 999 ; Garcia et al . 2000 ; Kennedy et al. 2003) .

At least two f indings seem to indicate an adequate performance of the method i n

this thesis. Fi rst ly , close agreement with the theoretical energy requ i rement for the

actual level of performance (Chapter 3 and present study) , and secondly, a s im i lar

release rate to that suggested by manufactu rers and the esti mation from a si ngle

test with CRD inserted in a rumen fistu lated animal (Chapter 2 ) .

The main objective of the grazing experimentation was to study the dai ly herbage

intake and its relationship to sward characteristics , maize g rain feeding and animal

performance. However, different indicators of diet characteristics were also used.

These included m icro-h istological evaluations of faeces and differences in nutrient

and component selection indexes (computed by pre- and post-g razing sward

measurements) in Chapter 2. The n-alkane method was also tested for diet

composition with no feasible solut ions (Chapter 2). Resu lts were not rel iable

enough , and they represented a s ubstantial i ncrease of research costs and

increased workload, practical ly unmanageable u nder commercial farm conditions .

The decision to concentrate effort on herbage i ntake in Chapter 3 and Chapter 4

(with eight and twelve animal groups , respectively) , seemed adequate as a close

relationship between herbage intake and animal performance was obtained ( Figure

6) . However , it is recognised that further research on diet composit ion u nder

changing g razing conditions is needed , and information obtained fro m a low t ime-

Effects of increasing herbage allowance with and without maize grain 1 05

space scale ( i .e . bite scale as reported by Cang iano et al . 2002) with daily grazing

activities needs to be investigated.

Visual measurements of ingestive behaviour in Chapter 2 and 3 provided additional

insights about animal responses. I t was demonstrated that maize feeding

sign ificantly increased the harvest ing rate in a l i near fash ion (Chapter 3 ) ,

expressed as g OM eaten kg LW -1 min . spent g razing - 1 (Krys l & H ess 1 993). The

use of autom atic recording equ ipment ( Rutter et al . 1 997) cou ld make it possible to

enhance the detail of behavioural responses. Automated observations were made

on animals i n the study described in Chapter 4 as part of another postgraduate

project (Sacido M . , unpubl ished) , and are not reported here.

5. Impl ications and conclusions

The n-alkane & 1 3C method proved to be reliable , at least for quantitative

est imates of herbage and grain i ntakes , and al lowed the estimates of important

variation in individual grain intake when animals are supplemented in groups. The

benefits that differences observed in herbage intake and substitution rate

est imations depend on the method used , h igh l ights the importance of

standardisation , and caution in com pari ng results.

The resu lts from th is series of short-term studies showed consistency in the

comparison of the effects of increasing herbage al lowance and supplementation on

herbage intake and animal LWG. Although the i r applicability in terms of practical

g razing management is l imited and needs further confi rmation , they have potential

to help the development of grazing models as sward description is avai lable, and

therefore to expand the understanding of the effects of manipulat ing herbage OM

allowance and grain feeding in a systems context. Data summarized in this chapter

wil l be used for model evaluation i n Chapter 7.

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pastura. Revista Argentina De Producci6n Animal 22:8-9.

Mayer D . G . ; Butler D . G. 1 993: Statistical val idation. Ecological Modelling 68:21 -

32.

Mayes R . ; Lamb C. ; Colgrove P. 1 986 : The use of dosed and herbage n-alkanes

as markers for the determi nation of herbage intake. Journal of Agricultural

Science, Cambridge 107: 1 6 1 - 1 70 .

Meijs J . A. C . ; Hoeskstra J . A. 1 984: Concentrate supplementation of grazing dairy

cows . 1 . Effects of concentrate intake and herbage al lowance on herbage

i ntake. Grass and Forage Science 39:59-66.

Molina D . 0.; Matamoros I . ; Pel l I . N. 2004 : Accuracy of estimates of herbage

intake of lactating cows us ing alkanes : comparison of two types of capsu les .

Animal Feed Science and Technology 1 14:24 1 -260 .

Morris S . T . ; Parker W. J . ; Grant D . A. 1 993: Herbage i ntake , l iveweight ga in , and

g razing behavior of Friesian , Piedmontese x Friesian, and Belgian Blue x

Friesian bul ls . New Zealand Journal of Agricultural Research 36:23 1 -236.

Okaj ima T . ; Kamijoh T. ; Yokota H . ; Ohshima M. 1 996: Relationsh ips between daily

herbage intake of graz ing cattle with dai ly herbage allowance and leafiness.

Asian-Australasian Journal of Animal Sciences 9:577-582.

Pearson C . J. 1 997: Agronomy of Grassland Systems. 2nd Ed. Cambridge

[England] ; New York, NY, USA. Cambridge Un iversity Press :222 p.

1 1 0 Part 11 - Chapter 4

Real in i C . ; Hodgson J . ; Morris S . T. 1 999: Effect of sward surface height on

herbage intake and performance of fin ish ing beef catt le. New Zealand

Journal of Agricultural Research 42: 1 55- 1 54.

Reardon T. F. 1 977 : Effect of herbage per u nit area and herbage al lowance on dry

matter intake by steers. Proceedings of the New Zealand Society of Animal

Production 37: 58-6 1 .

Rearte D . 1 998 : Beef cattle production and meat quality on grazing system in

temperate regions. Revista Argentina De Producci6n Animal 18: 1 29- 1 42 .

Redmon L. ; McCollu m T . ; Horn G . ; Matthew D . ; Cravey S . ; Gunter P . ; Beck J . ;

Mieres M . ; San Ju l ian R . 1 995: Forage intake by beef steers g razing winter

wheat with varied herbage al lowances . Journal of Range Management

48: 1 98-20 1 .

Reid T. 1 986: Comparison of autumn/winter with spring pasture for g rowing beef

cattle. Proceedings of the New Zealand Society of Animal Production

46: 1 45- 1 47.

Robaina A. C.; Grainger C . ; Moate P . ; Taylor J . ; Steward J . 1 998 : Responses to

g rain feeding by grazing dairy cows . Australian Journal of Experimental

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Rutter S. M . ; Champion R. A.; Penning P. D. 1 997: An automatic system to record

foraging behavior in free-ranging rum inants. Applied Animal Behavior

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Steel R . G. D. ; Torrie J . H . 1 980 : Principles and Procedures of Statistics: A

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forage crops. Journal of the British Grassland Society 1 8: 1 04- 1 1 1 .

Part 11

Herbage quality

Chapter 5

Seasonal variation i n the q ual ity of an alfalfa-based

pasture and its relationsh i p with morpholog ical and

matu rity esti mates

1 14 Par 11 - Chapter 5

Abstract

To monitor seasonal change of herbage qual ity of an alfalfa-based pastu re

( Medicago sativa, Dactylis glomerata and Bromus wildenowil) at the Chacra

Barrow Experimental Station- Argentina (38 0 20' S , 60 0 1 3' W, 1 20 m.a .s ) , was

sampled fortn ightly for 28 months. The pasture was strip-grazed and samples were

taken from the re-growth of a previously grazed paddock, which was ready for re­

g razing. At each sampling t ime, herbage mass was esti mated with a fortn ight ly

calibrated r is ing plate meter. Twenty herbage samples were cut to 4 cm above

g round level and immediately bulked and refrigerated . In the laboratory, a

subsample (0 .4 kg) was taken and divided into two parts ; o ne was freeze-dried and

analyzed for I VDM Digest ibi l ity, CP%, NDF% and NSC %, and the second fract ion

was quickly sorted out into dead and g reen com ponents. The g reen pool was

separated into grass and alfalfa, and later into lamina plus leaf let and stem p lus

pseudostem fractions. These fractions were oven dried (48 h at 60QC) , weighed

and expressed as OM %. At each sampling date, the quantitative maturity ( mean

stage by cou nt and mean stage by weight) of fifty t i l lers per grass species and fifty

alfalfa shoots were recorded . There was a decl ine in OM digestibi l ity (and

consequently ME) during autumn (71 .7 % and 1 0 .8 MJ ME kg DM·1 , respectively )

and i n CP during summer ( 1 7.3 %) , although herbage qual ity was consistently h igh

th roughout the year (overall average o f 1 1 .5 MJ M E kg DM-1 and 20 .6 %, for

m etabol isable energy content and crude protein leve l , respectively) . Duri ng

summer and autumn , the sward had the highest proportion of legume and dry

matter (averages of 59.3 and 28 .0 %, respectively) , with the lowest leaf to stem

ratio ( 1 .25) and green content (82 .6%) . Grasses had the highest maturity state

during spring and summer and M. sativa was more mature dur ing summer-autum n .

Morphological and maturity estimates predicted satisfactori ly t h e changes in the

main nutritional variables. Although promising i n terms of gain ing understanding of

the seasonal change of herbage quality, these relationships requ i re fu rther

i nvestigation to determine the i r impl ications under practical conditions .

Key-words : herbage quality; seasonal change; a lfalfa/grass ; maturity

Seasonal variation of herbage quality 1 1 5

1 . Introduction

Herbage nutrit ional qual ity at any t ime is the weighted average of the proport ions of

plant components and their n utrient content. Sward qual ity changes seasonal ly and

dynamically when physiological changes take place in the plants , and g razing or

harvesting conditions interact with those changes (Nelson & Moser 1 994) .

Match ing herbage mass and qual ity with the nutrit ional requirements of g razing

animals is one of the key chal lenges facing g raziers. Tactical decisions with in

g razing systems are often based on scant information about herbage qual ity, i n

spite of its importance in i nf luencing herbage intake and animal performance

( Pearson 1 997) . More recently, some dairy cattle studies have concentrated on

herbage quality (Moller 1 997 ; Cosgrove et al . 1 998) and systematic

characterisation of herbage qual ity for different sheep and beef cattle farms in four

reg ions of New Zealand has been undertaken ( Litherland et al . 2002 ) .

The need for a low cost and less t ime-consuming method of estimating herbage

qual ity is well recognized by pastoral researchers all around the world . A strategy

to lower the costs has been to derive one nutrit ional variable from another. Neutral

detergent f iber content (N DF) has been shown to be a good predictor of in vitro

digestibi l ity for alfalfa, alfalfa-grass pastures and winter and summer fodder crops

( Pagella et al . 1 996) . Another alternative has been use of faster analytical methods

than the traditional laboratory analysis , such as near infra-red reflectance

spectroscopy (Corson et al. 1 999). Others have establ ished quantitative

relationships between matu rity stages and herbage qual ity in non-defol iated

g rasses (Sanderson & Wedin 1 889; Berg & Hi l l 1 989; Sanderson 1 992 ; El izalde

et al . 1 999) and legumes (H intz & Albretcht 1 99 1 ; Owens et al . 1 995 ; Sulc et al.

1 997) and less frequently in grazed pastures (Smart et al . 200 1 ) . Simi larly the

relationships between maturity and accumu lated degree days (Frank & Hofmann

1 989; Smart et a l . 200 1 ) and water stress (Lynk et al. 1 990) have been reported .

It is worth noting that these attempts to predict pastu re qual ity are dependent on

chemical analysis for val idation , and the i r usefu lness is restricted to those short­

term tactical circumstances were forage testing is not feasible ( Fick et al . 1 994) .

1 1 6 Par 11 - Chapter 5

As forage matures, the general trend is for decreasing leaf to stem ratio ( Nelson &

Moser 1 994) both i n grasses and legumes. The h igher contribution of stems and

the decrease in their qual ity and digestibi l ity, is associated with an increase of N D F

with in the herbage (Sanderson & Wedin 1 889) . However, matu rity i s not a clear

concept in m ultispecies pastures, as different matu ration patterns have been widely

reported for different plant species under s imi lar c l imatic conditions (Sanderson

1 992). Herbage qual ity is affected by grazing management (Saul et al . 1 999;

Schlegel et a l . 2000 ; Frame et al . 2002) , and intensive g razing is a key too l to

maintai n nutritional value of pastures, by keeping the sward immature with a

greater leaf :stem ratio (Nelson & Moser 1 994; Clark 1 995; Smart et al . 200 1 ) by

preventing the development of structural t issue which leads to a decl ine of readi ly

digestible cell contents (Hodgson & Brookes 1 999) .

The relationsh ips cited are mainly developed for t iming of harvesting events when

monophytic stands grow u ninterrupted , but not when forage m aturity of mixed

pastures is prevented u nder an intensive grazing management . Therefore, the

main objective of th is experiment was to study the seasonal variation of pastu re

quality in an alfalfa-based pastu re , under an intensive beef cattle g razing syste m .

Secondly, the relationship between nutritional variables and potential predictors

from morphological and maturity variables was explored q uantitatively. The

experiment described in this Chapter was designed taken into account a

pre l iminary study of the seasonal variation on herbage qual ity of h and-plucked pre­

grazing herbage samples over th ree years from a New Zealand Friesian bul l beef

production system. Detai ls of this prel iminary study are given in Annex B .

2. Materials and methods

On a fortnightly basis between March 2000 and July 2002, herbage samples were

taken from an alfalfa-based pasture (Medicago sativa, Bromus wildenowii and

Dactylis glomerata) in its second year of production at the Chacra Barrow

Experimental Station , Argentina (38 �O'S, 60 01 3'W, 1 20 m .a.s) . The pasture was

strip-grazed regu larly by British bred cattle , and herbage samples were taken from

strips prior to grazing. Rainfal l , temperature and evapotranspiration were recorded

dai ly at an observation station with in the experimental site.

Seasonal variation of herbage quality 1 1 7

At each sampling t ime, herbage mass (kg d ry matter ha-1 ) was estimated with a

r ising plate meter ( Fi l ip's fold ing plate meter, New Zealand) , cal ibrated against

herbage height and herbage cutt ing (above 4 cm ground level) for the same period

and experimental site (Machado et al. 2003) according to Earle & McGowan

( 1 979) . Fifty herbage samples ( 1 .0 kg aprox. ) were cut to 4 cm above g rou nd level

us ing a knife, and they were bu lked in a plastic bag and refrigerated i mmediately.

Once in the laboratory , a sub-sample (0 .4 kg) was taken and divided into two parts.

The fi rst was sorted into dead , green and weed components. Subsequent ly , the

green pool was separated into grasses and alfalfa, then divided into g rass lamina,

legume leaf, grass sheath & stem and legume stem fractions. These fract ions were

oven dried u nti l constant weight (48 h at 60QC ) , weighed and expressed as dry

matter percentage ( DM%) . The other fraction was subdivided into two sub­

fract ions : one was freeze-dried and analysed for in vitro digestib i l ity ( Dig %) (Til ley

& Terry 1 962) ; crude protein (CP %) determined by mu ltiplying the N concentration

(the Micro Kjeldhal method (A.O.A.C. 1 960) by 62 .5 , neutral detergent f iber (NDF

%) (Goering & Van Soest 1 970) and non structural carbohydrates (NSC) by the

anthrone method ( Pichard & Alcalde 1 990) . The other sub fraction was sorted into

components as previously described , but samples were freeze-dried instead. I n

order to obtain an estimation of their quality variation over seasons, five samples

per season of each component were randomly selected from the whole set of

samples and analysed for in vitro digestibi l ity and crude protein .

At each sampling date , fifty ti l lers per grass species and fifty alfalfa shoots were

col lected at the same sampl ing site . Descript ions of morphological stages of

development were used for alfalfa (from 0-9 Fick & Muel ler 1 989) , and g rasses

(from 2 1 -93 , Simon & Park 1 983) . Values for each t i l ler and shoot were recorded

and a mean arithmetic stage by count (MSC) per species was estimated .

Additional ly , t i l lers and shoots were oven-dried (48 h at 60QC unt i l constant weight)

for each stage of development to estimate a mean stage corrected by weight

(MSW) . A regression analysis was performed between MSC and MSW for each

species.

The dataset was g rouped into nutritional variables ( Dig, CP, N DF and NSC) and

1 18 Par 11 - Chapter 5

morphological and maturity variables (herbage mass, dry matter content, g reen

content , leaf :stem ratio , mean stage by count or weigh ing for each of the three

species) . Analyses of variance by season for both groups of variables were carried

out us ing year as a block. Multip le regression analyses ( 'stepwise" model selection ,

Alpha=0 . 1 5) were performed for each season , us ing alternatively mean stage by

counting and mean stage by weigh ing as the maturity pred ictor. I n order to study

the overal l relationship between n utritional and morphological-maturity variables , a

multivariate canon ical correlat ion analysis was appl ied (Matthew et al . 1 994) .

Crude protein and ME of plant components were analysed with a completely

random design between seasons . Fol lowing a significant F-test ( P<0 .05) , least

squares means were separated using least significant differences (Steel & Torrie

1 980) . All analyses u sed the SAS statistical analysis system (SAS/STAT 2001 ) .

Additional ly, a 8ayesian smooth ing analysis was applied to the complete data set

of ME and Protein using Flexi 3 . 1 (Wheeler & Upsdel l 2003) , to reveal possible

underlying seasonal trends in herbage ME and Prote in . Th i s smooth ing tech nique

employs a variance components model to fit a constant term describing the general

level of the variable and a correlated random term to describe departures of the

curve from th is constant. A cycle of 365 days with fluctuati ng covariance (the cycle

is not forced to repeat exactly) and integral equal to 0 were u sed to model the data

as : Total fit of ME or GP = Seasonal component + long term component + error.

Graphs are presented with the confidence intervals (83 % as defau lt of Flexi) for

the fitted curve (Wheeler & Upsdell 2003).

3. Results

3. 1 . Climatic conditions

The annual means for rainfal l , mean daily temperature and hydric balance

(difference between rainfall and potential evapotranspi ration ) recorded duri ng the

trial were very s imi lar to the 1 8-year average (Table 1 ) . However, during the

experimental period, February, June and December were d rier and August and

October were wetter than normal .

Seasonal variation of herbage quality 1 1 9

3.2. Seasonal change in nutritional quality

OM digestibi l ity (and consequently ME) were lowest ( P<O .05) during autumn

(Table 2 ) , and for the rest of the year had a fairly stable pattern around 1 1 MJ kg

DM-1 (Table 2 and Figure 1 .a) . Crude protein was lowest (P<O.05) dur ing summer

(Table 2) , although variabi l ity for the rest of the seasons was greater than for ME

content (Figure 1 .b ) , with two peaks occurring at the start of winter and during

spring_ Non-structu ral carbohydrate was lower duri ng autumn, whereas NDF

content was constant across seasons (Table 2 ) .

Table 1 . Means of rainfal l , daily temperature and hydric balance recorded over last 1 8 years and duri ng the trial.

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC x Historical ( 1 8 yrs . )

Rainfal l (mm) 96 78 82 80 58 43 39 47 61 76 98 87 70 Mean daily temp. C 23 21 1 9 1 4 1 1 8 7 9 1 1 1 4 1 8 2 1 1 5 Hydric balance* (mm) -95 -77 -35 9 1 6 1 1 3 - 1 1 - 1 8 -35 -53 - 1 08 -33

During trial

Rainfal l (mm) 1 1 1 59 1 1 6 90 60 29 43 72 79 1 39 79 70 79 Mean dai ly temp. C 23 22 1 8 1 4 1 1 8 6 9 1 0 1 4 1 7 2 1 1 4 Hydric balance " (mm) -89 - 1 34 -4 9 1 2 -8 7 1 1 1 59 -67 - 1 6 1 -30 "difference between rainfall and potential evapotranspi ration and X = overall mean of the year.

Table 2. Seasonal variation of nutritional variables in an alfalfa-based pasture.

Autumn Winter Spring Summer #PSE

Nutritional variables I n vitro d igestib i l ity (% of d ry matter) 7 1 .7 a 76.8 b 78.8 b 75.7 ab 1 .83 Metabo l izable energy (MJ ME kg dry matte(' ) 1 0 .8 a 1 1 .6 b 1 1 .9 b 1 1 .4 ab 0.27

Neutral detergent fiber (% dry matter) 34 .5 33.7 33.3 32 .2 1 .60 Crude protein (% d ry matter) 2 1 .5 b 22.2 b 22.0 b 1 7.3 a 1 .00 Non structural carbohydrates (% of dry matter) 6 .3 a 8.8 b 1 0 .0 b 8 .6 b 0.58 #PSE= pooled standard error and d ifferent letters within rows indicate s ig nificant d ifferences between seasons (P<0.05) .

Seasonal variations in contents of ME and C P in different sward compo nents are

presented in Table 3 . The ME of grass parts was significantly lower (P<O.05) in

summer, although sheath & stem ME started to decrease during spring. Alfalfa leaf

ME decreased duri ng autumn , and legume stem during summer. Dead material

had the lowest M E value during autumn . The content of m etabol isable energy in

leaf fractions (computed mean between legume and g rasses) had a trend to lower

coefficients of variation (CV) than stem fract ions (9.4 and 1 3 . 3 % CV, respectively) .

Legume fractions tended to be more stable in M E than grasses fractions , whi le the

ME of dead components had the highest seasonal variabil ity. Crude protein

120 Par 11 - Chapter 5

content declined s ignificantly (P<0 .05) i n summer i n m ost of the sward

components , but in legume leaflets it was s im i lar throughout the seasons , and i n

g rass stem i t was also lower dur ing autumn . Crude prote in content was h igh ly

variable between seasons i n a l l the components, with an averaged CV of 20.6 %.

However, crude protein in the legume leaf was the least variable component ( 1 0 .6

%) .

Table 3. Seasonal variat ion in contents of metabo l isable energy and crude p rotei n i n d i fferent sward components.

Autumn Winter Spring Summer lIPSE *CV

Metabol izable energy (MJ ME kg dry matte(1 )

Grass lamina 1 0 .2 ab 1 2. 1 b

1 1 .8 b

9.9 a 0 .33 1 0. 7 Grass sheath & stem 1 1 . 1 ab 1 2.7 b

1 0 .0 a 9.9 a 0.53 1 4. 7 Legume leaf 1 1 .3 a 1 2.6

b 1 3 .2 b 1 2. 3 ab 0.35 8 . 1

Legume stem 1 0.9 b 1 2.8 c 1 0 .4 ab 9.6 a 0.29 1 2.0 Dead 5 .2 a 7 . 1 b

7 .3 b

6.0 ab 0 .54 22.2 Crude protein (% of d ry matter)

Grass lamina 22. 1 20. 1 2 1 . 3 1 8 .6 1 .90 2 1 .0 Grass sheath & stem 1 0. 8 a 1 5.9

b 1 1 .9 ab 1 0. 0 a 1 .40 29.0

Legume leaf 32.0 b 29.4 ab 29.5 ab 26.7 a 1 .20 1 0.6

Legume stem 1 6.0 ab 1 9. 1 b 1 6.4 ab

1 2.6 a 1 .40 23.0 Dead 8 .2 ab 9.3

b 8.6 ab 7 . 1 a 0.7 1 9. 1

D ifferent letters within rows indicate significant d ifferences between seasons ( p<0.05), lIPSE pooled standard error and *CV = coefficient of variat ion.

a) 1 3

1 0

o o 0 B 0

o

1 0 0 2 0 0 day

'b 0

0 0

o o 0

300

A M J J A S O N D J F M

Month

b) 3 0

2 5 � CL <.) 20

1 5

0 o 0

1 00 200 day

300

A M J J A S O N D J F M

Month

Figure 1 . Seasonal change in herbage metabol isable energy (a) and crude p rotein (b) i n pre­g razing samples during the sampling period .

Plotted confidence bands represent 83% probabi l i ty i n the smoothing analysis .

Seasonal variation of herbage quality 1 2 1

3.3. Seasonal change of morphological and maturity variables

The seasonal variation in morphological and maturity variables in the pasture is

shown in Table 4. Herbage mass (>4cm above ground level) was s im i lar through

the seasons ( P>0.05) . During s ummer and autumn , the sward h ad a h igher

proport ion of leg u me and dry m atter, with lower leaf to stem ratio and green

content . This last variable also decl ined duri ng autum n ( P<0.05) .

Grasses had a h igher maturity state during spring and summer, us ing both MSC

and MSW. M. sativa was more mature during summer-autumn u si ng MSC. A

s im i lar pattern was obtained for MSW but with spring differing also from winter.

Mean stages by count and by weight were highly correlated in the t h ree species

(with correlat ion coefficients of 0 .93 , 0 .92 and 0 .97 for M. sativa, B. willdenoii and

O. glomera ta, respectively) .

Table 4. Seasonal variation of morpholog ical and maturity variables in an alfalfa-based pasture.

Autumn Winter Spring Summer #PSE

Herbage mass (kg dry matter ha" above 4 cm) 1 51 5 1 673 1 51 9 1 639 1 49.8

D ry matter content (% of herbage mass) 24.6 b 1 9 .6 a 1 7.6 a 3 1 .3 c 1 .29 Green (% of d ry matter) 79 .2 a 84. 1 a 97.5 b 86.0 a 2.47 Leaf:stem ratio 1 .51 ab 2.82 c 1 .98 b

1 .00 a 0.29 Legume (% of Green) 64.2 b

43.3 a 54.3 ab 85 . 1 c 6.45 M sativa (mean stage by counting)+ 1 .86

b 0.61 a 1 . 1 a 3.0 c 0.20

B. wildenowii (mean stage by counting) * 23.6 a 25.8 a 47. 1 b 40 .5

b 2.82

D. glomerata (mean stage by counting) * 22.2 a 25.8 a 36.4 b 36.6 b 2. 1 2 M . sativa (mean stage by weighing) + 2 . 1 b 0.89 a 1 .6 b 3.4 c 0.26 B . wildenowii (mean stage by weigh ing) * 24.5 a 26.3 a 47.3 b 53.2 b 3.74 D . glomerata (mean stage by weighing) .. 22.7 a 25.9 a 40.3

b 39.6 b 2.96

# PSE=pooled standard error, + Quantitative scale ( Fick & Muelier 1 989), * Quantitative scale (Simon & Park 1 983)see material and methods for further explanation about scales and d ifferent letters within rows ind icate significant differences between seasons (P<0.05).

3.4. Relationships between variables

Digestibi l ity was significantly correlated with NDF, NSC and Protei n (correlation

coefficients of -0 .62 , 0 .41 and 0 . 36 , respectively) , and NDF content with NSC (-

0 .32) . "Best fit" equations from "step-wise" regression analysis with in each season

relating herbage qual ity variables and morphological and maturity variables of the

pastu re are presented in Table 5. During summer and autumn , nutrit ional variables

were mostly predicted from morphological variables. Protein could not be predicted

either i n summer or winter , and autumn presented the lower reg ression coefficient

122 Par 11 - Chapter 5

( R2=O.54). The content of non-structural carbohydrates was on ly predicted

significantly in spring . I n the cases when maturity corrected by weigh ing was

selected, they could be replaced acceptably by the corresponding mean stage by

counting without much loss of predict ion capabi l ity, except for the case of Dig in

winter.

Table 5 . Regression equations of herbage quality variables and morphological and maturity variables in an alfalfa-based pasture.

Reg ression model Season R2 RMSE P value

Autumn Dig = - 1 83 + 0 .24 Green - 0 .009 HM + 1 1 .23 fDACTw 0 .86 3 .63 0.00

Dig = -238 - 0 .0 1 HM + 1 4 .7 fDACTc 0 .80 3 .69 0.00

NDF = 30.7 + 1 .23 DM - 0 .33 Green 0 .65 4 .34 0.00

CP = 21 .9 - 0 .003 HM + 0.06 Legume 0 .54 2 .36 0 .02

Winter Dig =1 03.9 - 0.006 HM - 4.67 fALFw - 0.44 fBROw 0.54 5 .60 0 .02

Dig = 87.3 - 0 .006 HM 0.27 6.60 0 . 04

N D F = 24. 1 + 5.33 fALFw + 0 .003 HM 0.63 3 .89 0 .02

NDF = 22. 1 + 8 .37 fALFc + 0 .003 HM 0 .52 4.41 0 . 0 1

Spring D ig = 81 .6 - 0 .003 HM - 0 . 1 5 fBROw 0.69 2.26 0 . 0 1

Dig = 99 . 1 - 0 .33 fDACTc - 0 . 1 6 fBROc 0.78 1 .9 1 0 .00

N D F = 24.2 + 0 . 1 1 fBROw + 0 .002 HM 0.65 1 .76 0 . 0 1

N D F = 21 .6 + 0 . 1 3 fBROc+ 0.003 HM 0.69 1 .63 0 .0 1

CP = 47.3 -0.004 HM - 0 .76 DM -3.39 fALFw 0.75 3 .08 0 .0 1

CP = 47.3 + 0.95 Green - 0.005 HM - 8 .9 fALFc 0 .87 2 .21 0 .0 1

NSC= 7 .9 + 0 .35 D M - 0 .07 fBROw 0 .58 1 .60 0 .03

NSC= 20 .4 - 0 .06 Legume - 0 . 1 53 fBROc 0 .59 1 . 57 0 .02

Summer Dig = 41 .2 + 0.4 G reen 0 .58 3 . 1 0 0 .00

NDF = 73.3 - 1 4 .9 Green - 0.3 LeafSt 0.87 2 .40 0.00

RMSE=root mean square error, fALFc= Mean stage by counting of M. sativa, fALFw= Mean stage by weighing of M. sativa, fBROc= Mean stage by counting of B. wildenowii, fBROw= Mean stage by weigh ing of B. wildenowii, fDACTc= Mean stage by counting of D. glomera ta, fDACTw= Mean stage by weigh ing of D. glomera ta, HM= herbage mass DM= dry matter percentage, Green=green percentage, Legume= legume percentage and LeafSt=leaf:stem ratio .

The overall relationship between nutritional and morphological-matu rity estim ates

was explored with a canonical correlat ion analys is . From the possible fou r pairs of

canonical factors (the four nutrit ional variables constrained by the number of

canonical pairs ) , one pai r had significant differences ( p<O.05) with plant maturity

expressed in mean stage by cou nt ing or mean stage by weigh ing , hence both

alternatives are shown (Table 6 ) . With in the nut ritional variables, Protei n and

Seasonal variation of herbage quality 123

m etabol isable energy contributed the most to their canonical factor. I n the case of

morphology and m aturity variables, matu rity stage of M . sativa, dry matter content

and herbage m ass had the highest (and negative) coefficients, fol lowed by a

positive coefficient of green content of the dry matter. Comparing both methods of

expressing m aturity, mean stage by weighing tended to present a s l ightly higher

explanation of the mu ltivariate dispers ion (R2 =0 . 81 ) than mean stage by counting

( R2 =0 . 77) .

