विभभन्न मौसमीय पररस्थितियों में फसऱ िाष्पोत्सर्जन का आॊकऱन

ESTIMATION OF CROP EVAPOTRANSPIRATION

UNDER VARIABLE WEATHER CONDITIONS

AMIT KUMAR SINGH

DIVISION OF AGRICULTURAL PHYSICS

INDIAN AGRICULTURAL RESEARCH INSTITUTE

NEW DELHI - 110012

ESTIMATION OF CROP EVAPOTRANSPIRATION

UNDER VARIABLE WEATHER CONDITIONS

A Thesis

By

AMIT KUMAR SINGH

Submitted to the Faculty of Post-Graduate School,

Indian Agricultural Research Institute, New Delhi,

in partial fulfillment of the requirements

for the award of the degree of

MASTER OF SCIENCE

in

AGRICULTURAL PHYSICS

2012

Approved by:

Dr. (Ms). Ananta Vashisth (Chairperson) : …………………………………………...

Dr. (Ms). U.K. Chopra (Co-Chairperson) : …………………………………………...

Dr. S.D. Singh (Member) : …………………………………………...

Dr. R.N. Sahoo (Member) : …………………………………………...

Division of Agricultural Physics,

Indian Agricultural Research Institute

New Delhi-110012, India

Dr. (Mrs.) Ananta Vashisth,

Senior Scientist

CERTIFICATE

This to certify that the thesis entitled ―Estimation Of Crop Evapotranspiration

Under Variable Weather Conditions‖, submitted to the faculty of the Post

Graduate School, Indian Agricultural Research Institute, New Delhi, in partial

fulfillment of the requirements for the degree of Master of Science in Agricultural

Physics by Amit Kumar Singh, Roll no. 20027 embodies the results of bonafide

work carried out by him under my supervision and guidance. No part of the thesis

has been submitted by him for any other degree or diploma.

I further certify that any help or any information received during the work on

this thesis has been duly acknowledged.

Place: New Delhi (Ananta Vashisth)

Date: June 30, 2012 Chairperson

Advisory Committee

Dedicated

to

My Beloved Parents

ACKNOWLEDGEMENT

I humbly and whole heartedly bow my head before Lord Almighty for blessing me

all his grace to successfully complete my two years study and research endeavour in IARI.

I was lucky to get Dr. (Mrs.) Ananta Vashisth, Senior Scientist, Division of

Agricultural Physics, as Chairperson of my Advisory Committee. I express my profound

sense of gratitude and sincere obligation to mam for his valuable suggestions and kind

help rendered during the entire research programme.

I owe my heartfelt thanks to Dr. (Mrs.) U.K. Chopra. Principal Scientist,

Division of Agricultural Physics, Co-chairperson of advisory committee and Professor of

Division for all his help, encouragements and valuable suggestions during the research

work, expert guidance, trustworthy inspiration, unwavering perseverance, unabated

encouragements, constructive criticisms, indomitable spirit and support which motivated

and helped for the successful completion of my thesis. I express my profuse thanks to

the members of my Advisory Committee, Dr. R.N. Sahoo, Senior Scientist, Division of

agricultural Physics, Dr. S.D. Singh, Principal Scientist, Division of Environmental

Science, IARI, New Delhi for his perpetual support and ever-willing assistance and

guidance given to me throughout this endeavour.

My sincere thanks to Dr. Ravender Singh, Head for his guidance and allowing

me to use facilities available in division.

I express my profuse thanks Dr. (Mrs.) Pramila Aggrawal, Dr. V.K. Sehgal, Dr.

D.K. Das, Dr. K.K. Bandopadhyay, Dr. Debashish Chakraborty, Dr. R.N. Garg and Dr.

Sanatan Pradhan for providing me the necessary facilities and encouragement throughout

the research work.

I am deeply gratified to Dr. V.K. Gupta, Dr. D. K. Joshi and Shri P.K. Sharma,

Division of Agricultural Physics, IARI for extending timely and valuable help during the

experimental work of this study.

I am extending my sincere gratitude to Vipin mam, Kanchan, Rawat Sir and

Rajender ji Division of Agricultural Physics all other technical, administration and

supportive staff members in the Division of Agricultural Physics, IARI, New Delhi for

the constant support, affection and motivation which helped me to complete my study in

the Division.

I articulate my appreciation to all my seniors of the Division especially Rajeev

Bhaiya, Rakesh Bhaiya, Jitendra Bhaiya, Nilimesh Sir Sudipta Sir, Rajkumar Sir, Saurav

Sir, Debasish Sir and Sarath Sir. I am earnestly thankful to my torch bearing and adored

senior Mondal Bhaiya and Pankaj Bhaiya, Harvinder Bhaiya, Jayant Bhaiya, Praveen

Bhaiya, Subhashish Bhaiya, Dinesh Bhaiya and Gaurav Dhir Sir for their ever-willing

lending hands, support, guidance and encouragement poured on me during my study. It is

a matter of immense privilege for me to express my whole hearted thanks to my beloved

friends Prasanna, Monu, Paritosh, Sanjay, Sumit,Debrup ,Golui, Ranjan ,Prakash and

Soobedar whose moral support, advice and assistance helped me in all walks of my life. I

extend my whole hearted thanks to my classmates Bappa and Mukhtar for their nice

company, immeasurable help and support rendered to me during the two years of study in

the division. I express my thanks to my juniors Vidyasagar, Abhishek, Ashish, Paulson,

Amit and Rekha for their nice companionship.

I am grateful to the Director, IARI, New Delhi, for providing me necessary

facilities to carry out my research work at this Institute. I extend my gratitude to IARI

for the financial support in the form of IARI Fellowship.

IARI, New Delhi (Amit Kumar Singh)

Date: June 30, 2012

CONTENTS

Sl. No. Chapter Page No.

1. INTRODUCTION 1-3

2. BACKGROUND 4-15

3. MATERIALS AND METHODS 16-24

4. RESEARCH PAPER I 25-40

5. RESEARCH PAPER II 41-50

6. DISCUSSION 51-52

7. SUMMARY 53-56

8. ABSTRACT 57-58

9. साॊराश

59-60

10.

11.

BIBLIOGRAPHY

APPENDIX

i-xiii

xiv-xviii

1

LIST OF TABLES

Table

No.

Title Page

No.

1. Soil properties at experimental site. 17

2. Average Reference evapotranspiration estimated at different

stage by Penman-Monteith equation.

30

3. Crop stage of different varieties of mustard at variable weather

conditions

32

4. Adjusted Single crop coefficient of mustard at different stage 33

5. Adjusted basal crop coefficient of mustard at different stage 34

6. Average value of crop evapotranspiration estimated using single

crop coefficient approach in mustard at different stage

35

7. Total Crop evapotranspiration estimated using different approach

in mustard at different stage

36

8. Average value of crop evapotranspiration estimated using dual

crop coefficient of mustard at different stage

37

9. Seed Yield (Kg/ha) in different varieties of mustard grown at

variable weather conditions

45

10. Percentage oil content in mustard grown at variable weather

conditions

46

11. Radiation use efficiency of different varieties of mustard grown

under different weather conditions

48

2

LIST OF FIGURES

Fig.

No.

Title After

Page No.

1. Weekly observed and normal minimum (min) and maximum (max)

temperature during rabi season 2011-12 at IARI, New Delhi.

28

2. Weekly observed and normal minimum (min) and maximum (max)

rainfall during rabi season 2011-12 at IARI, New Delhi.

28

3. Observed and normal bright sunshine hours during rabi season

2011-12 at IARI, New Delhi.

28

4. Observed and normal evaporation during rabi season 2011-12 at

IARI, New Delhi.

28

5. Observed and normal bright wind speed during rabi season 2011-

12 at IARI, New Delhi.

28

6. Observed and normal relative humidity during rabi season 2011-12

at IARI New Delhi.

28

7. Daily net radiation estimated at experimental site 29

8. Daily Reference Evapotranspiration (mm/day) estimated at experimental

site.

29

9. Relation between reference evapotranspiration estimated through

Penman-Monteith equation and Pan Evaporation

29

10. LAI of different varities of Mustard under variable weather conditions 31

11. Height of Mustard at Different Varieties at Variable Weather Conditions 32

12. Adjusted Single Crop Coefficient at Experimental Site. 33

13. Adjusted Basal Crop Coefficient at Experimental Site. 34

14. Variation in Soil Evaporation Coefficient (Ke) with crop growing

period

34

15. Calculated Crop Evapotranspiration through Single Crop

Coefficient under variable weather conditions in different varieties

of mustard

35

16. Calculated Crop Evapotranspiration through Dual Crop Coefficient

under variable weather conditions in different varieties of mustard

36

17. Biomass of different varieties of mustard sown under variable

weather conditions

44

3

Fig.

No.

Title After

Page No.

18. Thermal response curve of biomass for Pusa Gold under variable

weather conditions

45

19. Thermal response curve of biomass for Pusa Jaikisan under

variable weather conditions

45

20. Thermal response curve of biomass for Pusa Bold under variable

weather conditions

45

21. Water use efficiency of different varieties of mustard estimated by

different methods in variable weather conditions

46

4

1. INTRODUCTION

Oilseed crops play an important role in India‘s economy. Among

different oilseed crops, mustard (Brassica spp.) is the second most important crop

contributing nearly 30 percent of total oilseed production in the country. In India

Brassica species are mostly grown in northern India consisting of Uttar Pradesh,

Rajasthan, part of Madhya Pradesh, Punjab, Haryana, part of Himachal Pradesh and

Jammu and Kashmir. Accessible irrigation water needs to be utilized in a manner

that matches the water needs of this crop. Water requirements of this crop vary

substantially during the growing period due to variation in crop canopy and climate

conditions. Knowledge of crop water requirements is an important practical

consideration to improve water use efficiency in irrigated agriculture. Many studies

have been carried out related to irrigation water requirements of Indian mustard for

different agro-climates.

Evapotranspiration (ET) is the combined water loss from a vegetative surface

through evaporation and transpiration. Understanding the nature and applications of

evapotranspiration is very important in the field of agriculture. Many management

practices use evapotranspiration based criteria for both dry land and irrigated

agriculture. Information on ET is particularly important in irrigation scheduling

where soil water depletion can be estimated.

Evapotranspiration is the major cause of loss of water received by crop

through irrigation and rainfall. Less than one percent of water used in

evapotranspiration is consumed in metabolic process. Evapotranspiration represents

a major portion of total water budget of crop. Estimation of evapotranspiration is

very critical for proper crop planning. An important requirement for attaining proper

irrigation scheduling is the determination of actual crop evapotranspiration (ETc)

during the growing season (Hunsaker et al., 1996).

Evapotranspiration (ET) being the major component of hydrological cycle

will affect crop water requirement and future planning and management of water

resources. Precise estimates of evapotranspiration are essential for maximum

production of crop. Efficient irrigation water management is very important to

conserve water and optimize crop yield.

The crop coefficient (Kc) methodology (Doorenbos and Pruitt, 1977) was

developed to provide growers with a simple ETc prediction tool for guiding irrigation

5

management decisions. The development of regionally based and growth-stage-

specific Kc will help in irrigation management and provides precise water

applications for the region. The Food and Agricultural Organization (FAO) of the

UN, Paper 56 (FAO-56) (Allen et al., 1998) presented the dual crop coefficient

method with Kcb (Transpiration) and Ke (Soil evaporation) allows computation of

more precise estimates of daily ETc, particularly for days following irrigation or rain.

However, there is no simple way to calculate those Kc, as these coefficients are

function of climate, soil type, the particular crop and its varieties, irrigation method,

soil water, nutrient content and plant phenology (Allen et al., 1998). Consequently,

specific adjustment of crop coefficients in various climatic regions is necessary.

Unfortunately, values of Kc for mustard growing in the Delhi region are not currently

available. FAO-56 guidelines recommend that Kcb values should be adjusted to

account for variations in effective ground cover to obtain site specific crop

coefficients (Allen et al., 1998).

FAO-56 are intended to strictly represent conditions for standard crop

densities and optimum agronomic and water management practices, the publication

strongly encourages local calibration of development lengths and, if warranted from

research findings, recommends modifying Kcb curves to more adequately reflect the

crop water use behavior under the local conditions. In the semi arid region of Delhi,

high water use requirements coupled with increasing costs for water require mustard

growers to implement irrigation practices that will lead to increased water use

efficiency.

FAO developed dual crop coefficient approach for estimation of crop

evapotranspiration. The effect of crop transpiration and soil evaporation are

combined into a single Kc coefficient. In the dual crop coefficient approach the

effects of crop transpiration and soil evaporation are determined separately. The dual

crop procedure is more accurate for real time irrigation scheduling. Estimation of

evapotranspiration of mustard crop will help in better water management practices,

improving irrigation scheduling and increasing their estimation of evapotranspiration

in mustard crop using different methods under variable weather conditions.

6

With this background in view, the present study was taken up with the following

objectives-

To estimate the evapotranspiration in mustard crop using different methods

under variable weather conditions

To estimate the water use efficiency of mustard crop under variable weather

conditions

7

2. BACKGROUND

This section gives a brief outline of the relevant literature available on

different approaches in estimating crop evapotranspiration with special emphasis on

Food and Agricultural Organisation (FAO)-56 dual crop coefficient approach. In

research area II, relevant studies on water use efficiency of crop are discussed.

2.1. Research Area I

The relevant materials to the first objective of the present study (Research

area I) are given under following topical heads.

2.1.1 Crop Evapotranspiration

ET0 can be estimated by many methods (Jensen, 1971).These methods range

from the complex energy balance equations (Allen et al., 1998) to simpler equation

that require limited meteorological data (Hargreaves and Samani, 1985).

Determination of crop evapotranspiration (ETc) includes various measurement

(direct) and modelling (indirect) techniques. An accurate estimation of crop ET is an

important factor for efficient water management (Tyagi et al., 2000). Field water

balance is commonly used to measure total water use or actual crop

evapotranspiration (ETa) when lysimeter facilities are not available (Parihar and

Sandhu, 1987; Bandyopadhyay and Mallick, 2003; Kar et al., 2007). Direct

measurement methods of ETc are expensive and involve hard work and the results

apply only to the exact or similar conditions in which they are measured.

The indirect estimation using the FAO-56 model (Penman-Monteith

reference evapotranspiration approach, Allen et al., 1998) has often been preferred

because it requires phenological data and standard meteorological parameters which

can be easily obtained. The FAO Penman-Monteith method was developed by

defining the reference crop as hypothetical crop with an assumed height of 0.12 m,

with a surface resistance of 70 s m-1

and an albedo of 0.23, closely resembling the

evaporation from an extensive surface of green grass of uniform height, actively

growing and adequately watered (Allen et al., 1998). The method overcomes the

shortcomings of the FAO-24 Penman method (Doorenbos and Pruitt, 1977) and

provides values that are more consistent with actual crop water use data worldwide.

Scientific community has accepted the FAO-56 Penman-Monteith model as the most

precise one for its good results when compared with other models in various regions

of the entire world (Chiew et al., 1995; Garcia et al., 2004; Gavilán et al., 2006).

8

The semi-empirical FAO model provides a simple calculation of both, soil

evaporation and plant transpiration, based on crop specific coefficients and water

balance. The crop coefficient method has been applied to estimate water use and

irrigation requirements of a wide range of agricultural crops under different climatic

conditions (Abdelhadi et al., 2000; Poulovassilis et al., 2001; Howell et al., 2004;

Zhang et al., 2004; Kar et al., 2006; Bodner et al., 2007). For most agricultural crops

a relation can be established between evapotranspiration and climate by the

introduction of the crop coefficient (Kc), which is the ratio of crop evapotranspiration

(ETc) to reference evapotranspiration (ET0) (Doorenbos and Kassam, 1979).

Reference crop evapotranspiration (ET0) can be estimated by many methods.

Estimation of actual crop evapotranspiration by the modified FAO-56(Allen

et al., 1998) preferable due to its accuracy compared to other methods (Jensen et al.,

1990) and the single and dual crop coefficients (Allen et al., 1998) are introduced to

evaluate its magnitude. This method overcomes the shortcomings of the previous

FAO-24 Penman method and provide ETc more accurately.

2.1.2 Leaf Area Index (LAI)

Allen and Morgan (1975) observed that the number of pods/plant and the

seeds/pod were positively related with LAI at the time of onset of flowering which

resulted in higher seed yield. Specifically, LAI continues to increase till maximum

vegetative growth LAI plays a dominant role in seed formation, and thereafter it

starts declining. Chauhan (1980) based on his C14

study on Pusa Bold concluded

that upper two leaves translocated photosynthates to the seed in the range of 11 to

17%. Maximum LAI was attained at 80 days after sowing (DAS). Niwas et al.

(1999) from the field study conducted at Hisar, Haryana, reported that LAI of

Brassica species (cv. Laxmi, HC-2, GSH-2 and BSH-1) was the highest in the crop

sown on 20th

October among the crops sown on 20th

October, 30th

October and 10th

November. Working on B.O. 54, Pusa bold and Toria T-9, Kar and Chakravarty

(2000b) reported that LAI and pod area index (PAI) were lower in a season with

higher temperatures (2-30C) during vegetative and grain filling stages as compared to

the season with relatively lower temperatures in the same period. Roy et al. (2003)

observed that delay in fortnight sowing from 1st

October to 1st

November reduced

LAI by 8.3 and 52.8% in Agrani, 12.9 and 45.4% in Pusa Jaikisan and 8.8 and

45.6% in Varuna varieties than first sowing.

9

2.1.3 Crop coefficients

The concept of crop coefficient (Kc) was introduced by Jensen (1968) and

further developed by the other researchers (Doorenbos and Pruitt, 1977; Burman et

al., 1980; Allen et al., 1998).

To estimate ETc the crop coefficient Kc, which is the ratio of ETc to grass

reference evapotranspiration ETo, is needed. The crop coefficient Kc value represents

crop-specific water use and is required for accurate estimation of irrigation

requirements. The morphological and eco-physiological characteristics of the crop

and the adopted cultural practices are taken in to account through crop coefficient.

