SIMULATION OF TIMOTHY NUTRITIVE VALUE : A COMPARISON OF THREE PROCESS-BASED MODELS

Persson, T.1, Höglind, M.1, Van Oijen, M.2, Korhonen P.3, Palosuo, T.3, Jégo, G.4, Virkajärvi, P.3, Bélanger, G.4, Gustavsson, A.-M.5

1 Norwegian Institute of Bioeconomy Research (NIBIO), Særheim, Norway

2 Centre for Ecology and Hydrology, UK

3 Natural Resources Institute Finland (Luke), Helsinki, Finland

4 Agriculture and Agri-Food Canada (AAFC), Québec, Canada

5 Swedish University of Agricultural Sciences (SLU), Umeå, Sweden

BACKGROUND

– Timothy is a widely grown forage grass in cold temperate regions.

– Physiological characteristics and nutritive value vary between cultivars.

– Climate, weather, soil conditions, and mangement practices affect yield and quality

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BACKGROUND

– Process-based simulation models consist of equations, which represent the physiological processes behind sward growth and development and affect chemical composition.

– Different models respond differently to environmental and management factors, affecting the model prediction accuracy.

– Cultivar specific calibration improved the prediction of timothy yield with the models BASGRA, CATIMO and STICS (Korhonen et al

2018, Field Crops Research 224: 37-47).

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OBJECTIVES

– Compare the models BAGSRA, CATIMO and STICS as regards prediction accuracy of crude protein (CP) and fibre (NDF) concentration, and fibre digestibility (dNDF)

– Determine how cultivar-specific calibration affects the prediction accuracy of these attributes

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MODEL CHARACTERISTICS

– BASGRA, CATIMO and STICS:

– All models calculate growth by radiation use efficiency (RUE).

– All models simulate leave and stem fractions. BASGRA also simulates vegetative and generative tillers.

– All models calculate phenological development by growing degree days. In BASGRA day length also affects development.

– All models simulate nitrogen responses.

– STICS simulates soil water and nitrogen in multi-layers, while BASGRA and CATIMO simulate the soil in one single layer.

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MODEL CHARACTERISTICS

– In CATIMO and STICS, plant N requirements decrease with increasing shoot biomass. In BASGRA, plant N requirements are determined by the sward LAI and the extinction coefficient.

– The amount of cell walls (NDF)/the digestibility of cell walls (dNDF), increases/decreases with phenological stage in all three models.

– All models: Different rates of NDF change in stems and leaves

– CATIMO and STICS: NDF/dNDF also directly affected by temperature

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– Data on crude protein, NDF and dNDF from field trials in four countries

– six varieties

– a wide range of climate and soil conditions, and N fertilizer regimes

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SIMULATION SETTINGS

– Two types of calibration

1. Cultivar-specific calibration

2. Calibration of all cultivars together (generic calibration)

– Separate calibration and validation datasets.

– All simulations were initiated at the beginning of the growing season in the spring

– Calibrations techniques were allowed to differ between the models

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CRUDE PROTEIN

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calib

ration

valid

ation

BASGRA

Observ

ed C

rude P

rote

in (

g g

DM

-1)

0.0

0.1

0.2

0.3

CATIMO

BASGRA

0.0 0.1 0.2 0.3

0.0

0.1

0.2

0.3

CATIMO

Simulated Crude Protein (g g DM-1)

0.0 0.1 0.2 0.3

STICS

STICS

0.0 0.1 0.2 0.3

NEUTRAL DETERGENT FIBRE

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BASGRA

Simulated Neutral Detergent Fibres (g g-1 DM)

0.4 0.5 0.6 0.7

0.4

0.5

0.6

0.7

CATIMO

0.4 0.5 0.6 0.7

STICS

0.4 0.5 0.6 0.7 0.8

ca

libra

tio

n

Champ Nuutti GrindstadTammisto IIIki JonatanClimax1:1 line

va

lidatio

n

BASGRA

Obse

rve

d N

eu

tra

l D

ete

rgen

t F

ibre

s (

g g

-1 D

M)

0.4

0.5

0.6

0.7

0.8

CATIMO STICS

DIGESTIBILITY OF NDF

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BASGRA

Observ

ed d

igestibili

ty o

f N

eutr

al D

ete

rgent F

ibre

s (

g g

-1 N

DF

)

0.4

0.5

0.6

0.7

0.8

0.9

BASGRA

Simulated digestibility of Neutral Detergent Fibres (g g-1

NDF)

0.4 0.5 0.6 0.7 0.8

0.4

0.5

0.6

0.7

0.8

CATIMO

0.4 0.5 0.6 0.7 0.8

STICS val

0.4 0.5 0.6 0.7 0.8 0.9

STICS

STICS

calib

ration

valid

ation

CATIMO

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Number of

observations

Mean of

observation

Mean of

simulation

Normalised

RMSE (%)

rMBE

(%)

d-

index

Crude protein concentration (g g-1 DM)

BASGRA

173 0.14

0.15 16 -0.8 0.89

CATIMO 0.26 101 87 0.36

STICS 0.17 38 25 0.92

NDF concentration (g g-1 DM)

BASGRA

252 0.56

0.56 8.8 -0.4 0.72

CATIMO 0.55 17 -3 0.49

STICS 0.56 12 0.8 0.46

Digestibility of NDF g g-1 NDF)

BASGRA

28 0.78

0.75 9.3 -4 0.59

CATIMO 0.51 38 -34 0.34

STICS 0.79 6.4 0.23 0.82

CONCLUSIONS – NDF concentration and

dNDF predicted with similar accuracy as DM yield in previous studies

– Prediction of CP was poor compared with NDF and dNDF

– Cultivar-specific calibrations improved the performance of CATIMO and STICS, but not of BASGRA.

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