Table 6. Summary of canonical correlatio n analysis of the whole sample set of herbage nutritional variables and morpholog ical variables and two options of expressing maturity variables in an alfalfa­based pastu re.

Correlations between n utritional variables

and their canon ical variables

Metabol isable energy (MJ ME kg dry matte(l )

Neutral detergent f iber (% of dry matter) C rude protein (% of d ry matter) Non structural carbohydrates (% of d ry m atter)

Correlations between morphological and maturity variables

and their canonical variables

Herbage mass (kg DM ha·1 above 4 cm)

D ry matter content (% of herbage mass) Green (% of DM) Leaf:Non leaf ratio Legume (% of Green) M. sativa (mean matu rity stage) B. wildenowii (mean maturity stage) O. glomerata (mean maturity stage) Summary statistics

Canonical factors

CF-MSW1

0 .77

-0 .53 0 .85 0 .36

-0 .56

-0 .59 0 .44 0 .30

-0.72

CF-MSC2

0 .79

-0.51 0 .83 0 .43

-0.57

-0.60 0.49 0.31

-0.72

Canonical R2 0 .8 1 0 .77

3pmv 76.9 0 .69 4% Raw var. 26.7 22.6

Significance * * **

Coefficients less than 0 .25 supressed, 1 Canonical factor for mean maturity state estimated by weighing, 2 Canonical factor for mean maturity state estimated by counting , 3 Proportion of multivariate dispersion explained, 4 raw variance of herbage qual ity variables explained, ** P<0.01 and NS= not significant.

4. Discussion

4. 1 . Seasonal change in nutritional quality

The herbage qual ity of bu lk harvests was maintained to a h igh standard throughout

the experiment , although protein decl ined in summer and M E and NSC decl ined

124 Par 11 - Chapter 5

during autum n . Th i s general pattern is l ikely to be a consequence of the relatively

intensive g razing management carried out du ring this study. The demonstration

beef cattle un it of wh ich this trial pastu re is a part ( Di Nezio et a l . 2002) , makes

intensive use of the pasture area ( less than 50% of the beef area, the rest are

stubble from cash c rops and forage crops) in a combination of cow-calf and beef

cattle f in ishing systems with stocking rate and animal production levels well above

the values for the region . I ntensive use of pastures is a key tool to keep the sward

immature by moderating the development of structural t issue ( Nelson & Moser

1 994 ; Clark 1 995; Hodgson & Srookes 1 999) , and these results are also in

agreement with the nutritional observations in a New Zealand beef cattle pasture

managed intensively (Annex 8) .

The observed pattern of protein levels throughout the seasons were s im i lar to that

obtained with pastures of sim ilar characteristics managed under i ntensive rotational

systems during two years (Kloster et al . 2000) , where the authors observed protein

values by season of 23.7 , 26 .0 , 25.8 and 23 .4 for autumn , winter, spring and

summer, respectively. The protein values of the present study clearly exceeded

(except for the summer) the recom mended protein content of herbage requi red for

very you ng g rowing animals, i .e . 1 5- 1 8% crude protein (Hodgson & Srookes 1 999) .

The relationships between the qual ity change of the bulk samples (Table 2 ) and

that of plant parts (Table 3) must be accepted with caution , as sample numbers

were not the same. However, they provide some indication of qual ity changes

within the canopy, showing that ME content of grass fract ions and alfalfa stem

declined during sum mer, and in the case of grass stem there was some decrease

during spring , wh ich is in agreement with the changes in DM digestib i l ity observed

by Mowat et al . ( 1 965) in M. sativa and Serg & Hi l l ( 1 989) in O. g/omerata. Leaf

components had a lower coefficient of variatio n in M E content and less marked

variation in prote in , i n comparison with that observed in stems. I n a study carried

out during two springs on alfalfa, leaf had 4 and 1 9 % CV in digestibi l ity and

Protein values , respectively, whereas stem presented 23.0 and 40 .5 % CV for the

same nutrients (Chr istian et al . 1 970) .

Seasonal variation of herbage quality 1 25

4.2. Seasonal change of morphological and maturity variables

The h igher proportion of alfalfa during summer-autu mn ( 75%) than winter-spring

(48%) i n th is experiment is in agreement with the resu lts of Kloster et al . (2000) in

pasture of s im i lar composition (80 and 59 % of alfalfa i n canopy OM for spring

summer and autumn-winter, respectively) . This seasonal pattern is l ikely to be

associated with the different growth patterns of the species and to the different

factors for which g rass and legume in mixture compete (McKenzie et al. 1 999) . The

i ntensive sward uti l isation moderated the normal maturity process , although leaf to

stem ratio declined during sum mer. High temperature promotes plant matu ration

with proportio nal ly more stem growth (Buxton & Fales 1 994) , decreasing the leaf to

stem rat io , with a marked decl ine in the qual ity of the stems and increased

senescence of green material (Ch ristian et a l . 1 970; Nelson & Moser 1 994) .

Spring showed the highest level of green percentage , s imi larly to that observed by

Kloster et a l . (2000) in an alfalfa-grass pastu re u nder grazing . These authors

reported averages of 87.5 and 99.0 of green percentage for autumn-winter and

spring-sum mer, respectively, for two years of measurement. Dry matter content of

the pasture increased significantly during su mmer, wh ich i s in agreement with that

reported by Ch ristian et al . ( 1 970 ) .

Clearly the combination of species resu lted in a spread o f pasture maturity.

Although the three species were most mature during summer , g rasses increased in

matu rity during the spring , and alfalfa extended maturity to the autu mn (Table 4) .

The effect of increased temperature on plant maturity has been well establ ished

(Buxton & Fales 1 994) , but plant maturity integrates multip le environmental factors

and represents the physio logical and ecological status of the plant stand

(Sanderson 1 992).

Although there was a s ignificant seasonal pattern th roughout the year, the

intens ive use of the pasture m eant that it was maintained in a relatively immature

state. Consideration must be taken of bloat incidence as the relationship between

h igh ly immatu re alfalfa and the problem level has been establ ished (Thompson et

al . 2000 ) . However, on the beef cattle unit where th is study was developed , bloat

126 Par 11 - Chapter 5

was control led us ing d ifferent management alternatives (Majak et a l . 200 1 ) .

There was a very h igh correlation between M S C and MSW, with an overal l

correlation between species of r=0.94 , which is in agreement with that reported by

others using alfalfa pastu re (Muel ler & Fick 1 989) . However, a better capabi l ity of

MSW has been mentioned ( Fick et al . 1 994) , but MSC is less t ime demanding , and

is simply the arithmetic average of the quantitative maturity stage of individual

t i l lers and stems. As MSC does not take into accou nt the change in s ize of the

stems and t i l lers, th is could be an important bias when substantial morphological

plant changes are occurring. One study u nder g razing condit ions (Smart et a l .

2001 ) reported that M SC became an increasingly poor predictor of leaf to ste m

ratio as g razing progressed i n the growing season of a big bluestem pasture . The

prevention of matu rity by intensive grazing in th is study is l ikely to have contributed

to the higher association between the two used methods for obtain ing maturity

estimates .

4.3. Relationship between variables

The important correlation between N D F and Dig is i n agreement with the

observation of Pagel la et a l . ( 1 996) . As expected by the seasonal changes in

nutritional variables (Table 2) and morphological and matu rity variables (Table 4 ) ,

nutritional predicto rs changed seasonal ly (Table 5 ) . For th is reason , no s ign ificant

predictor could be identified when the overal l dataset was u sed without g rouping by

season (result not shown) . It is worth noting that changes in g rasses becam e

important during spring , with earl ier matu rity than alfalfa (Table 4 ) , where t h e g rass

stem plus sheath started to decrease in M E and protein content (Table 3 ) .

Canonical corre lation analysis was an interesting tool for exploring the g lobal

relationship between the two sets of variables ( i .e . morphological -maturity and

nutritional i ndicators) . Maturity of M. Sativa, and the increase of herbage mass and

dry matter percentage affected negatively the M E and protein contents . I t is l i kely

that these resu lts may be associated with changes occurring main ly in alfalfa

stems. Stems decrease in qual ity faster than leaves in most forage plants ,

special ly when plants approach matu rity (Nelson & Moser 1 994) , and a close

association between M SW and digestibi l ity of alfalfa stem has been demonstrated

Seasonal variation of herbage quality 127

(Sanderson & Wedin 1 889; Sanderson et al. 1 989) .

Poor prediction capabi l ity of plant maturity indicators has been observed when the

herbage is used more intensive ly , therefore moderating the u sual changes that

affect herbage qual ity. The d isturbance of the sward structure caused by low

herbage al lowance, resu lted i n poor prediction of mean stage by count of leaf :stem

ratio , protein and N DF , especial ly when compared with non-grazed or laxly g razed

swards of Adropogon gerardii (Smart et al . 200 1 ) . However , the combination of

est imates of morphological and m aturity variable applied here seemed to i mprove

the herbage quality predictors.

Although there was a decl ine of O M digestibi l ity (and consequently M E) during

autum n and of crude protein during summer, herbage quality was consistently high

throughout the year. This was in agreement with a NZ pasture ( ryegrass and white

clover) managed u nder an i ntensive system (Annex B ) . The significant

relationships establ ished by d iffe rent stat istical tests between morphological and

maturity estimates and herbage n utritional variables in a pasture are promising in

terms of gain ing understanding of the seasonal change of herbage qual ity .

However, their potential applicabil ity in a wider context i s h igh ly restricted at

present. Further investigation is requ ired to determine their imp licat ion under

alternative pasture managements.

References

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Berg C . ; H i l l R . 1 989: Maturity effect on yield and quality of spring harvested

o rchardgrass forage. Crop Science 29:944-948.

Buxton D. R . ; Fales S. L . 1 994 : P lant-environment and qual ity. In : Fahey , G. C. Jr . ;

Col l ins , M . ; Mertens , D . R . ; Moser, L .E . Eds. Forage Quality, Evaluation, and

Utilization. Publ ished by the American Society of Agronomy, Crop Science

Society of America, and Soi l Science SOciety of America. 1 55- 1 99 .

Christian K . R . ; Jones D. B . ; Freer M . 1 970 : Digestibi l ity and chemical com position

128 Par 11 - Chapter 5

of fractions of lucerne during spring and su mmer. Journal of Agricultural

Science. Cambridge 75:21 3-222.

Clark E . A. 1 995 : M aintaining forage q uality by intensive management.

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l ivestock feeding. Proceeding of the New Zealand Grassland Association

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Cosgrove G . P. ; Betteridge K. ; Thomas V . J . ; Corson D . C . 1 998 : A sampl ing

strategy for estimating dairy pastu re qual ity. Proceedings of the New

Zealand Society of Animal Production 58:25-28.

Di Nezio L. ; Duhalde J . ; Jensen M . y . B. C. 2002 : Resu ltados de la Unidad

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plate meter for esti mating dry matter yield of pasture . Australian Journal of

Experimental Agriculture and Animal Husbandry 19:337-343.

El izalde J. C . ; Merchen N . R.; Fau lkner D. B. 1 999 : Fractioning of fiber and crude

protein in fresh forages during the spring growth . Journal of Animal Science

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Fick G . W. ; Muel ler S . C . 1 989 : Alfalfa : qual ity, matu rity , and mean stage of

development . Information Bulletin Nr.21 7, College of Agriculture and Life

Science, Cornel! University, Ithaca, NY. 1 3 p.

Fick G. W.; Wi lkens P. W . ; Cherney J . H . 1 994 : Model ing forage qual ity changes in

the g rowing crop. In: Fahey, J . R . ed. Forage Quality, Evaluation and

Utilization. American Society of Agronomy, I nc . :757-795.

Frame H . ; Warn L. ; McLarty G . 2002 : Effects of g razing system and phosphorus

application on pasture quality. Wool Technology and Sheep Breeding

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Frank A. B . ; Hofmann L. 1 989 : Relationship among g razing management, g rowing

degree days , and morppholog ical development for native g rasses on the

North ern G reat Plains . Journal of Range Management 42: 1 99-202.

Goering H . K. ; Van Soest P . 1 970 : Forage fiber analysis (apparatus reagents,

p rocedu res and some applications). Agriculture Handbook Nr 379, USDA.

Washington, DC. 20 p .

H intz R . W. ; Albretcht K. A. 1 99 1 : Predict ion of alfalfa chemical composition from

maturity and plant morphology. Crop Science 3 1 : 1 5 6 1 - 1 565.

Hodgson J . ; Brookes I . M. 1 999: N utrition of grazing animals. In: White, J .&

Hodgson , J . Eds. New Zealand Pasture and Crop Science. Oxford

U niversity press. 1 1 7- 1 32 .

Kloster A. M. ; Latimori N . J . ; Amigone M. A. 2000 : Evaluaci6n de dos s istemas de

pastoreo rotativo a dos n iveles de asignaci6n de forraje en una pastura de

alfalfa y g ramfneas . Revista Argentina De Produccion Animal 20: 1 87 - 1 98.

Litherland A. J . ; Woodward S. J. R. ; Stevens D. R . ; Mcdougal D . B . ; Boom C. J . ;

Knight T. L . ; Lambert M . G . 2002: Seasonal variations i n pasture qual ity on

New Zealand sheep and beef farms . Proceedings of the New Zealand

Society of Animal Production 62: 1 38- 1 42.

Lynk S . 0.; Gee G. W. ; Downs J . L. 1 990: The effects of water stress on

phenological and ecophysiological characteristics of cheatgrass and

Sandberg's b luegrass . Journal of Range Management 43:507-5 1 3 .

Machado C. F. ; Berger H . ; Morris S. T. ; Hodgson J . ; Copes M . ; y Duhalde J .

2003 : Calibracion estacional d e mediciones de altura del canopeo con plato

y con baston de altu ra para estimar la biomasa de forrajera de una pastura

base alfalfa. Revista Argentina De Produccion Animal 23:73-74

( resumen) .

Majak W. ; Hal l J . W. ; McAl l ister T. A. 200 1 : P ractical m easures for reducing r isk of

alfalfa bloat in catt le . Journal of Range Management 54:490-493.

M atthew C . ; Lawoko C. R. 0.; Korte C . J . ; Smith D. 1 994 : Applicatio n of canon ical

130 Par 11 - Chapter 5

discrim inant analys is , principal component analys is , and canon ical

correlation analysis as a tool for evaluat ing d ifferences in pastu re botan ical

composit ion. New Zealand Journal of Agricultural Research 37:509-520 .

McKenzie B. A . ; Valent ine I . ; Matthew C . ; Harrington K. C . 1 999: Plant interact ions

in pastures and crops. In: White, J .& Hodgson , J . Eds. New Zealand Pasture

and Crop Science. Oxford Un iversity press . 29-44 .

M ol ler S . N . 1 997: An evaluation of major nutrients i n dairy pasture in New Zealand

and their effects on mi lk production and herd reproductive performance .

PhD Thesis. Massey University, Palmerston North, New Zealand:342 p.

Mowat D . N. ; Fulkerson R . S. ; Tossell W. E . ; Winch J . E. 1 965 : The i n vitro

digestibi l ity and protein content of leaf and stem portions of forages.

Canadian Journal of Plant Science 45: 32 1 -330 .

Mueller S . C . ; Fick G . W. 1 989 : Converting alfalfa development measurements

from mean stage by count to mean stage by weight. Crop Science 29:4 1 6-

420.

Nelson C . J . ; Moser L. E. 1 994 : Plant factors affecti ng forage qual ity. I n: Fahey , G.

C. J r. ; Col l ins , M . ; Mertens, D . R . ; Moser , L .E . Eds. Forage Quality,

Evaluation, and Utilization. American Society of Agronomy: 1 1 5- 1 54.

Owens V. N . ; Albretch K. A. ; Hintz R. W. 1 995: A rapid method for predicting a lfalfa

qual ity in the field. Journal of Production Agriculture 8:491 -495.

Pagel la J. H . ; Jouve V . V . ; Strizler N. P . ; Vigl izzo E . F . 1 996: Est imaci6n de la

digestibi l idad in vitro de especies forrajeras uti l izando parametros de

composici6n qu fmica. Revista Argentina De Producci6n Animal 16:25-34 .

Pearson C . J . 1 997: Agronomy of g rassland systems . 2nd Ed. Cambridge [England] ;

New York, NY, USA. Cambridge University Press :222 p.

P ichard G . ; Alcalde J. A. 1 990 : Determinaci6n de carbohidratos no estructurales.

In: Ruiz , M . E. and Ruiz, A Eds. Nutrici6n De Rumiantes: Gufa metodol6gica

de Investigaci6n. I I CA. San Jose De Costa Rica:3-20. :3-20.

Seasonal variation of herbage quality 1 3 1

Sanderson M . 1 992 : Morphological development of Swithchgrass and Kleingrass.

Agronomy Journal 84:4 1 5-4 1 9.

Sanderson M . ; Hornstein J . ; Wedin W. 1 989 : Alfalfa morphological stage and its

relation to in situ d igestibi l i ty of detergent fiber fractions of stems. Crop

Science 29: 1 3 1 5- 1 3 1 9 .

Sanderson M . ; Wedin W . 1 889 : Phenological stage and herbage qual ity

relat ionship in temperature grasses and legumes. Agronomy Journal

8 1 :864-869.

SAS/STAT. 200 1 : Release . 8 SAS. Inst. Inc. , Cary, NC.

Saul G . R . ; Kearney G . A. ; F l inn P. C . ; Lescun C. L. 1 999 : Effects of

superphosphate fert i l iser and stocking rate on the nutrit ive value of perenn ial

ryegrass and subterranean c lover herbage. Australian Journal of Agricultural

Research 50: 537-545.

Schlegel M. L . ; Wachenheim C . J . ; Benson M. E . ; Ames N . K.; Rust S. R. 2000 :

Grazing methods and stocking rates for di rect-seeded alfalfa pastures : 11.

Pastu re quality and diet selection. Journal of Animal Science 78:2202-2208 .

S imon U . ; Park B . 1 983 : A descriptive scheme for stages of development in

perennial forage grasses. Proceedings of the XIV International Grassland

Congress: 4 1 6-41 8 .

Smart A. J . ; Schacht W. H . ; Moser L. E . 200 1 : Predicting leaf/stem ratio and

nutritive value in g razed and nongrazed big bluestem. Agronomy Journal

93: 1 243- 1 249.

Steel R . G . D. ; Torrie J . H . 1 980 : Principles and Procedures of Statistics: A

Biometrical Approach. .2nd Ed. McGraw-Hi l l Publ ish ing Co . , New York. 631 p.

Su lc M . ; Albretcht K . A. ; Cherney J . H . ; Hall M. H . ; Muel ler S . C . ; Orloff S . 1 997 :

Field testing a rapid method for estimating alfalfa qual ity. Agronomy Journal

89:952-957.

Thompson D . J . ; Brooke B . M . ; Garland G . J . ; Hal l J . W. ; Majak W . 2000: Effect of

132 Par 11 - Chapter 5

stage of growth of alfalfa on the incidence of bloat in cattle. Canadian

Journal of Animal Science 80:725-727.

Ti l ley J. M. A . ; Terry R. A. 1 962: two-stage technique for the in vitro digestion of

fa rage crops. Journal of the British Grassland Society 18 : 1 04- 1 1 1 .

Wheeler D. M . ; Upsdell M . P. e. 2003 : Flexi 3 .0 . 8ayesian Smoother. Reference

Manual :306 p.

Part III

Modelling

Chapter 6

Model l i n g studies of gra i n feed ing i n a g raz i ng­

based beef f in ish i n g operation :

1 . Development of a "beef cattle fin ish ing un it" s imulator

136 Part 11/ - Chapter 6

Abstract

This chapter describes a dynamic "beef cattle f in ishing u nit" s imu lator ( 8eefSim)

that represents the main biological and some economic indicators for intensively

grazed beef cattle systems using strategic grain supplementation . The model is

bui lt i n Extend package™ and driven by herbage allowance and stocking rate, with

an associated procedu re for pasture conservat ion. The model runs from a day to

several years , and can s imulate different levels of g rain supp lementation , stocking

rates , and herbage allowances . 8eefSim is organised in three modules,

biophysical , decision and economic. I n the biophysical module dai ly nutrit ional and

feed budgets are est imated , and from the metabolisable energy and crude prote in

intake an imal performance is computed. The decision module incorporates a

monitoring system for pasture cover and mean animal l iveweight in re lation to pre­

defined targets as a basis for management adjustments. The economic modu le

gives a valorization to different physical inputs and outcomes of the model to

estimate a g ross marg in . Three different run modes can be appl ied , fu l ly interactive

(requ i res the user to take the decis ion in real t ime) , automatic (decisions are

summarized in a user-defined grazing strategy) and automatic p lus optim ization . I t

can be concluded that the model contains enough specification for use as a

research too l for i ntensively managed rotational grazing beef cattle fin ish ing

systems.

Key-words: Simu lator; dynamic; beef cattle f in ish ing; g razing ; rotational ; g rain

supplementation

1 . Introduction

One chal lenging and sometimes frustrating issue that must be faced by pastoral

researchers , is to demonstrate quantitatively the value of research in a whole­

system context . This is relevant for tech nology transfer to the industry , and also for

funding applications. However, i n order to have some contro l , m ost of the analytical

information about grazing systems is obtained at scales of space and t ime that are

smaller than the scales at which it is desired to apply the knowledge ( Demment et

a\ . 1 995) . Consequently , the contribution of s imulation in the study of grazing

Development of a "beef cattle finishing unit" simulator 137

systems has been recognised (Wright & Dent 1 969) , and there are many different

models avai lable where pastoral-based beef production has been s imulated ( Doyle

et al. 1 989; Marshal l et al. 1 99 1 ; Freer et al. 1 997; Loewer 1 998) .

One o f the major problems of developing s imu lation models o f grazing systems is

the lack of su itable biological data (Wright & Dent 1 969; Dove 1 996) , and this can

also apply where there is a need to adapt a model developed for different

conditions. In projects of a wider scope and magnitude, th is issue has been solved

by cooperative research between field researchers and model developers (Cohen

et al. 2003) . In a more restrictive appl ication , the development of a specific model

s imultaneous to research, has proved to be a h igh ly effective way to expand the

scope of the experimentation (McKinion 1 980; Walker et al. 1 989; Garcia et al.

2000) .

The i mplications to the animal and to beef catt le systems of "starch" grain

supplementation u nder grazing conditions has been sum marised in Chapter 1 , and

studied in short-term grazi ng experiments in Chapters 3 and 4. However, a more

i ntegral quantification of potential on-farm benefits of th is information is not easi ly

predicted. When component information is incorporated i nto a grazing-based

system , often interactions operate in complex ways and may depend on different

characteristics of the farm, which h igh l ights the importance of a whole-farm

approach to study problems (Pannell 1 999) . Farmlet-based studies h ave been

shown to be effective for research into beef cattle fin ish ing systems (Garcia et al.

1 998 ; Kloster et al . 2003) , but they are costly and l im ited by the n umber of

treatments, replications and duration of the study. In a sense , the use of a

s imulation model can be regarded as a whole-farm research approach (Wright &

Dent 1 969).

The main objective of this chapter was to develop a s imu lation too l in the shape of

a generic "beef cattle finish ing unit" simu lator (hereafter: BeefSim), to evaluate in a

system context the implication of alternative grazi ng strategies. Th is task was

developed by synthesizing pertinent information for a pasture-based beef catt le

f in ish ing system , and this chapter detai l s the rationale and procedures used in the

s imulator. I n th is approach , model l ing was centred on bio logical processes to study

the impacts of alternative grazing and s upplementation strategies involving maize

1 38 Part 1/1 - Chapter 6

grain feeding, but economic assessment was included as a necessary constraint in

order to ensure that technical effic iency was not pursued at the expense of

economic efficiency.

2. Model description

2. 1 . Specificity, justification and main features

This model represents a rotational grazed beef cattle f in ishing system , u sing daily

herbage DM al lowance as the u nit for herbage allocation . "Beefsim" represents

intensive beef cattle systems, particu larly 1 2-month systems that use strategic

grain supplementatio n to fin ish heifers and steers of B ritish breeds (280 and 340-

420 kg LW, respectively) before new weaners ( 1 50-200 kg LW) enter in the autumn

(Arosteguy 1 984; Rearte 1 999 ; Machado et a l . 2001 ) . To be able to operate at a

farm level and different scales of t ime ( i .e . from single day to mu lt iple years) and

include n utrient and feed budgeting , d ifferent components and procedu res have

been used directly or adapted from the l iterature. The possibi l ity of u sing d i rectly an

existing model was evaluated and discarded at an early stage of the study, based

on data availabi l ity and flexibi l ity to m atch the objectives of th is thesis. The model

runs in dai ly t imesteps, wh ich have been adequate in other whole-farm models

(Doyle et al . 1 989; Herrero et al . 2000; Cros et al . 2003) . Spatial ly , the lowest

level at which the model operates is the grazing strips with in a paddock and

individual animals with in a si ngle mob.

The fol lowing processes are model led : herbage growth , herbage, s i lage and g rain

intakes , diet composition , animal g rowth , feed allocation , s i lage-making , annual

sales and purchases , and ferti l izat ion (uncalibrated) . Herbage and total i ntake are

estimated dai ly from sward characteristics , supplements (if any) and animal

characteristics , and animal performance is estimated for each ind ividual animal

based on metabolisable energy and crude protein intake.

Commercial pastoral farms with s imi lar biophysical and economic characteristics

frequently are different in their outputs , as a consequence of differences in the

management ski l l of graziers (Cros & Duru 1 997) . These authors developed an

Development of a "beef cattle finishing unit" simulator 139

expl icit and rigorous model l ing of alternative management strategies for their dairy

model (SEPATOU) , i n the format of a decis ion module associated with a module of

biophysical parameters (herbage g rowth , herbage consumption , animal

performance) . A s imi lar approach has been fol lowed in cow-calf systems (Romera

et a l . 2004) and a s impl ified option h as been adopted for this model , i n the shape

of a "manager" block, constitut ing the decis ion module. The "manager" is in charge

of applying the g razing strategy and adj usting components (contingency actions) i n

agreement to prior researcher-defined criteria. A n economic module was added to

the biophysical and decision modules for a more complete assessment of grain

feeding strategies . A general out l ine of h ow the three modu les interact is shown in

Figure 1 . The three modules are subsequently described separately.

Management actions

Economic module Decision modu le

Contingency actions Tactical Operational

_---- Control

Planni ng Strategic Tactical Operational

Biophysical module

S ubmodels

G lobal J optimizer

{ Animal s ubmodel Pasture submodel P lant-animal i nterface s ubmodel

• Structures { Paddocks and strips

Mobs

Outcomes

Monitoring

)

Figure 1 . Cybernetic representation of the biophysical, decision and economic modules i n BeefSim.

The m odel is determinist ic, h owever when standard deviations of the imputed

herbage accu mulation rates are provided , different stochastic s imu lat ions can be

generated. I n the way time is treated, the model is dynamic, and i n terms of the

description for the u nderstanding of the system , the model is m echanistic (France

140 Part 11/ - Chapter 6

& Thornley 1 984} . However , specific components and relationsh ips are empirical.

The model uses the modular-based s imulation shel l provided by 5.0 Extend™

( I magine That, I nc) using its compi led language (ModL). The main advantages of

this software are its interactivity (model parameters and logic can be changed in

real t ime) , reusabil ity of developments, visual transparency (structure and

behaviour can be conveyed at a glance) , i nterappl ication communication ( i .e . easi ly

connected with spreadsheets, etc . ) and a bu i lt- in evolutionary opt imiser ( Krah l

2002) .

;.I ARRAY BLOCKS -/o/2!J Y.5.o 537k 11lm12OO4 08:51

Cl Monogor _ � _fied:l'()312004 14:21 . Con {;] MoIrixSplill.r (3) �

- _f .. d:2711112OO3 12:30 • Co .=J GJJ =�::;'�:l 1 1 '41 r-" M

Libraries of the b locks Dialog Help Icon Initial data Behaviour (code)

Block extended d ialog Input data S imulation res ults

Figure 2. Extend model l ing structure in the BeefSim model .

Code

The model has 1 78 blocks communicated using resizable dynamic arrays . The

customized blocks (main blocks ordered by functional ity and s imu lat ion orders are

presented in Appendix 6 .A . ) present a code (set of equations written in ModL) , that

provides their behaviour , and some present dialogs to enter input data to observe

the s imulation resu lts ( Figure 2) . Blocks are stored in l ib raries, from where they are

copied to the model worksheet when needed, and connected with other blocks to

develop the whole model . The potential of using Extend™ to develop whole-farm

models has been described elsewhere ( Brennan & Gooday 1 998 ; Barioni et al .

1 999) . Model abbreviations and their descriptions are alphabetical ly presented in

Development of a "beef cattle finishing unit" simulator 1 4 1

Table 1 (pp 1 72 ) , and constants are presented i n Tables 2 (p. 1 76 ) , and 3 (p. 1 77) .

2.2. Biophysical module

This module represents the main biolog ical components of a pastoral-based beef

f in ish ing system . This module is divided into s ubmodels , structures and

management actions.