Crop coefficient specific to each crop, evapotranspiration varies in the course of the

season because morphological and eco-physiological characteristics of the crop do

change over time. The FAO and WMO (World Meteorological Organization) experts

have summarised such evolution in the ‗‗crop coefficient curve‘‘ to identify the Kc

value corresponding to the different crop development and growth stages (Tarantino

and Spano, 2001).

Value of Kc for most agricultural crops increase from planting and reach at

maximum at full canopy cover and then declines. Crop growth characterstics are

primarily responsible for this declination (Jensen et al., 1990). The FAO-56 paper

presents a procedure to calculate ETc using three Kc values that are appropriate for

four general growth stages (in days) for a large number of crops. Single crop

coefficient considers the effect of crop transpiration and soil evaporation in a

combined single crop coefficient while dual crop coefficient approach considers the

effect of specific wetting events on the value of Kc. ETc is determined by splitting Kc

into two separate coefficients: one for crop transpiration i.e. the basal crop

coefficient (Kcb) representing the transpiration of the crop and another for soil

evaporation, the soil water evaporation coefficient (Ke). Allen et al. (1998) reported

that in situations where evaporation from soil is not a large component of ETc, use of

single crop coefficient approach will provide reasonable results. Majority of the

studies considers single crop coefficient approach. Dual crop coefficient used less

frequentlywhere the effects of crop transpiration and soil evaporation are determined

separately (Casa et al., 2000; Lopez-Urrea et al., 2009). Discussions of both

approaches are dealt separately.

2.1.3.1.1 Single crop coefficient approach

10

To make the use of Kc operational, research and experiments have been

carried out worldwide, and they have led to determination of the average value that

Kc may take in the course of the season over the years (Grattan et al., 1998). It is

worth highlighting that the Kc is affected by all the factors that influence soil water

status, for instance, the irrigation method and frequency (Doorenbos and Pruitt,

1977; Wright, 1982), the weather factors, the soil characteristics and the agronomic

techniques that affect crop growth (Stanghellini et al., 1990; Tarantino and Onofrii,

1991). The crop coefficient values are different at various growth stages of mustard.

In mustard Kc values of 0.39, 0.92, 1.31 and 0.42 were achieved at initial,

development, mid season and late season, respectively at Bhubaneswar (Kar et al.,

2007). There is a constantly increasing trend in Kc during the crop development

growth stage. The slow increase in the Kc values from equal to or less than 0.3 to

equal to or less than 0.6 can be attributed to slow increase in LAI from below 0.2 to

near 0.8 during this period. The peak Kc values of 1.12 during mid season stage

because of LAI during this period (41st-70

th DAS) acquired the peak value around

1.5 for the crop. In the late season crop growthstage starting from 71 to 90 DAS, the

crop coefficient rapidly decreased to 0.35, likely due to fast decline in LAI (0.8)

(Kumar et al., 2012).

2.1.3.2 Dual crop coefficient approach

An evaluation of the amount of water lost by direct soil evaporation needs a

partitioning of total evapotranspiration in to soil evaporation and plant transpiration

components. Separate and direct measurements of transpiration and soil evaporation

have been successfully obtained (Williams et al., 2004), but difficult to employ. On

the other hand, the dual crop coefficient can be successfully used in detailed soil and

water balance studies (Allen et al., 1998). Lopez-Urrea et al. ( 2009) reported that

the dual crop coefficient approach is more reliable than the single crop coefficient in

onion crop grown under semi arid conditions, for estimating high values of

evaporative component existed during the the entire crop cycle. Accurate estimates

of Kcb and fractional coverage are necessary for FAO-56 dual crop coefficient

application. The derived Kcb for winter wheat at mid season crop growth stage was

found to be considerably lower from suggested by FAO-56 (Er-Raki et al., 2007).

2.2 Research Area II

11

2.2.1. Water use efficiency

Crop yield response to water input can be useful for various water

management practices in the crops. Water production functions for several

agronomic crops are used for optimal water use. A large number of work done at the

use of crop water production functions in evaluating the economic implications of

crop water use at different levels (Stegman et al., 1980; Ayer and Hoyt, 1981;

English, 1990; Helweg, 1991). Water production functions relates yield to applied

water for the satisfaction of crop water requirements by irrigation water in addition

to rainfall and available soil moisture. (Hexem and Heady, 1978). Vaux and Pruitt

(1983) combined the yield of a given crop to cumulative ET as a linear function.

Field studies with cotton by Grimes et al. (1969) and Gulati and Murty (1979) with

wheat, barely, and sugarcane and it was reported that the Yield-ET relations for these

crops were best described by quadratic functions. The yield response factor to

describe the relationship between ET deficit and yield reduction was introduced by

Doorenbos and Kassam (1979). This approach expresses yield reductions, and ET

deficits in relative terms based on the maximum crop yield and corresponding ET at

maximum yield. According to them ET deficit spread similarly over the total

growing period affects crops differently. For example, a particular seasonal ET

deficit for crops where KY < 1; such as groundnut, grape, cotton, and soybean should

result in a smaller yield reduction than would the same ET deficit for crops where

KY > 1, such as banana, maize, and pepper. Crop water use efficiency (WUE), where

WUE ¼ Y=ET, represents the productivity of the water utilized by the crop for yield,

and has been often taken as an important comprehensive index (Eck, 1986; Turner,

1987; Wang, 1987; Hunsaker et al., 1996). For sorghum a linear relationship was

observed between grain yield and ET by Garrity et al. (1982) and found that the

intercept of the ET governs whether WUE increases or decreases with ET deficit.

Addition of nutrient like P to chickpea (Cicer arietinum L.) increased yield,

water use, and WUE (Singh and Bhushan, 1980). The increase in WUE was from 8.5

to 12.2 kg ha-1

mm-1

at 0 and 100 kg P ha-1

respectively. This gain was due to a

greater depletion of soil water with fertilizer and a yield increase. Zhang et al. (2000)

estimated water use and water-use efficiency of chickpea and lentil from 3

experiments over 12 seasons, 1986–87 to 1997–98, in northern Syria. Rainfall is the

strongest determinant of grain yield of chickpea and lentil and their water use under

12

rainfed conditions. Both the average water use efficiency and potential transpiration

efficiency for lentil and chickpea were lower than those for cereals. Lower water use

efficiency was associated with low seed yield in the study. Brown et al. (1989)

observed that Chickpea may adapt to drought stress by maximizing its water uptake

through continuous root growth up to seed-filling and by maintaining substantial

water uptake until the fraction of extractable moisture in the root profile falls to 0.4.

Tanner and Sinclair (1983) suggested that semiarid region may have the most

potential for improvement in WUE because the water vapor gradient between plants

and the atmosphere is small and evaporation rates may be reduced. Increasing the

efficiency of water use by crops continues to escalate as a topic of concern because

of the increasing demand for water use and improved environmental quality by

human populations.

Efficiency is a term that creates a mental picture of a system in which we can

twist dials, tweak the components, and ultimately influence the efficiency of the

system (Hatfield et al., 2001). Tanner and Sinclair (1983) summarized the different

forms of relationships that have been used to characterize water use efficiency.

Earlier summaries developed by Power (1983) and Unger and Stewart (1983)

provided a strong foundation for understanding role of soil management on WUE.

Much of the research that forms the foundation for understanding the

relationships among precipitation, soil water, plant water use, and crop response has

been conducted in semiarid regions (Hatfield et al., 2001) and it was suggested that

semi-arid region may have the most potential for improvement in WUE because the

vapour gradient between plants and the atmosphere is small and evaporation rates

may be reduced (Tanner and Sinclair, 1983).

Increasing the soil water availability in the absence of any other yield-

limiting factor can lead to increased WUE (Hatfield et al., 2001).Wheat yield

increased from 1000 to 3000 kg ha-1

as available soil water at planting increased

from 220 to 400 mm (Good and Smika, 1978). Improvement in efficiency was

possibly due to reduced use of fallow seasons and using water for crop growth that

otherwise would have lost by evaporation, runoff or deep percolation during fallow

(Farahani et al., 1998). Application of phosphorous also improved the yield, water

use and WUE of chickpea (Singh and Bhushan, 1980), possibly due to greater

utilization of soil water with fertilizer and corresponding yield increase associated

with low seed. Increased water use efficiency of field crops was possible through

13

proper irrigation scheduling by providing only the water that matches the crop

evapotranspiration and providing irrigation at critical growth stages (Eck, 1984;

Turner, 1987; Wang, 1987; Wang et al., 2001; Hunsaker et al., 1996; Kipkorir et al.,

2002; Norwood and Dumler, 2002; Kar et al., 2005)

A study from 1998 to 2000, to evatulate seasonal water use and soil-water

extraction by Kabuli chickpea was conducted by Anwar et al., 2003. The response of

three cultivars to eight irrigation treatment in 1998/99 and four irrigation in

1999/2000 at different growth stages was studied on a Wakanui silt loam soil

moisture in Canterbury, New Zealand. Evapotranspiration was computed from soil

moisture data monitored though a neutron moisture meter and water use efficiency

(WUE) was examined at crop maturity. There were also highly significant (p<0.001)

interacting effect of irrigation, sowing date and cultivar on WUE and the trend was

similar to that for seed yield. The estimated WUE ranged from 22-29 kg dry matter

ha-¹ mm

-¹ and 10-13 kg seed yield ha

-¹mm

-¹ water use. Despite the importance of

water use by legumes, little attention has been given to the pattern of water use and

the water-seed yield relationship of legumes (Zhang et al., 2000).

High water use efficiency (WUE), ability of the plant to produce dry

matter/unit of water is a genetic character, dependent on photosynthetic rate (Pn) and

transpiration rate (T), is very crucial for productivity of the crop under drought

stress. Since low photosynthetic efficiency is believed to be one of the major

constraints in achieving high yield (Thurling, 1992) because enhancing

photosynthetic efficiency would be a possible approach in improving yield.

Seed yield is also limited by the relatively short duration of the growing

season and the severity of soil moisture deficits experienced during the later phases

of reproductive development. The contribution of photosynthesis of pods to the seed

yield in Brassicas is about 70%, therefore, genotypes with high WUE during this

phase would be highly desirable.

2.2.2 Radiation use efficiency

The amount of biomass produced by crops can be defined via a simple

physiological framework based on the amount of solar radiation intercepted and its

efficiency of conversion to dry matter. The efficiency of conversion (light use

efficiency, LUE) is often a constant for a crop grown in a non-stress environment,

but is affected by some environmental conditions. However, it was observed that

decrease in biomass production in maize, sorghum and pearl millet in response to

14

water stress was primarily associated with a reduction in LUE rather than a reduction

in radiation interception (Muchow, 1989). Further, the LUE of maize was more

severely affected by water stress than was sorghum and pearl millet. In another

study, LUE of rice was found to be more sensitive to water stress than that of

sorghum and maize (Inthapan and Fukai, 1988).

In most crops, development of water stress slow during early stage of growth,

and severe stress development after maximum light interception was achieved. In

these cases, water stress had a small effect on light interception but a large effect on

water use efficiency (toal dry matter produced per unit of solar radiation intercepted).

However, for the chickpea sown in April, water stress developed during leaf area

expantion, and severely reduced light interception with little adverse effect on light

use efficiency. The results suggest that weather water stress affects light interception

or light use efficiency depends on the timing of water in relation to the canopy

development (Thomas and Fukai, 1995).

2.2.3 Biomass Production

Studies on biomass production and its partitioning assume greater importance

to crop management because grain yield is greatly dependent on the partitioning of

photosynthesis towards grain filling after anthesis. The division of assimilates among

different parts of the plant termed partitioning; affect both productivity and survival

of the plants. Thurling (1974a) pointed out that in Brassica campestris about 85

percent of total dry matter was accumulated before anthesis while in Brassica napus

it was only 50 percent. Rao (1992) reported that under Delhi conditions 12 to 17

percent dry matter production was accumulated before flowering and rest after

flowering in a mustard crop. Krishnamurthy and Bhatnagar (1998) in their study

conducted at Varanasi during 1982-83, reported that dry matter (DM) increased

with a positive correlation between DM and LAI at flowering and final DM

production, CGR increased until flower cessation and then dropped. Studies on

Brassica juncea and Brassica napus shown that when the crop was exposed to

moderate temperature stress (28/150C) for short term period 6 days than 7, dry

matter production was unaffected, while there was a significant reduction when the

crop was exposed to high temperature stress (35/150C). A study conducted by Kar

and Chakravarty (2001) at sandy loam soils of Delhi during 1993-94 and 1994-95

reported that 6 and 22 percent reduction in biomass production in the crop sown

15

on 1st

and 3rd

week of November, respectively as compared to crop sown on third

week of October in Brassica (cv. B.O.54 and Pusa Bold). From the field experiment

conducted at Delhi, Giri (2001) observed that in October sown crop there was

higher dry matter production (cv. Pusa Jaikisan) as compared to November sown

crop. Under semiarid conditions of Haryana, Singh et al., (2002) reported highest

biomass allocation in leaves (59 percent) followed by stems and roots at the first

flower appearance. At the onset of seed filling, the highest biomass was recorded in

stems, followed by leaves, roots and siliquae while at the end of seed filling and at

maturity, the greatest biomass allocation was evident in stems (43 to 59 percent)

followed by siliquae (32 to 63 percent, and leaves 9.5 to 20 percent). Dastider and

Patra, (2004) observed that there was a slight delay in flowering and maturity due to

delay in sowing. The biological yield could be increase with increase in number of

pods.

2.2.4. Seed Yield

Several workers have reported the declining trend of mustard seed yield due

to delay in sowing under various agro-ecological conditions across the country.

Same results were also supported by Gross (1963); Scott et al., (1973) and Thurling

(1974b). However under semi-arid environment of Delhi, earlier workers (Babu,

1985; Ravindra, 1985 and Prasad, 1989) reported that delayed sowing (1st

week of

November) causing increase in the duration of vegetative phase while that of

flowering, seed filling and maturity phases got reduced. Babu (1985) examined the

morpho-physiological factors causing reduction in yield in rapeseed-mustard when

the sowing was delayed beyond October under north Indian conditions and

concluded that such an yield reduction was associated with reduced plant height,

inflorescence length, number of branches, pod number, pod weight and 1000 seed

weight i.e., all together leading to a reduction in total dry biomass and harvest index.

This view was also supported by Prasad (1989). A reduction in seed yield in the

range of 7.94 to 4.97 g for per day delay was observed by Bhargava (1991) when

crops were sown from 12th

October to 11th

December at an interval of 10 days.

Nanda et al., (1994) viewed that reduction in seed yield of Brassica species under

late sown condition might be attributed to increase in temperatures at the time of pod

growth and seed filling stage, which reduced the dry matter accumulation into the

seed and shortened the seed-filling period. On red soils of West Bengal, Chakraborty

16

(1994) studied the effect of date of sowing on physiological processes and yield and

found that yield was higher in the plants sown earlier than sown after 31st

October.

Meherchand et al., (1995) from Hissar also reported similar result that seed

yield/plant and seed production decreased by delayed sowing. Das (1995)

observed that delay in sowing from October to November reduced seed yield in

the range of 8 to 65 percent. Kar (1996) reported that due to delay in sowing under

Delhi conditions, reduction in seed yield ranged from 7.6 to 23.3 per cent in different

cultivars of Brassica (B.O.54, Pusa Bold and Toria T-9). Dahal et al., (1997)

reported that the yield of Brassica campestris (var. toria cv. Vikas) under the

mid-hill rainfed bari land conditions in Nepal was highest when crop was sown

on 30th

September and generally decreased when sowing was delayed beyond 15th

October Similarly, Shahidullah et al., (1997) reported that the number of

branches/plant and seed yield were higher with the 27th

October and 6th

November

sown crop than 16th

November sown crop Plant height was highest in 6th

November

sown crop, while number of pods/plant and number of seeds/pod decreased with

delay in sowing. Khushu et al., (2000) found that minimum temperature between

flower bud initiation and pod formation was negatively associated with seed yield.

Working on early sown and late sown crops, under field conditions at Hisar,

Khichar et al., (2000) reported higher seed yields of 2049.7 kg/ha from 20th

October sown Brassica cultivars. Based on field experiment conducted in Uttar

Pradesh, Singh and Singh (2002) observed more growth characteristic with respect to

plant height, leaves/plant, leaf-area index, dry- matter accumulation/plant, primary

and secondary branches/plant, yield attributes (siliquae/plant, length of siliqua),

yields (biological, seed and stover yields) and qualities (yields seed protein and oil)

with 14th

October sown crop as compared to 29th

October, 13th

November and 28th

November sown crops. Sharma et al., (2002) developed multiple linear regression

models that included three independent variables viz., the weighted sum of

maximum temperature, minimum temperature and relative humidity during the

entire cropping season, to forecast crop yield for cv. Kranti and concluded that the

maximum temperature and relative humidity at 1400 hrs played a significant role

in deciding the crop yield. In an experiment conducted by taking eight Indian

mustard (RC-781, T-6342, B-85, RW-351, RW-4-C-6-3/2, Kranti, NDR-8501 and

RLM-619) and their 28 F1 hybrids, Dastidar and Patra (2004) found delayed sowing

17

beyond 16th

October significantly reduced crop growth, yield attributes and seed

yields. Similarly, Panda et al., (2004) reported higher seed yield (1945 kg/ha) in case

of crop sown on 16th

October than the crops sown on 31st

October (1556 kg/ha) and

15th

November (872 kg/ha) during winter season 1997-98 in New Delhi. Moreover,

while working in growth chambers, wherein the Brassica spp. was exposed to

short-term high temperature stress during different growth stages Gan et al., (2004)

observed a reduction in main stem pods by 75 percent, seeds pod 25 percent, and

seed weight 22 percent when the plants were grown under 350/18

0C stress as

compared to the control plants. Further, they reported a reduction of seed yield

per plant by 15 percent when plants were severely stressed during bud formation,

58 per cent when stressed during flowering and 77 per cent when stressed during

pod development. Plants stressed at earlier growth stages exhibited recovery,

whereas stress during pod development severely reduced most of the yield

components. Adak (2008) worked on the two mustard varieties (Pusa Jaikisan and

Bio 169-96) in two season 2005-06 and 2006-07, under Delhi condition , reported

that when debranching was done at 50 DAS the seed yield of Pusa Jaikisan

significantly increased by 9 to 10 per cent and that of BIO169-96 by 6 to 8 per cent

in both the sowings. Delay in sowings by 15 days decreased seed yield of Pusa

Jaikisan by 25.0 and 20.5 per cent in control and debranched (at 50 DAS) plots

respectively while the respective yield reduction in BIO169-96 were 24.3 and 26.2

percent.

2.2.5 Oil Content

Oil production of an oil crop requires certain ambient temperature as well as

accumulated heat over growing period. Variation of these two parameters causes

great variations in the oil content. As growing degree days (GDD) are varying with

sowing dates, oil content may vary but again it is the genetic variation that influences

more under a given environment. On the other hand, oil productivity which is a

function of seed yield definitely varies as seed yield is reduced owing to delay in

sowing. From four years field experiment taking eight early, medium and late

Brassica juncea cultivars, Ghosh and Chatterjee (1988) concluded that each fortnight

delay in sowing from 3rd

week of October to 3rd

week of November, oil content of

early varieties reduced by 7 to 15 per cent while that of medium varieties 10 to 17

per cent and for long duration varieties it was around 4 to 12 per cent. Mishra and

18

Verma (1994) found that delay in sowing reduced significant amount of seed yield

and oil content. Panda et al., (2004) reported 2 to 5 per cent reduction in oil content

due to delay in sowing by a fortnight. Neog et al., (2005) concluded that the mean oil

productivity which was observed to be maximum 11.7 q/ha and 10.5 q/ha in Pusa

Jaikisan and Varuna respectively in last week of October sowing dropped sharply to

2.6 and 2.5 q/ha in December 1st

week sowing, registering a decline of 350 and 320

percent. These findings provided the information that right sowing time for optimum

seed and oil productivity of mustard in the Delhi region to be between 22nd

to 30th

October and thereafter they decline, while sowing beyond 5th

November is highly

uneconomical.

19

3. MATERIALS AND METHODS

In order to achieve the objectives set out, field experiments on Mustard crop

(Brassica juncea) were conducted during 2011-12 the Rabi season (October-April) of

three different varieties of mustard sown on three different dates for creating variable

weather for different stage of the crop. The details of the materials and methods

adopted during the course of present investigation are given in this chapter.

3.1 Field Experiments

3.1.1 Location of the Experimental Site

The present study was carried out in the experimental farm of Indian

Agricultural Research Institute (IARI), New Delhi located at 28o35' N latitude,

longitude of 77o12' E and at an altitude of 228.16 m above mean sea level. The field had

a fairly leveled topography.

3.1.2 Climate

The climate of the research farm is semi-arid with dry hot summer and cold

winters. May and June are the hottest months with mean daily maximum temperature

varying from 40-45oC, while January is the coldest month with daily mean temperature

ranging from 8-12oC. The mean annual rainfall is 710 mm, of which 80 per cent is

received during southwest monsoon period from July to September and the rest is

received through western disturbance from December to February. Air remains dry

during most of the part of the year. The relative humidity varies from J u n e to

D e c e m b e r . T h e Mean daily values (during different weeks) of meteorological

parameters recorded at the IARI Meteorological observatory adjoining to the

experimental site during the rabi season are presented in the annexure 1.

3.1.3 Soil

The soil of the experimental site belongs to the major group of Indo-Gangetic

alluvium. The soil texture of experimental site is sandy clay loam and belongs to

Holambi Series, which is a member of non-acidic mixed hyper thermic family of Typic

Ustocrepts, with medium to weak angular blocky structure. The physical properties of

the soil of experimental sites are shown in table 1. The soil is non-calcareous and

slightly alkaline in reaction.

20

Depth (cm)

Bulk Density

(Mg m-3) pH

EC

(dS m-1)

Organic C

(g kg-1)

Particle size distribution Soil Moisture

Sand Silt Clay 0.033 MPa 1.5 MPa

0-15

1.54 7.1 0.46 4.2 77.0 11.6 11.4 21.7 7.4

15-30 1.55 7.2 0.24 2.2 74 11.1 14.9 21.5 8.1

30-60 1.56 7.5 0.25 1.6 76.9 10.1 12.9 21.0 8.2

60-90 1.60 7.5 0.25 1.2 70 14.0 16 22.8 8.6

90-120 1.60 7.7 0.30 1.1 66 16.1 17.9 23.9 13.8

Table 1: Soil properties at experimental site.

3.2 Field crops

Three varieties of mustard (Brassica juncea). viz., Pusa Gold, Pusa Jaikisan and

Pusa Bold were grown during the rabi season of 2011-12, following the standard

recommended agronomic practices. The brief chracteristics of these varieties are given

below.

3.2.1 Pusa Gold

It is early duration variety (120-125 days) highly susceptible to aphid

infestation.

3.2.2 Pusa Jaikisan

It is a Somaclonal variety. This is a high yielding variety of mustard with a plant

type of semi compact and medium plant height (approximately 185 cm). It is a bold

seeded and lodging resistant variety. Oil content of this variety is nearly about 40

percent.

3.2.3 Pusa-Bold

It is also medium duration variety of mustard with semi-compact and medium

plant height and lodging resistance timely sown suitable for both irrigated and rainfed

condition.

3.3 Dates of Sowing

Three varieties of Pusa Gold, Pusa Jaikisan and Pusa Bold were sown on 14th

October, 31st

October and 16th

November 2011 with three replications.

3.4 Experimental Layout

The experiment was laid out in a randomized block design with three

replications of each variety of three treatments of sowing in a 5 m x 5 m plot.

21

3.5 Irrigation and Fertilizer Application

Three irrigations were given in first and third sowing while only two irrigations

provided for second sowing according to demand of crop. After pre-sowing irrigation

when the soil was pliable, ploughing of the field had been taken up to good tilth. Just

before ploughing DAP @ 50 kg/ha was applied, so that it gets mixed well in to the soil.

3.6 Sowing Operations

Sowings were done with hand drill, maintaining a row-to-row spacing of 30 cm

and plant- to-plant 15 cm and seed rate of 6 kg/ha was maintained.

3.7 Cultural Operations

Weeding was done manually in each plot as and when needed. Gap filling

was done after 3 days after germination and irrigations were given as per crop water

requirement.

3.8 Stages of Observations

During the period of study, observations on plant and meteorological parameters

were taken at ten days interval.

3.9 Biomass Accumulation and Partitioning

Three plants were randomly selected in each plot and cut at ground level for this

study. Different plant parts like leaf, stem, pods were then separated. Those plant

materials were oven-dried at 650C for 48 hours or more until constant weight was

achieved as recommended. Dry biomass produced was expressed as g/m2.

3.10 Leaf Area Index (LAI)

Field measurements of LAI were carried out in using LAI-2000 Plant Canopy

Analyzer (LI-COR, USA), which is based on fish-eye measurement of diffuse radiation

interception by measuring gap fraction at five zenith angles (0–13, 16-28, 32–43, 47–

58, 61–74°) simultaneously. The measured gap-fractions are inverted to obtain the

effective LAI and the instrument was set to take four below and one above canopy

measurements to estimate the LAI. Three LAI readings were recorded in each plot

and two to three such readings were averaged out to represent the field LAI.

3.11 Soil water monitoring

In the absence of lysimetry, soil water balance method is a sound alternative for

22

determining ETc. The soil water balance method (Hanks and Ashcroft, 1980) determined

the components (all expressed in mm) of the water balance equation for total volume

defined by the soil profile of a given root zone depth and is written as :

ETc = (P + I + U) – (R + D) – (∆S + ∆V)…………………..(1)

Where ETc denotes estimated crop evapotranspiration (mm), P is precipitation

(mm), I is irrigation (mm), D is deep percolation below the root zone (mm), R is runoff

(mm) and ∆S is the change in profile soil moisture (mm), and ∆V is increment of water

content in plants.

Runoff (R) was assumed to be assumed to be negligible as the field plots were

bunded (30 cm height) and no bund over flow occurred. ∆V is considered to be

insignificant and was, therefore, ignored. The water table was below 15 m and therefore,

capillary rise (U) was assumed to be zero. Change in soil moisture content (∆S) was

calculated for each period of crop growth from the initial and final soil moisture.

The reference evapotranspiration, ET0, was calculated according to the FAO

Penman-Montieth equation (Allen et al., 1998).

……………….(2)

Where

ET0 expressed in (mm day-1

),

Rn net radiation at crop surface (MJ m-2

hr-1

),

G soil heat flux density (MJ m-2

day-1

)

T air temperature at 2 m height (0C),

u2 wind speed at 2m height (m s-1

),

es saturation vapour pressure (kPa),

ea actual vapour pressure (kPa),

Δ the slope of the vapour pressure curve (kPa 0C

-1),

γ is the psychrometric constant in (kPa ºC-1

).

3.12 Net radiation calculation:

Rn = (1−r)(0.25 + 0.5 n/N)So−(0.9 n/N + 0.1)(0.34 – 0.14 √ea) σT4…………(3)

23

where

S0 is the extraterrestrial radiation (MJ m-2

day-1

),

ea the vapor pressure (kPa),

σ the Stefan-Boltzmann constant (4.903×10-9

MJ m-2

K-4

day-1

),

T the air temperature (K),

r the reflection coefficient (observed mean value, 0.24),

n the number of hours of bright sunshine per day (h),

N the total day length(h).

3.13 Crop coefficient Kc

3.13.1 Single crop coefficient approach

ETc =Kc× ET0………………………………………(4)

Calculation- Adjustment was done according to experiment.

Kc ini = fw × Kc ini(Tab)………………………….……….……(5) Where fw the fraction of surface wetted by irrigation or rain (0-1)

Kc ini(Tab) = 0.15……………………………………………….(6)

Crop coefficient for the mid-stage season (Kc mid)

Kc mid= Kc mid (Tab) + [0.04(u2-2)-0.004(RHmin-45)](h/3)0.3

………..(7)

Where Kc mid( Tab) value for Kc mid given by FAO-56

u2 = mean value for daily wind speed at 2m height over plant during the mid-season

growth stage [m s-1

], for 1 m s-1

≤ u2 6 m s-1

,

RHmin = mean value for daily minimum relative humidity during the mid-season growth

stage [%], for 20%≤RHmin≤80%,

h = mean plant height during the mid-season stage [m] for 0.1m<h<10m.

Crop coefficient for the end of the late season stage (Kc end)

Kc end= Kc end(Tab) + [0.04(u2-2)-0.004(RHmin-45)](h/3)0.3

…………...(8)

Where Kc end (Tab) value for Kc end given by FAO-56

u2 = mean value for daily wind speed at 2 m height over grass during the late season

24

growth stage [m s-1

], for 1 m s-1

≤ u2 6 m s-1

,

RHmin = mean value for daily minimum relative humidity during the late season growth

stage [%], for 20%≤RHmin≤80%,

h = mean plant height during the late season stage [m] for 0.1m≤h≤10m.

For calculating wind speed at 2 m height the wind speed data taken from the

agromet observation adjustment to the experimental sites were corrected by multiply the

correction factor.

3.13.2 Dual Crop Coefficient approach:

ETc= (Kcb+Kc) E…..………………………..…………….. (9) The basal crop coefficient (Kcb) is defined as the ratio of the crop

evapotranspiration over the reference evapotranspiration (ETc/ET0) when the soil

surface is dry but transpiration is occurring at a potential rate, i.e., water is not limiting

transpiration. Adjustment were done according to prevalent conditions-

Kcb ini = 0.15…………………………………..………….. (10)

Kcb= Kcb (Tab) + [0.04(u2-2)-0.004(RHmin-45)](h/3)0.3

………..…(11)

Where

Kcb (Tab) = the value for Kcb mid or Kc end (if ≥0.45)

u2 = mean value for daily wind speed at 2m height over grass during the mid or late

season growth stage [m s-1

], for 1 m s-1

≤ u2 6 m s-1

,

RHmin = mean value for daily minimum relative humidity during the mid or late season

growth stage [%], for 20%≤RHmin≤80%,

h = mean plant height during the mid or late season stage [m] for 20%≤RHmin≤80%.

Evaporation component is given by

Ke= Kr × (Kc max-Kcb) ≤few × Kc max…………………………..……... (12)

Where

Ke soil evaporation coefficient and estimated by the following equation.

Kcb = basal crop coefficient; Kc max maximum value of Kc following rain or irrigation,

Kr = dimensioless evaporation reduction coefficient dependent on the cumulative depth

of water depleted (evaporated) from the topsoil

Few = fraction of the soil that is both exposed and wetted, i.e., the fraction of soils urface

from which most evaporation occurs.

25

Upper limit Kc max: Kc max ranges from about 1.05 to 1.30 when using the grass reference

ET0 and Kcmax estimated using following equation.

Kcmax= max({1.2+[0.04(u2-2)-0.0004(RHmin-45)](h/3)0.3

},{Kcb+0.05})……(13)

h = mean maximum plant height during the period of calculation (initial, development,

mid-season, or late season) [m],

Kcb = basal crop coefficient,

u2= wind speed at 2m height at experimental sites,

RHmin = minimum value of relative humidity at experimental sites

Soil evaporation reduction coefficient (Kr) is calculated as follows when the soil surface

is wet, Kr = 1.

When the water content in the upper soil becomes limiting, Kr decreases and becomes

zero.

When the total amount of water that can be evaporated from the topsoil is depleted.

Kr=TEW-De,i-1/TEW-REW for De, i-1>REW………………(14)

Where Kr = dimensionless equation reduction coefficient dependent on the soil water

depletion (cumulative depth of evaporation) from the top soil layer (Kr=1 when De,i-

1≤REW),

De,i-1 = cumulative depth of evaporation(depletion) from the soil surface layer at the end

of the dayi-1(the previous day) [mm],

TEW = maximum cumulative depth of evaporation (depletion) from the soil surface

layer when Kr=0(TEW=total evaporable water) [mm]

REW = cumulative depth of evaporation (depletion) at the end of stage 1(REW=readily

evaporable water) [mm].

3.14 Water use Efficiency (WUE)

Root water uptake was computed from the depletion of soil moisture during the

same period for 0-30 cm soil, assuming no drainage occurred as the soil moisture was

very less during this period. Water use efficiency of the crop was calculated as:

……(15)

3.15 Radiation Characteristics

Both incoming and outgoing Photosynthetically Active Radiation (PAR) values

26

were measured at top of crop canopy middle of crop height and bottom of crop

throughout the season using line quantum sensor (LICOR-3000). To get reflected

radiation from top and bottom ground, the sensor was held in inverse position. The

above measurements were taken in all the six growth stages on clear days between 11:30

and 12:00 hours IST when disturbances due to leaf shading and leaf curling and solar

angle were minimum. These data were further used to derive radiation use efficiency.

3.15.1 Absorbed Photosynthetically Active Radiation (APAR)

APAR by the whole canopy = {Incident radiation on the top of the canopy–

reflected radiation by the top of the canopy – incident radiation at the

bottom(transmitted radiation) + reflected from the ground}

3.15.2 Intercepted Photosynthetically Active Radiation (IPAR)

Radiation interception (percent) by the whole canopy = {(Incoming PAR at

top –reflected from the canopy –incoming PAR at bottom)*100}/ (Incoming PAR

at the top)

Radiation penetration from top to bottom (B/T) = {(Incoming-outgoing) PAR at

bottom*100}/(Incoming PAR at mid)

Radiation penetration from mid from mid to bottom (B/M) = {(Incoming –

Outgoing) PAR at bottom*/(Incoming PAR at mid)

3.15.3 Accumulated PAR and incoming short wave solar radiation

The mean daily values of incoming shortwave solar radiation were estimated

using Angstroms equation and the PAR was calculated by multiplying it with

0.48 falling Monteith (1972). The extra terrestrial radiation for daily periods (Ra) was

calculated following Allen et al., 1998 with the following equation

Ra =[(24×60)/π×Gsc×dr×{Ws×Sin(Φ)×Sin(σ)+Cos(Φ)×Cos(σ)×Sin(Ws)}]

where Ra = Extra terrestrial radiation (MJ/m2/day)

Gsc = Solar Constant =0.082 Mj/m2/min

Dr = Inverse relative distance earth –sun

Ws = Sunset hour angle Φ = Latitude (radian) σ = Solar delination

27

dr was estimated by the following equation

dr ={1+0.033xcos( 2π/365×j)}

Where J is the Julian day The incoming shortwave solar radiation (Rs) is calculated by the Angstrom formula

Ra = Ra × (0.32+0.46×n/N) For Delhi condition following Gangopadhyay et al.,

(1970)

Where, n: actual bright sunshine hours for a day N: maximum possible sunshine

hours for the same day.

Where N = (24/π)×Ws Ws is the sunset hour angle (Radian) =Arc Cosine [-ten (Φ) ×tan (σ)] Φ =Latitude in radian, For IARI, Delhi Φ = (28.12×π)/180

σ =solar declination in radiation, calculated as follows σ =0.409 × Sine [(2×π×J)/d-

1.39] Where J = Julian days (1 to 365/366) and d=no. of days in the year Finally, PAR was calculated by PAR =0.48×Rs The cumulated APAR was calculated by =Rs × fAPAR × 0.48 fAPAR: Fraction of Absorbed PAR Radiation use efficiency (RUE) was calculated at weekly interval .

3.15.4 Radiation use efficiency (RUE):

RUE of the crop was expressed as the slope of the curve of biomass

accumulated at various stage and cumulative APAR at that particular stage as given

by

……(16)

28

4. RESEARCH PAPER-I

Estimation of evapotranspiration in mustard crop using different methods

under variable weather conditions.