2.2. 1 . Submodels

2.2. 1 . 2. Pasture submodel

The objective of this sub-model was to devise a tool that al lows pasture production

and uti l ization to be integrated with in a whole-farm model , al lowing short- and long­

term evaluat ion of maize grain s upplementation strateg ies. The processes that

govern pri mary production are relatively well documented , and there are in the

l iterature c l imate-driven models of herbage growth with varying g rades of real ism ,

accuracy and pu rpose (Doyle et a l . 1 989; Herrero et al . 1 998 ; Moir et al . 2000 ;

Thornley 200 1 ; McCal l & Bishop-Hurley 2003) . The construction of a detailed

mechanistic description of herbage g rowth including weather and soil components

was beyond the scope of this study. Soil models are h igh ly demanding i n

appropriate data for parameterisation and validation (Herrero e t a l 2000) and

Argentinean beef cattle systems consist of heterogeneous pastures and soi ls

(Rearte 1 998) . However, a climate and soi l component could be added where

specific soi l , plant and cl imatic parameters are avai lable.

The model u ses user-defined monthly herbage accumulation rates ( HAR) , as done

by Woodward et al . ( 1 993) and Garcia et al . (2000) . However, average herbage

accumulation rate is converted to herbage gross growth rates (GG R) through the

procedu re described below. Although this procedure requ i res addit ional

assumptions to be made, it does al low the division of herbage mass into leaf (L) ,

sheath and stem (St) and dead (0) proportions ( Doyle et al . 1 989 ; Cacho et al .

1 995) . This division is important for an expl icit representation of sward condit ions

(eg . qual ity losses during the carry-over of herbage between grazings) . Additional

advantages of this alternative are described in the herbage i ntake and diet

composition sections (2.2. 1 .2 . and 2.2 . 1 .3 . , respectively) . Some authors have

1 42 Part 1/1 - Chapter 6

suggested incorporation of the spatial structure of the sward (Herrero et a l . 1 998) ,

but the lack of information about L, St and 0 proportions with in the sward strata in

the cu rrent project l im ited its incorporation . Therefore :

HM = L + St + D Eqn . 1

Where HM is herbage mass above ground level (kg OM ha-1 ) and L , St and D

represent kg OM ha-1 of leaf , sheath p lus stem and dead herbage m asses above

g round level , respectively.

U nder grazing , herbage accumulation rate is a fu nction of s imu ltaneous and

interacting processes of herbage gross growth , senescence and consumption

( Lemaire & Agnusdei 2000) . These factors make it u nfeasible to convert

realistically between HAR and GGR u nder grazing conditions. However , most of

the avai lable HAR (and those used in the model) are estimated by cutting

techniques (Corral l & Fenlon 1 978) . Hence, HAR recorded under cutt ing conditions

is :

HAR = GGR - S Eqn. 2

Where HAR i s the herbage accumu lation rate (kg OM ha-1 d-1 ) , GGR i s herbage

g ross g rowth rate (kg OM ha-1 d- 1 ) and S is the senescence rate (kg OM ha-1 d-1 ) .

GGR was model led as a function o f leaf mass, i n a Mitscherl ich-type function

(Thornley & Johnson 1 990) as :

GGR = HAR,nput f adi (1 - e(P1L» ) Eqn. 3

Barioni ( 1 997) used HAR,nput estimated by cutting techn iques to calcu late GGR

i ncluding a d imensionless factor ( f adi ) cal ibrated through a regression analysis

from a constructed dataset. Here f adi i s i ncluded but in a different approach ,

estimat ing fadi mathematical ly from leaf cei l ing mass and P1 4 ( Eq n . 4) . A

description of assumptions and procedure used to estimate Eqn . 4 are shown in

4 Constant descriptions are presented in Table 1 and 2 and not mentioned i n the text, except when needed for explanation.

Development of a "beef cattle finishing unit" simulator 1 43

appendix 6 .B . Therefore, HAR,nput f adj converts HAR,nput ( input of the model ,

assumed as the maximum achievable HAR) backwards into GGR expressed in (kg

DM ha-1 d-1 ) , L is leaf mass (kg DM ha-1 ) and P1 is a seasonal parameter (Table 2)

that represents the partition of the aerial biomass production among leaf and stem

parts ( Doyle et al . 1 989; Cacho et al. 1 995). Therefore :

Where Lcei/ is leaf cei l ing mass (kg DM ha-1 ) described as :

L -MaxLcei/ + MinLcei/ (M L _ M" L ) (2 t -P2 ) Ceil - 2 + ax Cei/ In Cei/ cos Jr 365

Eqn. 4

Eqn. 5

Where MaxLcei/ and MinLcei/ are defined l imits for the seasonal variation of cei l ing

mass (Cacho et al . 1 995) , with values of 4500 and 2000 kg DM leaf ha-1 and t is

the s imu lation t ime (day of the year, 0= 1 March by defau lt) . Leaf senescence ( SL '

kg DM leaf ha-1 d-1 ) is described as a l i near function of L as :

Eqn. 6

And

HAR f . (1 - e(P,'-<:eil ) ) P _ Input ad} 3 - Lcei/

Eqn. 7

Where f33 is the slope of the li near function of leaf senescence versus leaf mass

expressed in kg DM ha-1 (Appendix 6 . B . ) , HAR,nput fadj converts HAR,nput ( input of

the model) backwards into GGR ( kg DM ha-1 d-1 ) and LCei/ is leaf mass (kg DM ha­

\ Senescence of St component (SS!, kg DM ha-1 d-1 ) is assu med to be s imi lar to

that est imated for the L fraction . The rate of decomposition of D components has

been model led as affected by temperature and humidity (McCall & Bishop-Hurley

2003) . However, i n the absence of soi l and cl imate submodels, a s impl ified

1 44 Part 1/1 - Chapter 6

approach has been fol lowed (Cacho et al . 1 995) as :

Eqn. 8

Where DR is dead decay rate (kg OM ha-1 d-1 ) and D is dead herbage mass (kg

OM ha-1 ) . Final ly , t h e dynamics of herbage accumulation rate in t h e absence of

grazing for each existing strip of the farm (section 2.2 .3 . 1 . Feed al locat ion) is

estimated from pre-grazing herbage conditions as5:

L � L + PSGGR -sL

Sf � Sf + (1 - Ps )GGR -sSt

D � D +sL+SSt-DR

Eqn. 9

Eqn. 1 0

Eqn. 1 1

Where L is leaf mass ( kg OM ha-\ GGR is herbage g ross growth rate (kg O M ha-

1 ) , SL is leaf senescence rate (kg OM ha-\ Sf is sheath & stem mass (kg OM h a-

\ SSt is the sheath & stem senescence rate ( kg OM ha-1 d-1 ) , D is dead herbage

mass (kg OM ha-1 d-1 ) and DR is dead herbage decay rate (kg OM ha-1 d-1 ) . A

styl ized view of the structure of the pasture submodel is shown in Figure 3 .

.. .. . . . .. . . .. . . . ... . .. .. '

---""---Gross herbage

growth rate

Herbage Intake and silage mak ing

'------v------' Green pools (kg DM ha�-_----.-/

Herbage mass (kg DM ha" )

Figure 3. Structure of the pasture submodel used i n the s imulator.

5 The operator <- is used here to indicate that the value of the variable is updated at each simulation step, and its value on the previous step is part of the right hand of the equation.

Development of a "beef cattle finishing unit" simulator 1 45

2.2. 1 .2. Herbage intake

I n g razing animals , feed intake constraints have been divided into nutrit ional and

non-nutrit ional factors (Pappi & Hughes 1 987) . Nutrit ional factors may be related to

metabol ic demand ( 1 l I ius & Jessop 1 996) or to d igestive capacity (Al ien 1 996) . Non­

nutritional factors are associated with herbage avai lab i l ity , and how it constrains

the abi l ity and behaviour o f the animal in harvesting pastu re (Hodgson 1 990) .

There i s some data available from Argentina (Cangiano & Gomez 1 985; Cangiano

et al . 2002) and elsewhere (Woodward 1 997; Herrero et al . 1 998 ; Hodgson &

Brookes 1 999) to calculate herbage intake from grazing behaviour measurements.

However, because of l imitations mentioned recently by Herrero et al . (2000) for

integrat ing this approach in a h igher t ime and space scale within a whole-farm

model , a s impler alternative was preferred . Daily herbage i ntake without

concentrate feeding was represented as:

Eqn. 1 2

Where I (kg OM d-1 ) is the herbage intake (s i lage intake is i ncluded if present) ,

Ip is potential i ntake , IF is f i l l capacity i ntake (both in kg OM d-1 ) and fA is a non­

nutrit ional factor (0- 1 ) associated with herbage availabi l ity (Section 2.2 . 1 .2 . 3 . ) .

2.2. 1 .2. 1 . Potential intake

Potential intake ( Ip) is estimated fol lowing the approach of Freer et al . ( 1 997) as :

And

N z = SRW

i f W�SRW Eqn. 1 3

Otherwise

Eqn. 1 4

146

BC = L W Actual

N

Part 11/ - Chapter 6

Eqn . 1 5

Eqn. 1 6

Where SRW is the standard reference weight as defined by (SeA 1 990) , Z is the

relative ani mal size , BC is the animal body condit ion , N i s the normal an imal weight

for the cu rrent animal age , L WActua/ is animal l ive weight (kg ) , WBirth i s animal birth

weight (kg) and A is animal age (days) .

2.2. 1 .2.2. Rumen fill intake

The digestive capacity intake is estimated fol lowing Fin layson et al . ( 1 995) as :

And

'F = K�RC

1 - Dig Herbage

Eqn. 1 7

Eqn. 1 8

Where 'F i ntake is constrai ned by digestive capacity (kg OM d-1 ) , RC represents

the rumen capacity (kg OM d-1 ) , L WActual is animal l ive weight (kg ) , DigHerbage i s the

herbage digestibil ity (as fraction of OM) incorporating a term FSFactor to represent

hay or si lage effect (fibrous supplements , FS) on fi l l i ntake (as fraction) added by

Barioni ( 1 997) to yield :

Eqn. 1 9

Where FS is fibrous supplement eaten (kg OM d-1 ) assuming the offered FS is

eaten (constrained arbitrari ly to a maximum of 5 kg d-1 less a feeding loss, as in

this kind of system th is feed is not the main part of the diet as stated by Rearte

( 1 998) , and DigFs represents the digestibi l ity of the FS .

Development of a "beef cattle finishing unit" simulator 147

2.2. 1 .2.3. Intake constraints associated to herbage allowance

I n a quantitative dai ry catt le study about the sward characteristics that affect

herbage intake u nder rotat ional g razing , pre-grazing mass , herbage al lowance and

post-grazing mass were the relevant factors identif ied (Stockdale 1 985) . An

asymptotic relationsh ip between herbage intake and herbage al lowance is

general ly accepted (Hodgson 1 984; Redmon et a!. 1 995) . Herbage mass has also

been shown to affect i ntake positively ( Penn ing et a l . 1 994; Wales et al . 1 999)

however there is a negative effect when herbage mass and maturity are

confounded ( Reardon 1 977; Stakelum 1 986; Ferrer Cazcarra & Petit 1 995) . For

th is reason the effectiveness of sward characteristics as animal performance

p redictors can be improved when they are expressed in terms of g reen rather than

total herbage dry matter (Baker et al . 1 981 ) . McCall ( 1 984) inc luded green herbage

mass and green herbage allowance as the major d river variables in a rotational

g razing i ntake model for sheep. Th is model has been appl ied to other whole-farm

models , but bui ld ing the g reen component from leaf and stem that were model led

i ndependently (F in layson et al . 1 995; Barioni et al. 1 999) . I n the case of models for

rotat ional grazing systems, g reen herbage al lowance ( Ieaf+sheath+stem ) has been

used in a bul l beef model (Doyle et al . 1 989) and herbage dry matter mass in a

dairy catt le model (Cras et al . 2003).

There is further evidence that sward characteristics expressed i n leaf mass rather

than g reen herbage mass can improve the u nderstand i ng of what drives herbage

i ntake (Butler & Hoogendoorn 1 987; Penn ing et aL 1 994 ; Okaj ima et al. 1 996 ;

Wade & Carvalho 2000) . Thus was recognized by Herrero et al . (2000) , who

i ncluded in their model herbage intake as a function of defol iat ion of leaf area i ndex

( LA I ) based on Johnson & Parsons ( 1 985) . Additional ly , Herrero et al . (2000)

inc luded explicit ly the effect of the animal l ive weight , as is i t is known that large

ruminants are less able to exploit low herbage avai l abi l ity than smal l ones ( 1 l I ius &

Gordon 1 987; Ferrer Cazcarra et al. 1 995) . I n the p resent mode l , th is component

of i ntake was assumed to be driven by leaf mass and leaf al lowance, and i ncluded

the effect of animal size. The relat ionship to LW (direct or al lometric) was found to

be impo rtant i n faci l i tating the derivation of functions for an imals of different size, as

the an imals i n a beef catt le fin ish ing system can i ncrease their l ive weight by two to

1 48

four t imes during the f in ish ing process. Therefore :

Where

1 +

L

L W K",o 1(g Actual

L W K",o Kg Actual

Part III - Chapter 6

Eqn. 20

Eqn. 2 1

Where fL is a dimensionless factor depending on leaf mass (0< fL < 1 ) adapted

from Herrero et al. (2000) by converting empirical ly L to LA I assuming a l i near

relationship (Ooyle et a l . 1 989) , L is leaf mass (kg OM ha-1 ) and L WActual is an imal

l ive weight (kg) .

Herrero et a l . (2000) d id not include a reference to herbage al lowance , which is

known to affect herbage i ntake in rotational grazing systems (Hodgson 1 984 ;

Stockdale 1 985) . Information relat ing leaf al lowance and herbage intake is scarce ,

and most is from dairy cattle systems (Chapter 1 , Table 2) . Although some authors

have reported a l inear function of herbage i ntake as leaf al lowance i ncreases

(Butler & Hoogendoorn 1 987) , there is evidence of an asymptotic relationship ( Kim

et al . 1 995; Peyraud et al . 1 999; Oelagarde 2000 ) , and i ntu itively that seems a

more l ikely relationsh ip . Here, fLA is a dimensionless asymptotic value (0< fLA < 1 )

associated with the effect of leaf allowance on herbage intake. From the

observation of K im et a l . ( 1 995) , assuming a zero intercept and asymptotic

situation of intake at the maximum leaf al lowance of 1 84 g leaf (kg LWo.75r1 , the

fol lowing equation was fitted (R2=0.99, residual SS = 1 .3-0.4 , P<0.05) .

Development of a "beef cattle finishing unit" simulator

{ 'LA = 1 - exp(K12 LA)

'LA = 1

if LA< 1 84 g (kg LWo.75r1 Eqn. 22

Otherwise

1 49

Where LA is leaf al lowance expressed in g kg LWo.75, and is estimated from the

herbage al lowance (% LW) allocated to the animal and leaf percentage of the

sward in the correspo nding strip that animals are currently grazing. I n Figure 4 and

Figure 5 the response su rface of the relative i ntake as affected by LW, leaf

al lowance and mass for two different animal sizes are shown . The data suggest

that a heavier ani mal (450 kg , Figure 5) is more demanding in the sward cond it ions

to achieve a s imi lar relative i ntake than a l ig hter one ( 1 00 kg , Figure 4) . I n Figure 6

a general outl ine of the different constraints to intake is presented .

Leaf Allo.,..,ce (% LW)

o Leaf mass (kg/ha)

fA relative Intake

(0-1 )

[] 0·0 . 1

0 0 .2-0.3

. 0 .4-0.5

. 0. 6-0 .7

. 0 .8-0.9

. 0. 1 ·0 .2

0 0 .3-0.4

[] 0.5-0.6

0 0 .7-0.8

. 0 .9 - 1

Figure 4. Response surface of relative intake (0-1 ) of a 1 00 kg LW animal to i ncreasing levels of leaf herbage masses and leaf h erbage allowances as predicted by Eq n.20. Leaf allowance is expressed here as % LW.

1 50

Leaf AlIo'MWlC8 ('Y. LW )

o Leaf mass (kg/ha)

fA relative Intake

(0- 1 )

Part 111 - Chapter 6

0-0.1 . 0. 1 -0.2

0 0.2-0.3 0 0 .3-0.4

• 0.4-0.5 [J 0. 5-0.6

. 0.6 -0 .7 0 0 .7-0.8

. 0.8-0.9 . 0.9-1

Figure 5. Response surface of relative intake (0-1 ) of a 450 kg LW animal to i ncreasing levels of leaf herbage masses and leaf herbage allowances as predicted by Eq n.20. Leaf al lowance is expressed here as % LW.

Herbage mass Herbage allowance

_. __ ._ .. _. __ ._---_ . . _ .. _._ ...... _--_._ .... _\ (_ .. _._._._._ .. _--_ .. _. __ . __ .. __ ._ .. _ .. _._-_._-_ .. \ ( Non Nutritional nstraints I Nutritional constraints

I 1 I I ; � 1 Allowance I I Rumen fill Potential ! , - -1- - - - - - intake I ,i - - intake intake i ! L.... lA ) : L-=:: IF I p_d

r-__ �_-_-�- - - - - - - - - - - - - - - - - r - - - - - -

Total intake

Figure 6. A genera l outl ine of how different constraints to intake are estimated and integrated in the "beef cattle fi nishing unit" simu lator (continuous li nes indicate mass fluxes, dashed l ines indicate i nfluence) .

2.2. 1 .3 . Diet co mposition

A sward is not u niform and the heterogeneous distribut ion of its co mponents

provides rumi nants with the opportunity to graze selectively (Gordon & Lascano

1 993; Hodgson & Srookes 1 999). Species composit ion and g razing management

p lay a major role in determining the diet selected by the an imal , which is

dependent on stocking rate, environmental condit ions or other disturbances

Development of a "beef cattle finishing unit" simulator 1 5 1

(Herrero et al . 1 998) . Sward heterogeneity can affect the relationship between

herbage intake and herbage allowance : leaf versus stem, g reen versus dead and

species differences (M i lne 1 991 ) . Animals fi rst consume leaf and then stems, and

once material other than leaves is eaten , there is a marked decl ine in digestibi l i ty of

the diet selected (Penn ing et al . 1 994) . However, marked dietary enhancement

with respect to the proport ions of leaf over stem and dead material present in the

sward does not necessarily reflect an act of conscious selectivity , it may be s imply

a consequence of canopy structure (Dougherty et al . 1 988) .

As described previous ly for the pasture mode l , herbage mass was divided i nto L ,

St and D, and these sward components were assumed to be selected as the diet

according to the sequence LeabStem>Dead ( El l is 1 978; Dougherty et al . 1 992 ;

Nielsen et al . 2003) . Adapting from the approach of Johnston and Parsons ( 1 985)

for the three sward components ( L, St and D) , the component proportions under

continuous grazing are described as:

Where

S / -L1C13 e L - -----'-=-----

L 1C1 3 + Sf1C14 + D1C15 Eqn. 23

Eqn. 24

Eqn . 25

Se/ L ' Se/ Sf and Se/D are the proport ions of leaf, stem and dead

components contributing to the diet under continuous condit ions, L , Sf and D

represent kg DM ha'1 of leaf, sheath & stem and dead herbage masses above

ground level , respectively and K1 3 , K14 and K15 represent the degree of preference

(active , caused by sward structure or both ) for each component. These equations

imply that the grazing an imals eat different p roport ions of leaf, stem and dead

components than those avai lable in the sward ( L %, St% and D% respectively) , and

the degree of selectivity is controlled by the herbage mass and the preference

parameters of each sward component.

The abi l ity of the animal to harvest through grazing a nutrit ionally enhanced mix of

1 52 Part 111 - Chapter 6

sward components decl ines as herbage al lowance decreases (Geenty & Sykes

1 982; Dougherty et al . 1 989; Dougherty et al . 1 990) . Therefore , u nder rotat ional

grazing co nditions, the diet component proportions adapted from Barioni ( 1 997)

are described as :

DietL = L% + (SelL - L%)fLA

DietSt = St% + (SeISt - St%) fLA

DietD = 1 - (DietL + DietSt )

Eqn. 26

Eqn. 27

Eqn. 28

Where DietL , DietSt and DietD represent the proport ion of leaf, stem and dead

material in the diet. L % , St% and D% are the proport ion of leaf, stem and dead

material in the sward . SelL ' Sel St and Sel D are the proportions of leaf, stem and

dead components contributing to the diet under conti nuous grazi ng, respect ively.

'LA is a factor associated to leaf allowance as defined in Eqn. 22.

F igure 7 shows an example of how the composition of the d iet changes when the

animal is allocated a h igh ly restrictive leaf al lowance ( 'LA =0. 1 ) , where the

component propo rtions are simi lar to those of the sward (0.4 1 , 0.29 and 0.29

proportions of leaf, stem and dead respectively in th is example) . In contrast , when

animals are allocated to un restrictive co nditions ( 'LA = 1 ) , the component

proportions are similar to those esti mated by Eqns. 23-25 . This s ituation is h igh ly

dynami c and i s affected by the leaf content of the sward.

c 0 '';:::; 'in c 0 0 a. ',;:::; E u o � u !:S a; 0

1 .0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 .0

Leaf Al l owance factor

• Leaf • Stem o D ead

Fig ure 7. Diet composition at increasing level of leaf al lowance with a sward composition i n fractions of 0 .4 1 , 0 .29 and 0.29 o f l eaf, stem and dead respectively (as estimated by Eqns. 26-28) .

Development of a "beef cattle finishing unit" simulator 1 53

Figu re 8 shows the i nf luence of leaf allowance on leaf select ion across the ful l

range of possible sward leaf content, i .e . when the leaf % of the sward increases,

only those animals a l located a higher leaf al lowance are able to obtain a h igher

leaf proport ion than available in the sward. With a very restrictive leaf al lowance

( (A =0. 1 ) , proport ions of sward and diet components are s imi lar.

1 .0

0.9

0.8

C 0.7 0 -f) 0.6 �

0.5 Cii Q) -1 0.4

Q) 0 0.3

0.2

0 . 1

{ -- 0 1 - - - - 0.2

fLA -- 0:4 - · - · - 0.6

- - 0.8 1 .0

0.0

0.0 0.2 0.4 0.6 0.8 1 .0

Sward Leaf (fraction of sward m ass)

Figure 8 . Effect of leaf fraction in the herbage O M (Eqn.26) and leaf al lowances (0< (A <1 ,

1 =unrestricted) on the fraction of leaf in d iet OM.

2.2. 1 .4. H igh starch grai n feeding a n d substitution rate

Grain feeding under g razing conditions adds complexity and incorporates further

interactions to the al ready complex pasture-animal interface ( Dixon & Stockdale

1 999) . Frequently herbage intake decl ines when g rain is fed ( Horn & McCol lum

1 987) and substitution rate is defined as the change i n forage intake per unit

increase i n concentrate intake (SCA 1 990) . The substitution rate can be influenced

by many factors i ncluding herbage avai labi l ity and quality, nature of the

supplement used and potential animal performance (Mayne 1 988) . There is strong

evidence for sheep and beef cattle (Dixon & Stockdale 1 999) and dairy cattle

(Grainger & Mattews 1 989; Robaina et al. 1 998 ; Wales et al. 1 999) of a l i near

relationship between u nsupplemented herbage i ntake and substitution rate . This

response seems to j ustify representing grain feeding to g razing animals as a two­

step procedure , computing fi rst the unsupplemented herbage intake, then the

1 54 Part 1/1 - Chapter 6

substitution rate and final ly the actual herbage and total dai ly i ntake, as done by

other authors (Neaves et al . 1 996 ; Woodward et al. 2000) , therefore :

11 000 SR = K16 + K17 0.75 L WActual

Eqn. 29

Where SR is the substitution rate (kg OM herbage kg-1 OM grain) when g rain is

fed , I is the herbage intake prior to g rain feeding (Eqn . 1 3) but expressed i n g OM

d-1 , and L WActualo.75 is the animal metabol ic l iveweight.

2.2. 1 .5. Quality of herbage on offer and quality of herbage eaten

Herbage qual ity is a concept that include mu ltiple factors i n addition to herbage

composit ion , such as nutrient digestion and diet selection (Bush & Burton 1 994) .

The term herbage qual ity is restricted here to the percentage of crude protein and

metabol isable energy content. A range of alternatives for model l ing herbage qual ity

has been used in the l iterature. Some authors have i ncorporated it as a user­

def ined variable (Woodward et al . 2000 ; Garcia et al . 2000) estimated from the

contribut ion of g reen and dead herbage material (McCall 1 984) , bui l t from leaf ,

stem and dead mass (Doyle et al . 1 989 ; Cacho et al . 1 995) or from sward

structu re and maturity (Woodward 200 1 ) . I n th is model herbage qual ity is adapted

from the method used by Doyle et al . ( 1 989) , and is g iven by :

ME Herbage = L%MEL + (St%MESt )fseason + D%MED

CPHerbage = L %CPL + (St%CPSt )fseason + L %CPD

ME HerbageEaten = Diet L ME L + (Diet St ME St )fSeason + Diet D ME D

CPHerbageEaten = Diet L CPL + (Diet St CPSt ) 'season + Diet DCPD

Eqn. 30

Eqn. 3 1

Eqn. 32

Eqn. 33

Eqn. 34

Where ME Herbage ' Dig Herbage and CPHerbage are the metabol isable energy (MJ kg DM­

\ in vitro OM digestibi l ity of the herbage and crude protein (% OM), respectively;

ME HerbageEaten and CPHerbageEaten represent the metabol isable energy (MJ kg DM-1 )

and crude protei n (% OM) of the herbage eaten , L % , St% and D% are the

Development of a "beef cattle finishing unit" simulator 1 55

fraction of leaf, stem and dead material i n the herbage, DietL , DietSt and

Dieto represent the fraction of leaf, stem and dead material in the diet,

ME L , ME St ' ME 0 and CPL , CPSt , CPo are the annual average of metabol isable

energy (MJ kg OM-1 ) and crude protein (fraction of the OM) of the leaf, sheath &

stem and dead component of the sward and 'season is a seasonal factor affecting

stem quality, to take account of plant maturation during late spring and summer,

i .e . decrease in the leaf to stem ratio and a decl ine in the qual ity of the sheath and

stems (Nelson & Moser 1 994) .

I n this model leaf, stem and dead masses and their qual ities are treated separately,

therefore g reen to dead ratio or leaf to stem ratio implicitly affect herbage qual ity .

The seasonal change of herbage qual ity is present (although more moderate)

when an intensive g razing system is fol lowed (Hodgson & Brookes 1 999) .

Therefore :

. (2tr( t - 1 50 )) 'season = 1 + K19 Sin

365

Eqn. 35

Where t is the s imulation t ime (day of the year, 0= 1 st March) and 1 50 adjusts the

seasonal ity to yield the lower value of 'season at the end of January , and

K19 regu lates the magn itude of the seasonal change.

Herbage qual ity and herbage intake are l i nked to the sward components. In Figure

9 is shown an example of a 30-d g razing period , describ ing the sward composition

of a series of dai ly strips, before and after g razing. In this particu lar example ,

animals were al located to a herbage allowance of 5 % of LW d-1 .

1 56 Part III - Chapter 6

IV 3500 .c Cl 3000 �

b) IV 3500 .c Cl 3000 �

2500 III Q) 2000 III III

� 2500 III Q) 2000 III III III 1500 E III 1500 E Cl 1000 Cl 1000

10 20

c: N 500 � 0 Cl

3 0 U; 0

c: 500 N � 0 Cl I!! 0 10 20 30

0 c.. c.. Grazing day Graz ing day

I �ad • Stem . Leaf Dead • Stem . Leaf

Figure 9. Simulation of Herbage mass composition a) pre-g razing and b) post-grazing in a series of strips of herbage grazed daily.

Herbage al lowance was of 5 % LW d·1 in a period of 30 days by animals weigh ing 3 1 0 kg LW at day O .

2 . 2 . 1 .6. Total daily nutrient intake

In the fo l lowing equation the herbage i ntake and supplemented feeds ( if any) are

integrated to calculate the metabol isable energy and crude prote in eaten daily as :

TI = I H + IFS + I Maize Eqn. 36

Eqn. 37

Eqn . 38

Eqn. 39

Eqn. 40

Where TI is total dry matter intake (kg OM d-1 ) , IH is herbage intake (kg OM d-\

IFS is f ibrous supplement intake (kg OM d-\ if any. IMaize is maize g rain i ntake ( kg

OM d-1 ) , SR is the substitution rate (kg OM herbage kg-1 OM grain) when grain is

fed . MaizeFed is the amount of g rain fed (kg OM grain kg LW-1 d-1 ) which is

arbitrari ly restricted to a maximum of 2 % of LW per day, DietME is the total

metabol isable energy eaten daily (MJ ME d-\ MEHerbageEaten , MEFS and

MEMaiZe are the metaboli sable energy density (MJ M E kg-1 OM) of herbage eaten ,

Oevelopment of a "beef cattle finishing unit" simulator 157

f ibrous supplement and maize grain , respectively, Dietcp is the total protein eaten

daily (kg ep d-1 ) , CPHerbageEaten , CPFS and CPMa;ze are the crude protein density (g

ep kg-1 DM) of herbage eaten , fibrous supplement and maize grain , respectively.

2.2. 1 .7 . Animal performance

Animal growth rates and consequently the updated an imal I iveweights are

est imated daily i .e . s imulated animals gain or lose weight , depending on their

nutritional balance associated with animal characteristics and the corresponding

individual ME and ep eaten daily (Eqns. 39 and 40 respectively) . Partit ion of

dietary metabol isable energy and crude protein to maintenance and production are

calculated from Freer et al . ( 1 997) and based on publ ished requ i rements for

maintenance and growth (SeA 1 990) , hence a fu l l detai led descript ion is not

presented here.

2.2.2. Structures

Paddocks , g razi ng strips and animals with in mob constitute the structu res of the

model . By default , the model specifies twe lve paddocks , but th is feature can be

easi ly modified. For each paddock, width and length (in meters) , L St and D mass

and monthly herbage accumulat ion rates must be provided by the user. The size of

the g razing strips are internally estimated by the model (section 2 .2 .3 . 1 . ) .