4.1 Abstract

Understanding crop water needs is essential for irrigation scheduling and

water saving measures in a semi-arid region because of its limited water supply. This

study was performed using single crop coefficient, the dual crop coefficient and soil

water balance equation for predicting seasonal changes in evapotranspiration

(ETc) for mustard crop under variable weather conditions in IARI, New Delhi

during 2011-12. The reference crop evapotranspiration ET0 is an important

parameter f o r calculating the actual crop evapotranspiration (ETc), was estimated

using FAO Penman–Monteith equation. The values suggested by FAO-56 for the

basal crop coefficients (Kcb) after adjustment for the specific climatic condition in

the study area were used. The soil evaporation coefficients (Ke) was determined for

the climate, the soil, the mustard growing stages under variable weather conditions.

The results showed that the value of soil evaporation coefficient was low except

during irrigation and precipitation events. The value of crop evapotranspiration was

found to be more during mid stage in all varieties with respect to different weather

condition. ETc values estimated using dual crop coefficient were low during initial

stage (average value between of 0.53 to 1.32 mm day-1

) except during irrigation

events in the initial stage of crop growth. The ETc value increased during the crop

development stage (average value between 1.01 to 1.89 mm day-1

) and reached its

peak during the mid-season stage (average value between 2.45 to 4.63 mm day-1

),

then the ETc value declined rapidly during the last crop growth stage (average value

between 1.84 to 2.06 mm day-1

). The crop evapotranspiration calculated from dual

crop coefficient was better and more accurate for estimating water needs for mustard

crop.

Key words: Evapotranspiration; dual crop coefficient; mustard, FAO-Penman

monteith equation; crop water requirement

4.2 Introduction

Mustard is a major oilseed crop grown during rabi season. Determination of

crop coefficient under local climatic condition is the base to improve planning and

efficient irrigation management in many field crops.

29

Very cost-effective yields are frequently obtained, as a result of high

radiation rates in summer, combined with modern management techniques. Since

the climate is very dry in the region, irrigation is absolutely necessary for obtaining

reliable yields. Under normal conditions, three irrigations are recommended for

optimum production (ICAR Hand book, 2010). However, considerable amounts of

water diverted for irrigation are not effectively used for crop production (FAO,

1992). The dependence on water for food production has become a critical

constraint to increasing food production. Therefore, the great challenge facing the

agricultural sector is to produce more food from less water by increasing crop

water productivity (Kijne et al., 2003). To improve efficiency of water use in

irrigated agriculture, a comprehensive knowledge of crop water requirement, critical

crop growth stages, and irrigation schedules for maximizing production are highly

desirable along with the availability of adequate amount of water to meet the crop

requirement (Yitaew and Brown, 1990; Kang et al., 2003; Li et al., 2003). Crop

water requirements vary substantially during the growing period due to variation

in crop canopy and climatic conditions (Allen et al., 1998), these are commonly

estimated through the reference crop evapotranspiration (ET0) and crop-coefficient

(Kc). The reference crop evapotranspiration (ET0) can be calculated using many

methods (Shuttleworth, 1992; Kashyap and Panda, 2001; Zhao, 2003; Moges et al.,

2003; Zhao et al., 2005). Among those, the FAO Penman Monteith method is

recommended as the standard method. The single crop coefficient and dual crop

coefficient approaches were used to estimate the ETc. The single crop coefficient

approach is much simpler and more convenient than the dual crop coefficient

method. However, a few studies in the semi-arid region of north-western China

reported that the dual crop coefficient method had a higher accuracy in estimating

the ETc than the single crop coefficient method (Fan and Cai, 2002; Li et al., 2003).

Although some studies on the maize ETc have been documented (Zuo and Xie,

1991; Kang et al., 1994; Su et al., 2002), In mustard no much work has been

done for determining ET c using the dual crop coefficient approach. Therefore

the dual crop coefficient is used in this study because accurate ETc values are

important for real time irrigation scheduling in the crop. The main objective of the

study is to determine water needs of mustard using the basal crop coefficient (Kcb)

and soil evaporation coefficient (Ks) of mustard, and to examine seasonal changes

in the ETc.

30

4.3 Materials and methods

Field experiments

Field experiments were conducted during 2011-12 Rabi season at research

farm of IARI, New Delhi, India. The climate of the station is semiarid with dry hot

summers and cold winter. For creating variable weather conditions three varieties of

mustard viz., Pusa Gold, Pusa Jaikisan and Pusa Bold were sown on 14th

October,

31st October and 14

th November 2011. The crops were raised following the standard

recommended agronomic practices with three replications in a randomized block

design (RBD). Leaf area index (LAI), plant height, and weather parameters were

taken. Number of days required to attain different stages were recorded. The leaf

area index was measured using canopy analyzer (model LICOR-3100). The plant

height was calculated by taking average of height of ten plants from each plot. The

above measurements were taken at weekly intervals. Daily data of maximum and

minimum temperatures, morning and evening relative humidity, rainfall, wind speed,

bright sunshine hours and evaporation rates for season were obtained from the

records of the meteorological observatory of the Division of Agricultural Physics,

located adjacent to the experimental site. Potential evapotranspiration (ET0) were

estimated using FAO Penman–Monteith equation 2 as given in chapter 3. The crop

evapotranspiration were estimated using single crop equation 4, dual crop coefficient

equation 9 and soil water balance equation 1 as given in chapter 3.

4.4 Results

4.4.1 Weather conditions during crop growth and development

The daily weather data recorded at IARI observatory (adjoining the

experimental plots) during the crop growing season were noted for a detailed

analysis. From the daily data collected, mean daily values during different weeks

starting from the date of sowing were computed (in case of rainfall weekly total was

computed) and analyzed to study the effect of weather factors on crop growth and

development.

The maximum temperature during different standard meteorological weeks in

the rabi 2011-12 was observed to be lower than normal except during 45th

, 49th

, 1st

and 8th

to 12th

standard meteorological weeks it was found to be more than normal

and in 42nd

, 46th

and 5th

standard meteorological weeks it was equal to the normal.

The maximum temperature was 0.6 to 2.6°C higher than normal and 0.2 to 2.5°C

lower than normal in different standard meteorological weeks. The difference

31

between observed and normal maximum temperature was 0.2 to 2.6°C during

different standard meteorological weeks.

The minimum temperature remain lower than normal except during 49th

, 1st

and 12th

standard meteorological weeks it was higher than normal and in 45th

, 47th

,

3rd

and 8th

standard meteorological weeks it was equal to the normal. The minimum

temperature was 0.9 to 3°C higher than normal and 0.04 to 7.3°C lower than normal

in different standard meteorological weeks. The difference between observed and

normal maximum temperature was 0 to 7.3°C during different standard

meteorological weeks (Fig. 1).

Total rainfall of 34 mm (normal value 101.4 mm) was received during rabi

2011-12 on 1st (6.6 mm), 3

rd (8.2 mm) and 11

th (19.2 mm) standard meteorological

weeks. The rainfall during the Rabi season was 66 percent less than the normal.

However, the rainfall was received in 3 out of 23 weeks of this season (Fig. 2).

Bright sunshine hours were found to be lower than normal except during 5th

and 9th

standard meteorological weeks it was higher than normal and during 4

th and

8th

standard meteorological weeks it was equal to the normal (Fig. 3). The bright

sunshine hours was 0.7 to 1.2 hours higher than normal and 0.6 to 7.2 hours lower

than normal in different standard meteorological weeks. The difference between

observed and normal maximum temperature was 0 to 7.2 hours during different

standard meteorological weeks.

Evaporation during different weeks in the rabi 2010-11 was observed to be

lower than normal except during 2nd

, 3rd

and 5th

to 10th

standard meteorological

weeks it was more than normal and 4th

and 12th

standard meteorological weeks it was

equal to normal (Fig. 4). The difference between observed and normal pan

evaporation was 0 to 3.2 mm/day during different standard meteorological weeks.

The pan evaporation was 0.1 to 1.8 mm/day higher than normal and 0.1 to 3.2

mm/day lower than normal in different standard meteorological weeks.

Wind speed was found to be lower than normal except 2nd

to 4th

, 6th

, to 10th

and 12th

standard meteorological weeks it was higher than normal value and in 1st

standard meteorological weeks it was equal to normal value (Fig.5). The difference

between observed and normal wind speed was 0 to 3.5 km/hours during different

standard meteorological weeks. The wind speed was 0.8 to 2.8 km/hours higher than

normal and 0.4 to 3.5 km/hours lower than normal in different standard

meteorological weeks.

32

Relative humidity measured at 7.21 A.M. was found to be higher than normal

throughout the crop growing period except 45th

, 52nd

2nd

, 4th

, 6th

to 10th

and 12th

standard meteorological week it was lower than normal. The difference between

observed and normal maximum relative humidity during different standard

meteorological weeks was 0.1 to 13.9. The maximum relative humidity was 1.7 to

9.1 higher than normal and 0.1 to 13.9 lower than normal in different standard

meteorological weeks.

Relative humidity measured at 2.21 A. M. was found to be lower than normal

throughout the crop growing period except 47th

, 49th

, 1st and 3

rd standard

meteorological week it was higher than normal. The difference between observed

and normal minimum relative humidity during different standard meteorological

weeks was 1.0 to 23.3. The minimum relative humidity was 8.3 to 23.3 higher than

normal and 1.0 to 17.4 lower than normal in different standard meteorological weeks

(Fig. 6).

4.4.2 Net radiation

The net daily radiation, the difference between the incoming net shortwave

radiation and the outgoing net long wave radiation, is the fundamental variable for

calculation of evapotranspiration. However, direct measurements were not available

for the study area therefore equation 3 given in the chapter 3 was used for

estimating net radiation. The term in Equation 3 is the net shortwave radiation

an d net long wave radiation was calculated from meteorological data. The net

radiation during crop growing period was sown in the (Fig. 7).

4.4.3 Reference evapotranspiration (ET0)

The Penman-Monteith equation was used for calculating the reference

evapotranspiration. The value of estimated reference evapotranspiration estimated

daily by Penman-Monteith equation is given in Figure 8. Because no reference

evapotranspiration was measured at the experimental site, the estimated reference

crop evapotranspiration was validated by pan evaporation by multiplying the pan

evaporation data with correction factor. The Figure 9 showed the relationship

between estimated reference evapotranspiration and calculated by pan evaporation

had good correlation at R2 = 0.91. The average value of reference

evapotranspiration for all three varieties during different crop stage at different

weather conditions sown are shown in the Table 2.

33

Table 2: Average Reference evapotranspiration estimated at different stage by

Penman-Monteith equation

Varieties Initial Stage Development Stage Mid Stage Late Stage

First Sowing

Pusa Gold 2.99 2.09 1.96 3.66

Pusa Jaikisan 2.88 2.16 2.14 4.27

Pusa Bold 2.88 2.16 2.14 4.27

Second Sowing

Pusa Gold 2.99 2.11 2.05 3.86

Pusa Jaikisan 2.99 2.09 2.04 3.83

Pusa Bold 2.99 2.04 2.06 3.83

Third Sowing

Pusa Gold 2.88 2.16 1.63 2.68

Pusa Jaikisan 2.88 2.08 2.16 2.16

Pusa Bold 2.88 2.08 2.16 2.16

The average reference evapotranspiration had higher value during initial stage of

crop decrease during development stage, reached minimum during mid stage of crop

and again increase during late stage of crop. The higher value of reference

evapotranspiration during initial phase is due to lese crop coverage as compared to

other stage. During mid stage the crop coverage is maximum than other stages.

During late stage crop leaves start drying and crop coverage decrease.

4.4.4 Leaf area index (LAI)

Leaf area index is an important parameter for the crop growth studies since it

is useful in interpreting the capacity of a crop for producing dry matter in terms of

the intercepted utilization of radiation and amount of photosynthesis synthesized.

During the crop season (rabi 2011-12), the maximum leaf area indices under

different weather condition were found to be 3.12, 4.62, 3.89 in Pusa Gold , Pusa

Jaikisan and Pusa Bold respectively at 80 days after sowing (DAS) for first sown

crop. For second sown crop the peak value of LAI at 80 DAS was found 2.09, 3.30

and 2.95 in Pusa Gold, Pusa Jaikisan and Pusa Bold respectively. For third sown

crop the peak LAI was found to be 2.06, 2.95, and 2.87 at 70 DAS in Pusa Gold,

Pusa Jaikisan and Pusa Bold respectively.

The first sown crop has higher value of LAI as compared to second and third

34

sown crop. The percentage reduction in peak LAI was 33, 29 and 24 in Pusa Gold,

Pusa Jaikisan and Pusa Bold respectively in second sown crop as compared to first

sown crop and 34, 36 and 26 percent in third sown crop with respect to first sow crop

in Pusa Gold, Pusa Jaikisan and Pusa Bold, respectively. It was further observed that

the maximum LAI was at 80 DAS in case of early sown crops while in contrast with

the early sowing, the late sown crops achieved maximum LAI ten days earlier. It was

observed that the leaf area index (LAI) was higher in Pusa Jaikisan followed by Pusa

bold and Pusa Gold. The first sown crop showed 16% and 32% higher LAI in Pusa

Jaikisan as compared to Pusa Bold and Pusa Gold. The second sown crop showed

10% and 36% higher LAI in Pusa Jaikisan as compared to Pusa Bold and Pusa Gold.

The third sown crop showed 3% and 43% higher LAI in Pusa Jaikisan as compared

to Pusa Bold and Pusa Gold. The peak leaf area index was highest in Pusa Jaikisan

followed by Pusa Bold and Pusa Gold in three respective sowings (Fig. 10). Leaf

area index increased and reached a maximum around pod formation stage (45%

flower to 85% pod stage) irrespective of weather conditions. Later, the leaf area

index declined rapidly. Senescence and abscission coincided with onset of flowering

and completed well before maturity. In all the three cultivars maximum leaf area

index was attained in the first sowing.

4.4.5 Crop stages:

The number of days taken to reach different stages during the crop growth

period were noted carefully from day-to-day observations. According to FAO-56 the

different stage initial, the development, mid- season, and late season stages were

calculated based on the crop coverage data as suggested by FAO-56 given in Chapter

3 and shown in the (Table 3)

Pusa Gold

This variety had initial stage up to 40 days after sowing in first and second

sowing and up to 45 days after sowing in the third sowing. The development stage

was between 40 to 60, 40 to 55 and 45 to 60 days after sowing in different weather

conditions. The mid stage was between 60 to 120, 55 to 125 and 60 to 100 days after

sowing in different weather conditions. The late stage was between 120 to 150, 125

to 140 and 100 to 124 days after sowing in different weather conditions (Table 3).

Pusa Jaikisan:

Pusa Jaikisan had initial stage up to 45 days after sowing in first and third

sowing and up to 40 days after sowing in the second sowing. The development stage

35

was between 45 to 60, 40 to 60 and 45 to 65 days after sowing in different weather

conditions. The mid stage was between 60 to 130, 60 to 125 and 65 to 130 days after

sowing in different weather conditions. The late stage was between 130 to 164, 125

to 149 and 130 to 145 days after sowing in different weather conditions.

Pusa Bold

Pusa Bold had initial stage up to 45 days after sowing in first and third

sowing and up to 40 days after sowing in the second sowing. The development stage

was between 45 to 60, 40 to 65 and 45 to 65 days after sowing in different weather

conditions. The mid stage was between 60 to 130, 65 to 125 and 65 to 130 days after

sowing in different weather conditions. The late stage was between 130 to 164, 125

to 149 and 130 to 145 days after sowing in different weather conditions.

Table 3 Crop stage of different varieties of mustard at variable weather

conditions

Varieties Initial Stage Development Stage Mid Stage Late Stage

First Sowing

Pusa Gold 40 40-60 60-120 120-150

Pusa Jaikisan 45 45-60 60-130 130-164

Pusa Bold 45 45-60 60-130 130-164

Second Sowing

Pusa Gold 40 40-55 55-125 125-140

Pusa Jaikisan 40 40-60 60-125 125-149

Pusa Bold 40 40-65 65-125 125-149

Third Sowing

Pusa Gold 45 45-60 60-100 100-124

Pusa Jaikisan 45 45-65 65-130 130-145

Pusa Bold 45 45-65 65-130 130-145

4.4.6 Height of mustard

The temporal variations of height of mustard are shown in figure 11. The

height of mustard increased slowly in initial stages, more rapidly in development

stages and after mid stage it becomes constant with respect to varieties and weather

conditions. During mid stage the average height was between 0.8 to 1.5 m, 1.8 to

1.822 m and 1.81 to 1.83 m during first sowing, 0.39 to 1.49m, 1.66 to 1.72 m

during second sowing and 0.8 to 1.36, 1.56 to 1.64 and 1.50 to 1.57 during third

36

sowing for Pusa Gold, Pusa Jaikisan and Pusa Bold, respectively. During late

stage the average height was 1.50 m, 1.82 m and 1.83 m during first sowing,

1.49 m, 1.72 m and 1.70 m during second sowing and 1.33 m, 1.64 m and 1.57

m during third sowing for Pusa Gold, Pusa Jaikisan and Pusa Bold, respectively.

4.4.7 Single crop coefficient approach for mustard

The calculated crop coefficient for initial stage, the mid stage and late stage for

different variables of mustard under weather conditions are shown in table 4. Plant

parameters influencing the crop coefficient calculation are soil cover and plant height

and a climatic correction for relative humidity and wind speed. The value of Kc for Pusa

Gold during mid stage ranged between 1.186, 1.179 and 1.178 under different weather

conditions. For Pusa Jaikisan the value of Kc during mid stage ranged between 1.118,

1.121 and 1.127 under different weather conditions. The Kc values for Pusa Bold

ranged between 1.114, 1.118 and 1.125 under different weather conditions. Using

values of Kc ini, Kc mid and Kc end the crop coefficient curve for mustard was

developed which is presented in Figure 12.

Table 4 Adjusted Single crop coefficient of mustard at different stage

Varieties Initial Stage Mid Stage Late Stage

First Sowing

Pusa Gold 0.350 1.186 0.350

Pusa Jaikisan 0.350 1.118 0.333

Pusa Bold 0.350 1.114 0.331

Second Sowing

Pusa Gold 0.350 1.179 0.350

Pusa Jaikisan 0.350 1.121 0.333

Pusa Bold 0.350 1.118 0.331

Third Sowing

Pusa Gold 0.350 1.178 0.350

Pusa Jaikisan 0.350 1.127 0.331

Pusa Bold 0.350 1.125 0.330

4.4.8 Basal crop coefficient (Kcb) for Mustard

The Kcb curve is divisible into four growing stage periods, i.e. the initial

stage, the development stage, the mid- season stage, and the late season stage. The

observed dates and lengths of the four growing stages for mustard are given in

37

Table 5. The Kcb values suggested by FAO-56 are 0.15, 1.15, and 0.25,

respectively, for the initial, mid-season, and late season stages. They were adjusted

using Equation (11) for the climatic conditions of the study area. After adjustment,

the Kcb of mustard in the initial stage was 0.15 for all varieties under different weather

conditions but the Kcb value in mid stage were different for different varieties under

different weather conditions. The value for Kcb in mid stages for Pusa Bold were

1.1358, 1.1292 and 1.1275, for Pusa Jaikisan it was 1.067, 1.071 and 1.0767 and for

Pusa Bold the value of Kcb at mid stage was 1.0643, 1.068 and 1.0745 respectively

under different weather conditions (Table 5). The daily Kcb values were determined

using Equation (11) as given in chapter 3, and the crop coefficient curve could then

be drawn (Figure 13).

Table 5: Adjusted basal crop coefficient of mustard at different stage

Varieties Initial Stage Mid Stage Late Stage

First Sowing

Pusa Gold 0.15 1.14 0.22

Pusa Jaikisan 0.15 1.07 0.23

Pusa Bold 0.15 1.06 0.23

Second Sowing

Pusa Gold 0.15 1.13 0.21

Pusa Jaikisan 0.15 1.07 0.23

Pusa Bold 0.15 1.07 0.23

Third Sowing

Pusa Gold 0.15 1.13 0.21

Pusa Jaikisan 0.15 1.08 0.23

Pusa Bold 0.15 1.07 0.23

4.4.9 Daily calculation of soil evaporation coefficient (Ke)

Soil evaporation coefficient Ke, as a function of growth period is affected by

the soil water characteristics, exposed and wetted soil fraction, and soil water

balance. The variation of Ke in the mustard growing season are shown in the figure

14 .In the initial stage, the effective fraction of soil surface covered by crop was

small, and thus, soil evaporation losses were high during the period. Following

irrigation, Ke reached its maximum values (1.04 to 1.07). Ke had a sharp fall when

38

the soil evaporation switched from stage 1 to stage 2. In the development stage, the

effective fraction of soil surface covered by mustard crop increased, and the Ke

value decreased step by step. In the mid-season stage, the effective fraction of soil

surface covered by mustard reached maximum and the soil water losses mainly

depended on the crop transpiration. The small exposed soil fraction resulted in a

small Ke value. The value of Ke increased after irrigation and precipitation and

decreased simultaneously.

4.4.10 Crop evapotranspiration (ETc) using single crop coefficient

The crop evapotranspiration (ETc) calculated using single crop coefficient for

different varieties under different weather condition are sown in the Figure 15.

During the initial stage of crop growth, which is the period from sowing through

40-45 days, the ETc values are very low except during irrigation events. The ETc

values increase during the crop development stage (40-65 days) and reach its peak

during the mid-season stage (55-130 days). The ETc values decline rapidly during

the last crop growth stage, the period from 120 to 164 days. The average values of

ETc for Pusa Gold in the initial stage, development stage, mid stage, and late stage

are 1.048, 1.631, 2.330 and 1.297 mm/day; 0.85, 1.31, 2.971 and 1.432; 0.68,

1.434, 3.153 and 1.424 respectively under different weather condition (Table 6).

Table 6: Average value of crop evapotranspiration estimated using single crop

coefficient approach in mustard at different stage

Varieties Initial Stage Development Stage Mid Stage Late Stage

First Sowing

Pusa Gold 1.048 1.631 2.330 1.297

Pusa Jaikisan 1.008 1.607 2.396 1.434

Pusa Bold 1.008 1.702 2.389 1.426

Second Sowing

Pusa Gold 0.850 1.310 2.971 1.432

Pusa Jaikisan 0.850 1.320 2.924 1.516

Pusa Bold 0.850 1.228 3.032 1.501

Third Sowing

Pusa Gold 0.680 1.434 3.153 1.424

Pusa Jaikisan 0.680 1.138 4.001 1.803

Pusa Bold 0.680 1.273 3.993 1.793

39

For Pusa Jaikisan the average values of ETc in the initial stage, development stage,

mid-season stage, and late season stage are 1.008, 1.607, 2.396 and 1.434 mm day-1

,

0.85, 1.32, 2.924 and 1.516 and 0.68, 1.138, 4.001and 1.803 respectively under

different weather condition (Table 6). For Pusa Bold the average values of ETc in

the initial stage, development stage, mid stage, and late stage are 1.008, 1.702, 2.389

and 1.426 mm day-1

, 0.85, 1.228, 3.032 and 1.501 and 0.68, 1.273, 3.993 and

1.793 respectively under different weather condition (Table 6). In general, the c rop

evapotranspiration (ETc) value was 254, 284 and 213mm for Pusa Gold; 287, 288

and 341mm for Pusa Jaikishan and 288, 284 and 343 mm for Pusa Bold at the

experiment site in the growing season (Table 7).

C rop evapotranspiration (ETc) value estimated using soil water balance

equation was 307, 284 and 274 mm for Pusa Gola; 335, 328, 300 mm for Pusa

Jaikisan and 343, 310 and 293 mm for Pusa Bold atbthe experiment site in the

growing season (Table 7).

Table 7 Total Crop evapotranspiration estimated using different approach in

mustard at different stage

Varieties Single Crop Coefficient Dual Crop Coefficient Soil Water Balance

First Sowing

Pusa Gold 254.26 288.22 306.50

Pusa Jaikisan 286.94 335.87 335.43

Pusa Bold 287.57 334.96 342.98

Second Sowing

Pusa Gold 283.97 308.46 284.00

Pusa Jaikisan 287.54 312.25 328.12

Pusa Bold 283.50 310.04 310.43

Third Sowing

Pusa Gold 213.11 236.79 274.00

Pusa Jaikisan 341.13 368.61 300.00

Pusa Bold 343.20 370.65 293.00

4.4.11 Crop evapotranspiration (ETc) using dual crop coefficient

The crop evapotranspiration (ETc) calculated using dual crop coefficient for

different varieties under different weather condition are sown in the Figure 16. During

40

the initial stage of crop growth, which is the period from sowing through 40-45

days, the ETc values are very low except during irrigation events. The ETc values

increase during the crop development stage (40-65 days) and reach its peak during

the mid-season stage (55-130 days). The ETc values decline rapidly during the last

crop growth stage, the period from 120 to 164 days. In general, the c rop

evapotranspiration (ETc) value was 288, 308 and 237 mm for Pusa Gold; 336, 312

and 369 mm for Pusa Jaikisan and 335, 310 and 371 mm for Pusa Bold at the

experiment site in the growing season (Table 7). The average values of ETc for Pusa

Gold in the initial stage, development stage, mid-season stage, and late season

stage are 1.32,1.81, 2.46 and 1.68 mm/day; 0.68, 0.94, 3.39 and 1.95 mm/day; 0.53,

1.23, 3.70 and 1.88 mm/day, respectively under different weather condition (Table

8).

Table 8 Average value of crop evapotranspiration estimated using dual crop

coefficient of mustard at different stage

Varieties Initial Stage Development Stage Mid Stage Late Stage

First Sowing

Pusa Gold 1.32 1.81 2.46 1.68

Pusa Jaikisan 1.21 1.89 2.60 2.06

Pusa Bold 1.21 1.88 2.59 2.05

Second Sowing

Pusa Gold 0.68 0.94 3.39 1.95

Pusa Jaikisan 0.68 1.08 3.37 1.81

Pusa Bold 0.68 1.11 3.52 1.81

Third Sowing

Pusa Gold 0.53 1.23 3.70 1.88

Pusa Jaikisan 0.53 1.21 4.63 1.26

Pusa Bold 0.53 1.35 4.47 1.25

For Pusa Jaikisan the average values of ETc in the initial stage, development stage,

mid season, development and late season stage was 1.21, 1.89, 2.6, 2.06 mm/day;

0.68, 1.08, 3.37, 1.81 mm/day and 0.53, 1.21, 4.63, 1.26 mm/day respectively under

different weather conditions (Table 8). For Pusa Bold the average values of ETc in

the initial stage, development stage, mid season, development and late season stage

41

was 1.21, 1.88, 2.59, 2.05 mm/day; 0.68, 1.11, 3.52, 1.81 mm/day and 0.53, 1.35,

4.47, 1.25 mm/day respectively under different weather conditions (Table 8).

4.5 Discussion

Among the plant growth parameters, Leaf area index is the most important

parameters exhibiting overall performance of the growth and development under

varying weather conditions. During the crop season (rabi 2011-12), the maximum

leaf area indices were found to be at 80 days after sowing (DAS) for first sown crop,

78 DAS for second sown crop and at 70 DAS for third sown crop. The first sown

crop has higher value of LAI as compared to second and third sown crop in all three

cultivars. Rao and Agarwal (1986) reported that, the maximum LAI was found at 90

DAS and thereafter declined towards maturity. Working on Brassica napus cv. B.O.

54, B. juncea cv. Pusa bold and B. campestris, Kar and Chakravarty (2000) reported

that LAI was lower in a season with higher temperatures (2 to 3°C) during vegetative

and grain filling stages as compared to the season with relatively lower temperatures

in the same period. Working on three varieties Pusa Jaikisan, Varuna and Agrani

under Delhi condition, Roy (2003) reported that delay of a fortnight sowing from 1st

October to 15th

October and to 1st November reduced LAI by 8.3 and 52.8 per cent in

Agrani, 12.9 and 45.4 per cent in Pusa Jaikisan and 8.8 and 45.6 per cent in Varuna

varieties. In late sown crop was infested by aphid and Leaf area index was found to

be higher in healthy crop as compared to aphid infested crop. This was most likely

due to degenerated internal leaf structure, reduced leaf area and stunting plant growth

caused by aphid feeding (Castro and Rumi, 1987, Castro et al 1988, Morgham et al.,

1994).

Although the Kc and Kcb exhibited similar kind of response to differential

weather conditions but Kcb was able to produce some higher difference during mid

stage with respect to weather conditions and varieties. During this active growth

period the percentage of bare ground was very less owing to the faster development

of canopy which made the transpiration component much higher than that of

evaporation. As the Kcb is more related to transpiration than Kc which integrates both

the transpiration and evaporation components, made use of Kcb better indicator of

crop water use per se. In one experiment total ETc at experimented sites during crop

growing period ranged between 213-343 estimated by different approach for

different varieties at variable weather conditions. Bandyopadhyay and Mallick

(2003) observed that no significant difference existed between estimated and FAO

42

reported Kc values of predetermined four stages for wheat but our results suggest that

the average Kc and Kcb values in mustard differed. This may be attributed to the fact

that earlier study was made under the humid tropical climatic conditions whereas our

study was conducted under the semi-arid subtropical climatic regions of Delhi. This

ushered the need of development of regional and growth stage-specific crop

coefficient values of different crops for precise application of irrigation water which

otherwise lead to over or under irrigation having negative impacts.

Su et al. (2002) reported the seasonal maximum water use of maize at the

experimental site has been reported to be 651.6 mm under surface irrigation

conditions, with the average values of water use 1.2, 2.7, 5.3, and 3.3 mm day-1

,

respectively, in the initial, development, mid-season and late season stages. Cai et

al. (2003) reported an ETc value of 600 mm for the maize growing season in the

Jingtai irrigation district. An average ETc value of 621 mm has been reported for the

Hexi area of Gansu province in which the study area is located (Liu et al., 2005).

Crop water requirement vary during the growing period, mainly due to

variation in crop canopy and climatic conditions, and related to soil-crop

management and irrigation methods. Nearly 99% of water uptake by plants accounts

for evapotranspiration (ET) and hence that the management of actual crop

evapotranspiration (ETc) on a daily scale over the whole vegetative cycle can be

assumed as equivalent to the water requirement of the given crop. Knowledge of

precise crop water requirement is crucial for water resources management and

planning in order to improve water use efficiency at regional, national and global

scale (Hamdy and Lacirignola, 1999; Katerji and Rana, 2008). Therefore, it is

necessary to improve the water use efficiency in agriculture to sustain the natural

input resource (water) and the crop production in order to meet the demand of food

for billions of people in coming future. This can only be achieved by employing

proper technologies for saving of irrigation water at each application time.

4.6 Conclusion

The FAO Penman-Monteith method was used to estimate the ET0 value in

the s tudy are had good correlation with evaporation calculated by pan

evaporation (the correlation coefficient, R2 = 0.91). The value ET0, ranged from

1.4 to 6.5 mm day-1

at the experiment site during crop growing period. The total

ETc value calculated by dual crop coefficient was ranged between 237 mm to 371

mm; 213 mm to 343 mm as calculated by single crop coefficient and 274 mm to 343

43

mm as calculated by soil water balance. Since ETc calculated by dual crop

coefficient consider the effect of soil evaporation and crop transpiration both

therefore the value calculated by dual crop coefficient are more reliable as compared

to single crop coefficient. The ETc value was found to be less during initial stage,

increased during development stage and reached maximum during mid stage.

44

RESEARCH PAPER II

Estimation of water use efficiency of mustard crop under variable weather

conditions

5.1 Abstract

Field experiment was conducted at IARI during Rabi 2011-12 for

understanding crop water needs required for irrigation in mustard. Three varieties of

mustard were sown at three different dates for creating different weather condition

for different crop stages. Crop evapotranspiration were calculated using single crop

coefficient, dual crop coefficient and water balance equation. The crop biomass,

radiation interception, soil moisture at regular interval and seed yield were measured.

Results showed higher value of biomass, seed yield, water use efficiency and

radiation use efficiency in Pusa Jaikisan followed by Pusa Bold and Gold. The value

of biomass, seed yield and radiation use efficiency was found to be more in first

sowing followed by second and third sowing. The value of water use efficiency

calculated using dual crop coefficient were more near to the value of water use

efficiency calculated by soil water balance as compared to the value calculated by

single crop coefficient. From the above studies it can be concluded that water need

requirement in mustard crop can be estimated more accurately by dual crop

coefficient approach as compared to single crop coefficient and soil water balance

because water use calculated by dual crop coefficient consider both soil evaporation

coefficient and basal crop coefficient.

Key words: Water use efficiency, biomass, seed yield, Radiation use efficiency.

5.2 Introduction

Long spell of drought and competing water demands in most parts have put

enormous pressure on water resources. Steps need to be taken to conserve both the

quantity and quality of water and appropriate strategies will have to be developed to

avoid risk to future water supplies. One of the ways by which we can reduce the total

water used for irrigation is to employ practices that improve crop yield per unit

volume of water used i.e., water use efficiency. Incresed water use efficiency of

crops was possible through proper irrigation scheduling by providing only the water

that matches the crop evapotranpiration and providing irrigation at critical stages

(Eck, 1984; Turner, 1987; Wang, 1987; Hunsaker et al., 1996; Wang et al., 2001;

45

Kipkorir et al., 2002; Norwood and Dumler, 2002; Kar et al., 2005). For most

agricultural crops a relation can be established between evapotranspiration and

climate by the introduction of the crop coefficient (Kc), which is the ratio of crop

evapotranspiration (ETc) to reference evapotranspiration(ET0) (Doorenbos and

Kassam, 1979). Refernce crop evapotranspiration (ET0) can be estimated by many

methods. Since localized Kc values are not always available in many parts of India

and due to lack of locally determined crop water use data, the values of Kc as

suggested by FAO (Allen et al., 1998) are being widely used to estimate crop water

requirements. In all cases, no or very little attempt was made to estimate crop water

requirements. In all cases, no or very little attempt was made to experimentally

verify the estimates locally. Much of known about the crop water requirements of

important cereals like wheat, rice etc. using field water balance and/or lysimeter

study in field experimental plots at various agro-ecological conditions of India

(Prihar et al., 1976; Singh and Sinha, 1987; Singh, 1989; Tyagi et al., 2000) but

crop water requirements of some pulses and oilseeds are to be known. In the

present paper, the water use efficiency were calculated using crop

evapotranspiration estimated by single crop coefficient and soil water balance

equation.

5.3 Material and Methods

Field experiments were conducted during Rabi 2011-12 at IARI, research

farm. Three varieties of mustard were sown at three different dates for creating

different weather condition for different stages. The crop parameters and soil

moisture at different intervals and final seed yield were measured. The water use

efficiency was calculated using the equation given in chapter 3.

The samples collected for estimating leaf area index were utilized for

assessing the biomass production. Plants samples were oven dried at 65°C for 48

hours or more until constant weight is achieved in order to estimate the accumulation

of dry matter in different plant parts. The both incoming and outgoing

Photosynthetically Active Radiation (PAR) values were measured at three heights

viz. top, middle (50 percent canopy height) and bottom of mustard crop throughout

the season using line quantum sensor (LICOR-3000). To get reflected radiation from

top, middle and bottom ground, the sensor was held in inverse position. The above

measurements were taken at weekly intervals on clear days between 11:30 and 12:00

hours IST when disturbances due to leaf shading and leaf curling and solar angle

46

were minimum. These data were further used to derive radiation use efficiency by

the equation 17 given in chapter 3. The summation of mean temperature above a

base value represents the growing degree-days (GDD) or thermal time. Daily data of

maximum and minimum temperatures, for growing season were obtained from the

records of the meteorological observatory of the Division of Agricultural Physics,

located adjacent to the experimental site. These data were used to calculate GDD.

GDD = (Tmax + Tmin) / 2 – Tb °D

Where, Tmax and Tmin represent the daily maximum and minimum temperatures and

Tb is considered as 5°C.