The mob i s constituted as groups of i nd ividuals , and may be of different l iveweight,

breed or sex. This information is provided to the model by different parameters

requi red for nutr it ional requirement estimat ion (Freer et al. 1 997) , that i nternally i n

the model are organ ized as a look up table (24 breeds and with the corresponding

SRW for female , castrates and males) . Although the animal performance model

takes individual animals as units , feed al location routines use the mob as the unit

(sect ion 2 .2 .3 . 1 . ) . For the sake of simpl icity i n the grazing plan, the present version

of the model uses a single mob, but this feature can be easi ly expanded when

needed. In this case, when animals enter the farm they need to be "tagged"

individually to a mob for identification.

1 58 Part III - Chapter 6

2.2.3. Management actions

2.2.3 . 1 . Feed allocation

It is important to emphasise that although herbage i ntake is part ly control led by leaf

mass and al lowance (Section 2 .2 . 1 .2 .3 . ) , daily herbage al locat ion is based simply

on OM herbage al lowance (% LW d·1 , cut above ground level ) . This means that any

given herbage al lowance can produce different values of herbage intake, diet

composit ion and animal performance, depending on sward characteristics .

Herbage al lowance can provide a more sensible control of the plant-animal

interface than stocking rate alone. A g razing management based on herbage

residuals may be more robust (Hodgson 1 984) , but this latter approach was not

used given the difficulty to control residuals in a dai ly rotational model (one new

strip day"1 ) when the s imulation step is of 1 day, which creates difficu lties i n

constrain ing the herbage intake function . Addit ional ly, herbage al lowance was the

variable of control in the f ield experiments, and these two sward characteristics are

normally h ighly correlated (Stockdale 1 985) .

The mob is the unit for feed allocations, and to run the model, herbage al lowances

(% l ive weight in herbage OM above g round level) must be ass igned to the mob on

a monthly basis . When grazing starts , the paddock with the h ighest herbage mass

is allocated for grazing (Woodward et al . 1 993) . Once a paddock is allocated to a

mob, the model checks for the assigned herbage al lowance, calculates the LW of

the mob, avai lable herbage mass and size of the paddock, and creates a g razing

strip6. In practical terms, each strip cou ld be defined by temporary e lectric fences.

This is repeated dai ly unt i l the whole paddock is grazed. Most of the t ime the size

of the paddock does not yield an ent ire number of strips ( i .e . exact g razing days) .

To avoid herbage wastage when the paddock is almost f in ished, i f the ung razed

area left is less than 60 % of the present strip it is added to the current day and the

paddock is completely g razed. Conversely, if the ungrazed area left is higher than

60 % of p resent strip , this area is al located as a new strip next day with a lower

allowance than programmed this day. I n this way the complete paddock is uti l ised .

6 Fraction of the paddock al located daily.

Development of a "beef cattle finishing unit" simulator 1 59

I n Figure 1 0 .a) an example shows how the model selects the paddock with the

h ighest mass (start ing at paddock 1 1 ) and then al locates daily the planned

herbage allowance in different strips (5 strips for paddock 1 1 ) . Although on the

second day, paddock 1 1 does not have the highest herbage mass ( Figure 1 0 .b) ,

the an imals remain i n paddock 1 1 unt i l the whole paddock i s ut i l ised (Figure 1 O .a) ,

and then move to paddock fou r.

12 r--��-���--------.: 11 ... Q) 10

.Q 9 E 8 ::s 7 ,; 6 I.) 5 o 4 :g 3 � 2

1 o �----�--��------�

10 20

Grazing day

Strip -Paddock nurrber

6

... Q)

4 .Q E

3 ::s c:

2 .9-... 1 tii

12 11 ...

Q) 10 .Q 9 E 8 ::s 7 c: .la:: 6 I.) 5 0 4 '0 3 '0 III 2 Q. 1

0

r--------�------�___, 3200 3 100 Cl 3000 ::. 2900 � __

2800 E ,E 2700 Q) :E 2600 Cl Q III 2500 -e 2400 � 1---___ ----"'------' 2300

10 2 0

Grazing day - Paddock nurmer

30

- Herbage mass in the highest mass paddock

Figu re 1 0. a) Paddock currently under grazi ng and number of allocated strips and b) herbage mass and number of the paddock with the h ighest herbage mass in a regrazing sequence of 30 days with 1 2 paddocks.

All the strip i nformation is stored i n a resizable dynamic array (paddockdata,

Appendix 6 .A. ) that keeps in memory the leaf, stem and dead masses, start and

end points of the strips with in the paddocks. The herbage accumu lation rate

(model i nput) is the same for the whole paddock, but the gross g rowth rate ( Eqn . 3)

is estimated dai ly for each strip of the farm (the model keeps a memory of

d imensions , posit ion and post-grazing sward condit ions, which are updated dai ly) .

I n the same way as herbage is al located, si lage and grain al lowance may be

def ined for the mob.

"BeefSim" is essential ly a pasture-based s imulator, wh ich allows the i nclusion of

g ra in and s ilage supplementation . I ntegration of a smal l feedlot i n to the pastoral

system can be s imu lated by assign ing the mob to a paddock with zero herbage

mass and HARlnput ,=O, so animal performance wi l l be estimated from the allocated

s ilage and g rain feeds , and the i ntake i n th is case wi l l be determined by nut rit ional

constraints only ( Eqn. 20= 0) .

1 60 Part 11/ - Chapter 6

2.2.3.2. Pasture fertilization

Argentinean beef f in ishing systems commonly on ly use pastu re fert i l ization at

seeding t ime, with application of phosphorus and nitrogen at levels below 70 and

1 00 kg ha- 1 respectively (Rearte 1 998) . Th is situation is incorporated impl icit ly to

the model through the magnitude of the herbage accumu lation rates that are

inputted (HARlnput) into the pasture mode l .

The strategic role o f fert i l izer in other countries (e .g . New Zealand) enhancing the

g rowth of perenn ial pastures to cope with herbage deficits is not used widely in

Argentina, where strateg ic g rain supplementation is generally preferred (Sant in i &

El izalde 1 993 ; Rearte & Pieroni 200 1 ) . Nevertheless , the increasing recognit ion of

the potential importance of strategic fert i l ization for dairy and beef cattle fin ish ing

systems is reflected in research efforts in the use of n itrogen ferti l isers (Guaita et

al . 1 996 ; Lattanzi & Mazzanti 1 997; De Battista & Costa 1 998 ; Martin & Refi

2002) and phosphorous ferti l isers (Berardo & Marino 2000; Marino & Berardo

2000 ; Picone et al. 2003) . However, most of the information is presented as extra

kg OM produced per annum, so the cal ibration of dynamic equations associated

with dose rate and time of applicat ion to add this functional ity of the model was not

feasible at th is stage. However, the u ndoubted high potential strategic value of

ferti l ization is recognized in the model by presenting an uncal ibrated alternative ,

that wi l l be explored beyond this in itial study.

Typical fert i l izer response equations are in the quadratic form y = ao + a1 X + a2 X 2

or the inverse polynomial y = � (Thompson et al . 2000 ) , where y is the X + b

described pasture yield as a function of fert i l izer x ; aa , a1 , a2 , a and b are

empi rical constants . An alternative approach is the quadratic fert i l iser response

function, using the square root of ferti l iser rate (Colwell 1 994) . Good and poor

featu res for each of these approaches are quoted in the l iterature, but one

important issue is to select statistically the model which f its the data best

(Ratkowsky et al. 1 997; Belanger et al . 2000) . I n the absence of adequate data,

the static (no t ime i nvolved) fert i l iser response here uses the quadratic approach

Development of a "beef cattle finishing unit" simulator 1 6 1

i . e :

Eqn. 41

Where FR is the fert i l iser response (kg OM ha-1 ) and FD is the quantity of

fert i l iser appl ied (kg ha-\ The use of th is equation is l imited to the period of t ime

represented by original experimental data used for fitt ing the parameters .

Following the approach of Barioni ( 1 997) , the dynamics of the fert i l iser response i n

the absence o f c l imate and soil models can be simpl if ied i nto two parameters, the

total du rat ion of the fert i l iser response (Spel l , expressed in d ) and the i n itial

fert i l iser response (kg OM ha-\ defined by :

Eqn. 42

Where Spell is the total durat ion of the fert i l iser response (d) , GGR is the

herbage gross g rowth rate (kg OM ha-1 d-1 ) as defined by Eqn 3. The inclusion of

GGR i n the response to fert i l iser defines i n a s impl ified way how the current

environmental and structural condit ions of the sward constrain the fert i l iser

response (Kemp et al . 1 999) . It i s worth noting that i n the model GGR depends on

the HARlnput , but also to the leaf mass in that particu lar paddock ( i .e . two paddocks

with the same inputted HAR and equ ivalent fert i l iser dose but with different leaf

mass, wi l l respond to the applicat ion differently) .

For the sake of s impl icity, the response to fert i l iser is assumed to be at maximum

immediately after appl ication date, described l i nearly as :

IFR = 2FD

Spell

Eqn. 43

Where IFR i s the i nitial fert i l iser response (kg extra OM ha-1 d-\ FD is the

quantity of fert i l iser appl ied (kg ha-1 ) and Spell is the total duration of the fert i l iser

response (d) . Final ly , the dai ly fert i l iser response is represented with a

hyperlog ist ic equation (Haefner 1 997) as :

1 62

AHAR = II�J 1 - ( t AfterF JJ F III Spell

Part /11 - Chapter 6

Eqn. 44

Where AHA RF is the extra herbage accum ulation rate above unfert i l ised

accumulation rate (kg OM ha-1 ) , IFR is the i nit ial fert i l iser response ( kg extra OM

ha-1 d-\ t AfterF i s the t ime (d) after fert i l iser was applied. Spell is the total duration

of the fert i l iser response (d) .

Eqns. 41 -44 are dependent on the i nformation provided by the researcher about

the paddock/s to be fert i l ized , type of fert i l izer and dose and t iming of applicat ion .

This i nformation is used later for economic analysis of the particu lar fert i l izer pol icy

(Eqn. 58) .

2 . 2 .3.3. Si lage making

Feed-budget deficits and surpluses normal ly occur in grazing systems , and si lage

making is i ncorporated here to make the pastoral system more f lexible and

sustainable (Parsons 1 998; Holmes & Matthews 200 1 ) . In Argent inean beef cattle

systems, hay is usually made at the end of the g rowing season when rainfa l l

decreases, al lowing rapid drying under f ie ld conditions, however at th is time of the

year g rasses have a low nutritive value (Vivian i Rossi & Gutierrez 1 995) . S i lage is

a preferred conservation process to ensure quality, but it is not widely used in

Argentina for beef cattle systems (Rearte 1 998) . For conservat ion purposes, the

paddock is the m in imum unit, so when a paddock is cut, al l the i nformation about

its individual strips is deleted ( i .e . paddock= 1 strip) .

The user must provide information about the t ime interval where conservat ion is

al lowed, and the farm pasture cover that triggers conservat ion. I f condit ions for

pasture conservation are achieved , a currently u ngrazed paddock with the highest

herbage mass is selected , i nformation about g razing strips (if g razed previously) is

deleted, and the paddock is harvested . By default, herbage mass after cutt ing of

1 000 kg OM ha-1 is assumed. This procedure is repeated unt i l pastu re cover

conditions are met. The post-harvesting leaf , stem and dead material mass are

related to their values in pre-harvesting conditions (Barioni 1 997) , and described

Development of a "beef cattle finishing unit" simulator

as:

LAH = LBH 1 - ----'--( HMpreH J HMpostH

Sf AH = Sf BH 1 - -----'--( HMpreH J HMpostH

D AH = D BH 1 - -----'--( HMpreH J HMpostH

1 63

Eqn . 45

Eqn. 46

Eqn. 47

Where LAH , SfAH and DAH are the leaf, sheath & stem and dead masses in the

paddock after harvesti ng (kg OM ha-\ LBH , Sf BH and DBH are the leaf , sheath &

stem and dead masses in the paddock before harvest ing (kg OM ha-1 ) and

HMpr eH and HMpostH are the herbage mass pre- and post-harvesting in the paddock

(kg OM ha-\ The type of storage affects harvest rates, forage losses and nutritive

value of feeds produced (Buckmaster et al . 1 989). For s impl icity, harvested mass is

converted here to si lage mass assuming 1 5 % of OM losses i n the whole process

(Viviani Rossi & Gutierrez 1 995) , and for the nutritional value of pasture si lage

reg ional information is used (Guaita & Fernandez 2002) _

2.2 .3 .4 . Animal sale and purchases

By default animals are bought as 6-month-old weaners ( 1 March) , but an imals can

enter at any t ime of the year. An imal information can be incorporated individual ly ,

inc lud ing age (d) , LW (kg) and breed and sex. Alternatively if desired , an imals can

be generated randomly providing mean, max imum and min imum values of these

parameters . This information is used to estimate the i r n utritional requirements

(sect ion 2 .2 .7 . ) .

An imals can be sold unfinished or i ncorporated into a feed lot with in the farm, but

the default procedure is to identify them as a "fin ished an imal" when a target

l iveweight is reached. By default (this can be changed in the block dialog) , heifers

are f in ished at 280 kg LW and steers at 380 kg LW (Brit ish bred animals) . Sel l ing

procedu re is activated only when the min imum number for trading is reached. By

default and for practical reasons, th is is defined as a truck u nit (30 steers of 380 kg

1 64 Part 11/ - Chapter 6

l iveweight) , so although some animals can be f in ished, they are kept i n the mob

(eating and growing) unt i l the min imum animal number for trading is achieved .

Animals are ' lost" random ly in agreement to the defined mortality rate with i n each

animal type on the farm . I nternal ly, stocking density expressed as animals ha-1 and

kg LW ha-1 are estimated dai ly.

2.3. Decision module

Grain feed ing may be incorporated , with in other technologies, i nto the farm plan.

However, typical ly the use of th is technology is not s imply decided at the beg inning

of year, and its implementation is part of a more structured process, l i nked to the

dynamic conditions of the components with in the farm system . The same is valid

for the herbage al lowance al located to animals. Garcia et al . (2000) introduced the

interactivity between model and user to i ncorporate human management.

Simi larly, the software used here al lows i nteractivity, as the model parameters and

logic can be changed in real t ime (Krah l 2002) . Addit ionally, for more i ndependent

runs, th is module incorporates the concept of "grazing strategy" s impl if ied from

Gros & Ouru ( 1 997) . Within this module, the general strategy and contingency

procedures are designed as a sequential set of management ru les , which are

applied for a "manager" block. Parker ( 1 999) mentioned that translat ing strategy

effectively i nto an act ion requires a clear l ink with the monitoring processes. I n

practice, good graziers design a strategy, i n it iate i t and then monitor it i n cyclical

procedu res (m icro-budgets) to manage "deviations" from the plan due to variation

in the envi ronment, combining formal and i nformal procedures (Gray et al. 2003) .

In this model , "deviations" are simpl ified into two aspects : a) pastu re cover

deviation , and b) target l ive weight deviation, def ined as :

_ ( PCOV erT arg et - P cov erActua/ ) J PCover Dev - P COV erTarg et

Eqn. 48

Where PCover Dev is the (positive or negative) deviation of the current pasture

cover ( PCover Actual ) from pre-defined target pastu re cover for different t imes of the

year ( PCover Targ et ) ' both expressed i n kg OM ha-1 and PCover Actual represents

the weighted average of herbage mass (Eqn. 1 ) over a l l strips of the farm .

Development of a "beef cattle finishing unit" simulator

L W = ( (L Wrarget - L WActual ) J

Dev LW rarg et

1 65

Eqn. 49

Where LW Dev is the (positive or negative) deviatio n of the current average of

animal l iveweights ( L W Actual ) from pre-def ined targets for animal l iveweight for

different t imes of the year ( L Wr arg et ) ' both expressed in kg LW.

PCover Dev and L W Dev are checked by defau lt on a weekly and monthly basis ,

respectively. This means that although pasture cover, individual i ntakes or animal

l iveweights can be obtained daily from the model , mon itoring is constrained to

measurable variables on a real farm. On this point, Wright and Dent ( 1 969)

suggested that there is l ittle point in insisting that the model should predict to a

given degree of accuracy, if this accu racy cannot be i ncorporated into the

management decisions of real systems.

The "manager" block, together with the i nformation obtained from this simple

monitor ing routine, has the chance to adjust the d ifferent variables according to

pre-defi ned grazing strategies containing contingency actions (F igure 1 1 ) .

Management tasks have a temporal and hierarch ical organisation (Gros et al .

2004) , hence the grazing strategy used here ensures the important issues are

addressed and al lows a clear course of action to proceed. Associated with the

in it ial objective , the adjustment of grazing strategy in the current vers ion of the

model is restricted to : herbage al lowance adjustment and si lage making associated

with PCover Dev ' and grain feeding associated with L W Dev . I n formation on the

threshold deviat ion and levels of adjustment of the variables are provided as tables

in the dialog of the "manager" block ( Figure 1 2) . Once a g rain supplementation is

in it iated , it is maintained for a m in imun period of 30 days, after which the decision

is evaluated again based in the updated deviation of the target l iveweight and the

def ined condit ions in the "manager" block. This complete feature may be tu rned

on/off, i n order to run particular short-term trials without any adjustment.

1 66

l Each 7 days I I

Management module

Deviation of Deviation of pasture cov� � l!iveweights

. - - - - � - - - -

Part 111 - Chapter 6

� - - - - - � - - - - - · - - - - - - - - - - - - - 1 .. ." .. .. ." Supplement

feeding Silage

makl1g Herbage

allowance

I I I

Fertilisation Sell & purchase

an imals

I _ _ _ _ _ � - - - - - + - - - - - - - _ _ _ _ _ _ I

Biophysical module Each 30 days I I

� - - - - - - - - - - - _ _ _ _ _ _ 1

Figure 1 1 . Main view of the relationship between the decision and the biophysical module.

;.J EKtend :,

P" [nllble mllnllgement bllsed in mobs

Supplementlltion Period

Stllrtlng DoY fi58 Ending DoY �

o ·· · · · ··1·

· · · ··2······ ··· ··········

m ::::) . . _ . . .. __ . .

-........ -� --...... --

Rdjustments

Jan Fob Mw i ....

OK J j [lIneel I

DoIII.tion i Grain 1110...,,0 i ... Deullltlon - ((Tllrget - RctulIl) I TIIrget) Mob Rug LW o �; 0,

·········· 1 ·········· __ ·······ir········-O'OO3' - · ··-·······i5f--· ···---O'OO�l

::::�� 3 �.::=:::==:=:::::r=::=== o��] ..!!i!J"'-- . .. _-.... .... L .......... _.}TJ �

Figure 1 2. Extended dialog of the manager block.

2.4. Economic module

This module gives an ind ication of the economic response of the systems, act ing

as a constraint when h igh levels of technical efficiency (LWG, herbage uti l isation ,

LW ha·1 ) are pursued including off-farm inputs , such as maize grain .

Development of a "beef cattle finishing unit" simulator 1 67

G razed herbage is usual ly less expensive than concentrate , and feeding

supplement and silage making are additional expenses to the system .

Supplementary feeding to g razing animals i s a "process technology" that interacts

with d ifferent components of the system , with d i rect and indirect effects on the

an imal and to the system (Chapter 1 ) . I n g razing systems , economic impacts a re

rarely related l inearly to biolog ical impacts, therefore i n the case of g rain feeding ,

whole-farm models have been used for economic appraisal in beef (Ru iz et a l .

2000 ; Peri l lat et al . 2004) and dairy cattle systems (Sod er & Rotz 200 1 ) .

There are different approaches and methodologies to evaluate a l ivestock

business. I n th is study, a s imple methodology widely used in Argentina (AACREA

1 990) for assessing bio-economic farm ing performance of a complete year

(expressed as gross marg in ) is incorporated as :

Eqn. 50

Where APcosts are the animal purchase costs ($US y(\ L Win is the individual

an imal l iveweight at purchasing (kg) , P,n is the purchase price ($US kg lW-\

Pcosts are the purchase costs (proport ion of animal cost) and TCosts are the

transportat ion costs (proport ion of animal cost ) .

Eqn. 5 1

Where ASReturns are the an imal sale returns ($US y(\ L Wout i s the ind iv idual

an imal l iveweight at sale ( kg ) , Pout is the sale p rice ($US kg LW-\ Scosts are the

sale costs (proport ion of an imal cost) and TCosts are the transportation costs

(proportion of animal cost) .

NI = ASRe turns - APcosts Eqn. 52

1 68 Part 111 - Chapter 6

NI i s the net income ($US) , ASRe turns are the animal sale returns ($US y(1 ) and

APcosts are the animal purchase costs ($US y(\

DC = LaboUfCosts + TAHcosts + TFCcosts + T sup PCosts + Tfertcosts Eqn. 53

DC are the di rect expenses of production ($US y(1 ) and LaboUfCosts are the labou r

expenses ($US y(1 ) .

Eqn. 54

TAHCosts are total animal health expenses expressed i n $US y(1 , A Hcosts are

an imal health expenses ( $US hd y(\ AnTot is the number of animals in the farm at

starting of s imu lation ( input) plus those pu rchased dur ing the run.

TFCcosts = FCcosts Aconserved Eqn. 55

TFCcosts are total expenses of forage conservat ion , FCCosts are the expenses for

conserving forage ($US ha-1 ) and Aconserved is the pasture area conserved (ha y(1 ) .

Eqn. 56

TSUPPcosts are the total supplementation expenses ($US y(\ MaizeFed is the

amount of maize g rain fed (ton y(1 ) , PMaize i s the g rain price ($US ton-1 ) and

DMaize are the maize grain processing and distribution expenses ($US ton-\

I n mU lti-year simulat ions, the negative difference of si lage i nventory (if any)

between starting conditions and the end of the productive cycle expressed in ton

OM mu lt ipl ied by the price of the si lage ($US ton-1 ) , is added to the total

supplementation expenses of that year. Addit ional ly, in mu ltiyear s imu lation ,

f in ished and unfinished animal prices need to be provided to estimate the

economic resu lts when the productive cycle is closed.

Development of a "beef cattle finishing unit" simulator 1 69

Eqn. 57

Tfertcosts are the total fert i l isation expenses ($U S y(1 ) at any moment different than

seedi ng t ime ( i ncluded in pastu re seeding costs) , FertpaddoCk is amount of fert i l iser

appl ied in each paddock (kg ha \ Apaddock is the area of each fert i l i sed paddock

( h a) , PFert i s the p rice of the fert i l iser ($US ton-1 ) and APPcosts i s the cost of

ferti l isation ($US ha-1 ) .

GM =

(NI - DC - PAmort) Eqn. 58

Arot

GM is the g ross margin of the farm ($US y(1 ha- 1 ) , NI is the net income ($US) ,

DC are the di rect costs of production ($US y(\ PAmort i s the amortisation cost of

the pasture ($US y(1 ha·1 ) assuming fou r years as the duration of the pastu re and

ATot is the total area of the farm (ha) and :

Eqn. 59

PAmort are the costs of pasture renovation ($US y(1 ) i ncluding fert i l isation at

seeding t ime, PRcost are the cost or seedi ng a pasture ($US ha-\ PRpr op is the

proportion of pasture renovation (% farm area y(1 ) and ATot is the total area of the

farm (ha). In Fig ure 1 3 so me of the i nputs and outputs of the economics block are

shown.

1 70

u( mod� P ....... n ,--=.:3��_,_._

'rtc .. 5,.1. ,rice (SUSITon) r­Slle •• ,rtn r-Inlme' d.te - ....... ....

I I

.!.l

( .. pen •••

I G.

......cNon -I "

·1

Monthly u.n.tlon .f cottle ,rk. Ind ..... I_no ••••• n.llty

... G I

I I I I I

-I

: .1 !.U .!.l !.-.l

',..d ..... ('I. .r enlme' price) 'urd ••• es ra--- Sel.. pa-l .... ur (SUS/Mr) � IftlmeI N...... � , •• ture ... ,..... Conservetlon r- amertlzeUH � ',..1. ,rocelllnl en. "endlle. (SUS/ton) � SHe,e mMlnl ee.t (lUSlTen) � .lOOI.Ic=:::::J .!.l

Part 11/ - Chapter 6

Re.u .... .. 1I,ra.

F � �··I ..... pr r I � :

., _. I - pi

.!J .iJ .1J .iJ Purth •• es S.les

Tot •• 1111 ..... Ib./F) r-­Tml., tlSt. I" Inlm.1 ,rite) r--Tot •• n ••• h.d •• Im.', r-­T.t •• I.co .... n.l.h.d •• 1 ..... ISUS/yr) r-­Totll •• n.I •• ed lRimI" r-­T •• II lilt .... ... nni.hed .. 1nI1I. ISUS/yr) r--

• iro .. ........ ISMS/bl/gr) r--Figure 1 3. Extended dialog of economics block. White rectangles indicate requi red inputs and g rey rectangles are estimated with in the block.

3. Running mode

The model can be run in three different modes, "fu l l i nteractive", "automatic" and

"automatic associated with optimization". For the ful l i nteractive option , m in imum

information is requi red of the featu res of the s imulated farm , and then the model

stops when a user's decision is needed . Extend allows efficient interactions with

the user , so parameters and log ic can be changed in real time following the

approach of Garcia et al . (2000) . The automatic alternative involves applicat ion by

the manager of a grazing strategy def ined by the researcher in the form of s imple

rules associated with PCover Dev and to L W Dev (sect ion 2 .3 . ) . Most of the rule-

based models enunciate the rules that control the run (eg . SEPATOU , Cros et al .

2003) , whereas BeefSim al lows one to track the magnitude of the adjustment and

when they occu r ( Figure 1 4) . This offers the possib i l ity of understanding the

behaviou r of the system through requi red adjustment (with in the allowed range) ,

and not on ly through its outputs ( i .e . the behaviour of the ru le is fol lowed) .

The th i rd option is associated with a bu ilt- in global opt imiser that can manipu late

different variables (eg . i n itial stocking rate, herbage al lowances, g ra in al lowances ,

etc) to maxim ise others individual ly or in combinat ion (eg . harvesting efficiency,

animal production per ha, g ross margi n etc) or min imize them (herbage wastage ,

grain fed etc). Figure 1 5 shows t he dialog o f the global optim iser.

Development of a "beef cattle finishing unit" simulator

J Extem.l '

610bel Slm D1t,

I �

1 71

Figure 1 4. Tracking of adjustments made to grai n supplementation by the manager block in association with deviation between actual l iveweight and targets liveweight ( Eqn. 50).

,;J EKtend • Fie Ecit Lilrary Model Text Define Rl.n Window �

IOiil1Ii1a1 W!:U�5 itQ�!!l� jrlf !fl {tlli e ii I\lclltiQ1� I$l,HtJ

Optimlzes a model. New Run I ____ ...J Ueriebles Table Enter limits for uerieble. to be modified. leeue blank for model outputs. limits entel'1ld with declmel points el'1l l'1lel, without al'1l Integer.

·lol2tl

\hrN Block LIb,1 i Block Nunber . Variable Nwnt Ro ..... Col i .n Umit i Mu: Umit i .. j:::: :=��i==�[===f���:t==========:::=:t:======::�_�==t==:====j �___ �"i!!: __ j. __ !!l ___ -L St;or(�g)!LL _____ -l ________ -L ________ ._-.l 1 ___ .. _�i!! __ i lQl ! Y I __ . ___ -l __________ L_. ______ .j

:��= �����::i====:==-l-:==:=:==�:�:����:�:�:��t���:�==:: �iJ _=====:t==:=:=:t==:=:==t==== .. ===:=t::=::===== -=======�l1 Enter an equation In the form: Mlneost - uarN ... ; or M aHProflt - uarN ... ; where N I'1lfers to the row number in the aboue table of uariables.

MaHProflt = Uar 4;IIOptlmlzatlon of gross margin U$S/Ha managing J animal pun:hases. herbages allowances and period of grain supplementation � l!!JJ!J1 U J �

Figure 1 5. Dialog of the evolutionary optimizer block (front tab).

4. Conclusions

A beef cattle f inishing unit simu lator (8eefSim) has been developed , representing

the main biological processes of the system, together with some i ndication of

econom ics. This research too l can s imu late different levels of g rain

supplementation, stocki ng rates, and herbage al lowances, from one day to multi­

year ru ns. I n the next chapter, the model wi l l be tested for its capabil ity to

reproduce g razing trials developed with in th is thesis and also from the l iterature .

1 72 Part /11 - Chapter 6

Table 1 . Alphabetical order of model definitions,

Abbreviations A

Aconserved

AHCosts

ATot

AnTot

Apaddock

APCosts

APPCosts

ASReturn

BC

CPo

C POiet

C POiet

C PMaize

CPHerbage

C P HerbageEaten

DietME

D ietcp

Dieto

DietSt

Description Animal age

Surface of forage conserved

Health expenses per animal

Total surface of farm

Animals in the farm when simulation starts

Area of each paddock

Total expenses of buying animals

Expense of applying fert i l iser

Animal sale returns

Relative animal body condition

Crude protein in the dead component

Crude protein eaten

Animal diet crude protein content

Fibrous supplement crude protein content

Maize grain crude protein content

Herbage crude protein content

Crude Protein of the herbage eaten

Crude protein in the leaf component

unit d

ha y(1

$US hd'1 y(1

ha

hd

ha

$US y(1

$US ha'1

$US

Ratio of normal

weight (N )

Fraction of DM

kg d'1

% DM

% DM

Fraction of D M

Fraction of DM

% DM

Fraction of D M

Crude protein in the sheath & stem component Fraction of DM

Dead mass kg DM ha'1

Fraction of dead in the herbage

Dead masses after harvesting

Dead masses before harvesting

Di rect costs

Total expense of p rocessing and

supplement

Metabolisable energy eaten

Crude protein eaten

feeding

Fraction of dead components i n the diet under

rotational conditions

Fraction of leaf component in the d iet under

rotational conditions

Fraction of sheath & stem in the d iet under

rotational conditions

Fraction of DM

kg DM ha'1

kg DM ha'1

$US ton'1

MJ M E d'1

g CP d'1

Fraction

Fraction

Fraction

Development of a "beef cattle finishing unit" simulator

Abbreviations

DigHerbage

DigFs

OR

fAdj

FCcosts FD

Fertpaddock

fLA

FR

FS

fSeason

FSFactor

GGR

GM

HAR

HARlnput

HARActual

HM

HMpostH

HMpreH

Table 1 . Alphabetical order of model definitions (Cont.)

Description

In vitro herbage OM digestibi lity

In vitro OM digestibi l ity of fibrous supplement

Dead decay rate

I ntake restriction factor associated to herbage

availabil ity

I ntake restriction factor associated to herbage

Conversion factor from herbage accumulation rate

to herbage growth rate

Expenses of conserving an hectare

Fertil izer rate

Total fert i l izer per paddock

Intake restriction factor associated to leaf mass

Intake restriction factor associated to leaf

allowance

Fertilizer response

Fibrous supplement eaten

Seasonal factor associated with the decline of

Unit

Fraction of O M

Fraction of O M

Fraction d-1

Fraction

Ratio of HARlnput

$US ha-1

kg ha-1

kg y(1

Fraction

Fraction

kg OM ha-1

kg OM d-1

stem component Fraction

Reduction of rumen capacity by feeding fibrous

supplement kg OM d-1

Herbage gross g rowth rate kg OM ha-1 d-1

Gross margin $US ha-1 y(1

Herbage accumulation rate kg OM ha-1 d-1

Herbage accumulation rate ( input) kg OM ha-1 d-1

Herbage accumulation rate actual kg OM ha-1 d-1

Herbage mass kg OM ha-1

Post-harvesting herbage mass kg OM ha-1

Pre-harvesti ng herbage mass kg OM ha-1

I ndividual herbage intake and si lage intake (if any)

when grain is not fed kg OM ha-1

Individual Intake estimated from rumen fi l l

constraints kg OM ha-1

I n itial ferti l iser response kg OM ha-1

Fibrous supplement intake kg OM d-1

Herbage intake kg OM d-1

1 73

1 74 Part 11/ - Chapter 6

Table 1 . Alphabetical order of model defi nitions (Cont.)

Abbreviations

L%

LA

Labou r Costs

LAH

LSH

LCeit

LCeit

LW

LWSirth

LWActual

LWDev

LW1n

LWOut

LWTarget

MaizeFed

MaxLceil

M ED

M EDiet

M EMaize

M EHerbage

M EHerbageEaten

Description Unit

Maize grain intake kg DM d-l

I ndividual Intake estimated from potential and

rumen fi l l constraints kg DM d-1

Leaf mass kg DM ha-1

Fraction of sheath & stem in the herbage

Leaf allowance

Labour expenses

Leaf mass after harvesting

Leaf mass before harvesting

Leaf cei l ing mass

Leaf cei l ing mass

Animal l iveweight

Animal b i rth liveweight

Actual animal l iveweight

Deviation of animal liveweight on a g iven date

Liveweight of each animal at purchase

Liveweight of each animal at sale

Target average animal l iveweight of the farm for

Fraction of DM

g (kg LW o 75r1 d- 1

$US y(l

kg D M ha-1

kg DM ha-1

kg DM ha-1

kg DM ha-1

kg

kg

kg hd-1

kg

kg

specific date kg hd-1

Maize grain fed

Seasonal maximum leaf ceil ing mass

Metabolisable energy in the dead component

Metabolisable energy in the animal d iet

Metabolisable energy in f ibrous supplement

Metabolisable energy in maize grain

Metabolisable energy

Metabo l isable energy of the herbage eaten

Metabolisable energy i n the sheath & stem

% LW d-1

kg DM ha-l

MJ M E kg DM-1

MJ M E kg DM-1

MJ M E kg DM-1

MJ M E kg DM-1

MJ M E kg DM-1

MJ M E kg DM-1

component MJ M E kg DM-1

Development of a "beef cattle finishing unit" simulator

Table 1 . Alphabetical order of model defin itions (Cont. ) .

Abbreviations

MEst

MinLceil

N

N I

PAmort

PCosts

Pcovercurrent

PCoverDev

PcoVerTarget

PFert

Pin

POut

P RCost

PRprop

RC

S

SCosts

Selo

SelSt

SL

Spell

SR

SRW

Description Un it

Metabolisable energy in the leaf component MJ ME kg DM-'

Seasonal min imum leaf ceil ing mass kg OM ha-'

Normal liveweight as defined in SCA ( 1 990) fraction

Net income $US

Pasture amortisation cost $US y(' ha-'

Fraction of animal

Purchase costs cost

Current average pasture cover on the farm kg OM ha-'

Deviation of target pasture covers on a

given date %

Target average pasture cover on the farm

for specific date kg OM ha-'

Price of ferti l iser $US ton-'

Price of an imals at purchase $US kg LW'

Price of animals at sale $US kg LW-'

Price of maize grain

Expense of seed ing a pasture

Proportion of total farm pasture renovated

Rumen capacity

Total senescence rate

Sale costs

Fraction of leaf, sheath & stem and dead

components under continuous g razi ng

management

Fraction of leaf component under

$US ton-'

$US ha-'

proportion y('

kg

kg OM ha-' d-'

Fraction of animal

cost

Fraction

continuous g razing management Fraction

Fraction of sheath & stem component under

continuous g razi ng management Fraction

Leaf senescence rate kg OM ha-' d-'

Total duration of the ferti l izer response d

kg OM herbage kg

Substitution rate O M grain-'

Standard reference animal weight as

defined in SCA ( 1 990) kg

1 75

1 76 Part '" - Chapter 6

Table 1 . Alphabetical order of model definitions (Cont.) .

Abbreviations Description Unit

SSt

St

St%

StAH

StBH

SUPPFed

TAHCasts

TCasts

TFCCasts

TFertcasts

TI

Sheath & Stem senescence rate

Sheath & stem mass

Fraction of leaf in the herbage

Sheath & stem mass after harvesting

Sheath & stem mass after harvesti ng

Total supplement fed

Current simu lation time

Time after fert i l isation

Total costs of an imal health

Transportation costs

Total costs of forage conservation

Total costs of ferti l isation

Total dry matter i ntake

in the

kg DM ha·' d·'

kg DM ha·1

Fraction of DM

kg DM ha·1

kg DM ha·1

Ton y(1

d

d

$US y(1

% animal cost

$US y(1

$US y(1

kg DM d·1

TotMaizeEaten

TSuPPCasts

Total amount of maize grain eaten

farm

Total costs of supplementation

Relative animal size as defined in

ton. DM y(1

$US y(1

SCA

z ( 1 990) Fraction

Extra herbage accumulation rate above

herbage accumulation rate of the

unferti l ised pasture

Table 2. Constants used in the pasture sub-model.

Constant

�5

Description S lope of herbage gross g rowth rate

Day when min imum LCeil occurs

Slope of the l inear leaf senescence Maximum and minimum proportion of dead decomposition

Seasonal partitioning fraction of g ross herbage growth rate to leaf component

Value Vegetative = - 0 .00 1 4 Reproductive = - 0 .0023 355

Estimated by Eqn.7 1 5 February = 0.02 1 July=0.05, i nterpolated the rest of the year Vegetative = 0 .70 Reproductive = 0.65

Source Barioni ( 1 997)

Cacho et al . ( 1 995)

Cacho et al . ( 1 995)

Cacho et al . (1 995)

Development of a "beef cattle finishing unit" simulator 1 77

Table 3. Model constants.

Constant Definition U nits value Reference

K1 Relative size kg kg" 0.025 (SCA 1 990)

K2 1 .7 (SCA 1 990)

K3 Growth rate constant Kg LWo 27 d·1 0 .0 1 57 (SCA 1 990)

K4 Allometric scalar for animal 0 .27 (SCA 1 990) g rowth rate

K5 I ntake parameter 0.063 ( Fi n layson et al . 1 99 5)

K6 OM Losses when fibrous 0 . 1 Assumed supplement is fed

K7 I ntake parameter 0 . 1 7 ( Fin layson et al . 1 995)

K8 Conversion L to LAI kg 290 (Doyle et al . 1 989)

Kg I ntake parameter 0 .229 (Herrero et al . 2000) K1 0 Al lometric parameter 0 .36 (Herrero et a l . 2000 ) K1 1 I ntake parameter 3 (Johnson & Parsons

-0 .0344±0.8'3

1 985) K1 2 Slope for Leaf al lowance effect Fitted from King et al .

( 1 995) K1 3 Leaf preference Proportion 0 .6 Barion i ( 1 997) K14 Stem preference Proportion 0 .3 Barioni ( 1 997) K1 5 Dead preference Proportion 0 . 1 Barioni ( 1 997) K1 6 Regression intercept -0. 1 2 Dixon and Stockdale

( 1 999) K1 7 Reg ression slope 0 .0079 Dixon and Stockdale

( 1 999) K1 8 Conversion factor of ME to Dig 0 . 1 57 AFRC ( 1 993)

OM K1 9 Seasonal variation of Stem % Proportion Default=0 . 1 User-defined

quality of average value

K20 Losses in grain feed i ng Proportion of 0 .05 User-defined OM grain fed

K21 Fert i l iser parameter Uncal ibrated K22 Ferti l iser parameter Uncal ibrated K23 Ferti l iser parameter K24 Ferti l iser parameter 80 Barioni ( 1 997) K25 Ferti l iser parameter 0.6 Barioni ( 1 997) K26 Shrunk LW loss Proportion of 0 .04 Assumed

LW

1 78 Part 11/ - Chapter 6

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Development of a "beef cattle finishing unit" simulator 189

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1 90

Appendix 6.A. Main structure of the model and s imu lation order.

Biological blocks

Padc!ockOata

.. _s ... ilagu.;

e

;....IL SlageData

111 -- r GrainData Grain L

.... 'I.IOI.j"I .-_"..-

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Decision & Management blocks

I Silage Making I 34 t xp

USERMSGOMSG ,

Paddoc��c�tionData t -p_a_ddockA __ ",,",_"""_' _D_a_ta ---.! ... """' .... _ ..

I MobAlowancesL USERMSGIMSG . BI.. r Manager

40 I XD

sellAnimalS I 46lxp

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BUYAnima5 1 16! XD USERMSGIMSG

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41 ..

References

BID'" N. name; "' sinulation order; XD? XD I bbck has an eKtended diabg, - I not

I D.Jtjl Source I ()- � o.:� use Ink 8bck l/:SE'rrequi'es data o from bbck OrJtil Source

D.� modification link 8bck tJ.sermodTes data 0 from bbck DiKiJ Source

I Odta Source fc:: D � Dat� use and modifbtion ink Block Useorrequres data 0 from bbck {)Ma Source and modfEs part of t

Message Ink 6bck Sender sends message Msgto bbck Receiver

Part 11/ - Chapter 6

Development of a "beef cattle finishing unit" simulator

Appendix 6 .B. Derivation o f Eqns. 4 and 7

Assumptions :

a) CCR ( L) = HARtnpUl fadj ( 1 - eflJL )

1 9 1

(Gross growth rate)

b) HA RAcrual ( L ) = HARtnpuJadj (I - eM ) -/3,L (Herbage accumulation rate)

c) S ( L ) = /3,L

1 ) CCR ( LCeil ) = s ( LcelI ) or

2 ) mfx {HARAClual ( L)} = HAR1n[JUl

Working :

from 1 )

3)

from 2)

(Senescence rate)

HAR Actual ( LCd! ) = 0

mfx {HARActual ( L)} occurs when aHAR�zal ( L) = 0

Differentiat ing from b)

aHAR . ( L) => Ailual = -HAR f .j3 efl1L -/3 = 0 aL

tn[JUI . Ad} I .1

Substituting with 3) and solving tor L , therefore max { HARAClual ( L) } occurs when L

4) In (-1 + efllLco1 )

L = j3ILcdl j3I

From 2) and substitut ing L from 4)

1 92

max { HARAC{ual ( L )} = HAR/IlPIlI L

In ( - l + efJ1LC"'{ J H R fJlLCeil :::::> A ACtual

PI

Part 111 - Chapter 6

2) above

= HAR InplII

from using b) to expand the left hand side to give the equation and

replacing L from 4)

Substituting P3 with 3) (- I + efJ..Lc,,{ J /ijl<:,,{ I n _ HAR/IlPlII fadj ( I - e ) PI LCeil

= HAR L P

/llfJllI Ceil I

An rearranging the equation and solved for fad} gives

f. = PI LCei/ adj p L . + [ I _ l n (- l + efJ

ILc,d J: ( l _ efJILc,,{ ) 1 Cell

P L 1 Ceil

When al l is put together it ends up with :

Development of a "beef cattle finishing unit" simulator 1 93

Chapter 7

Model l i n g stud ies of gra in feed ing i n a g razi ng­

based beef cattle f i n ishing operation :

2 . Model evaluation and system simulations

1 96 Part '" - Chapter 7

Abstract

The objective of this study was to evaluate the model developed in Chapter 6 in

terms of its capabil ity for predicting experimental data of herbage i ntake and animal

performance , and to use the model to compare the impacts of the strategic use of

maize grain in beef cattle f inishing systems. The model predicted the experimental

measurements of l iveweight gain and herbage i ntake with accu racy (P<0.05) and

without bias. A "basel ine system" with a stocking rate of 2.5 steers ha-1 was bui lt

from experimental data, and the model was used to evaluate additional stocking

rates of 3.5 , 4.5 and 5 .5 hd ha-1 . These stocking rates were s imulated without

maize feed ing and with a strategic maize feeding strategy in which maize g rain was

added when the model led monthly LW fell below pre-defi ned LW targets .

Liveweight production per ha on the g razi ng only treatments i ncreased markedly to

when stocking rates i ncreased from 2 .5 to 3.5 hd ha-1 (from 487 to 644 kg LW ha-1

y(1 , respectively) , with no improvement thereafter. Gross marg ins in the g razed

only treatment decreased l inearly as stocking rate i ncreased, reflect ing i ncreasing

numbers of unfi nished animals. I n the case of supplemented treatments, there was

a l inear i ncrease in production per ha as stocking rate increased (from 487 to 1 070

kg LW ha-1 y(1 , respectively) . Gross marg in reached the highest value at 4.5 hd ha-1 . Other sward parameters are also presented . It is concluded that the model

showed accuracy for predicting animal performance and herbage intake under a

range of experimental c i rcumstances , and provides an adequate tool for

i nvestigation of alternative g razing-based beef cattle f in ishing systems.

Keywords : system model l ing; beef cattle production ; intake ; l iveweight gain .

1. Introduction

In Chapter 6 a dynamic model representing the main biology and some economic

i ndicators of a grazing-based beef cattle f in ishing un it was described . Model

evaluat ion is a critical step to quantify the degree to which the objectives of model

development are addressed (Carlson et al . 1 993) . However, this procedure is not

straightforward, as the lack of or i ncomplete avai labil ity of independent field

information for an adequate evaluation of agricultural s imu lation models is a

Model validation and system simulations 1 97

common problem (Herrero et aL 1 998 ; Cohen et al . 2003 ) .

I n part icular, i nformation on the plant-animal interface is an important component i n

the u nderstanding of the behaviour of g razing systems , but information o n th is

topic is often the major constraint to model development (Dove 1 996) . For effective

validation, a model was developed i ndependently of the data collected from field

experiments against which it was tested.

The objectives of th is chapter were to : a) evaluate the model developed in Chapter

6 in terms of i ts capabil ity for predicting herbage i ntake and animal performance

from field experiments; and b) explore different system comparisons of the

strategic use of maize grain i n beef catt le f in ish ing systems.

2 . Model evaluation

The predictive capacity of the beef catt le f in ish ing un i t s imulator (Chapter 6) was

assessed against information from Chapters 3 and 4 i .e . LWG and herbage intake

over a range of treatments and sward conditions, using the method suggested by

Mayer & Butler ( 1 993) . In Table 1 the main i n itial parameters used in the

s imulations are detailed. Simi lar to the field experiments, the model was set up to

g raze in a s ing le grazing cycle . To represent herbage quality associated with the

herbage components, average values from Chapter 5, Table 3, were used to

represent MEL , MESt ' MED ( 1 1 .7 , 1 0 .9 , 7 .0 ) and CPL , CPSt , CPo ( 25 .0 , 1 4. 1 and

8 .3) the annual average of metabol isable energy (MJ ME kg DM-1 ) and crude

protein (% OM) of the leaf , sheath & stem and dead components of the sward ,

respectively. Explanation of how herbage qual ity is handled is presented in

Chapter 6, sect ion 2 .2 . 1 .5.

Table 2 shows the fitted regressions using the two steps ( i .e. with and without an

intercept) suggested by Mayer and Butler ( 1 993) for assessment of model

predict ion. A sign ificant (p<0.05) agreement was obtained between modelled

values for LWG (Figure 1 ) and herbage i ntake (Figure 2) with values recorded

under experimental condit ions. I n both cases the l i near regressions fitted

signif icantly , the R2 value obtained for LWG being sl ightly h igher than that

1 98 Part /11 - Chapter 7

observed for herbage i ntake (Table 2 ) , and for both variables, i ntercepts were not

significantly ( P>0.05) different from zero (Table 2). Similarly, both variables

adjusted sign i ficantly to l inear regressions without i ntercept (P<0 .0 1 ) and the

s lopes were not different f rom 1 .0 . There were no i ndications that the relationships

shown i n Figure 1 and 2 were affected by supplementation or herbage al lowance

level . From this in itial evaluation , it can be concluded that the model predicted the

experimental i nformation with acceptable accuracy and without bias.

Table 1 . I nit ial parameters from Chapter 3 and 4 considered for the simu lations.

Chapter 3

Animal class heifers I n itial l iveweight (kg) 223 Duration (d) 49 cTreatments 4 Pre-grazing leaf mass (kg O M ha-1 ) 2452 Pre-grazing sheath & stem mass (kg OM ha-1 ) 1 866 Pre-grazing dead mass (kg OM ha-1 ) 1 0 1 3 HARlnput (kg O M ha-1 d -1 ) 1 6 c = See detai ls i n Chapter 3-4 and HARlnput = herbage accumulation rate estimated from accumulated mass i n first grazed strips in each treatments.

1 .2

1 .0 •

"0 0.8 -01 6 • • CJ 0.6 • • S ...J • "0 CD 0.4 > (jj (J) • .D 0.2 y=x 0 • •

-0.2 0.0 0.2 0.4 0.6 0.8 1 .0 -0.2

Predicted LWG (kg d-')

Chapter 4

steers 258

57 6

682 1 0 1 1 658

41

Figure 1 . Regression of observed l iveweight gain on predicted liveweight gain from the model (Chapter 6) of beef bred animals grazing at different herbages al lowances and maize supplementation levels (Chapter 3 and 4) .

Model validation and system simulations 1 99

Table 2. Regression analysis of observed (Y) values (Chapter 3 and 4) and predicted (X) values for l iveweight gain and herbage i ntake using the model developed in Chapter 6.

LWG (kg d·l) Sig. Herbage intake (kg OM Sig. d ·1 )

a 0 . 1 7±0.08 b 0.73±0 . 1 3 RMSE 0 . 1 3 R2 0.78 b 0.98±0.08 R2 0.93

NS * *

* * *

P(b * 1 ) NS

1 .0 1 ±0.63 0 .69±0 . 1 7 0 .54 0 .66 0.93±0.04 0.98

LWG= l iveweight gain , Sig .= significance level , NS= non significant d ifferences ( p>0.05), **=P<0.0 1 and ***=P<O.01 .

QJ -'" <tf

6.0

� � 4.0 ell 'C <tf 'O -2 :2E J! o '0 ell QJ -'" > � m 2.0 (/) .0 o

0.0 "" 0.0 1 .0 2.0

• • •

/ . / .

.... y=x

3.0 4.0 Predicted herbage intake

(kg DM d ' )

• • •

• •

5.0 6.0

NS

* *

N S

Figu re 2 . Regression of observed herbage i ntake o n predicted herbage i ntake from a model (Chapter 6) of beef bred animals grazing at different herbages a llowances and maize supplementation levels (Chapter 3 and 4).

3. Model application

The development of the model in Chapter 6 was ori ented towards intensive beef

catt le grazing systems, part icularly 1 2-month systems that f in ish steers before new

weaners enter the farm in the autumn . I n Chapter 1 it was emphasised that in such

systems, adequate animal performance during the summer is important so as to

avoid feed requi rements of f in ish ing animals overlapping with those of weaners

entering i n the autumn . G ra in supplementation during summer may be used when

there is r isk of not being able to f in ish the animals within the productive cycle.

P re l im inary research suggests that a level of g rain feedi ng above 1 .4 % LW d-1

(approximately half of the total OM i ntake) is needed to achieve the target date for

fin ish ing (Machado et al . 200 1 ) . However , it is wel l recognised that heavier cattle

200 Part '" - Chapter 7

are less efficient in terms of feed conversion eff iciency (SCA 1 990 ; NRC 2000) ,

and that heavier animals in summer require a higher absolute i nvestment than

l ighter animals ( i .e . 1 .4 % LW d-1 for an animal of 320 kg LW represents 4.5 kg OM

grain d-\ One option cou ld be to feed grain ( i f necessary) , earl ier in the productive

cycle as a more "pro active" alternative to preventing l iveweight fal l i ng below

targets, and together with the adjustment of stocking rate, th is may allow a more

efficient use of grain supplements (Hodgson & Tayler 1 972 ; Rearte & Pieron i

200 1 ) . Therefore , "BeefSim" was used to investigate the effects o f i ncreasing

stocking rate ( including herbage allowance management) and maize g ra in

supplementat ion associated with farm monitoring , on sward , an imal and system

variables.

3. 1. Development of a "baseline" farm

Prior to establ ish ing system comparisons , it is necessary to define a "basel ine"

system. This may be defined arbitrari ly , but it was preferred to use avai lable

information from farm lets developed at INTA Balcarce Experimental Station -

Argentina, in order to reduce the number of assumptions. Therefore, the model

was set up to resemble some of the characteristics of the control ( unsupplemented)

farm let described in Garcia et al . ( 1 998) .

Addit ional ly, th ree variables were necessary to set up the model ; monthly herbage

accumulat ion rates (HARlnput) , monthly target pastu re covers and pre-defined

herbage allowances.

3 . 1 . 1 . Herbage accumulation rates

I nformation on HARlnput for beef cattle f in ishing g rass/legumes pastures i n

Argentina i s scarce. I nformation i s not avai lable from the most relevant farmlet

t rials (Mezzadra et al . 1 992 ; Romera et al . 1 998 ; Garcia et al . 1 998 ; Mezzadra et

al. 2003) , or the information is condensed into average figures between seasons

(autumn-winter and spring-summer) as presented by Kloster et al . (2000) . To

establ ish the s imulation to represent the observat ions of Garcia et al . ( 1 998 ) ,

i nformation on HAR was used from an alfalfa-grass pasture i n the southeast of

Model validation and system simulations 20 1

Buenos Aires province reported by Machado et a l . (2003)7, plus 4 years of data for

a pastu re of s im ilar characteristics using simi lar measu rement procedures (Corrall

& Fenlon 1 978) recorded at the CHE I-Barrow- Experimental Station - Argentina.

These values were condensed with in months in Table 3 .

Table 3 . Mean and standard deviations o f month ly herbage accumulation rate (kg O M ha-1 d· 1 ) for an alfalfa-grass pasture in the southeast of Buenos Aires province (Tres arroyos, n=6), and pasture cover target assumed in the simulations.

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV O EC Mean HAR 33.5 3 1 .5 32.7 30.3 20.6 1 3.5 1 1 .7 1 4.2 23.7 47.6 57. 1 43.3 SO 1 2.3 8 .8 9 .3 9.8 5 .7 4.5 9 . 1 1 4.3 1 3 .8 1 9 . 1 22.3 1 7 .0 Target PC 2400 2400 2000 2000 2000 1 800 1 800 2000 2300 2300 2300 2300 HAR= herbage accumulation rate, SO= standard deviation of the mean, PC= pasture cover.

3 . 1 .2. Target pasture cover and its monitoring

Seasonal sward targets of pastu re cover are well defined i n New Zealand

(Matthews et al . 1 999) . However, in Argentina no such targets were avai lable. For

the purpose of the model comparison , values presented in Table 3 were assumed

as reference values in the s imulations, based on i nformation from New Zealand

(Coutinho et al . 1 998) .

The (positive or negative) deviation from these targets ( PCover Dev ' Chapter 6 ,

Eqn . 48) are estimated weekly, representing what i s a "farm walk" i n a real system .

When the herbage adjustment is "on" i n the "manager" block, it can adjust the pre­

defined herbage al lowances downward or upwards in response to variat ion in

pastu re cover. The procedure is controlled by a set of rules avai lable in tables in

the "manager" block l inked to PCover Dev (presented g raph ical ly in Figure 3) . The

formula that describes this part of the management control is given by:

HA HA (1 Ad" pcoverDev )

Applied = Input + � HA 1 00

Eqn . 60

Where HAApplied i s the DM herbage al lowance (% LW d-1 ) actually appl ied and

estimated weekly when the herbage adjustment is "on" in the "manager" block,

HA,nput is the pre-defined monthly DM herbage al lowance (% LW d-1 ) at the start of

7 Corresponding measurements made for the experimental site and t ime interval presented in Chapter 5.

202 Part 11/ - Chapter 7

the simulation , ADJ HA is the factor adj ustment ( Figure 3) and PCover Dev is the

positive or negative deviation from the target pasture covers (kg ha-1 ) _

The adjustment of herbage al lowance i s intended t o manipulate herbage intake i n

response t o herbage deficit and surplus, ru nn ing t h e model i n "automatic" mode

and with herbage adjustment set to "o n" (Chapter 6, sect ion 3 . ) . I f the "fu l l

interactive" mode is chosen instead, desi red adjustments at different t imes in the

s imu lation are made directly by the user.

For example, if during the fi rst week of September, a pasture cover of 2600 kg ha- 1

is present, th is means that PCover Dev = +300 kg ha- 1 (2300 kg ha-1 of target

pasture cover in Table 3, subtracted from actual pasture cover) . The

ADJ HA corresponding to th is PCover Dev and month of the year is +0.05 ( Figure 3 ) .

I n th is example, an HA,nput 6 % LW d-1 (0.06 in model notat ion) was allocated to the

ani mals. Substituting this information in Eqn. 1 leads to an HA Applied = 0.07 (7 %

LW d-1 ) as the final result . If the PCover Dev = +300 kg ha-1 was in April at the same

HA,nput as in the September example, ADJHA in th is month would be 0. 1 2 , and

HAAPplied would be in this month 0 .082 (8.2 % LW d-1 ) . HA Applied i s arbitrari ly

constrai ned to 1 2 % LW d-1 as done by McCall ( 1 984 ) , a level where maxi mum

intake i s reached (Hodgson 1 984) .

ADJ HA ( F igure 3) i s d ivided into two periods, one with a tighter co ntrol o f herbage

al lowance adjustment in the reproductive season (assu med here from September

to February inclusive) , where there is the risk of losing sward conditions at h igh

herbage al lowances (Chapter 1 , section 4) . If the sward is managed more tightly,

this al lows the excess to be harvested when the pastu re cover th reshold for

conservation is ach ieved. Pos itive adjustments after +600 kg OM to + 1 500 kg OM

are the same as shown for + 600 kg OM (Figure 3). I f PCover Dev is below -400 kg

OM or above 1 500 kg O M , the simu lation is abo rted as the outcome was

considered not to be feasible, because of excessive or too low stocking rate for the

system.

Model validation and system simulations

c Q) E Cii :::J '0 CIl Q)

+ 0.25 + 0.20 -+ 0. 1 5 + 0. 1 0 -+ 0.05 ,J"'- - _r - - ... . . . .

� ,11 � � -600 -500 -400 -300 -200 - 1 .05 Q + 1 00 + 200 + 100 + 400 + 500 + 600 + 1 500 CIl Q) Cl CIl -e Q) I

--lIIar-Aug - - Sep-Feb

-0. 1 5 --0.20 -0.25 -

Pasture co-.er deviation (kg OM ha-1 )

203

Figure 3. Herbage allowance adjustment as a function of i ndicated pasture cover deviation from target.

The basic assumption and resu lts for the "basel ine" s imu lation are presented in

Table 4 _ At the start , the paddocks ( 1 2 as default number) were organised in a

sequence of re-growth states with an averaged pasture cover of 2000 kg OM ha-1

(pasture cover for March , Table 3 ) . The model was allowed to sell the an imals

individually, when animals reached 370 kg LW achieved (specified by Garcia et. al .

( 1 998) ) , and i n practice an imal sales were spread from 1 January to 26 February_

To adjust the system and define the value for ADJHA , different combinations of

HAlnput and ADJ HA were tested against the projected outcome for the basel ine

system. Final ly, an HAlnput of 3 % LW d-1 (0 . 03 in model notation ) duri ng autumn­

winter and 4 % LW d-1 (0.04 in model notation) dur ing Spring-Summer, were used.

It must be noted that the "manager" was adj usting the herbage al lowance weekly,

to yield an overall mean of HAAPPlied of 5 .2±2 % LW d-1 (0 .052 in model notation) .

The projected pattern of animal l iveweight (F igure 4) shows the expected seasonal

pattern , associated with a set of conditions def ined by the user (Le_ pre-defined

herbage al lowance, target pastu re covers, values for ADJ HA ' changes in herbage

qual ity) .

The h ighest LWG was in Spring (0.86 kg ha-1 d-1 ) i n agreement with the 0.84 kg ha-1 d-1 (average of two seasons) est imated from equat ions presented by Mezzadra et

204 Part 111 - Chapter 7

al . ( 1 992) for s im i lar stocking rates and cattle types. The combinat ion of

the ADJ HA being more restrictive in spri ng-sum mer together with the method used

for herbage conservation, meant that although there was a clear herbage surplus

which yielded 2700 kg OM ha·1 y(1 of harvested herbage for conservation (Table

4), the animals were not fed the h ighest possible al lowances .

Table 4. Results of setting the model as the referenced beef cattle fin ish ing farmlet for development of a "basel ine" system.