Percentage oil content of the seeds for each plot was measured using low

resolution pulsed H1 NMR (model no. PC20 Bruker made, frequency- 20MHz) in

the Nuclear Research Laboratory, IARI. For this purpose, 10g of dry and clean seeds

from each plot were kept for drying at 1050C in the oven and then kept for

desiccating till measurement was taken. About 2 to 3g desiccated seeds were inserted

into the NMR and the signal was recorded. Finally, oil content (percent) was

determined using calibration curves. Since sensitivity of the NMR instrument

depends on any change in Instrumental components, air temperature and relative

humidity, it is recommended to calibrate the instrument before taking reading in each

time. Equation of calibration curves with oil content is given as follows:

Oil content (per cent) = {(Signal + intercept)/ (Weight of seeds × slope)} × 100

Y= 1.3156× X -0.0727 (R2 =0.99)

5.4 Statistical Analysis

The data was analysed using the software SPSS 16.0 and MS office excel.

Computation of correlation coefficients, critical difference and student t test was

carried out using Excel and SPSS packages.

5.5 Results

5.5.1 Biomass production:

Biomass production of the plant is the process of organic substance formation

of carbohydrates, the products of photosynthesis and from small quantity of

inorganic substance absorbed by roots from the soil. The timely accumulation of dry

matter by the crop is important as it is followed by adequate translocation of

assimilates to the sink resulting in higher yield. The higher biomass in the first

sowing dates may be due to favourable weather during crop growth period. The

47

maximum above ground biomass in the first sowing was observed in first sowing

Pusa Jaikisan (1890 g/m2) followed by Pusa Bold (1734 g/m

2) and Pusa Gold (892

g/m2). Similarly, in case of second sowing, the corresponding behaviour of biomass

production was observed under Pusa Jaikisan (1260 g/m2) followed by Pusa Bold

(1252 g/m2) and Pusa Gold (713 g/m

2). In third sown crop the corresponding value

of biomass production were 677 g/m2 for Pusa Jaikisan, 642 g/ m

2 for Pusa Bold and

417 g/m2 for Pusa Gold (Fig. 17). The reduction in the magnitude of maximum

biomass production in second sown crop as compared to first sown crop was 20%,

33% and 28% in Pusa Gold, Pusa Jaikisan and Pusa Bold. Biomass production was

further reduced in third sown crop by 53%, 64% and 63% in Pusa Gold, Pusa

Jaikisan and Pusa Bold as compared to first sown crop. Thus, it may be inferred that

biomass production in all three varieties was higher in first sown crops as compared

to late sown crops, which might be due to more favourable weather condition for

first sown crop as compared to other two dates during crop growing period.

Among the three varieties, it can be concluded that Pusa Jaikisan produced

higher biomass as compared to Pusa Bold and Pusa Gold irrespective of sowing

dates which might be due to higher leaf area index, leaf area duration and more

proliferating nature. The biomass production was higher in Pusa Jaikisan by 8%,

0.7% and 5% as compared to Pusa Bold and 53%, 43% and 62% as compared to

Pusa Gold in first, second and third sown crop.

5.5.2 Thermal Response and Biomass Production

As the ambient daily temperatures are highly variable, the response of the

plants to the thermal environment for their growth and development can be better

expressed through the accumulated heat units instead of temperatures. Growing

Degree Days (GDD) are the most common and simple ways of quantifying the

thermal environment. Degree-day based approach is based on the premise that plants

need a certain definite amount of accumulated heat to fulfil their requirement for

phenological development. Differentiation in phenological events does not take place

until this requirement is met. The basic concept of heat unit assumes a linear or

logarithmic relationship between growth and temperature, which is predicted by

Vant Hoff‘s Law. Heat unit is a measure of departure of mean daily temperature

from a base temperature below which the internal biochemical activity ceases. The

response of plant growth parameters (LAI, biomass and seed yield) to the prevailing

thermal environment (represented by thermal units GDD) can be depicted by curves,

48

termed as ―thermal response curves‖. Thermal response curves may serve as ready

reference for expressing the relationship of growing degree-days with LAI and

biomass production and these curves can be used for predicting biological or

economical yield of a crop well in advance, besides in crop simulation studies.

LAI and biomass was significantly correlated with GDD. It was observed

third order polynomial equations in biomass that 97 to 100 per cent variation in

production could in Pusa Gold (Fig. 18) and Pusa Jaikisan (Fig.19) and 98 to 99 in

Pusa Bold (Fig. 20) could be explained through the accumulated heat unit (GDD),

when crop was sown in variable weather conditions.

5.5.3 Seed Yield

During the crop season the seed yields of Pusa Gold, Pusa Jaikisan and Pusa

Bold were 1225, 2522 and 2475 Kg/ha in first sown crop (14th

October). In second

sown crop (31st October) the seed yield were 1080, 2512 and 2258 Kg/ha, while the

yield were lowest in third sown crop (16th

November) and the value were 434, 1587

and 1583 Kg/ha in Pusa Gold, Pusa Jaikisan and Pusa Bold, respectively. Delay in 15

days sowing from 14th

October decreased seed yield to the 14% in Pusa Gold, 0.4%

in Pusa Jaikisan and 9% in Pusa Bold, respectively. Further delay in sowing by 15

days reduced the yield to 65% in Pusa Gold, 37% in Pusa Jaikisan and 36% in Pusa

Bold. Early sown crop yielded higher seed yield than late sown crop in all varieties.

The seed yield was found to be higher in Pusa Jaikisan followed by Pusa Bold and

Pusa Gold in all three date of sowing. Pusa Jaikisan have 51, 57 and 72% higher

yield than Pusa Gold in first, second and third sown crop respectively and 2, 10 and

0.3% higher yield than Pusa Bold in first, second and third sown crop respectively

(Table 9 ).

Table 9: Seed Yield (Kg/ha) in different varieties of mustard grown at variable

weather conditions

Varieties 14th October, 2011 31

th October, 211 16

th November, 2012

Pusa Gold 1225.0 106.1 1080.0 125.7 434.2 85.7

Pusa Jaikisan 2522.5 157.6 2512.5 149.4 1587.5 169.9

Pusa Bold 2475.0 274.1 2258.3 40.8 1583.3 102.7

49

5.5.4 Percentage Oil content:

The percentage oil content was 32.14, 31.33 and 30.05 in Pusa Gold, 35.85,

35.09, 33.89 in Pusa Jaikisan and 34.05, 33.99 and 32.09 in Pusa Bold respectively

grown in variable weather conditions.

Table 10: Percentage oil content in mustard grown at variable weather

conditions

Varieties First sowing Second sowing Third sowing

Pusa Gold 32.14 0.28 31.33 0.25 30.05 0.27

Pusa Jaikisan 35.85 0.29 35.09 0.44 33.89 0.36

Pusa Bold 34.05 0.42 33.99 0.45 32.09 0.44

The percentage oil content was found to be slightly more in the first sown

crop followed by second and third sown crop. The percentage reduction in oil

content for second sowing was 2.5, 2.1 and 0.2 % in Pusa Gold, Pusa Jaikisan and

Pusa Bold as compared to first sowing. However further delay in sowing by 15 days

for third sowing reduced the oil content by 6.5, 5.5 and 5.8% in Pusa Gold, Pusa

Jaikisan and Pusa Bold as compared to first sowing. The percentage oil content in

Pusa Jaikisan was higher than the Pusa Gold and Pusa Bold in all the three dates of

sowing. The percentage oil content was higher in Pusa Jaikisan by 10.3, 10.7, 11.3 %

and 5, 3.1, 5.3 % as compared to Pusa Gold and Pusa Bold respectively under

different weather conditions.

5.5.5 Water use efficiency (WUE):

The values of water use efficiency (WUE) calculated based on the single crop

coefficient were found to be 4.82, 3.80, 2.04 Kg/ha/mm for Pusa Gold, 8.79, 8.74

and 4.65 kg/ ha/mm for Pusa Jaikisan and 8.61, 7.97 and 4.61 kg/ha/mm for Pusa

bold under different weather conditions (Fig 20). The value of Water use efficiency

(WUE) calculated based on the dual crop coefficient were found to be 4.25, 3.50,

1.83 kg/ha/mm for Pusa Gold, 7.51, 8.05 and 4.31 kg/ ha/mm for Pusa Jaikisan and

7.39, 7.28 and 4.27 kg/ha/mm for Pusa bold under different weather conditions ( Fig

21b). The value of Water use efficiency (WUE) calculated based on the water

balance equation were found to be 4.0, 3.80, 1.59 kg/ha/mm for Pusa Gold; 7.52,

7.66 and 5.29 kg/ha/mm for Pusa Jaikisan and 7.22, 7.28 and 5.40 kg/ha/mm for

50

Pusa bold under different weather conditions ( Fig. 21c ).

The water use efficiency calculated by all three approaches was found to be

more in Pusa Jaikisan followed by Pusa bold and Pusa Gold. The value of water use

efficiency calculated using dual crop coefficient were more near to the value of water

use efficiency calculated by soil water balance as compared to the value calculated

by single crop coefficient. In case of Pusa Gold and Pusa Bold the water use

efficiency was more in first sown crop followed by second and third sown crop.

However in case of Pusa Jaikisan the value of water use efficiency was found to be

more in second sown crop followed by first and third sown crop when calculated by

soil water balance equation and dual crop coefficient approach. Pusa Jaikisan had 45

to 57% and 1 to 8.8% more value of water use efficiency as compared to Pusa Gold

and Pusa Bold respectively in different weather conditions, when calculated by

single crop coefficient approach. Water use efficiency when calculated by dual crop

coefficient had 43 to 57% and 0.8 to 9.5 % more value in Pusa Jaikisan as compared

to Pusa Gold and Pusa Bold respectively in different weather conditions. Pusa

Jaikisan had 47 to 70% and 0 to 5% more value of water use efficiency as compared

to Pusa Gold and Pusa Bold respectively in different weather conditions when

calculated by water balance equation.

5.5.6 Radiation Use Efficiency (RUE)

During the crop growing period the peak value of RUE (g/MJ) was 4.59,

5.73, 5.65 g/MJ and 4.01, 5.50, 5.33 for Pusa Gold, Pusa Jaikisan, Pusa Bold in first

and second sown crop at 100 days after sowing while the peak value of RUE for

third sown crop was 3.38, 4.02 and 3.70 at 70 days after sowing (Table.11). The first

sown crop had higher value of RUE as compared to second sown and third sown

crop. The percentage reduction of peak value of radiation use efficiency was 13, 4, 6

% and 26, 29 and 35% for Pusa Gold, Pusa Jaikisan and Pusa Bold respectively in

second and third sown crop as compared to first sown crop. Pusa Jaikisan has higher

value of RUE followed by Pusa Bold and Pusa Gold. Pusa Jaikisan had 20, 27 and

38% higher value of RUE as compared to Pusa Gold and 1, 3 and 16% as compared

to Pusa Bold in different weather conditions.

51

Table 11: Radiation Use efficiency (RUE) of different varieties of mustard

grown under different weather conditions.

DAS First Sowing Second sowing Third sowing

Pusa

Gold

Pusa

Jaikisan

Pusa

Bold

Pusa

Gold

Pusa

Jaikisan

Pusa

Bold

Pusa

Gold

Pusa

Jaikisan

Pusa

Bold

40 3.99 4.15 4.09 3.40 4.05 4.02 3.23 3.56 3.85

70 3.89 4.99 4.37 3.77 4.19 4.04 3.38 4.02 3.70

100 4.59 5.73 5.65 4.01 5.50 5.33 2.19 3.52 2.94

130 2.19 2.78 2.68 1.48 1.65 1.63 1.02 1.24 1.32

5.6 Discussion

Biomass production in all three varieties was higher in first sown crops as

compared to late sown crops, which might be due to more favourable weather

condition for first sown crop as compared to other two dates during crop growing

period. The biomass production levels obtained in the present study and the

reduction of biomass production due to late sowing are in conformity with the earlier

findings of Bhargava (1991), Das (1995) and Kar and Chakravarty (2001). Leaf area

index (LAI) and biomass production in Brassica species were reported to be

positively correlated with GDD accumulation during the crop growth period

(Chakravarty and Sastry, 1983 and Patel and Mehta, 1987). The response of plant

growth parameters (LAI, biomass and seed yield) to the prevailing thermal

environment (represented by thermal units GDD) can be depicted by curves, termed

as ―thermal response curves‖. Thermal response curves may serve as ready reference

for expressing the relationship of growing degree-days with LAI and biomass

production and these curves can be used for predicting biological or economical

yield of a crop well in advance, besides in crop simulation studies.

During the crop season the RUE (g/MJ) for the first sown crop had higher

value as compared to second sown and third sown crop. The results are in

conformity with the earlier findings of researchers (Kar and Chakravarty, 1999,

Dhaliwal and Hundal, 2004 and Pandey et al., 2007) who reported RUE in the range

of 1.0 to 5.0 g/MJ in different mustard varieties grown under varied thermal regimes.

Early sown crop yielded higher seed yield than late sown crop in all varieties.

Cold spell during initial period for third sown crop might have restricted growth.

52

Incidentally, relatively higher temperatures at the later stage of the crop growth

resulted early maturity.

There was reduction in seed yield due to delay of sowing (Roy and

Chakravarty, 2002). The yield attributes and yield of mustard significantly decreased

in delayed sowing even under protected conditions (Patel et al., 2004). The results

are in conformity with the earlier findings of researchers (Mendham et al., 1990;

Kar, 1996; Neog, 2003 and Roy, 2003) who reported a reduction in seed yield due to

delay of week /fortnight from the normal sowing. In the first sowing, the percentage

oil content was higher than late sowing. Reduction in oil content in delayed sowing

was reported by Bhattacharya et al. (2000), Saha et al. (2000) and Neog et al. (2005).

Our findings are in close conformity with their observations.

Gimenez et al. (1994) and Gardner et al. (1994) concluded that, for any given

canopy size (LAI), canopy structure (leaf angle and orientation) determine the

fraction of intercepted radiation, interception of PAR and its utilization efficiency

with which, PAR drives photosynthetic gain in terms of productivity

Increasing the efficiency of water use by crops continues to escalate as a topic

of concern because of increasing demand for water use and improved environmental

quality by human populations. Efficiency is a term that creates a mental picture of a

system in which we can twist dials, tweak the components, and ultimately influence

the efficiency of the system (Hatfield et al., 2001). Tanner and Sinclair (1983)

summarized the different forms of relationships that have been used to characterize

water use efficiency. Earlier summaries developed by Unger and Stewart (1983) and

Power (1983) provided a strong foundation for understanding role of soil

management on water use efficiency.

5.7 Conclusion

The Biomass and seed yield were relatively higher in the first sown crop

because of more congenial weather conditions during the entire crop growth period.

The RUE was found to be higher in first sown crop as compared to late sown crop.

Delay in sowing time reduces the yield significantly. The water use efficiency

calculated by all three approaches, single crop coefficient, dual crop coefficient and

soil water balance equation were found to be more in Pusa Jaikisan followed by Pusa

bold and Pusa Gold. The value of water use efficiency calculated using dual crop

coefficient were more near to the value of water use efficiency calculated by soil

water balance as compared to the value calculated by single crop coefficient. From

53

the above studies it can be concluded that water need requirement in mustard crop

can be estimated more accurately by dual crop coefficient approach as compared to

single crop coefficient because water use calculated by dual crop coefficient consider

both soil evaporation coefficient and basal crop coefficient.

54

6. DISCUSSION

The accurate estimation of total water needed during different stages of the

crop is required for efficient utilization of water. The estimation of crop

evapotranspiration by dual crop coefficient is more reliable approach for estimating

the water need in the crop. In our studies the crop evapotranspiration were calculated

using different approach and results showed that evapotranspiration estimated by

dual crop coefficient had better results as compared to other approaches. Allen et al.

(1998) reported that FAO-56 crop coefficient method is idely used approach to

estimate water requirement of agricultural crops.Percent ground cover by the plant

canopy is an essential parameter in the FAO crop coefficient method to calculate

evapotranspiration losses and the proportion of soil evaporation and plant

transpiration respectively, which is also used in some models (e.g. Van Dam, 2000).

Although leaf area index is generally preferred, Firman and Allen (1989), Siddique et

al. (1989) and O,Connell et al. (2004) showed a close relation between both, leaf

area index as well as ground cover in analyzing radiation interception. In our study

crop development stages were estimated using leaf area index data wgich is in

agreement with FAO-56.

In our study mid stage crop coefficients of the crop were high corrected for

full ground cover. Differences to tabulated values of mustard is probably related to

the different atmospheric conditions for the main crop Kcb coefficients. The crop

water use varies and crop coefficient was changed due to environmental factors and

any change in the Kc may affect directly the crop water use (Doorenbos et al., 1979

and Miseha, 1983). The seasonal evapotranspiration (ETc) of sunflower and water

use efficiency decreased by increasing available soil moisture depletion (ASMD)

percentage (Connor et al.,1985) .In our study total ETc for initial stage was less,

increased during developing stage reached peak during mid stage and then declines.

This is because of higher crop cover during mid stage. Leaf are index in our study

varies with variety as well as with respect to weather conditions .First sown crop had

higher LAI, biomass,radiation use efficiencies as compared to second and third sown

crop.Greater number of leaves ensured the better crop yioeld due to higher

photosynthetic capacity by increasing LAI and resultantly higher intercepted PAR

and radiation use efficiency. The results obtained in our study confirm the findings of

many scientists (Legha and Giri, 1999; Vahedi et al., 2010). Gardner et al. (1994)

55

concluded that, for any given canopy size (LAI), canopy structure (leaf angle and

orientation) determine the fraction of intercepted

Radiation interception of PAR and its utilization efficiency with which that

PAR drive photosynthetic gain in terms of productivity. Monteith (1977) defined

RUE as the amount of biomass (g m-2

) per unit of intercepted solar radiation

(Gimenez et al., 1994). The RUE may vary during the crop growthstages (Hall et al.,

1995). The planting pattern also affect RUE (Tollenaar and Aguilera, 1992).