(Garcia et al . 1 998) Model Pasture only

Animal class steers Stocking rate (hd ha·1 ) 2.5 Farmlet Area (ha) 1 0 I n itial l iveweight (kg) 1 75 Final l iveweight (kg) 370 Duration (d) 36 1 353 Mean l iveweight gain (kg d-1 ) 0.54 0 .55 c LW Production (kg LW ha-1 y(1 ) 492 487 cStock efficiency (%) 72 68 #Herbage harvested for conservation N/A 2700 (kg ha-1 y(1 ) Herbage O M allowance (% LW d-1) N/A 5.2±2 c = see text for defi nition , #= this value represents the total OM harvested, but not conserved reserves, where different losses are applied and N/A= not available .

Liveweight production and stocking efficiency are two production indicators used

widely i n Argentinean beef cattle systems. The former is given by :

LW production = (Animal sales-animal purchases ± i nventory d ifference) I production area

And

I nventory difference = Farm final LW (kg) - farm start LW (kg)8

Where LW production is the total LW production (total LW ha-1 y(\ animal sales

and an imal purchases are the sales and the pu rchases during the year ( in total LW

y(\ production area represents the grazing area (ha) effectively dedicated to beef

cattle production ( i .e . it cou ld be used temporari ly by other stock) . I nventory

difference (kg) is the difference between total LW on the farm at the end (kg) less

the start ing total farm LW (kg ) and stock efficiency is given by:

8 In the simulated alternatives, stock inventory difference is zero

Model validation and system simu/ations 205

Stock effic iency = ( LW production / average animal LW on the farm) 1 00

That is , stock efficiency (%) is of yearly LW product ion (total LW ha-1 y(1 ) d ivided

by the average yearly an imal LW on the farm expressed i n kg LW.

370 •

0> :::s 1:: 320 .>2> Ql :l: � 270 to E E <{ 220

1 70 l M ar-04 M ay-04 Jul-04 Sep-04 Nov-04 Jan-05

Figure 4. Monthly target animal liveweights i n the "baseli ne" system. Liveweights at the start of the corresponding month.

The model fitted very wel l to f ield data, but th is was not considered a model

validat ion , as different parameters were adjusted iteratively to obtain these results.

However, i t was shown that the model had enough flexib i l ity to represent

acceptably the field i nformation , which encourages the use of the "basel ine" system

for further comparisons to be made in the next sect ion.

3.2. Simulation of intensive beef cattle finishing systems

With the herbage adjustment i n "on" status i n the "manager" block, the fol lowing

stocking rates were simu lated : 2 .5 ("basel ine" system) , 3 .5 , 4 .5 and 5.5 hd ha-1 •

These stocking rates were s imulated without maize feed ing , and with strategic

maize feeding (Le. maize was fed when the defined monthly LW fel l below the

targets specified in Figure 4).

3.2 . 1 . Basic managemen t rules

• Maize supplementation starts if L WDev (negative) is g reater than the

min imum deviation defined i n the management table (Chapter 6 , F igure 1 2)

and the current simulat ion t ime is with in the supplementation period . I n th is

206 Part 11/ - Chapter 7

exercise , supplementation was allowed on any day of the year to increase

the flexibi l ity of the system ( i .e . start ing and ending days are 0 and 364,

respectively) . Once the condit ions for maize supplementat ion were met, i t

was maintained for a min imum of 30 days , unt i l the next animal weigh ing ,

whe re the decision to cont inue feeding was re-assessed.

- Herbage was conserved (Chapter 6, section 2 .2 .3 .3 . ) if pasture cover was

higher than 3000 kg OM ha·1 , there was a currently u ngrazed paddock with

herbage mass h igher than 3800 kg OM ha·1 and the s imulation time was

with i n the i nterval where harvest ing was al lowed ( 1 5 December to 1 5

February) .

.. Animals were i ndividual ly s imu lated , and HARlnput was set up as

deterministic value .

3 . 2 . 2 . M a i n results

At a stocki ng rate of 2 .5 hd ha·1 animal performance i n both the g razed only and

strategic maize grain treatments was exactly the same . This s imply shows that

there was no need to add supplement at this stocking rate, because there was no

deviation of the L W Dev from targets .

Increasing the stocki ng rate from 2.5 to 5.5 weaners ha·1 decreased the herbage

allowance al located to the animals, pre- and post-grazing herbage mass, and the

pastu re cover on the farm. The herbage allowances shown in Table 5 represent

averages of the weekly adjustments made by the "manager", based on the

information presented in Table 3 and Figure 3 , and the pasture cover est imated at

each weekly farm walk.

The durat ion of the process was 353 d in the "basel ine" system (Table 4) , and

between 343 and 362 days i n the supplemented an imals. I n the case of

unsupplemented animals grazed at stocking rates of 3 .5 , 4 .5 and 5.5 hd ha-1 , the

proport ion of unf in ished animals at the end of the cycle increased substantial ly

(Table 5) to 1 .0 at 5 .5 hd ha-1 .

Model validation and system simulations 207

Table 5. Effect of stocking rate and use of strategic feeding of maize grain in a simulated farm let ( 1 0 ha) with steers start ing on the 1 March at 1 75 kg LW.

Stocking rates (hd ha·' )

Sward parameters • Herbage allowance (% LW d·' ) • Pre-grazing herbage mass (kg ha·' ) 'Post-grazing herbage mass (kg ha·' ) ' Pasture cover (kg ha·' )

LHARActual (kg ha·' y(' )

LHARActual 1 LHARlnput Total herbage eaten (kg ha·' y(' ) Total g rain eaten (kg ha·' y(' )

Animal performance Average LWG (kg d·' ) MJ ME-LWG I MJ ME-maintenance

System performance #Herbage conservation (kg ha·' y(' ) cLW Production (kg LW ha·' y(' ) cStock efficiency (%) Unfinished animals at 28 February

Grazed on ly

2.5 3.5 4.5 5.5

5.0 4.8 4.7 3 .5 3750 3460 3 1 60 2920 21 60 1 790 1 590 1 395 2490 2220 2 1 40 20 1 0 8500 8700 9000 91 00 0.77 0 .79 0.82 0.83 5800 7400 8500 8500

0.55 0 .50 0.41 0.35 0.52 0.49 0.41 0.36

2700 1 300 500 600 487 644 673 71 1

68 66 59 54

Strateg ic feeding of grain

2.5 3 .5 4 .5

5.0 5 .0 4 .6 3750 3480 3067 21 60 1 900 1 670 2490 2322 2 1 50 8500 8600 8900 0 .77 0.78 0 .81 5800 6400 7700 -- 800 1 600

0 .54 0 .57 0 .55 0 .52 0 .54 0 .51

2700 2200 1 200 487 766 875

68 71 70

maize

5.5

3.2 291 0 1 563 2080 9000 0 .81

8000 2800

0.55 0 .52

1 000 1 070

7 1

(Proportion of the mob) 0.00 0 .7 1 0.88 1 .00 0 .00 0 .00 0.00 0.00 ' = Means of daily observations, LHARActual = is the yearly accumulated HARActuah LHARlnput= is the yearly accumulated HARlnput MJ ME-LWG = energy used for LWG, I MJ ME-maintenance= energy used for maintenance, #= this value represent the total OM harvested, but not present herbage reserves, where different losses are applied (Chapter 6, section 2.2.3.3. ) and c = see text for definition .

1 .00

� rJ. 0.90 « I w � 0.80 co ID ID Cl .2l 0.70 a:;

I

0.60 2.5

." .. . . ." .'

. . '

• Grazed only

- . - .- - - Strategic maize

3.5 4.5 5.5 Stocking rates (hd ha·' )

Figure 5. Effect of stocking rate and use of strategic feeding of maize g rain on the ratio between herbage eaten (kg ha·' y(' ) and Lherbage accumulation (kg ha·' y(' ) as an indicator of grazing efficiency in the s imu lated alternatives.

208 Part 1/1 - Chapter 7

I ncreasing the stocking rate increased substantially the total herbage eaten on the

farm (Table 5) . HARActual also increased sl ightly with increased stocking rate , but

there was a much more substantial increase i n the proport ion of HARActual that was

eaten ( Figu re 3) . Maize grain feeding decreased total herbage eaten (Table 5 ) ,

which led to a lower proportion o f the herbage produced being g razed (total

herbage eaten / LHARActual) than in grazing only alternatives ( Figure 5) . Herbage

conservat ion was lower in grazed only treatments, and decl i ned with stocking rate.

For each additional animal per ha, herbage eaten increased by 920 and 790 kg ha-1 y(1 , for grazing only and maize g rain supplemented treatments, respectively.

I ncreasing the stocking rate in the grazed on ly treatments decreased LWG

substantial ly , and i ncreased the proportion of the total M E that was used for

maintenance by the an imals. This level of LWG agreed with the resu lts of

Mezzadra et al. ( 1 992) but was sl ightly higher than that observed by Romera et al .

( 1 998) , both experiments using Angus steers of smal l -s ized biotype (Figure 6) .

Romera et a l . ( 1 998) suggested that their relative lowest animal performance was

l ikely to be caused by accumulated effects of continuous use of the pasture at the

allocated stocking rate on pasture composition.

0.70 -0.60 '

:: 0.50 -

� DAD �

� <.9 0.30 -� 0.20 1

0. 1 0 -

• Mezzadra et al. ( 1 992) • Rorrera et al. ( 1 998) x Beef Sim

0.00 �-----.---,--------.---------,

0.0 1 .0 2.0 3.0 4.0 5.0 6.0 Stocking rate (hd ha" )

Figure 6. Effect of the stocking rate (X) on total l iveweight gain (Y) of Angus steers obtained from the model and from the l iterature. For the fitted l inear functions: Mezzadra et al. ( 1 992) , Y=O.69-0 .06 X, R2=0.41 . Romera et al. ( 1 998) Y=O.82-0. 1 3X, R2=0.41 , and predicted with the model Y=O .73-O .07X, R2=O.98.

Supplementation maintained LWG at a level similar to the "basel ine" system (no

supplement , 2 .5 hd ha-1 ) . This is explained by the fact that the objective of the rules

Model validation and system simulations 209

for maize grain feeding was to maintain target LW, not exceed the targets. One

important advantage of the use of grain in this way is that the speed of the

production cycle does not increase, so there were animals avai lable to use the

herbage produced during summer. Reducing the stocking rate too far du ring

summer, or excessive use of g rain supplement ( i .e . f in ishing animals early) , could

lead to the need for a substantial increase in herbage conservat ion to control

pastu res. Additi onal ly, as L WDev was monitored from the beg inn ing of the cycle,

most of the g rai n was fed early in the cycle ( in winter). Supplementat ion level and

durat ion depended on the degree of monthly L W Dev observed i n each treatment

( resu lts not shown) . Unsupplemented LWG was low (this is the reason why LW

targets were not ach ieved) , and i n th is situation , the highest response to grain is

expected (Chapter 1 , Figure 1 ) and protein makes a higher contribution to the LWG

of younger an imals with more accret ion of water than fat tissue (SCA 1 990; NRC

2000) .

3 .2 .3 . Gross Margin and LW product ion

Gross Margi ns for the simulated farm were estimated as described in Chapter 6 ,

section 2 .4 , us ing assigned values for the economic parameters p resented i n Table

6. Animals are sold when the target LW is reached, but it is assumed that all

animals are sold at the end of the production cycle , either f in ished or unf in ished (at

a lower price , as i ndicated in Table 6) .

2 1 0

Table 6 . Assigned values for the economic parameters of the model .

Description Unit

Price of animals at purchase

Price of animals at sale

Animal purchase costs

Value of the u nfin ished animal

Animal sale costs

Labour costs

Animal health costs

Herbage conservation

Silage costs

Costs of pasture renovation (25 %

$US kg LW-'

$US kg LW-'

Proportion of animal value

Proportion of the value of a finished animal

Proportion of animal value

$US ha-' y ('

$US hd-' y('

$US ha-'

$US ton-'

annually) $US ha-'

Maize g rain cost $US ton"

Costs of maize cracking and cost of

distributing the maize grain $US ton"

Part 1/1 - Chapter 7

Value

0 .76

0 .72

0.08

0 .8

0 . 1 0

64

3 .2

7 . 1

7 . 1

33

88

6

Liveweight production on the g razed on ly treatments i ncreased markedly when

stocking rates i ncreased from 2.5 to 3 . 5 hd ha-1 , with no improvement thereafter

(Figure 7). Gross marg in in grazed on ly treatments decreased l inearly as stocking

rate i ncreased (most of the animals were sold in an unf in ished state) . As was

mentioned in Chapter 6, section 2 .4 . , the use of production per ha could be

misleading when off-farm inputs such as g rains are i ncorporated i nto the system . I n

the case o f supplemented treatments ( Figu re 7 ) , there was a l inear i ncrease i n LW

production as the stocking rate increased , but gross marg in reached the h ighest

value at 4 .5 hd ha-1 .

Model validation and system simulations

1 200

�

: 1 000 fa .c :s: ::::!. 800 � c 0 "TI :::J 600 " 0 0.. :s: --l 400

250

• � 200 �

U5 :::J - 1 50 � c '0, - 1 00 � E

Cl) Cl) e 50 c.?

� 0 2.5 3.5 4.5 5.5

Stocking rates ( hd ha-I )

c::===:J Grazed only-GM c::===:J M aize feeding-GM

� Grazed o nly-P ro d. � • •• • • • M aize feding-P ro d .

2 1 1

Figure 7. Effect of stocking rate and use of strateg ic feeding of maize grain on gross marg in (GM) and LW production ( Prod.) in the simulated alternatives.

Gross marg in was i ncluded here to establ ish comparisons between groups, and to

constrain the situation where a large amount of g rain is used. The values

presented are not adequate for bench marking with other systems, for reasons

associated with the design of the farmlet used to set up the basel ine system

(Garcia et al . 1 998) : No crops were included ( i .e . oat) in the system comparison , a

common sou rce of feeds that represents roughly between 1 9 % and 33 % of the

net i ncome (N I , Chapter 6 Eqn. 53) (AACR EA 2004) ; herbage is conserved but in

this exercise is not fed back; and in the s imu lation, no value is assigned to the

conserved herbage and for clos ing the production-economic cycle, animals

unf in ished are sold at a lower price.

2.2 . Variation of herbage accumulation rates

The previous exercise was done with average values for HARlnput (Table 3 ) . A f inal

exercise was run to test the chances that the "manager" block in a "baseline"

system can cope with variation of HARlnput and effectively sti l l achieve the target

LW for a pre-defined day of the cycle (day 270) . This is important for the system

performance and, in this s imu lation 1 December was used as the cut off date . One

hundred yearly HARlnput profi les were generated by random sampl ing, using the

standard deviat ion of the HARlnput (Table 3), for each of the twelve months i n

sequence. Each of the profi les was used to run a productive cycle with the herbage

adjustment tu rned "on" i n the "manager" block and with no g rai n feeding allowed ,

2 1 2 Part III - Chapter 7

and estimat ing the probabil ity of exceed ing the target LW ( 1 December) used i n the

def in it ion of the "basel ine" system ( Figure 4) . It was observed that in this example,

the manager had a 0 .62 chance of of exceeding the 345 kg LW target at the 1

December, without feeding grain (Figure 8) . This exercise was developed only to

show the kind of factors to be taken i nto account when the degree of physical

"riskiness" of alternatives is evaluated. Stochastic components can be added

relatively easi ly to the model (stochastic HARlnput , gra in and an imal prices , random

level of ind ividual grain intake from the average grain allowance to the mob, etc) .

I n this case systems comparison was done with a determin istic HARlnput , a

l im itation imposed by the fact that the basic data set to define the "basel i ne" system

(Table 3) covered only 6 years at the CHE I -Barrow experimental stat ion . Examples

in the l iterature show how different the model led response of a g razing system may

be when stochastic functions are added from rel iable sources of information

(Cacho & Bywater 1 994; Cacho et al. 1 999) . Although understandably more

l imited , a determin istic option was preferred for the system comparison , as a low

benefit was expected in terms of insights of model behaviour, when real iable data

were not avai lable.

1 .00 -r----------__

0.80 >-

� 0.60 :0 ct1

.0 o DAD et 0.20

0.00 +------------,--,----'-,---1 75 200 225 250 275 300 325 350 375

Animal livew eight exceeded (kg)

Figure 8. Accumu lated probabi l ity that animals exceed a g iven liveweight on 1 December. Constructed from the simu lation of 1 00 production cycles using stochastic herbage accumulation years (Table 3 ) . 345 kg LW represents the target LW for the "baseline" system.

3. Impl ications and conclusions

At the present state of development, the model can reproduce accurately the in it ial

set of information about l iveweight gain and herbage intake used for val idation.

Model validation and system simulations 2 1 3

Additional ly, i t has been shown that BeefSim has adequate flexibi l ity i n terms of the

target system where it is to be used . System comparisons developed to investigate

the effect of stocking rate and strateg ic use of maize g rain , show that

u nsupplemented options were in agreement with the l i terature. I n the case when

maize g rain was fed , it was demonstrated that a s imple mon itoring procedure can

help to use the g rain more sensibly , and gain insight into the system . Although the

i nclusion of "random" functions to the model may be tempting , use of a

determi n istic s imu lation is preferred u nt i l reliable data is avai lable. The model is

research oriented , but condenses practical i nformation with potential use for

education and decision-making.

References

AACREA. 2004 : Margen Bruto I nvernada 282:25.

Cacho O. J . ; Bywater A. C . 1 994 : Use of a g razing model to study management

and risk. Proceeding of the New Zealand Society of Animal Production

54:377-38 1 .

Cacho O. J . ; Bywater A. C . ; Di l lon J . L . 1 999 : Assessment of production risk in

g razing models. Agricultural Systems 60:87-98.

Carlson D. H. ; Thurow T. L . ; Jones C . A . 1 993 : Biophysical s imu lat ion models as a

foundation of decision support systems. In: J .W. Stuth and B .G . Lyons. Eds.

Decision Support Systems for the Management of Grazed Ecosystems.

UNESCO-MAB Book Series. Parthenon Press . New York. :38-68.

Cohen R . D . H . ; Stevens J . P . ; Moore A. D . ; Donnel ly J . R. 2003 : Validating and

using the G rassGro decision support tool for a mixed grass/alfalfa pasture in

western Canada. Canadian Journal of Animal Science 82: 1 7 1 - 1 82 .

Corral l A. J . ; Fenlon J . S. 1 978: A comparative method for describing the seasonal

distribut ion of production from grasses. Journal of Agricultural Science,

Cambridge 9 1 :61 -67.

Coutinho B. H . ; Matthews P. N. P . ; Morris S. 1. 1 998 : The effect of grazi ng

214 Part 111 - Chapter 7

management on pasture and an imal production in late autumn to early

spring in a one year bull beef g razing system . Proceedings of the New

Zealand Society of Animal Production 58:236-238.

Dove H. 1 996 : Constraints to the model l i ng of diet selection and intake in the

g razing ruminant. Australian Journal of Agricultural Research 4 7:257-275.

Garcia S. C . ; Santini F. ; Castano G. 1 998 : Producci6n de carne bajo pastoreo :

Alternativas de intensificaci6n. Material didactico Nro 1 4. I NTA, Cerbas . :52

p.

Herrero M . ; Dent J . B. ; Fawcett R . H. 1 998: The plant/animal interface in models of

grazing systems. In: Peart , R .M . & Curry, R .B. Eds. Agricultural System

Modeling and Simulation. New York. Marcel Dekker, I nc. 495-542.

Hodgson J. 1 984: Sward conditions , herbage al lowance and animal production .

Proceedings of the New Zealand Society of Animal Production 44: 99- 1 04.

Hodgson J . ; Tayler J . C. 1 972: The effect of supplementary barley upon growth

and efficiency of food conversion in calves kept at high grazing intensity.

Journal of the British Grassland Society 27:7-1 1 .

Kloster A. M . ; Lat imori N . J . ; Amigone M . A. 2000 : Evaluaci6n de dos sistemas de

pastoreo rotativo a dos niveles de asignaci6n de forraje en una pastura de

alfalfa y g ram f neas. Revista Argentina De Producci6n Animal 20: 1 87 - 1 98.

Machado C. F. ; Berger H . ; Morris S. T. ; Hodgson J . ; Copes M . ; y Duhalde J .

2003: Cal ibraci6n estacional de mediciones de altu ra del canopeo con plato

y con bast6n de altura para est imar la biomasa de forrajera de una pastura

base alfalfa. Revista Argentina De Producci6n Animal 23: 73-74

( resumen) .

Machado C. ; Striebeck G . ; Vasolo V. ; Di Croce F . ; Gonzalez F . ; Duhalde J . ;

Parente M . ; Auza N . 2001 : Suplementaci6n de g rana de mafz e n pastoreo y

a corral como alternativas de terminaci6n de novil los en el verano. Avances

En Producci6n AnimaI 26: 1 39- 1 46.

Matthews P . N . P . ; Harrington K. C . ; Hampton J . G . 1 999: Management of g razing

Model validation and system simulations 215

systems. In: White, J .& Hodgson , J . Eds. New Zealand pasture and crop

science. Oxford Univers ity p ress. 1 53- 1 74.

Mayer D. G . ; But ler D. G . 1 993 : Statistical val idat ion. Ecological Modelling 68:2 1 -

32 .

McCall D . G . 1 984 : A systems approach to research plann ing for North Is land H i l l

Country . PhD Thesis. Massey University. 26 1 p .

Mezzadra C . ; Escuder J . ; M iquel M. C . 1 992: Effects of genotype and stocking

density on post-weaning dai ly gain and meat production per ha i n catt le.

Animal Production 55:65-72 .

Mezzadra C . A . ; Me lucc; l . M. ; Vi l larreal E . L. ; Faverdin C . 2003 : Comparacion

del desempeno productivo de novi l los puros y cruza britanicos bajo

sistemas de engorde sem i- intensivos e i ntensivos . Revista Argentina De

Producci6n Animal 23:45-52 .

N RC. 2000 : Nutrient requirements of beef catt le. National Resarch Council.

National Academy Press. Washington, D. C.

Rearte D . ; P ieron i G. A. 2001 : Supplementat ion of temperate pastures.

Proceedings of the XV International Grassland Congress :679-691 .

Romera A. J . ; Mezzadra C. A. ; Vi l larreal E . l . ; Brizuela M . A . ; Corva P . M . 1 998 :

Productivity of graz ing Angus steers of different structural size. Animal

Science 67:455-460 .

SCA. 1 990 : Feeding Standards for Austral ian l ivestock. Standing Committee on

Agriculture, Ruminants Subcommittee. CS/RD Publications. 266 p.

Overview

2 1 8 C. F. Machado

1 . Introd uction

The objectives of th is thesis were specified as fol lows , with particular reference to

their importance to g razing-based beef catt le fi n ish ing in the context of Argenti na:

To provide information on herbage allowance-intake functions and the effects on

animal performance when maize grain is supplemented, in beef cattle finishing

systems.

To investigate the seasonal change of herbage quality in a beef cattle finishing

pasture.

To develop a "fi n ishing beef cattle unit" s imu lator driven by stocking rate and

herbage allowance, as a tool for information synthesis and research on the

biological and economic implica tions of grazing management and grain feeding in

intensive beef cattle systems.

The field research reported in this thesis was concerned with two key aspects of

intensive beef cattle f in ishing under rotational grazing systems: herbage allowance­

intake fu nctions and the effects on animal performance when maize grain is

supplemented , and seaso nal changes in herbage quality. These factors are h ighly

interrelated in grazing systems, but evidence on them is l imited for Argenti nean

conditions. Taken i nto account together with the dynamic changes and interactions

occurring naturally in the herbage and animal , model l ing was used as a research

tool to integ rate the information about herbage allowance, maize grain

supplementation and herbage quality in a systems context.

The main concl usions of the field research program me were considered in

Chapters 4 and 5 and the outcomes of the model l ing exercise were discussed in

Chapter 7. The field objectives were achieved , and the effectiveness of the model

was demonstrated in some prel iminary investigations of alternative management

strategies. This overview wil l integrate the main outcomes of the different parts of

the thesis , h ighl ighting the benefits of the combined approach for the advancement

of the topic and identifying needs of fu rther research .

Overview 2 1 9

2 . Main conclusions of the grazing stud ies

The n-alkane & 1 3C method proved to be accurate for quantitative esti mates of

herbage and maize grain i ntakes , and allowed the est imates of a substantial

variat ion in individual maize g rain i ntakes (between 31 to 41 % CV) when animals

are supplemented in groups . This variation is of potential importance in terms of

both system management and system model l ing , because it contributes di rectly to

variat ion in l iveweight gain , but there is l ittle publ ished info rmation about it . Thi s

method also allowed the establ ish ment of significant re lationships between

herbage dry matter allowance, herbage intake and animal performance , which

were dynamic in nature , but were also robust within about a range of experimental

conditions i ncluding season, sward com position and ani mal use.

The n-alkane & 1 3C method was the preferred basis for quantifyi ng the substitut ion

rate of grazed pasture for maize g rai n. Su bstitution rate is by definition a compou nd

variable ( i .e . a reference of u nsupplemented herbage intake is requ i red for its

estimation ) , therefore the term " individual substitut ion rate" used in Chapter 4 ,

should b e appl ied with caution . Also it was recognized that bias can be introduced

by the fact that unsuppleme nted animals tend to become prog ressively l ig hter than

supplemented animals. However, from this analysis it was possible to show a

consistent pattern of substitution rate over a range of h erbage al lowances and

levels of grain feeding.

The n-alkane method was effective in providing est imates of diet digestibi l ity, but it

was not successfu l in pred icti ng the botanical and morphological characteristics of

the diet selected . Other methods , such as micro-histological eval uation of faeces

and differences in nutrient and component selection indexes, were tested early i n

the prog ramme (Chapter 2 ) , but they were not co nsidered to be rel iable and they

we re too laborious for cont inued use under field conditions.

3. Seasonal variation of pastu re qual ity

The outcome of the studies on seasonal variation in herbage quality in it ial ly were

useful i n establ ishing a database of the range of values observed , and i n

demonstrat ing their relative robustness, at least under co nditions of good pasture

220 C. F. Machado

management. The study reported in Annex B for New Zealand conditions covered

a longer period (40 months) than that reported for an Argentinean pastu re in

Chapter 5 (28 months) . Comparison of herbage qual ity variables from different

pastures and weather conditions is l im ited . However, a clear and consistent

seasonal pattern in herbage nutritive value was observed in both studies. The

lowest values for crude protein occurring in summer in both cases , and the lowest

values of metabol isable e nergy content were in summer and autumn for the New

Zealand pasture (Annex B) , and during autum n i n Argentina (Chapter 5 ) . These

fl uctuations need to be compared with the relative nutritional requ i rement of the

animals. I n th is case, both studies gave levels of crude protein and metabolisable

energy content sufficient to meet or exceed those specified for growing beef

animals (Hodgson & Brookes 1 999) . I n agreement with the l iterature , i ntensive

pasture management is l i kely to be a key factor in achieving these results (Clark

1 995) , and care is needed in extrapolat ing to other management conditions . More

research is needed i nto feed protein fract ions to evaluate the implicat ions of

"excess" levels of crude p rotein on animal performance, rumen metabol ism and

animal nutritional requirements of free grazing an imals .

P rediction of the nutrient content of herbage from observations on sward

morphology (Chapter 5) was based on the approach suggested by Fick et al .

( 1 994) , but used a different combination of morpholog ical and maturity descriptors

as predictors of herbage qual ity. The sign ificant relationships establ ished between

morphological and maturity estimates and herbage nut ritional variables in a pastu re

u nder grazing conditions (Chapter 5) are promis ing in terms of potential i nferences

to the seasonal changes of herbage qual ity, but could not be taken further i n the

f ield studies. There is a need for fu rther work on the evaluation of these prel im inary

equations.

4. Model l ing for information synthesis in a whole-system context

An objective of this thesis was to develop a "fi n ish ing beef cattle unit" s imu lator, as

a tool for information synthesis (Chapter 6) and research on the biological and

economic impl ications of g razing management and gra in feeding in i ntensive beef

cattle systems (Chapter 7) . The model integrated in a simpl ified way (as does any

Overview 22 1

model) current knowledge of different discipl ines, in a form i n which the system can

be i nterrogated in terms of its response to different management conditions ,

scenarios and t imeframes.

The advantage of field experimentation l inked with model l ing is widely recognised

i n the l iterature. There are also frequent claims from model lers about l imited data

availabi l ity, or inadequate description of information ( Herrero et al. 1 998; Cohen et

al. 2003) . I n th is thesis , there was a "conceptual complementarity" between field

experimentation and model development, activities that were developed

s imultaneously. The selection of g razing management techn iques was i nf luenced

by the model l ing process and also by the fact that the experiments were mostly

developed on commercial farms. Conversely, choices between alternative

approaches for model components were affected by the feasibil ity of f ield

i nformation procedures .

The term model "evaluation " implies a more conservative expression than

"validation" (Oreskes 1 998) , and it was preferred here. Two key outcomes of the

model , l iveweight gain and herbage i ntake, were formally evaluated against

experimental information u nder d ifferent levels of herbage allowance and maize

feed ing , and were accurately predicted. However, i t is important to emphasise that

model development was completely independent from field experiments i n terms of

the use of data.

Overall outcomes of the model such as total l iveweight gain at different stocking

rates were also informally compared with the l iterature (Chapter 7, Figu re 6 ) , with

an acceptable agreement. The field studies on herbage i ntake and animal

performance were relatively short-term (Chapter 2 , 3 and 4) , but provided evidence

of responses to variation in herbage allowance and grain use which can effectively

be i ncorporated i nto a dynamic model of animal responses to management

decisions. Evaluation of other components of the model was not attempted.

Herbage accumulation rate is an input for the model , and the system for animal

requ i rements has been widely tested previously in g razing conditions (Donnel ly et

al . 2002) . Diet composition was predicted arbitrari ly from sward composition as

done i n other models (Doyle et al . 1 989 ; Cacho et al . 1 995) . L imitat ions of

222 C. F. Machado

i n formation on diet composition made its evaluat ion unfeasible. However, the fact

that the model was effective in predict ing l iveweight gain adequately indicates that

herbage intake was the main driver of th is response, in agreement with

observations in the f ield experiments (Chapter 4 ) .

Herbage dry matter al lowances and grain feeding were the main experimental

variables used in the grazing studies. Studies have been reported where fixed

herbage al lowances have been appl ied continuous ly over complete product ion

cycles (Bartholomew et a l . 1 98 1 ; Kloster et al . 2000 ) . However, herbage al lowance

appl ied in th is way seriously l imits management f lexibi l ity. One of the goals i n the

model l ing study was to i nteg rate the flexibi l ity of management decisions about the

al location of herbage dry matter al lowance, with stocking rate , grain feed ing and

herbage conservation , al l avai lable as management decis ions. When the model is

s imu lated under a "fu l ly i nteractive" mode (Chapter 6 , section 3 . ) , the user d i rectly

enters all the information in real t ime. Alternatively, Chapter 7 describes a s imple

but orig inal procedu re i ntended to manipu late herbage intake (and an imal

performance) in real t ime, in response to herbage deficit and surplus , identif ied by

a "manager" in a routine of pasture monitoring and budgeting . The functional ity of

this "automatic" feature was demonstrated in a system comparison (Chapter 7,

Table 5) where g rain feeding was managed under s imple rules driven by deviat ion

from pre-defi ned targets of an imal l iveweight and pasture cover for different months

of the year.

5. Further research and development

The variation of grain i ntake detected in the field experiments had important

practical impl ications, because highest individual g rain i ntakes were associated

with highest l iveweight gain (Chapter 3 and 4 ) . This function can be easi ly

i ncorporated in the model , by developing random intakes around an allocated

average level of maize g rain feeding. However, as the technique used to measure

g rain intake represents a 5 -day period, it is not known if th is variabil ity represents a

dai ly random variation between i ndividuals, or is i ndicative of consistent an imal

differences. These alternatives may have different impl icat ions for animal and

system response to g rain feeding . As l iveweight gain records cover 1 4 days, the

Overview 223

association with maize grain intake may indicate consistent animal d i fferences .

However, u nt i l more detailed i nformation becomes available i ncluding individual

record ing of animal behaviour with automated reco rders , th is question cannot be

resolved.

I nformation on herbage accumulat ion rates in Argent inean pastures is scarce. As

mentioned in Chapter 6, the development of a soi l -weather model to p redict

herbage accumulation is a complex task (Woodward & Rol lo 2002) , which was

beyond the objectives of th is study. However , the structure of the model

representing expl icitly leaf mass (clearly l inked to leaf area index) , and working with

a mathematical ly estimated g ross g rowth rate (easi ly replaced by a cl imate-driven

module that predicts the g ross g rowth rate d i rectly) from inputted herbage

accum ulation rate, provides the basis for a fu rther development in this area.

C l imate data is more readi ly avai lable in Argent ina than databases of herbage

accumulat ion rate, and these empirical databases take years to develop. This l im its

the model for use in other s ites. However, it is important to emphasise that th is

does not mean that pasture research efforts are not needed. On the contrary, they

shou ld be reinforced, but requ i re adequate descript ion of composit ion , structure

and dynamics of the sward , observations which wil l p rovide a comprehensive

means to calibrate and evaluate the best options from the l iterature. There is also a

need for more detai led information on pasture response to fert i l izer application .

The way that herbage quality is handled i n the model (Chapter 6, section 2 .2 . 1 .5 . ) ,

al lows the dynamic model l ing of different aspects of sward morphology and

herbage nutrit ive values. At th is stage of model development the herbage gross

g rowth rate is back-calculated from herbage accumulation rate, so caut ion is

requ ired in the analyses. However , it also provides opportunity for i nvestigat ion of

the effects of management on pasture changes i n metabolisable energy and crude

protein content of the sward , test ing d ifferent levels of seasonal change in stem

qual ity, eventual ly adding a penalty factor associated with stem mass on stem

quality when data becomes available.

224 c. F. Machado

6. Concl usions

The resu lts from this series of short-term studies showed consistency in the

comparison of the effects of herbage al lowance and level of maize

su pplementation on herbage intake and animal l iveweight gain . Although these

effects require further co nfi rmation and a wider scope of experi mental conditions ,

they constitute a good basis for model development . The n-alkane & 1 3C method

proved to be reliable for estimation of herbage and maize grain consu mption , with

an i mportant contribution in the identification of variabi l ity of grain intake between

animals fed in groups.

Although there was a clear seasonal pattern in herbage nutritive value, under an

i ntensive pasture management this did not seem to be a l imiting factor for growing

beef catt le , at least i n terms of the content of metabolisable energy or crude

prote in.

When tested against field observations, the model showed acceptable accuracy in

predicti ng l iveweig ht gain and herbage intake of beef cattle under a wide range of

herbage dry matter al lowances and maize grain i ntakes. The model has been

shown to be flexible when chal lenged with different grazing management

conditio ns, providing a good biological basis for a more holistic appraisal of the

effects of "process technologies" such as grain feeding in beef cattle f in ish ing

systems. This provides confidence in its futu re use as a research aid or an

educational tool for agricultural students and professionals.

References

Bartholomew P . ; McLauchlan W. ; Chestnutt D. 1 98 1 : An assessment of the

influence on net herbage accu mulation , herbage consumption and individual

animal performance of two length s of grazing rotation and three herbage

al lowances for g razing beef cattle . Journal of Agricultural Science,

Cambridge 96:363-373.

Cacho O. J . ; Finlayson J. D . ; Bywater A. C . 1 995 : A s imu lation model of g razing

sheep: 1 1 . Whole-farm model . Agricultural Systems 48:25-37.

Overview 225

Clark E. A. 1 995: Maintain ing forage qual ity by intensive management.

Proceedings of the Western Canadian Dairy Seminar 7: 1 35- 1 46.

Cohen R. D. H . ; Stevens J . P . ; Moore A. D. ; Donnel ly J . R. 2003 : Val idating and

using the GrassGro decision support too l for a mixed grass/alfalfa pasture in

western Canada. Canadian Journal of Animal Science 82: 1 7 1 - 1 82.

Don nelly J . R . ; Freer M . ; Salmon l. ; Moore A. D . ; Si mpso n R. J . ; Dove H . ; Bolger

T. P. 2002 : Evolut ion of the G RAZPLAN decision support tools and adoption

by the grazing industry in temperate Australia . Agricultural Systems 74: 1 1 5-

1 39 .

Doyle C. J . ; Baars J . A. ; Bywater A. C. 1 98 9 : A s imulation model of bu l l beef

production under rotational grazing in the Waikato reg ion of New Zealand.

Agricultural Systems 3 1 :247 -278.

Fick G. W . ; Wilkens P. W. ; Cherney J . H. 1 994: Model ing forage quality changes in

the growing crop. In: Fahey, J . R . ed. Forage Quality, Evaluation and

Utilization. American Society of Agronomy, I nc . :757-795.

Herrero M . ; Dent J . B. ; Fawcett R. H . 1 998: The plant/an imal interface in models of

grazing systems. In: Peart, R .M . & Curry , R .B . Eds. Agricultural System

Modeling and Simulation. New York. Marcel Dekker, I nc. 495-542.

Hodgson J . ; Brookes I . M. 1 999: Nutrition of g razing animals . In: Wh ite, J.&

Hodgson, J . Eds. New Zealand Pasture and Crop Science. Oxford

Un iversity press. 1 1 7- 1 32.

Kloster A. M . ; Latimori N. J . ; Amigone M. A. 2000 : Evaluaci6n de dos sistemas de

pastoreo rotativo a dos niveles de asignaci6n de forraje en una pastura de

alfalfa y gram fneas. Re vista Argentina De Producci6n AnimaI 20: 1 87- 1 98 .

Oreskes N . 1 998 : Evaluation (not validations) of quantitative models.

Environmental Health Perspectives 1 06: 1 453- 1 460.

Woodward S. J . R . ; Rollo M. D. 2002 : Why pasture growth prediction is difficult?

Agronomy 32: 1 7 -26.

Annexes

Annex A

Maize supplementation and feedlot as a n

alternative for sum mer steer f in ishing systems9

9 Machado, C . ; Striebeck, G. ; Vasolo, V. ;O i Croce, F . ; Gonzalez, F. ; Ouhalde, J . ; Parente, M . ;

Auza, 2001 . A vances en Producci6n animal 26: 1 39-1 46 [in spanish]

230 Annex A

Abstract

The aim of th is trial was to investigate tactical alternatives of maize grain

supplementat ion and feedlot in fi n ish ing systems of beef cattle during summer.

Ninety one Angus steers (31 0±2 .7 Kg LW) were randomly al located by l iveweight

to the fol lowing feed ing treatments on 5 January : grazing pasture on ly (P) , g razing

pasture plus concentrate at 1 % of l ive weight (S 1 ) , g razing pastu re plu s

concentrate at 2 % o f l ive weight (S2) and concentrate at 2 % of l ive weight p lus

alfalfa hay in feedlot (F) . An alfalfa-based pasture was used. Animals were strip­

grazed with a 4-day average break. On days 0, 1 5 , 33, 55, 82, 98, 1 1 4 , 1 27 , 1 42

and 1 56 , an imals were weighed unfasted and after a 1 5-h fasted period , and

l iveweight gain (LWG) estimated. Supplement levels were adjusted at each

weighing t ime. When steers in a treatment group reached an average of 400 kg

( 1 5-h fasted) , they were s laughtered. Before slaughter, animals were weighed fu l l

and after a 1 5-h fast to est imate shrunk l ive weight losses. Dry matter intake (OM I )

was est imated from pre- and post-grazing est imations of herbage mass. Feed­

intake of F was est imated from the difference between the offered and rejected

feeds (for g rain and hay) on a daily basis , and a s im i lar procedure was used to

estimate supplement i ntake in treatments S 1 and S2 . The animals in S2 and F

treatments were fed twice daily (8 am and 5 pm) , whi le S 1 were fed only once a

day (8 am) . I n S 1 , 1 00 % of cracked maize (OM basis) was used, but S2 and F

supplements were protein-corrected with brewer's grain ( 1 0 %) plus the addit ion of

urea. OM I plus concentrate (when present) were 2 .9 , 2 .7 , 2.9 and 3 . 1 % LW for P ,

S1 , S2 and F , respectively. Dry matter i ntakes dur ing the trial were more variable

when less concentrate was fed. Actual concentrate feeding levels were 0 .75 , 1 .46

and 1 .46 % of LW for S 1 , S2 and F respectively. LWG's were h igher (p<0.05) in S2

(0.90 kg d-1 ) and F (0 .90 kg d-1 ) than P (0.62 kg d-1 ) and S1 (0 .72 kg d-1 ) . Target

LW was reached at 1 54 , 1 1 9, 98 and 93 days for P, S 1 , S2 and F respectively.

Significant differences were detected for the percentage of shrunk l iveweight

losses, where P (5 . 1 %) and S1 (4.7 %) were higher than S2 (2 .6%) and F (3 .3%) .

Feeding supplements and feedlott ing improved LWG and the percentage of shrunk

l iveweight losses, although only S2 and F ach ieved f in ish in t ime to avoid the

requirement overlapping with new weaners entering the system in the autumn .

Maize supplementation and feedlot as alternative for summer steer finishing systems 23 1

Key-words: Supplement; summer; grazing ; beef catt le; system; maize.

1. I ntroduction

The success of any g razing system depends on the management ski l ls to produce

herbage of h igh quantity and qual ity, to use it adequately by grazing and to convert

it efficiently into animal products (Hodgson 1 990). The Buenos Aires Province,

Argent ina, is an agricultural area where beef product ion is based on perennial

pastures, which are part of the rotational system of soil use for cash crops. During

summer and autumn-winter it is difficult to achieve a satisfactory and predictable

an imal l iveweight gain (LWG) , so grain supplementation is frequently used in this

season, with variable resu lts .

I n these beef cattle production systems where animals are f in ished at 1 5-20

months of age , animal performance dur ing the f inal summer f in ish ing period is

extremely important to avo id feed requ irement overlap of f in ishing animals with that

of weaners entering the system in the autumn . Grain supplementation has been

used to overcame th is problem, with variable responses in animal production (Lake

et al. 1 974; Reardon 1 975 ; Boom & Sheath 1 998 ; Boom & Sheath 1 999) , and

also large f luctuations in herbage substitution rates (Mayne 1 988; Dixon &

Stockdale 1 999) .

Supplementation may increase animal performance or increase the carrying

capacity (Perry et al . 1 972 ; Mayne 1 988; Stockdale & Trigg 1 989; Prache et a l .

1 990) therefore allowing pastures to be used i ntensively , improving forage

uti l isation and ult imately provid i ng a better match of animal feed requirements

(Stockdale et al . 1 987). Although supplements increase feed costs , they may

provide more physical certainty to the system, accelerate the f in ish ing process ,

meet market specificat ion and release pastu re for other an imals (Taylor &

Gulbransen 1 990) . Particularly in mixed cash crop and l ivestock systems, a

predictable release of land use is important to optim ize the use of available

resources.

With i ntensificat ion of pasture-based beef cattle systems occurr ing i n Argentina,

232 Annex A

these seasonal constraints have become more evident (Rearte 1 998) . Strateg ic

use of fert i l isers, supplementation and i rrigation has been researched in beef steer

farmlets (Garcia et al . 1 998) , however local information about summer responses

to supplementation is scarce and variable . The aim of this research was therefore

to investigate the level of grain supplementation and the method of feeding

(grazing vs. feedlot) on animal performance as a tool to overcome summer

constraints for a pasture-based beefcatt le f in ishing system .

2 . Materials and methods

A trial was developed on a commercial farm ("Cacho-Rita", CREA San Cayetano)

situated 38 0 20' S, 60 01 3' W. Ninety one Angus steers (3 1 0±2 .71 kg LW) were

randomly allocated by l iveweight, to the fol lowing feeding treatments in 5 January

( i .e 22-23 animals per treatment) : g razing pasture only (P ) , g razing pasture plus

supplements a t 1 % o f LW (S1 ) , g razing pastu re plus supplements at 2 % of LW

(S2), and supplementation at 2 % of LW plus hay in a feedlot (F ) .

A 2-year alfalfa-based pasture (Medicago sativa, Trifolium pratense, Trifolium

repens, Bromus unioloides and Dactylis glomerata) was used. The previous

September, the paddock had been cut for haymaking . I n order to achieve a s im i lar

herbage condition amongst treatments, the available pasture area was categorized

into different sectors and then proportionally al located to the three g razing

treatments. Animals were strip-grazed with a 4-d break using electric fences with

no back-graz ing , and treatments were balanced to ach ieve a s im ilar herbage

uti l isation by adjusting the grazed area. Cutting or grazing with other animals

removed the surplus of herbage from the paddock. Feedlot animals were assigned

to a pen of 30 x 40 m , with shade avai lable. Al l supplements were fed in a t rough

with 0 .5 m hd-1 provided. Water was suppl ied ad libitum in a l l the treatments.

On days 0, 1 5 , 33, 55, 82, 98 , 1 1 4 , 1 27 , 1 42 and 1 56 , animals were fasted for 1 5 h

before weigh ing . Supplement levels were readjusted at each weighing. Steers

were slaughtered when a treatment group reached an average of 400 kg LW ( 1 5 h

fasted LW) . Prior to slaughter, animals were weighed straight off pastu re and then

again after a 1 5-h fasted period, to est imate fasted losses.

Maize supplementation and feedlot as alternative for summer steer finishing systems 233

Dry matter i ntake was estimated by using the pre- and post-grazing difference

method (Frame 1 98 1 ) , by cutti ng 1 0 quad rats before and after g razing a strip with in

each t reatment at ground level and d ry matter content est imated by oven-drying

the samples for 48 h at 60 CC unt i l constant weight. Soi l contamination of samples

was avoided. Estimated dry matter intakes were corrected for net herbage

accum ulat ion rate recorded simu ltaneously on the same pasture from the rate of

change in herbage mass (by cutting 1 0 quadrats per t ime) between two times

(Hodgson et al . 1 999).

Dry matter intake for the feed lot group was est imated dai ly from the difference

between feed offered and feed residual after 24 h (measured for concentrate and

hay separately) . S im i lar procedures were carried out for estimat ing supplement dry

matter intake for the S 1 and S2 t reatments. Supplement and its residual ( if any)

were sampled weekly and pooled for dry matter estimation . Fortn ightly, a fu rther

ten herbage quadrats were cut and a sub-sample obtained to estimate herbage

qual ity. Samples were analysed for in vitro dry matter digestibi l ity (Dig) (Ti l ley &

Terry 1 962) , crude protein (CP) by M icro Kjeldhal method (A.O.A.C. 1 960) , and

neutral and acid detergent fiber (NDF and ADF) (Goering & Van Soest 1 970) .

Treatments S2 and F were fed a maize-based mixture with 1 0% of brewer's grain

plus 22 g u rea d-1 twice daily (8 :00 am and 5 :00 pm) . S 1 were fed 1 00 % of

cracked maize only once a day (8 :00) . I n F supplementat ion level and t ime were

s im i lar to S2. The composition of the d ietary components is shown in Table 1 .

Table 1 . The percentage of dry matter d igestibi l ity, crude protein , neutral detergent fiber and acid detergent fiber of the dietary components.

Feeds Dig (%) CP (%) Maize grain 84.6 8.8 Brewery g rain 75.3 24.8

N D F (%)

Hay 51 .0 9.6 72. 1 Barley hay 59.0 6.6 74.4

ADF (%) 3 .0

1 8 .3

Dig= in vitro digestib i l ity, CP=crude prote in , NDF=neutral detergent fiber and ADF=acid detergent fiber.

Weather parameters were obtained from the nearby CHEI -Barrow Experimental

Station. Liveweights and l iveweight gains and the measurements of pasture were

analysed using a repeated measures design . PROC M IXED procedure and

234 Annex A

ESTIMATE state ment for compari ng means were used (L ittel l et al . 1 998). Total

l ive-weight gai ns and shrunk l iveweights were analysed as a completely random

design. Linear reg ressions were fitted between the different studied parameters. All

analyses used SAS system (SAS/STAT 200 1 ) .

3. Resu lts and Discussion

3. 1. Weather and pasture conditions

During the study, rai nfall was 1 8 % higher and potential evapotranspi ration was 5

% lower than h istorical i nformation for the same period. Pre-grazing herbage mass

averaged 2630±1 1 2 kg OM ha-1 and as a result of how the treatments were

al located , a sim i lar herbage use efficiency between treatments (43±3 %) was

observed. The requ i red areas per grazed treatment were 1 7 , 1 4 and 7 ha for P, S 1

and S2 respective ly. A higher carrying capacity of pastures is often the result when

energy supplements are fed (Perry et al . 1 972 ; Karnezos et al . 1 994; Boo m &

Sheath 1 998; Boom & Sheath 1 999) , but the level of im provement depends on the

complex interaction between supplementation leve l , type of concentrate , sward

condit ions, animal requirements and season and their effects on su bstitution rate

(Mayne 1 988) .

Esti mates of herbage qual ity are shown i n Table 2 . Pasture quality dur ing the f irst

60 days was lowe r than for the rest of the tr ial . Summer values of 1 5 to 20 % of ep

are normally reported on pastures havi ng simi lar characteristics (Kloster et a l .

2000) .

Maize supplementation and feedlot as alternative for summer steer finishing systems 235

Table 2 . Dry matter digestibi l ity, neutral detergent fiber and crude protein of the herbage during the trial .

Days 6

32 58 98

1 20 1 40

Dig (%) 60.3 56.4 6 1 .6 64. 1 72.5 75.0

NDF (%) 52.0 56.7 50.2 55.4 47.7 42.0

Dig= in vitro digestibi lity, N DF=neutral detergent fiber and CP=crude protein .

3.2. Dry matter intake

CP (%) 1 2. 7 1 2. 2 1 2.6 1 9 . 1 22.3 24.0

Herbage intake (expressed as % LW) plus concentrate and hay intake (when

present) were 2 .9±0 . 1 8, 2 .7±0 . 1 2 , 2 .9±0 .08 and 3 . 1 ±0 .05 for P, S 1 , S2 and F

respectively. When less concentrate was fed , total dry matter intakes were more

variable , with no significant differences (p<0.05) between grazing treatments, but F

had a higher value than S1 . Associated to the readjustment method for feed

allocation , supplement fed was 0.75 , 1 .46 and 1 .46 % of LW for S 1 , S2 and F

respectively, result ing in a lower level than originally planned. These resu lts are

important for futu re economic analysis, as normally these analyses are carried out

with "planned figures" rather than recorded ones.

I n the current study, an average of 2 .7 and 4 .7 kg maize d-1 were fed for S1 and

S2 respectively. Herbage substitution rates (SR) were h igh and variable (0 . 8 1 and

1 .03 for S 1 and S2 respectively) . Lower SR might be expected taking into account

that stocking density was modified for each treatment (adjust ing the grazed area)

to obtain a simi lar herbage ut i l isation . Highly variable substitution rates (0 .43 to

1 .24) with differences between replicates were reported by Boom and Sheath

( 1 998 ) , who included maize levels of 0, 2, 4 and 6 kg d-1 i n a grazing experiment

duri ng summer. These authors attributed this variation to differences in sward

conditions between replicates (54 vs . 35 % of green leaf fractions) . I n a more

recent study (Boom & Sheath 1 999) , 1 8-month steers were supplemented during

summer with 0 , 2 and 4 kg of protein-corrected cracked maize d-1 . Herbage i ntake

was the same in the control and the 2 kg (0 SR) supplemented g roup, but it was 22

% lower at 4 kg of concentrate fed (0 .69 SR) . However, conclusions about SR and

concentrate level are l imited by the method used for dry matter intake est imation in

236 Annex A

the present trial and by visual estimation in the previously quoted trials (Boom &

Sheath 1 998 ; Boom & Sheath 1 999) . Frequently when supplements are fed , the

control group of animals achieves a lower LWG (thus becoming l ighter than

supplemented groups during the tria l ) . This i ntroduces a bias if herbage i ntake as

recorded in the control is used for SR estimat ion. Although at present rel iable

techniques are available to record i ndividual herbage and supplement i ntake us ing

n-alkanes (Dove & Mayes 1 996) or other alternatives such as ytterbium (Yb) (Hoist

et al . 1 994) , a more precise SR est imation was beyond the objectives of th is pilot

study.

3. 3. Animal live weight, live weight gain and fasted L W loss

Changes in fu l l l iveweight during the trial are shown i n Figure 1 . Sign ificant

differences among treatments for total l iveweight gain over the trial were detected

(P<0.05) , being higher in S2 and F than in P and S 1 (Table 3 ) . The number of days

required to reach 400 kg LW were 1 54, 1 1 9 , 98 and 93 for P, S 1 , S2 and F ,

respectively. Only the S2 and F treatments avoided overlapping with the weaners

entering the system in the autumn . When fou r levels of maize supplementation

(zero , 33, 66 and 1 00 % of daily intake as concentrate) were fed to g razing steers

of a simi lar l ive weight to the present trial during 1 35 days of spring-summer ( Perry

et al . 1 972) , a l i near i ncrease of l ive weight gain was reported (0.4, 0 .8 , 1 . 1 Y 1 .4

kg d·1 respectively).

Table 3. Total liveweight gain and shrunk liveweight losses of the different treatments .

Parameter P S1 S2 F #PSE In itial LW (kg) 31 1 309 3 1 1 3 1 0 1 .4 Total LWG (kg dO' ) 0.62a 0 .72a 0.90b 0.90b 0.027 1 5-h shrunk LW loss (% LW) 5 . 1 b 4 .7b 2.6a 3 .3a 0 .207 P= pasture only, S1 = pasture plus supplement at 1 % LW, S2= pasture plus supplement at 2% LW, F= supplement 2% LW plus hay in a feed lot, #PSE=pooled standard error and different letters within a row indicate significant differences (P<0.05) .

Maize supplementation and feedlot as alternative for summer steer finishing systems 237

<4 20

<400

380 -Ol 6 360

E Ol Q) 340 ?; �

::i 3 20

3 00

-20 o 20

.. �, ' �, ':..L.. . . . . . • .. .. p

- e- S1 - · 6· S2

y F

4 0 60 8 0 1 00 1 20 1 40 1 60 Trial day

Figure 1 . Average animal liveweights (±standard errors) of the treatments throughout the trial. p= pasture only , S1 = pasture plus supplement at 1 % LW, S2= pasture plus supplement at 2% LW and F= supplement 2% LW plus hay in a feed lot.

The most direct explanation for the improvement i n animal performance that is

usual ly observed when grain is fed to g razing animals, is l i kely to be associated

with the extra energy provided (Horn & McCol lum 1 987; Mayne 1 988) .

Additionally, a possible explanation for improved LWG i n supplemented animals is

better use of excess ruminal ammonia N ( lower plasma u rea n itrogen) and a h igher

m icrobial protein flow to the gut, as has been associated with the improved LWG of

wether lambs g razing high protein herbage and fed with maize grain (Karnezos et

a l . 1 994) . However, some authors have reported LWG improvement in steers by

supplementation in high qual ity pastures without a s ign ificant improvement in net

rum inal m icrobial protein synthesis (El izalde et al . 1 998) . Crude protein levels were

relatively low (Table 2) on days 0 to 58 i n comparison with those observed in other

studies (Kloster et al . 2000) , and although diet select ion m ight improve these

values, LWG improvement in this trial is probably not associated with a better use

of protein in th is period . However, i t could be the case after day 58 of the trial ,

where level of protein (Table 2) and LWG response to maize feeding i ncreased

( Figure 1 ) . Changes in selectivity and a different SR between maize grain levels

have been mentioned as contributors to the non- l inear effect of supplements on

l ive weight gain (Petty et al . 1 998) . The extra l iveweight gain due to supplement

fitted to a l inear regression was : Extra LWG = 0.60±0 .04+ 0.06 ±0.0 1 S; R2 = 0 .97

238 Annex A

P<0.01 , where S is the supplement fed in kg d-1 . H igher extra LWG associated

with supplements (0. 1 7 kg d-1 ) than the present resu lts (0.06 kg d-1 ) were obtained

when 1 8-month steers at g razing were fed maize levels of 0 , 2 , 4 and 6 kg d-1

during summer (Boom & Sheath 1 998) . One factor that might contribute to th is

d ifference (further than the divergence in supplementat ion level ) , is the

performance of the control group (0%) , and so the potential response to

supplementation on this condition. Control LWG was zero in the results of Boom

and Sheath ( 1 998) , and was 0.62 kg d-1 in the present study (Table 3 ) .

A trend to improve the supplement conversion from S 1 to S2 was present. From

average supplement i ntakes of 2 . 7 and 4 .7 kg d-1 , extra LWG o f 37 and 60 g d-1

were found for S1 and S2 respectively. Sim i lar ly, when wether lambs grazing

i rrigated alfalfa were fed with 0 , 0 . 1 2 and 0 .25 kg of cracked maize per day

( Karnezos et al. 1 994) , feed conversion tended to improve with maize leve l .

Pasture only and S 1 treatments resu lted in a h igher loss of gastrointestinal content

on fast ing (p<0.05) than S2 and F (Table 3). S im i lar results were reported when

wether lambs grazing i rr igated alfalfa were supplemented with 0, 1 23 and 247 g d-1

of cracked maize (Karnezos et al . 1 994) and loss of gastrointestinal content

i ncreased quadratical ly ( p<0.01 ) with maize level fed . Other authors have obtained

in cattle a trend to a lower gastrointestinal f i l l with sorghum grain (Taylor &

Gulbransen 1 990) and maize grain (Reardon 1 975) . This emphasizes that the

combination of LWG per hectare , associated with a lower gut f i l l should be

considered when the economic value of supplementat ion is evaluated .

Conclusion and impl ications

Feeding maize grain u nder grazing and feedlot condit ions increased l iveweight

gain and decreased fasted l iveweight losses . Dry conditions during the summer

make i t difficu lt to ach ieve a satisfactory and predictable animal l ive weight gai n .

This led to a delay in an imal sales and to nutrit ional requ i rement for f in ish ing

animals overlapping with weaners entering the system in the autumn . Overlapping

with the entry of new weaners was avoided on ly in the treatment receiving the

Maize supplementation and feedlot as alternative for summer steer finishing systems 239

h ighest level of grain at grazing (S2) and under feed lot conditions. Th is impl ies that

a high level of supplement ( 1 .4 % l iveweight) might be needed to manage the

predictabil ity of the system. Variat ion in the qual ity and quantity of forage and in

concentrate level might be involved in the response of grazing animals to grai n

feeding (Dixon & Stockdale 1 999) . Thus, detailed complementary stud ies would be

needed to understand the trade-off between concentrate input and sward

condit ions, in order to improve the predictabi l ity of grain feeding responses.

From this pi lot study, although resu lts should be confi rmed, it is concluded that a

h igh level of maize feeding may be needed during summer to improve the

predictabi l ity of fin ish ing during the summer season. The feasibi l ity of the technique

should be assessed both from production and economic of the system perspective.

References

A.O.A .C . 1 960 : Official Methods of Analysis. 9th ed. AOAC. Wash ington :957 p .

Boom C. J . ; Sheath G . W. 1 998: Grain supplementation of f in ish ing beef catt le.

Proceedings of the New Zealand Society of Animal Production 58:239-242 .

Boom C. J . ; Sheath G. W. 1 999: Tactical supplementation of beef f in ish ing catt le.

Proceedings of the New Zealand Society of Animal Production 59: 1 62- 1 65.

Dixon R . M . ; Stockdale R . C . 1 999 : Associative effects between forages and

grains: consequences for feed uti l isation . Australian Journal of Agricultural

Research 50:757-753.

Dove H . ; Mayes R. W. 1 996: Plant wax components : a new approach to est imat ing

intake and diet composition i n hervibores : a review. Journal of Nutrition

126: 1 3-26.