Sowing of variety and planting geometrie are becoming increasingly

important components in gaining the economic and environmental viability of

various agroecosystems particularly under irrigated arid environments. Study by Kar

et al. (2007) showed that LAI is significantly correlated with Kc values, when LAI

exceeded 3.0, the Kc value was 1 in safflower and mustard. During the crop

development and maturity stages, the estimated Kc values were 11-23% higher in

crops than the values reported by FAO. Chuanyan et al. (2007) in their study

predicted crop evapotranspiration as a function of weather data, stage of crop

development and water availability. The simulated evapotranspiration in their study

ranged from 0.54 to 7.69 mm per day and the total ETc was 611.5 mm at the study

site for maize. The average value of ETc in the initial, development, mid season and

late season stages were 1.09, 3.67, 5.49 and 3.3 mm per day respectively. Similar

trend was observed in our study when ETc was calculated by using single and dual

crop coefficient approach. In the initial stage ETc value increases less while in the

development and mid stage it increases then decreases during late stage.

Bodner et al. (2007) reported that the FAO dual crop coefficient method

showed good results for two years of variable water availability and indicated

maximum additional profile depletion of 16% compared to fallow form dry

conditions during full cover crop growth. The FAO approach including water stress

compensation seems a reliable tool for water limited environments to obtain improve

estimates on water losses with readily available climatic, soil and plant data.

56

7. SUMMARY

An experiment was conducted during Rabi season 2011-12 at research farm

of IARI, New Delhi, with an aim to understand crop water needs in mustard. Three

varieties of Mustard viz., Pusa Gold, Pusa Jaikisan and Pusa Bold were sown on 14th

October, 31st October and 16

th November, 2011 respectively. The crop was raised

following standard recommended agronomic practices maintaining 40 cm and 15 cm

spacing between rows and plants respectively with three replications in a randomized

block design (RBD). Observations related to different weather elements viz.,

maximum and minimum temperature, morning and evening relative humidity and

rainfall were taken from the records of the Agrometeorological Observatory located

adjacent to the experimental plot. The different crop stages were calculated based on

crop coverage data. Different growth parameters viz., leaf area index, biomass, plant

height, soil moisture and radiation interception was measured at 10 days interval.

Seed yield and percentage oil content was measured. Radiation use efficiency and

growing degrees day (GDD) were computed. The crop evapotranspiration were

calculated using single crop coefficient, dual crop coefficient and water balance

equation. Water use efficiency were calculated using crop evapotranspiration

estimated by single crop coefficient, dual crop coefficient and water balance

equation. The single crop coefficient and basal crop coefficient were adjust based

upon the weather and crop data. The daily soil evaporation coefficient were

calculated. The reference evapotranspiration were estimated using Panman monteith

equation.

Reference evapotranspiration estimated by Penman-monteith equation and

calculated by pan evaporation had good correlation at R2 = 0.91.

The first sown crop has higher value of LAI as compared to second and third

sown crop. The percentage reduction in peak LAI was 33, 29 and 24 in Pusa

Gold, Pusa Jaikisan and Pusa Bold respectively in second sown crop as

compared to first sown crop and 34, 36 and 26 percent in third sown crop with

respect to first sow crop in Pusa Gold, Pusa Jaikisan and Pusa Bold respectively.

Leaf area index (LAI) was higher in Pusa Jaikisan followed by Pusa bold and

Pusa Gold. The first sown crop showed 16% and 32% higher LAI in Pusa

Jaikisan as compared to Pusa Bold and Pusa Gold. The second sown crop

showed 10% and 36% higher LAI in Pusa Jaikisan as compared to Pusa Bold

57

and Pusa Gold. The third sown crop showed 3% and 43% higher LAI in Pusa

Jaikisan as compared to Pusa Bold and Pusa Gold.

The number of days taken to reach different stages during the crop growth

period were noted carefully from day-to-day observations. According to FAO-56

the different stage initial, the development, mid season, and late season stages

were calculated based on the crop coverage data as suggested by FAO-56 was

found to be different for different varieties under different weather conditions

The adjusted value of single and basal crop coefficient had initially lower value,

increase during development phase reached maximum during mid stage and than

decline during late stage

Soil evaporation coefficient Ke, was low except during irrigation and

precipitation events.

The value of crop evapotranspiration was found to be more value during mid

stage in all varieties with respect to different weather condition. ETc values

es t imated using dual crop coeff ic ien t , s ingle crop coeff ic ien t

approach were low during initial stage increased during the crop

development stage and reached its peak during the mid-season stage then the

ETc value declined rapidly during the late stage.

The value of water use efficiency calculated using dual crop coefficient were

more near to the value of water use efficiency calculated by soil water balance as

compared to the value calculated by single crop coefficient.

The crop evapotranspiration calculated from dual crop coefficient was better and more

accurate for estimating water needs for mustard crop because water use calculated by

dual crop coefficient consider both soil evaporation coefficient and basal crop

coefficient. From the above studies it can be concluded that water need requirement in

mustard crop can be estimated more accurately by dual crop coefficient approach as

compared to single crop coefficient.

Cr op evapotranspiration value estimated by single crop coefficient approach was 254,

284 and 213 mm for Pusa Gold; 287, 288 and 341 mm for Pusa Jaikisan and 288, 284

and 343 mm for Pusa bold during crop growing period.

Crop evapotranspiration value estimated using soil water balance equation was

307, 284 and 274 mm for Pusa Gold; 335, 328 and 300 mm for Pusa Jaikisan

and 343, 310 and 293 mm for Pusa bold at the experiment site in the growing

season.

58

Crop evapotranspiration value estimated by dual crop coefficient approach

was 288, 308 and 237 mm for Pusa Gold ; 336 , 312 and 369 mm

for Pusa Ja ik i san and 335, 310 and 371 mm for Pusa bold at the

experiment site in the growing season

The c rop evapotranspiration value was found to be less during initial stage,

increased during development stage and reached maximum during mid stage

season.

The radiation use efficiency was found to be higher in first sown crop followed

by second and third sown crop. The percentage reduction of peak value of

radiation use efficiency was 13, 4, 6 % and 26, 29 and 35% for Pusa Gold, Pusa

Jaikisan and Pusa Bold respectively in second and third sown crop as compared

to first sown crop.

Pusa Jaikisan has higher value of RUE followed by Pusa Bold and Pusa Gold.

Pusa Jaikisan had 20, 27 and 38% higher value of RUE as compared to Pusa

Gold and 1, 3 and 16% as compared to Pusa Bold in different weather

conditions.

Biomass was found to be higher in first sown crop followed by second and third

sown crop. The reduction in the magnitude of maximum biomass production in

second sown crop as compared to first sown crop was 20%, 33% and 28% in

Pusa Gold, Pusa Jaikisan and Pusa Bold while the reduction in biomass

production was further reduced in third sown crop to 53%, 64% and 63% in

Pusa Gold, Pusa Jaikisan and Pusa Bold as compared to first sown crop.

Pusa Jaikisan produced higher biomass as compared to Pusa Bold and Pusa Gold

irrespective of sowing dates which might be due to higher leaf area index, leaf

area duration and more proliferating nature. The biomass production was higher

in Pusa Jaikisan by 8%, 0.7% and 5% as compared to Pusa Bold and 53%, 43%

and 62% as compared to Pusa Gold in first, second and third sown crop.

LAI and biomass was significantly correlated with GDD. It was observed third

order polynomial equations in biomass that 97 to 100 per cent variation in

production could be explained through the accumulated heat unit (GDD), when

crop was sown in variable weather conditions

Pusa Jaikisan had more value of water use efficiency as compared to Pusa Gold

and Pusa Bold respectively in different weather conditions, when calculated by

59

single crop coefficient approach, dual crop coefficient and water balance

equation

Seed yield was found to be higher in first sown crop followed by second and

third sown crop. Second sown crop had 14% in Pusa Gold, 0.4% in Pusa

Jaikisan and 9 % in Pusa Bold less value as compared to first sown crop. In third

sown crop the yield was reduced by 65% in Pusa Gold, 37% in Pusa Jaikisan

and 36% in Pusa Bold.

Pusa Jaikisan had higher seed yield followed by Pusa Bold and Pusa Gold in all

three date of sowing. Pusa Jaikisan have 51%, 57% and 72% higher yield than

Pusa Gold in first, second and third sown crop respectively and 2%, 10% and

0.3% higher yield than Pusa Bold in first, second and third sown crop

respectively.

From the above studies it can be concluded that water need requirement in

mustard crop can be estimated more accurately by dual crop coefficient

approach as compared to single crop coefficient because ETc calculated by dual

crop coefficient consider both soil evaporation coefficient and basal crop

coefficient. The value of water use efficiency calculated using dual crop

coefficient were more near to the value of water use efficiency calculated by soil

water balance as compared to the value calculated by single crop coefficient.

60

ESTIMATION OF CROP EVAPOTRANSPIRATION UNDER

VARIABLE WEATHER CONDITIONS

ABSTRACT

Accurate estimation of evapotranspiration is required for proper irrigation

scheduling for crops and their survival during adverse conditions. Weather

variability causes substantial fluctuations in crop productivity of any crop. In order

to optimize the growth and seed yield in any crop, quantification of Crop-Weather

relationships could help in determining proper time for sowing. Evapotranspiration

accounts for major water loss from the agricultural fields.

Keeping these in mind, a field experiment was conducted at research farm of

IARI, New Delhi during Rabi 2011-12 for understanding crop water needs for

irrigation in mustard. Three varieties of mustard viz., Pusa Gold, Pusa Jaikisan and

Pusa bold were sown on 14th

October, 31st October and 16th

November, 2011 by

creating different weather condition for different crop stages. The crops were raised

following the standard recommended agronomic practices with three replications in

a randomized block design. The crop evapotranspiration were calculated using single

crop coefficient, dual crop coefficient and water balance equation. The crop biomass,

leaf area index, radiation interception, soil moisture at regular interval and seed yield

were measured. Results showed higher value of biomass, leaf area index, seed yield,

water use efficiency and radiation use efficiency in Pusa Jaikisan followed by Pusa

Bold and Pusa Gold. The value of biomass, seed yield, leaf area index and radiation

use efficiency was found to be more in first sown followed by second and third

sowing. The results showed that the value of soil evaporation coefficient was low

except during irrigation and precipitation events. The value of crop

evapotranspiration was found to be more value during mid stage in all varieties with

respect to different weather condition. ETc values es t imated us ing dual crop

coeff ic ien t were low during initial stage increased during the crop development

stage and reached its peak during the mid-season stage then the ETc value declined

rapidly during the late crop growth stage. The value of water use efficiency

calculated using dual crop coefficient were more near to the value of water use

efficiency calculated by soil water balance as compared to the value calculated by

single crop coefficient.The crop evapotranspiration calculated from dual crop

coefficient was better and more accurate for estimating water needs for mustard crop

61

because water use calculated by dual crop coefficient consider both soil evaporation

coefficient and basal crop coefficient. From the above studies it can be concluded

that water need requirement in mustard crop can be estimated more accurately by

dual crop coefficient approach as compared to single crop coefficient.

62

विभभन्न मौसमीय विभभन्न मौसमीय पररस्थितियोंपररस्थितियों में फसऱ िाष्पोत्सर्जन का आॊकऱनमें फसऱ िाष्पोत्सर्जन का आॊकऱन

फसऱ िाष्पोत्सर्जन का सही आकऱन फसऱों और प्रतिकूऱ पररस्थितियों के दौरान उनके

अस्थित्ि के भऱए उचिि भस ॊिाई समयबद्धन के भऱए आिश्यक है. मौसम पररििजनशीऱिा ककसी भी

फसऱ की फसऱ उत्पादकिा में काफी उिार - िढाि का कारण बनिा है. आदेश में विकास और ककसी

भी फसऱ में बीर् उपर् का अनकूुऱन करने के भऱए, फसऱ मौसम ररश्िों की मात्रा का ठहराि बिुाई

के भऱए उचिि समय का तनर्ाजरण करने में मदद कर सकिा है. प्रमखु कृवि ऺेत्रों से पानी की कमी के

भऱए िाष्पोत्सर्जन फसऱों में पानी की ऺति के भऱए एक मखु्य कारक है। इन सभी बािो को

ध्यान में रखि ेहुए, एक ऺते्र प्रयोग आईएआरआई, नई ददल्ऱी के अनसुॊर्ान खेि में फसऱ पानी की

र्रूरि सरसों में भस ॊिाई के भऱए आिश्यक को समझने के भऱए रबी के दौरान 2012 में आयोस्र्ि

ककया गया. सरसों अिाजि के िीन ककथमों, में पसूा सोना, पसूा र्यककसान और पसूा बोल्ड अक्टूबर

14, 31 अक्टूबर और 16th निॊबर, 2011 को विभभन्न विभभन्न फसऱ िरणों के भऱए मौसम की स्थिति

बनाने के भऱए बोए गए. फसऱों मानक एक यादृस्छिक ब्ऱॉक डडर्ाइन में िीन अनकुरण के साि कृवि

प्रिाओॊ की भसफाररश के बाद उठाया गया. . िाथिविक फसऱ िाष्पोत्सर्जन के एकऱ फसऱ गणुाॊक,

दोहरी फसऱ गणुाॊक और पानी सॊिऱुन समीकरण का उपयोग कर की गणना की गई. बायोमास

फसऱ, ऱाइ, विककरण अिरोर्न, तनयभमि अॊिराऱ पर भमट्टी नमी और बीर् उपर् मापा गया.

पररणाम बायोमास के उछि मलू्य, बीर् उपर्, र्ऱ उपयोग दऺिा और पसूा र्यककसान में विककरण

उपयोग की ऺमिा से पिा िऱा पसूा बोल्ड और गोल्ड द्िारा पीिा ककया. बायोमास, बीर् उपर् और

विककरण उपयोग की ऺमिा का मलू्य पहऱे दसूरे और िीसरे बिुाई के बाद बिुाई में अचर्क होना पाया

गया. निीर् ेबिाि ेहैं कक भस ॊिाई और ििाज घटनाओॊ के दौरान िोड़कर भमट्टी िाष्पीकरण गणुाॊक का

मान कम िा. फसऱ िाष्पोत्सर्जन का मलू्य अऱग मौसम की स्थिति के सॊबॊर् के साि सभी ककथमों

में मध्य िरण के दौरान और अचर्क मलू्य का होना पाया गया. आदद दोहरी फसऱ गणुाॊक का उपयोग

कर का अनमुान मान फसऱ विकास के िरण के दौरान िवृद्ध हुई प्रारॊभभक िरण के दौरान कम िे और

63

मध्य - मौसम िरण के दौरान अपने िरम पर पहुॉि िो आदद मलू्य अॊतिम फसऱ विकास मॊि के

दौरान िरे्ी से चगरािट आई है. पानी का उपयोग दऺिा दोहरी फसऱ गणुाॊक का उपयोग कर की

गणना के मलू्य अचर्क पानी का उपयोग भमट्टी पानी सॊिऱुन के द्िारा की गणना की दऺिा के मलू्य

के भऱए तनकट के रूप में एकऱ फसऱ गणुाॊक द्िारा पररकभऱि मान की िऱुना में िे. दोहरी फसऱ

गणुाॊक से गणना की फसऱ िाष्पोत्सर्जन बेहिर और सरसों की फसऱ के भऱए पानी की र्रूरि का

आकऱन करने के भऱए और अचर्क सटीक िा क्योंकक पानी का उपयोग दोहरी फसऱ गणुाॊक दोनों

भमट्टी िाष्पीकरण गणुाॊक और बेसऱ फसऱ गणुाॊक पर वििार द्िारा की गणना है. उपरोक्ि अध्ययन

से यह तनष्किज तनकाऱा र्ा सकिा है कक सरसों की फसऱ में पानी की आिश्यकिा की र्रूरि है और

अचर्क सही दोहरी फसऱ गणुाॊक दृस्ष्टकोण द्िारा अनमुान ऱगाया र्ा सकिा है के रूप में में एकऱ

फसऱ गणुाॊक के भऱए िऱुना कर सकि ेहैं.

64

65

Fig. 1: Weekly observed and normal minimum (min) and maximum (max)

temperature during rabi season 2011-12 at IARI, New Delhi

Fig. 2 Weekly Observed and normal rainfall during rabi season 2011-12 at IARI,

New Delhi

66

Fig. 3 Observed and normal bright sunshine hours (BSS) during rabi season 2011-12 at

IARI, New Delhi.

Fig. 4 Weekly observed and normal evaporation during rabi season 2011-12 at

IARI, New Delhi

67

Fig.5 Observed and normal wind speed during rabi season 2011-12 at IARI, New Delhi

Fig.6 Observed and normal minimum (min) and maximum (max) Relative

humidity during rabi season 2011-12 at IARI, New Delhi

68

Fig. 7 Daily Net Radiation (MJ/m2/day) estimated at experimental site.

Fig. 8 Daily Reference Evapotranspiration (mm/day) estimated at experimental site.

69

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0 15 30 45 60 90 105 120 135

heig

ht

(cm

)

Days After Sowing

Third Sowing (16th November)

Pusa Gold Pusa Jaikisan Pusa Bold

Fig. 11 Height of Mustard at Different Varieties at Variable Weather Conditions

70

Fig. 13 Adjusted Basal Crop Coefficient at Experimental Site.

71

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Initial Initial Mid MidSin

gle

Cro

p C

oe

ffic

ien

t (K

c)

Growing Period

Second Sowing (31st October)

Pusa Gold Pusa Jaikisan Pusa Bold

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Initial Initial Mid MidSin

gle

Cro

p C

oe

ffic

ien

t (K

c)

Growing Period

Third Sowing (16th November)

Pusa Gold Pusa Jaikisan Pusa Bold

Fig. 12 Adjusted Single Crop Coefficient at Experimental Site.