E l izalde J. C. ; Cremin J. D.; Fau lkner D. B . ; Merchen N. R. 1 998 : Performance and

digestion by steers g razing tal l fescue and supplemented with energy and

prote in . Journal of Animal Science 76: 1 69 1 - 1 70 1 .

Frame J . 1 98 1 : Herbage Mass. In: Hodgson , J . ; Baker R .D ; Davis, A , ; Laidlaw,

240 Annex A

A.S. ; Leaver, J .D. Eds . Sward Measurement Handbook. British Grassland

Society : Berkshire , UK. 35-70.

Garcia S. C . ; Santini F. ; Castano G. 1 998 : Producci6n de carne bajo pastoreo :

Alternativas de intensificaci6n. Material didactico Nro 1 4 . INTA, Cerbas. :52

p .

Goer ing H. K. ; Van Soest P . 1 970 : Forage fiber analysis (apparatus reagents,

procedures and some applicat ions) . Agriculture Handbook Nr 379, USDA.

Washington, DC. 20 p.

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Handbooks in agriculture, Longman Scientific and Technical co-publ ished

with John Wiley. New York: 203 p .

Hodgson J . ; Matthews P . N. P . ; Matthew C. ; Lucas R. J . 1 999: Pastu re

measurements. In: White , J .& Hodgson, J . Eds. New Zealand Pasture and

Crop Science. Oxford University press . 59-66.

Hoist P. J . ; Curtis K. M. S . ; Hal l D. G. 1 994: Methods of feeding grain supplements

and measuring their intake by adu lt sheep. Australian Journal of

Experimental Agriculture 34:345-348.

Horn G . W. ; McCol lum F. T. 1 987: Energy supplementation of grazing ruminants,

Ju ly 23-24. Proceedings of the Grazing Livestock Nutrition Conference : 1 25-

1 36 .

Karnezos T . P . ; Matches A. G . ; P reston R . L . ; Brown C. P . 1 994 : Corn

supplementation of lambs grazing alfalfa. Journal of Animal Science 72:783-

789 .

Kloster A. M . ; Latimori N . J . ; Amigone M . A. 2000 : Evaluaci6n de dos sistemas de

pastoreo rotativo a dos niveles de asignaci6n de forraje en una pastu ra de

alfalfa y gram fneas. Revista Argentina De Producci6n AnimaI 20: 1 87- 1 98.

Lake R. P . ; H i ldebrand R . L. ; Clanton D. C. ; Jones L. E . 1 974 : L imited energy

supplementation of yearling steers grazing irr igated pasture and subsequent

feedlot performance. Journal of Animal Science 39:5:827-833 .

Maize supplementation and feedlot as alternative for summer steer finishing systems 24 1

Littel l R . C . ; Henry P. R . ; Ammerman C. B . 1 998 : Statistical analysis of repeated

measures data using SAS procedu res. Journal of Animal Science 76: 1 2 1 6-

1 23 1 .

Mayne C . S. 1 988: Effects of supplementation on the performance of both growing

and lactating cattle at pasture. In: Frame , J . Ed. Efficient Beef Production

from Grass. Occasional Symposium . No. 25 , British Grassland Society,

Berkshire , UK . 55-7 1 .

Perry 1. W. ; Huber D . A. ; Mott G . 0.; Rhykerd C . L. ; Taylor R. W. 1 972: Effect of

level of pasture supplementation on pastu re, drylot and total performance of

beef catt le. 1 1 . Spring plus summer pastu re. Journal of Animal Science

34:647-652.

Petty S. R . ; Poppi D. P . ; Triglone T. 1 998 : Effect of maize supplementation ,

seasonal temperature and humidity on the l iveweight gain of steers grazing

i rrigated Leucaena leucocephala/Oigitaria eriantha pastures in north -west

Austral ia . Journal of Agricultural Science, Cambridge 130:95- 1 05 .

Prache S. ; Bechet G . ; Theriez M . 1 990 : Effects o f concentrate supplementat ion

and herbage al lowance on the performance of grazing suckl ing lambs .

Grass and Forage Science 45:423-429.

Reardon T. F. 1 975: Summer feeding of beef catt le . Proceedings of the Ruakura

Farmers ' Conference 27 : 1 4- 1 8.

Rearte D. 1 998 : Beef cattle product ion and meat qual ity on grazing system i n

temperate regions. Revista Argentina De Producci6n Animal 18: 1 29- 1 42.

SAS/STA1. 200 1 : Release. 8 SAS. Inst. Inc. , Cary, NC.

Stockdale R . C . ; Callaghan A . ; Trigg 1. E . 1 987: Feeding high energy supplements

to pastu re-fed dairy cows. Australian Journal of Agricultural Research

38:927-940.

Stockdale R. C . ; Trigg 1. E. 1 989: Effect of pasture feeding levels on the response

of lactating dairy cows to high energy supplements. Australian Journal of

Experimental Agriculture 29:605-6 1 1 .

242 Annex A

Taylor K. M . ; Gulbransen B. 1 990: Feeding grain for profitable fin ish ing of g razing

steers . Proceedings of the Australian Society of Animal Production 18:388-

39 1 .

Ti l ley J . M . A. ; Terry R. A . 1 962: two-stage technique for the i n vitro digest ion of

forage crops. Journal of the British Grassland Society 18: 1 04- 1 1 1 .

Annex B

Seasonal changes of herbage q ual ity within a New

Zealand beef catt le fi n is h i ng pastu re

244 Annex 8

Abstract

To estimate seasonal variation in herbage qual ity from a Friesian bull beef

production system, fifty hand-plucked herbage samples were taken fortnightly from

a paddock about to be grazed at the Tuapaka bul l beef un it , Massey Un iversity ,

between November 1 998 and March 2002 . The pastu res were perennial ryegrass

and wh ite clover. Herbage mass (kg DM ha-1 ) was estimated using a cal ibrated

rising plate meter. Herbage samples were assessed for crude prote in ( Protein ) ,

l ipid ( Lip) , ash (ASH) , acid detergent f iber (ADF), neutral detergent fiber (NDF) ,

non-structural carbohydrates (NSC) , and metabolisable energy content (ME) by

N IRS. A correlation analysis between variables was performed and an analysis of

variance was carried out by season us ing year as a block. P rotein and ME were

tested for fit to alternative l inear equations. Additional ly, 8ayesian smooth ing

analysis was applied to the complete data sets of protein and ME. F iber variables

(NDF and ADF) had the strongest relationship between themselves and with other

variables , and were negatively related to NSC, ME and prote in . The content of non­

structural carbohydrates was positively associated with herbage protein and ME .

On average, herbage nutrient qual ity was maintained at a high standard though

there was a sign ificant decl ine i n protein in summer and ME in summer and

autumn. Non-structural carbohydrates had the highest variabil ity (31 % coeffic ient

of variat ion) , fol lowed by the fiber variables ( 1 5 and 1 6 % for ADF and NDF

respectively) . Winter gave the highest levels o f ASH , OMD and M E, but lowest

levels of NSC. The spring period gave the highest levels of herbage mass, protein ,

NSC, and M E . The l ipid levels were higher i n autumn and ADF was highest during

winter. There were no differences between seasons for NDF. I n the smooth ing

analysis , both protein and ME showed a seasonal behaviou r with a tendency for

herbage protein to peak before ME in spring . Both protein and ME adjusted

sign ificantly to 2nd order polynomials (R2 of 0 .88 and 0 .66 , respectively). From

monthly averaged ME values, l iveweight gains ( LWG) of g razing 350-kg l iveweight

Friesian bul ls were modelled at different levels of dry matter i ntake. The computed

l iveweight gains ach ieved lowest and highest values in March and September ,

respectively. Impl ications to animal performance are discussed , with part icular

reference to the inf luence of h igh herbage protein levels on protein and energy

uti l isation .

Seasonal changes of herbage quality - New Zealand 245

Keywords: Herbage qual ity; seasonal change; beef catt le; protei n ; energy

1. Introduction

New Zealand beef cattle production fin ish ing systems evolved with high stocking

rates of 5-7 hd ha-1 to increase production per hectare through maximum herbage

ut i l isat ion (Cosgrove et al. 2003) _ Recent changes in market characteristics

(carcass grading, prices and the value of t iming of supply) have produced a sh ift to

a lower stocking rate and improving per an imal yield with l itt le decrease in per

hectare yield (McRae 2003) . In this new context, herbage qual ity is l ikely to be the

major constraint for ach ieving high animal performance ( Lambert & Litherland

2000 ) .

Although earl ier studies in New Zealand have focused on seasonal changes of

herbage qual ity (Metson & Saunders 1 978), herbage quantity has traditionally

received more attention . Consequently, herbage qual ity has not been appl ied

systematically i n catt le feed management (Parker & Ulyatt 1 996) . More recently,

some dai ry cattle studies have concentrated on herbage qual ity (Mol ler 1 997;

Cosgrove et al . 1 998) and systematic characterisation of herbage qual ity for

different sheep and beef cattle farms in four regions of New Zealand has been

undertaken ( Litherland et al . 2002) .

Herbage qual ity varies seasonally and is affected by many factors such as g razing

management and fert i l izer pol icy (Saul et al . 1 999; Schlegel et a l . 2000 ; Frame et

a l . 2002) . Additional ly , interpretation of herbage qual ity in terms of animal

performance must be done carefu l ly. The forag ing animal can exploit sward spatial

and temporal heterogeneity to consume a diet better than the herbage on offer to

meet its requ i rements (Coleman & Barth 1 973 ; Co hen & Garden 1 983; Sch legel

et al. 2000) . Litherland et al . (2000) mon itored the ME and CP content of both the

offered pastu re and hand plucked samples s imu lating the pasture selected by

animals in fou r seasons and four reg ions of New Zealand . From their data, a mean

improvement of 6 % in ME in hand plucked samples can be est imated . The

magnitude of th is effect has been explained as dependent on grazing management

246 Annex B

(Hodgson 1 990) , which also affects herbage qual ity (Saul et a l . 1 999 ; Frame et a l .

2002) .

In order to define the variation caused by the year effect u nder a com mon grazing

management and to min im ize the difference between pasture on offer and that

selected by the animals , an experiment was designed to describe the seasonal

variat ion on herbage quality of hand-plucked pre-grazed herbage samples over

three years from a Friesian bul l beef production system .

2. Materials and methods

Fortn ightly between November 1 998 and March 2002 , herbage samples were

taken from the Massey University Tuapaka bu l l beef u nit, located 1 0 km east of

Palmerston North . This 1 1 1 ha teaching and research farm is stocked with 2 .5

Friesian bul ls per hectare purchased as weaners ( 1 00 kg l iveweight i n November)

for slaughter at a target l iveweight of 500 kg at approximately 1 8 months of age

(McRae 1 988 ; Coutinho et al . 1 998) . The pastures were perennial ryegrass and

white clover based , and the area sampled was divided i nto 1 0 paddocks of

approximately 4 ha. each. Fortnightly, fifty hand-plucked samples s imu lating "bites"

(Mol ler 1 997) were taken from a paddock ready to be grazed. Herbage mass ( kg

OM ha-1 ) was est imated with a r is ing plate meter using a local equat ion (Cout inho

1 998) obtained through a double sampl ing procedure (Earle & McGowan 1 979) .

Herbage samples were transported with in 30 minutes of sampl ing to t he laboratory

and oven dried for 72 h at 60 °C to a constant weight. Subsequently, they were

assessed by Near I nfra-Red Spectroscopy (N IRS) (Corson et al . 1 999) for the

fol lowing nutrients (brackets indicate the abbreviations used in the text hereafter) :

crude protein (prote in ) , l ipid (Lip) , ash (ASH) , acid detergent fiber (ADF) , neutral

detergent fiber (NDF) , non-structural carbohydrates (NSC) , organic matter

digestib i l ity (OMD) and metabol isable energy content (ME) . Although both OMD

and M E resu lts are presented for reference, discussion is centred on ME .

Addit ional ly , protein to ME ratio was computed and analysed. Analysis of variance

was carried out by season using year as a block. Monthly-averaged protein values

and ME were analysed for curve fitt ing. Al l analyses used the SAS stat istical

analysis system (SAS/STAT 200 1 ) . Bayesian smooth ing analysis was appl ied to

Seasonal changes of herbage quality - New Zealand 247

the complete data sets of ME and protein using Flexi 3 . 1 (Wheeler & Upsdel l

2003) , to reveal possible underlyi ng seasonal trends. This smooth ing technique

employs a variance components model to f it a constant term describing the general

level of the variable and a corre lated random term to describe departures of the

curve from this constant. A cycle of 365 days with f luctuating covariance (the cycle

is not forced to repeat exactly) was used to model the data as:

Total fit (Protein , M E) = Seasonal component + long-term component + error Eqn. 1

Graphs are presented with the confidence i ntervals (83 % as default of Flexi) for

the fitted curve (Wheeler & Upsdell 2003) .

To obtain a reference of the potentially ach ievable l iveweight gains with the

observed pasture qual ities , the l iveweight gain (LWG) of a g razing 350-kg

l iveweight Friesian bu l l was model led using the performance model of Freer et al.

( 1 997) , with the monthly ME averages over a range of levels of OM intake ( 1 .7 to

3.2 % LW) . The pasture used for nutrit ional calcu lations was assumed to be a

ryegrass pasture with a clover percentage of 30 % located at a latitude 40· S.

3. Resu lts

Fiber variables (NOF and AOF) had the most sign ificant relationships between

themselves and with other variables. They were negatively correlated to NSC, M E

and protein (Table 1 ) . The non-structural carbohydrates content was positively

associated with herbage ME and protein . P rote in was negatively related to herbage

mass and ASH , and Lip positively correlated with NOF. Overal l means, coefficients

of variation (CV) and herbage nutritive values by season are presented in Table 2 .

On average, herbage nutrient qual ity was maintained at a h igh standard , however

there was a decl ine in summer for protein and in summer and autumn for M E

(Table 2 and Figure 1 ) . Winter gave the h ighest levels of ASH , AOF and M E, but

the lowest level of NSC. The spring period gave the highest levels of herbage

mass, prote in , NSC, and ME . The Lipid levels were higher in autumn . There were

no differences between seasons for NOF. Non-structural carbohyd rates had the

h ighest variat ion (3 1 % coefficient of variat ion) , fol lowed by fiber variables ( 1 5 and

248 Annex B

1 6 % for ADF and NDF respectively) .

Metabol isable energy content peaked around August, and reached its lowest value

in January ( Figure 1 .a) , with a trend of herbage protein to peak before ME ( Figure

1 .a and 1 .b) . Both M E and protein adjusted significantly (p<O.00 1 ) to a 2nd order

polynomial :

ME = 9 .7 + 0 .5 1 8X - 0.034X2 R2=0.88 P>0.01

Protein = 22.8+ 1 .75X - 0 . 1 58X2 R2=0.66 P>0.01

Eqn . 2

Eqn. 3

Where X is month of the year ( 1 = January) . Maximum values for protein and M E

occurred at months 5.6 and 7.6 i n Eqns. 2 and 3 , respectively. It is worth noting

that the above equations are not continuous over years , un l ike the seasonal

smoothing ( Figure 1 ) .

Table 1 . Overall correlation analysis of the indicated parameters in pre-g raz ing herbage samples (n=74).

HM Protein Li� ASH AOF N OF NSC M E Herbage mass (HM) 1 Protein -0.32 1 Lip -0. 1 4 0.27 1 ASH -0.07 0. 1 7 -0.30 1 AOF 0.08 -0.33 -0 . 1 0 0.44 1 NOF 0 . 1 2 -0.27 0.27 0.01 0.80 1 NSC -0.05 -0. 1 4 -0. 1 3 -0.22 -0.70 -0.66 1 ME -0.03 0.44 0.03 0.27 -0.56 -0.59 0.55 1 Sward mass = Kg OM ha·

', Protein = crude protein (% OM) , Lip = Lipids (% OM) ,

ASH = ash (% OM) , AOF = Acid detergent fiber (% OM) , N O F = Neutral detergent fiber (% DM) , NSC = non-structural carbohydrates (% OM) , M E= metabolisable energy MJ kg DM·' and sign ificant coefficients in bold (P<0.05) .

Seasonal changes of herbage quality - New Zealand 249

Table 2 . Annual means and standard deviations of the overall dataset (n=74) and seasonal variation of means of the different herbage qual ity parameters in pre-grazing herbage samples.

Mean *CV Autu mn Winter Spring Sum mer PSE

Protein 25.8 4.8 26.4 26.7 c 26.9 c 23. 1 a 0.80

Lip 4 . 1 0 .5 4.2 b 4. 1 ab 4 . 1 ab 3.9 a 0. 1 0

Ash 1 2 .0 0 .4 1 1 .2 ab 1 3. 2 c 1 2.2 a 1 1 .3 b 0.29

AOF 25.7 1 5.2 24 .8 a 27.5 b

24.7 a 25.9 ab 0.88

N O F 39.9 1 6.4 40.4 40.8 38.8 39.8 1 .24

NSC 8 .3 31 .8 7.9 ab

7.S a 9.3 b

8.4 ab 0.65

M E 1 1 .3 2.7 1 0.9 a 1 1 .6 b

1 1 .8 b

1 0 .9 a 0. 1 9

Protein :ME 22.7 1 3 .0 24. 1 b

23. 1 b 22.7 b 21 .3 a 0.56

Herbage mass 2250.0 6.2 2 1 60 a 2250 a 2450 b 21 40 a 69.88

*CV = coefficient of variation, PSE pooled seasonal standard error, Protein = crude protein (% OM), Lip = Lipids (% OM) , ASH = ash (% OM) , AOF = Acid detergent fiber (% OM) , NOF = Neutral detergent fiber (% OM) , NSC = non-structural carbohydrates (% OM) , ME = metabolisable energy kg OM·' , Protein :ME = crude protein :metabol isable energy ratio, Herbage mass = kg OM ha·' , means with different letters within rows indicates significant differences between seasons (P<0.05) .

The computed l iveweight gains achieved us ing the d ifferent monthly M E values of

the pasture are s hown in Figure 2 . The lowest and the h ighest l iveweight gains

were in March and August-September, respectively. Herbage intake levels fol lowed

the same month ly trend throughout the year, with l iveweig ht gains i ncreasing as

feed intake (% of l iveweig ht) i ncreased , reach ing a value above 1 .6 kg LW d-1 o r

h igher between August and October, for t he h ighest OM intake leve l .

a )

100 200 300 _ day

D J F M A M J J A S 0 N M:rlth

l5 b)

lO

� 0 � � 25 Q. U

20

o S 0

0 0

0 0 0 0

1 00

D J F M

0 0

0 0

0 0 0 0

0 0 0 0

0

200 300 day

A M J J A S 0 N M:>nth

Figure 1 . Seasonal change in herbage metabol isable energy (a) and crude protein (b) during the sampling period (November 1 998-March 2002) . Plotted confidence bands represent 83% probabi l ity.

250 Annex B

1 .6

1 .4

� 1 .2

U Cl 1 .0 6 C) 0.8 3: .....J 0.6

0.4

0.2 Herbage intake 0.0 (%LW)

Figure 2. Energy-based pred icted liveweight gains of a 350-kg LW Friesian bull fed at d ifferent levels of herbage i ntake during the year based on Freer et al. 1 997).

The pastu re used for nutritional calculations was assumed to be a perennial ryegrass-white clover pasture with a clover percentage of 30 % located at a latitude 40' S.

4. Discussion

4. 1. General seasonal and year variation

Herbage quality from hand plucked herbage samples showed a clear seasonal

variation , although i t was maintained to a high standard throughout the seasons.

There was a consistent pattern between years , as shown by the seasonal

smooth ings ( Figure 1 ) and a cons istent total f it adjustment over the sampl ing

period ( Figure 3) . The i nclus ion of long term trends in the analysis , al lows a better

definit ion of the seasonal variation (Wheeler & Upsdell 2003) , however as they

have l ittle biological meaning they are not presented . Typical ly the c l imate variation

between years alters herbage qual ity where pastures are harvested at a s imi lar

stage of g rowth (Buxton & Fales 1 994). However, the g razing management u sed

on the bul l beef uni t m ight contribute to the consistency achieved between years .

I ntensive g razing m ay be the key element to moderate seasonal changes of

nutritional value of pastures (Clark 1 995) . In a heavi ly stocked rotat ional g razed

bul l beef system (7 .4 hd ha-1 ) with two alternative managements during summer

Seasonal changes of herbage quality - New Zealand 25 1

and replicated over two years (Clark & Broughham 1 979) , protein and ME levels

were consistently high throughout all seasons (with min imum values of 1 1 MJ ME

kg OM-1 and 1 8.3 % o f protein recorded during summer) . I ntensive ut i l isation o f the

sward contributes to delay maturation and the consequent herbage qual ity decl ine

( Hodgson & Brookes 1 999) . As an indicat ion of l evel of maturity of the sward , pre­

g razing herbage mass (mean 2250 kg OM ha-1 ) in this study was low when

compared with the value programmed by Coutinho et a l . ( 1 998) in the same bul l

beef exper imental u nit (2750 kg O M ha-\ However, it was higher than the 1 907 kg

OM ha-1 reported by Litherland et a l . (2002) from monitored beef and sheep farms

from four reg ions.

a) b) 3 5

0 0 0 1 3

(1) 0 � 30 0 Cl 1 2 0>

'" �

� w Cl :; 1 1 t25 � a. w 0 :; 1 0

0 2 0 0 CP 0

0 0 0 0 0 0 0 0

200 400 800 1 ,000 200 400 600 800 1 ,000 T ri al day T ri al day

Figure 3. Total smoothing herbage metabolisable energy (a) and crude prote in (b) during the sampling period (November 1 998-March 2002).

P lotted confidence bands represent 83% probabi l ity.

4.2. Metabolisable energy and non-structural carbohydrates

Metabol isab le energy had a very low seasonal variat ion (CV 2 .7%) . The ratio of

summer to spring ME can be used as a measure of seasonal change of th is

variable and value of 0 .92 was estimated from Clark & Brougham ( 1 979) i n a bu l l

beef system sampled over two years, a s imi lar value estimated to that i n present

trial. Both experiments compare favourably with the estimated value of 0 .87 for

summer:spring ME ratio from four regions of New Zealand beef cattle and sheep

farms ( Litherland et al. 2002) . The lowest levels of ME were found in summer in

each year, consistent with the values reported elsewhere (Clark & Brougham 1 979 ;

Mol ler 1 997) , and the decl ine extended onto autumn s imi lar to that found by

252 Annex B

Litherland et al . (2000) and Moller et al . ( 1 996) . I n dry weather condit ions, Boom

and Sheath ( 1 999) reported a low herbage qual ity (8.7 MJ ME kg DM-1 ) . During the

summer months, plant m aturation decreases the leaf to stem ratio , decreases the

qual ity of the stems and i ncreases senescence of g reen material ( Nelson & Moser

1 994) . Likewise an autum n min imum value of ME was obtained when sampl ing

n ineteen farms from fou r different reg ions of New Zealand , coincid ing with an

increase of dead material content in the sward (Litherland et al. 2002) . I n the

current study, samples were hand-plucked and components were not assessed ,

but a possible increase i n dead contribut ion is l ikely to explain the M E decl ine in

th is season .

Non-structural carbohydrate content was h ighest in spring , s im i lar to that report of

Mol ler ( 1 997) . However, th is author obtained values of 1 2 % for sum mer and 1 0 .2

% for the overal l year value, wh ich are h igher than the present study (9 .3 and 8 .3

% for summer and the overall year, respectively) . Non-structu ral carbohydrates

are h ighly labi le and sensitive to the prevai l ing weather condit ions (Moore &

Hatfield 1 994) , and it was the most variable parameter recorded i n a study of the

yearly seasonal variat ion of herbage q ual ity in the lower North Island (Metson &

Saunders 1 978) . I n this study the variabil ity was 3 1 % CV.

4. 3. Protein

Herbage protein levels decl ined during summer, but they were cons istently h igh

th roughout the year, which i s in agreement with the report of Clark and Brougham

( 1 979) and Metson and Saunders ( 1 978) . These authors described a sharp

decrease in summer, and a more extended period of h igh n itrogen ( i .e . crude

protein) from late autum n to spring. I n spite of the sum mer decl ine of p rotein

previously mentioned, values reported h ere for th is season were h igher compared

with the 1 4.6 % (average between treatment replicates) reported by Boo m &

Sheath ( 1 998) and the 1 0 .8 % found by Boom & Sheath ( 1 999) u nder d ry weather

conditions. Higher average pre-grazi ng herbage masses in both q uoted

experiments (4250 for Boom & Sheath ( 1 998) and 3350 kg OM ha-1 for Boom &

Sheath ( 1 999) respectively) than in th is study may contribute to the differences.

The values clearly exceeded the recom mended protein content of herbage

requi red for very young growing an imals , i .e . 1 5- 1 8% crude prote in ( Hodgson &

Seasonal changes of herbage quality - New Zealand 253

Brookes 1 999) , and th is f inding supports assumpt ion of cons idering the prote in

level as non- l im it ing to animal response in the the model . The an imal performance

model used (Freer et al. 1 997) described a protein deficit penalty which reduces

potential herbage i ntake as a function of the ratio between the total i ntake of rumen

degradable protein (RDP I ) and the requ i rement for rumen degradable protein

(RDPR) , but has no factor when RDPR is i n excess of that requ i red by the an i mal .

As the animal grows at a faster rate , protein requirements increased more rapidly

than energy demand ( Preston 1 966) . However, cons idering that the protei n level

averaged 25.8 % i n the present study, i t is un l ikely that animals g rowing at a higher

rate are able to ut i l ise this amount of protein in the diet. The energy requ i red to

e l im inate any excess of blood ammonia could be considerable, and has been

estimated by Moore and Varga ( 1 996) in dairy cattle as 1 .3 MJ M E for each 1 00 g

prote in in excess.

The correlat ions between variables conf irm previously reported relat ionsh ips

between the main qual ity parameters (Mol ler 1 997) . Prote in and ME gave a s im ilar

seasonal pattern ( r=0 .44, Table 2), although protein had a clear trend to peak

before M E content (Figure 1 ) , and the fitted curves in Eqns . 2 and 3. Although both

protein and M E decl ined significantly duri ng summer, the former decreased

relatively more than ME , as shown by the corresponding C P : M E ratio. To balance

these nutr ients , supplementing a h igh starch feed has been suggested to

synchronize degradable crude protein and fermentable energy in the rumen in

pastu re-fed dai ry cows (Kolver 2002) and in beef cattle graz ing in h igh qual ity

temperate pastures (E l izalde & Sant in i 1 992; El izalde et al . 1 998) . Feeding h igh

starch feeds is not a common practice in the New Zealand beef cattle industry ,

although some research studies have reported on its use ( Reardon 1 975; Boom &

Sheath 1 998; Boom & Sheath 1 999) . A ful l evaluat ion of the potential benefits of

concentrates in New Zealand beef cattle should be considered as a mean to a

better use of protein levels found in pasture. Other authors have stated that more

research shou ld be carried out both i n dairy (Mol ler 1 997) and beef and sheep

product ion ( Litherland et al. 2002) to be able to identify adequately the potential

effects of protein and NSC contents on animal performance levels .

254 Annex B

4.4. Modelling of animal performance

The requ i rement model used in th is study has been exten sively val idated and its

prediction capabi l ity demonstrated under g razing condit ions ( Donnel ly et al. 2002 ;

Cohen et al. 2003) . Model l ing us ing energy content of the herbage predicted

maximum LWG values in spring that were s im i lar to those reviewed by M atthews et

al . ( 1 999) in a i ntensive bul l beef system with an imals eat ing to appetite ( 1 .5-2 .0 kg

LW d-1 ) . As the same animal weight was used to model the performance in each

season , differences in LWG over the seasons at the same level of i ntake were

determi ned by the combination of the seasonal change i n M E and the conversion

efficiency of metabol isable energy to LWG (Kg) , which is a seasonal ly affected

factor with the lowest values in autumn and winter ( Freer et al . 1 997). Therefore ,

although summer and autumn herbage had a s im i lar M E content, lowest LWG was

predicted during March . Potential LWG was restricted duri ng summer and autum n

in a study reported for a warmer location (Waikato) where d iet M E values between

9-9 . 1 MJ kg DM-1 were obtained during these seasons ( Litherland et al . 2002) .

Although with h igher M E values for the same seasons, LWG predicted wi th i ntakes

of 3.2 % LW in the current study gave a lowest value of 1 . 1 5 kg LW d-1 in March ,

wh ich indicates that observed M E are l ikely to be constra in ing potential LWG . I n

add ition , i f any addit ional constraint on herbage i ntake may be operat ing under th is

circumstance (Al ien 1 996; Hodgson & Brookes 1 999) , a lower animal performance

may be obtained . In this s ituat ion , i ndependence between qual ity and intake

assumed in th is s imple model l ing exercise can be questionable.

5. Conclusions and i mpl ications

This experiment provides an i ndication of the magnitude and consistency of

seasonal variation of the main n utritional variables i n a New Zealand beef cattle

fi n ish ing pasture. The results show that u nder adequate grazing m anagement

herbage nutritive value can be of a high standard throug hout the year. However

two of the most i mportant nutrients for an i mal performance (M E and protein )

consistently decl i ned during summer and i n the case of M E , th is was extended into

autumn . Model l ing the influence of herbage energy on an imal performance

indicated that dur ing summer and autumn M E values m ight be constrain i ng a

Seasonal changes of herbage quality - New Zealand 255

maxim u m LWG , whereas spri ng predict ions are in agreement with l iveweight gains

reported from field studies . Protein was considered as non- l im it ing in the model ,

but i t i s clear that i t was in excess t o that requ i red by t h e animals. A more complex

model including herbage intake prediction and a penalty for excess protei n in the

diet m ight provide some extra u nderstandi ng . More research into feed prote in

fractio ns is needed to quantify the impl icat ions of the h igh level of protei n reported

here on rumen metabolism and an imal nutrit ion requ i rements.

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