72

0.0

1.0

2.0

3.0

4.0

5.0

40 50 65 80 90 115 135

LA

I

DAS

LAI First sowing

Pusa Gold Pusa Jaikisan Pusa Bold

0.0

1.0

2.0

3.0

4.0

40 55 70 80 100 120

LA

I

DAS

LAI second sowing

Pusa Gold Pusa Jaikisan Pusa Bold

0.0

1.0

2.0

3.0

40 50 70 85 110

LA

I

DAS

LAI Third sowing

Pusa Gold Pusa Jaikisan Pusa Bold

Fig.10 LAI of different varities of Mustard under variable weather conditions

73

(a)

0

2

4

6

8

10

First Sowing Second Sowing Third Sowing

WU

E (k

g/h

a/m

m)

Pusa Gold Pusa Jaikisan Pusa Bold

(b)

0

2

4

6

8

First Sowing Second Sowing Third Sowing

WU

E(kg

/ha/

mm

)

Pusa Gold Pusa Jaikisan Pusa Bold

(C)

0

2

4

6

8

10

First Sowing Second Sowing Third Sowing

WU

E (k

g/ha

/mm

)

Pusa Gold Pusa Jaikisan Pusa Bold

Fig.21 Water use efficiency of different varieties of mustard estimated by (a)

single crop coefficient (b) dual crop coefficient and (c) soil water balance under

variable weather conditions

74

0

500

1000

1500

2000

46 70 83 102 113 143

Bio

ma

ss

(k

g/h

a)

DAS

First Sowing (14th October)

Pusa Gold Pusa Jaikisan Pusa Bold

0

200

400

600

800

1000

1200

1400

1600

29 53 66 85 96 126

Bio

ma

ss

(k

g/h

a)

DAS

Second Sowing (31st October)

Pusa Gold Pusa Jaikisan Pusa Bold

0

100

200

300

400

500

600

700

800

37 50 69 80 110

Bio

mass (

kg

/ha)

DAS

Third Sowing (16th November)

Pusa Gold Pusa Jaikisan Pusa Bold

Fig .17 Biomass of different varities of mustard sown under variable weather

conditions

75

Fig .14 Variation in Soil Evaporation Coefficient (Ke) with crop growing period

76

Fig.16 Calculated Crop Evapotranspiration through Dual Crop Coefficient

under variable weather conditions in different varieties of mustard

77

Fig.15 Calculated Crop Evapotranspiration through Single Crop Coefficient

under variable weather conditions in different varieties of mustard

78

y = 0.6741x + 0.5252

R2 = 0.9112

0

1

2

3

4

5

6

7

8

0 2 4 6 8 10

Observed Pan evaporation

Estim

ated

ET

0

Fig 9 Relation between reference evapotranspiration estimated through

Penman-Monteith equation and Pan Evaporation

79

First Sowing

y = 12.312x3 - 91.46x2 + 285.41x - 193.94

R2 = 0.9898

0

200

400

600

800

1000

793

1042

1134

1258

1345

1679

GDD

Bio

mass (

kg

/ha)

Second Sowing

y = 10.031x3 - 86.076x2 + 313x - 248.85

R2 = 0.9723

-100

0

100

200

300

400

500

600

700

800

476 724 816 940 1027 1362

GDD

Bio

mass (

kg

/ha)

Third Sowing

y = -3.012x3 + 55.688x2 - 138.04x + 91.798

R2 = 1

-50

0

50

100

150

200

250

300

350

400

450

450 542 611 753 1088

GDD

Bio

mass (

kg

/ha)

Fig.18 Thermal response curve of biomass for Pusa Gold under variable

weather conditions

80

First Sowing

y = 20.543x3 - 130.97x2 + 401.41x - 289.63

R2 = 0.9711

0

400

800

1200

1600

2000

793 1042 1134 1258 1345 1679

GDD

Bio

mass (

kg

/ha)

Second Sowing

y = -26.806x3 + 302.81x2 - 718.17x + 463

R2 = 0.9971

0

200

400

600

800

1000

1200

1400

476 724 816 940 1027 1362

GDD

Bio

mass (

kg

/ha)

Third Sowing

y = 2.4739x3 + 27.045x2 - 71.766x + 47.086

R2 = 0.997

0

100

200

300

400

500

600

700

800

450 542 542 753 1088

GDD

Bio

mas

s (k

g/h

a)

Fig.19 Thermal response curve of biomass for Pusa Jaikisan under variable

weather conditions

81

First Sowing

y = 42.454x3 - 354x2 + 990.89x - 669.27

R2 = 0.9862

0

500

1000

1500

2000

793 1042 1134 1258 1345 1679

GDD

Bio

mass (

kg

/ha)

Second Sowing

y = -18.543x3 + 241.49x2 - 643.24x + 454.75

R2 = 0.9812

0

400

800

1200

1600

476 724 816 940 1027 1362

GDD

Bio

mass (

kg

/ha)

Third Sowing

y = 16.32x3 - 93.851x2 + 214.41x - 130.73

R2 = 0.9866

0

100

200

300

400

500

600

700

450 542 542 753 1088

GDD

Bio

mass (

kg

/ha)

Fig 20. Thermal response curve of biomass for Pusa Bold under variable

weather conditions

82

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Appendix

Weather table

Date T MAX.

(oC)

T MIM.

(oC)

Rain

(mm)

Avg. Wind

Speed

(kmph)

RH

mornin

g (%)

RH

evening

(%)

BSS

(hours)

14.10.2011 34.9 19.6 0.0 4.5 69.0 28.0 6.8

15.10.2011 34.0 18.2 0.0 6.1 74.0 26.0 8.0

16-10-2011 34.0 15.7 0.0 4.4 80.0 21.0 9.5

17-10-2011 34.0 15.5 0.0 2.7 77.0 27.0 9.5

18-10-2011 32.5 15.2 0.0 2.3 82.0 27.0 8.3

19-10-2011 31.9 15.0 0.0 4.0 81.0 20.0 7.0

20-10-2011 32.8 15.0 0.0 3.0 90.0 35.0 8.7

21-10-2011 32.2 16.2 0.0 0.6 81.0 38.0 3.3

22-10-2011 31.0 15.9 0.0 0.5 86.0 29.0 0.5

23-10-2011 32.5 15.4 0.0 1.9 84.0 33.0 4.0

24-10-2011 33.0 16.4 0.0 2.5 88.0 34.0 6.9

25-10-2011 31.8 14.6 0.0 1.7 82.0 34.0 4.4

26-10-2011 30.8 14.4 0.0 1.5 89.0 34.0 1.8

27-10-2011 30.4 14.3 0.0 1.9 72.0 25.0 3.5

28-10-2011 29.8 11.9 0.0 4.0 84.0 29.0 7.5

29-10-2011 29.6 12.6 0.0 2.4 85.0 35.0 7.0

30-10-2011 29.4 14.8 0.0 1.5 85.0 31.0 5.8

31-10-2011 30.4 13.0 0.0 2.1 89.0 29.0 3.5

01-11-2011 31.0 14.7 0.0 0.8 93.0 26.0 1.7

02-11-2011 32.0 13.0 0.0 2.1 79.0 25.0 5.6

03-11-2011 32.0 13.0 0.0 2.8 76.0 22.0 6.6

04-11-2011 31.6 12.5 0.0 2.3 89.0 28.0 7.0

05-11-2011 31.5 14.3 0.0 0.5 94.0 35.0 2.3

06-11-2011 31.0 16.7 0.0 2.8 74.0 31.0 1.4

07-11-2011 30.0 13.0 0.0 4.9 87.0 30.0 6.5

08-11-2011 29.5 11.0 0.0 2.5 90.0 24.0 7.7

09-11-2011 30.0 13.5 0.0 1.7 89.0 35.0 4.2

10-11-2011 30.5 18.2 0.0 4.7 69.0 38.0 5.6

11-11-2011 31.0 15.9 0.0 5.6 81.0 52.0 7.6

12-11-2011 28.8 16.4 0.0 7.0 77.0 30.0 6.0

13-11-2011 29.2 11.7 0.0 4.6 86.0 32.0 8.3

14-11-2011 29.6 11.8 0.0 1.7 88.0 32.0 7.9

15-11-2011 29.0 12.2 0.0 2.1 89.0 27.0 5.0

16-11-2011 29.3 13.2 0.0 2.8 84.0 35.0 6.3

17-11-2011 28.8 12.6 0.0 3.9 89.0 29.0 6.0

18-11-2011 29.0 11.6 0.0 4.0 88.0 30.0 6.7

96

19-11-2011 29.8 13.0 0.0 2.7 93.0 58.0 6.9

20-11-2011 26.0 11.3 0.0 2.5 96.0 51.0 2.2

21-11-2011 26.8 12.4 0.0 1.6 95.0 51.0 2.4

22-11-2011 27.6 12.5 0.0 1.4 93.0 55.0 0.0

23-11-2011 27.0 12.8 0.0 0.8 89.0 49.0 0.0

24-11-2011 26.5 11.4 0.0 1.2 82.0 44.0 0.0

25-11-2011 27.5 10.3 0.0 1.2 88.0 28.0 1.0

26-11-2011 27.2 9.5 0.0 1.7 90.0 32.0 4.3

27-11-2011 26.0 9.4 0.0 1.9 93.0 35.0 0.2

28-11-2011 25.8 9.4 0.0 1.8 95.0 27.0 0.6

29-11-2011 24.5 12.0 0.0 5.1 82.0 36.0 2.2

30-11-2011 25.4 9.2 0.0 6.9 78.0 27.0 3.7

01-12-2011 25.4 8.8 0.0 3.0 85.0 34.0 6.8

02-12-2011 25.0 10.6 0.0 2.0 88.0 47.0 1.9

03-12-2011 27.0 11.4 0.0 1.0 93.0 46.0 3.2

04-12-2011 26.5 9.3 0.0 2.9 88.0 34.0 4.0

05-12-2011 26.5 8.0 0.0 2.6 92.0 30.0 5.4

06-12-2011 28.5 11.6 0.0 1.1 88.0 42.0 5.5

07-12-2011 27.1 13.8 0.0 4.2 98.0 48.0 4.7

08-12-2011 26.0 13.9 0.0 3.2 94.0 47.0 3.4

09-12-2011 28.0 15.3 0.0 4.5 92.0 77.0 4.4

10-12-2011 22.0 12.8 0.0 5.3 93.0 46.0 0.0

11-12-2011 23.0 7.5 0.0 5.3 81.0 40.0 2.5

12-12-2011 22.3 5.2 0.0 2.8 94.0 34.0 4.9

13-12-2011 22.4 4.2 0.0 1.7 97.0 38.0 4.9

14-12-2011 22.0 4.0 0.0 2.8 97.0 29.0 4.1

15-12-2011 21.2 3.7 0.0 2.9 90.0 32.0 3.8

16-12-2011 21.2 2.4 0.0 2.3 84.0 31.0 4.4

17-12-2011 20.8 2.3 0.0 2.3 100.0 33.0 2.8

18-12-2011 20.5 2.2 0.0 2.7 90.0 25.0 3.1

19-12-2011 22.0 1.3 0.0 1.2 96.0 43.0 1.7

20-12-2011 21.0 3.0 0.0 1.5 91.0 47.0 0.0

21-12-2011 19.4 4.8 0.0 2.9 94.0 40.0 3.3

22-12-2011 22.5 3.2 0.0 0.5 90.0 42.0 3.5

23-12-2011 20.5 2.8 0.0 2.0 93.0 42.0 2.1

24-12-2011 18.5 0.0 0.0 2.8 83.0 43.0 3.2

25-12-2011 18.2 0.0 0.0 2.8 82.0 37.0 3.9

26-12-2011 20.0 0.2 0.0 1.4 83.0 37.0 4.2

27-12-2011 20.0 0.9 0.0 0.8 93.0 45.0 3.8

28-12-2011 20.5 2.3 0.0 0.4 97.0 50.0 0.0

29-12-2011 22.5 3.6 0.0 0.7 97.0 62.0 0.4

97

30-12-2011 21.5 3.0 0.0 1.1 97.0 70.0 0.0

31-12-2011 19.5 3.7 0.0 0.7 97.0 58.0 0.0

01-01-2012 22.0 9.0 0.0 1.8 93.0 96.0 0.0

02-01-2012 18.5 10.7 0.0 1.0 100.0 74.0 0.0

03-01-2012 19.5 5.8 0.0 1.1 97.0 57.0 0.0

04-01-2012 19.6 5.3 0.0 1.9 97.0 49.0 0.1

05-01-2012 21.0 7.8 0.0 0.8 95.0 75.0 0.0

06-01-2012 19.0 11.3 0.0 3.7 98.0 82.0 0.0

07-01-2012 17.0 11.5 6.6 5.3 100.0 80.0 0.0

08-01-2012 18.0 10.4 0.0 3.8 85.0 66.0 0.0

09-01-2012 17.0 7.3 0.0 5.2 87.0 47.0 0.0

10-01-2012 16.5 1.3 0.0 3.0 89.0 36.0 3.7

11-01-2012 17.0 1.7 0.0 3.9 84.0 42.0 5.6

12-01-2012 17.3 0.7 0.0 3.4 89.0 38.0 4.2

13-01-2012 18.0 1.7 0.0 4.7 93.0 39.0 5.9

14-01-2012 18.9 2.7 0.0 3.5 94.0 40.0 6.0

15-01-2012 21.0 7.4 0.0 3.1 92.0 33.0 5.4

16-01-2012 24.6 12.3 8.2 5.9 91.0 72.0 4.9

17-01-2012 19.0 8.2 0.0 6.3 97.0 79.0 0.3

18-01-2012 13.6 3.2 0.0 3.8 97.0 65.0 0.0

19-01-2012 15.0 3.0 0.0 2.8 94.0 63.0 3.3

20-01-2012 16.2 0.7 0.0 2.4 97.0 79.0 0.0

21-01-2012 12.5 4.6 0.0 5.4 86.0 35.0 0.0

22-01-2012 17.5 4.7 0.0 6.0 83.0 41.0 8.1

23-01-2012 17.0 3.0 0.0 3.3 85.0 42.0 0.9

24-01-2012 21.5 5.5 0.0 3.5 81.0 32.0 7.8

25-01-2012 20.5 8.8 0.0 6.3 78.0 41.0 8.6

26-01-2012 20.5 8.4 0.0 5.5 76.0 42.0 7.0

27-01-2012 20.5 4.6 0.0 5.1 94.0 43.0 5.9

28-01-2012 19.9 2.0 0.0 8.7 97.0 42.0 7.4

29-01-2012 20.3 1.7 0.0 2.5 97.0 50.0 6.9

30-01-2012 19.9 3.4 0.0 3.9 88.0 43.0 8.9

31-01-2012 21.0 0.4 0.0 3.2 96.0 48.0 7.6

01-02-2012 20.4 5.0 0.0 3.5 97.0 49.0 6.6

02-02-2012 21.0 6.4 0.0 5.6 87.0 40.0 6.8

03-02-2012 22.0 7.8 0.0 4.3 78.0 24.0 7.4

04-02-2012 22.5 7.5 0.0 2.8 87.0 32.0 5.1

05-02-2012 23.0 9.6 0.0 5.0 88.0 40.0 1.4

06-02-2012 23.0 9.8 0.0 6.3 95.0 43.0 4.6

07-02-2012 21.0 6.7 0.0 6.9 64.0 35.0 6.4

08-02-2012 19.5 7.7 0.0 5.0 62.0 19.0 5.2

98

09-02-2012 18.8 0.7 0.0 5.9 81.0 31.0 8.2

10-02-2012 18.0 5.0 0.0 10.5 59.0 23.0 8.5

11-02-2012 19.6 4.6 0.0 2.9 91.0 36.0 6.2

12-02-2012 20.6 9.0 0.0 3.5 76.0 43.0 4.0

13-02-2012 24.3 12.5 0.0 4.8 69.0 43.0 7.4

14-02-2012 23.2 9.0 0.0 4.0 88.0 42.0 5.9

15-02-2012 22.5 10.2 0.0 6.1 52.0 24.0 0.4

16-02-2012 20.5 5.0 0.0 6.5 65.0 31.0 8.1

17-02-2012 19.5 5.0 0.0 5.5 62.0 29.0 8.0

18-02-2012 21.5 6.8 0.0 6.1 86.0 25.0 4.0

19-02-2012 23.0 9.3 0.0 8.3 61.0 32.0 7.5

20-02-2012 23.5 9.4 0.0 7.7 70.0 35.0 7.8

21-02-2012 24.0 8.2 0.0 4.2 95.0 30.0 7.2

22-02-2012 28.2 11.0 0.0 2.7 84.0 37.0 7.6

23-02-2012 29.5 12.2 0.0 4.8 80.0 36.0 7.7

24-02-2012 26.8 9.1 0.0 5.1 80.0 37.0 7.4

25-02-2012 25.0 9.5 0.0 7.4 63.0 23.0 4.4

26-02-2012 22.5 8.0 0.0 8.7 58.0 24.0 8.3

27-02-2012 23.2 8.2 0.0 9.5 65.0 21.0 9.5

28-02-2012 24.4 9.6 0.0 7.5 78.0 34.0 8.0

29-02-2012 25.2 7.7 0.0 5.7 92.0 24.0 8.2

01-03-2012 25.7 6.8 0.0 3.2 86.0 19.0 8.6

02-03-2012 28.5 8.8 0.0 2.8 85.0 16.0 7.8

03-03-2012 28.5 9.0 0.0 4.9 74.0 19.0 8.6

04-03-2012 28.5 11.2 0.0 4.6 73.0 32.0 6.8

05-03-2012 31.5 16.0 0.0 3.7 75.0 31.0 4.8

06-03-2012 31.7 18.3 0.0 5.6 81.0 28.0 6.4

07-03-2012 28.5 9.7 0.0 6.4 63.0 19.0 3.6

08-03-2012 28.5 9.5 0.0 3.3 83.0 27.0 8.3

09-03-2012 26.5 9.0 0.0 4.8 77.0 25.0 5.3

10-03-2012 24.4 5.4 0.0 4.0 83.0 20.0 7.4

11-03-2012 25.5 4.6 0.0 3.0 83.0 22.0 9.4

12-03-2012 27.3 6.8 0.0 2.1 87.0 31.0 6.8

13-03-2012 28.5 14.0 19.2 5.0 75.0 38.0 7.1

14-03-2012 26.2 10.8 0.0 4.7 84.0 31.0 6.7

15-03-2012 25.0 12.2 0.0 7.2 70.0 27.0 6.8

16-03-2012 27.5 13.0 0.0 6.8 78.0 31.0 7.7

17-03-2012 30.0 12.0 0.0 4.1 91.0 21.0 6.1

18-03-2012 33.5 13.5 0.0 1.9 92.0 24.0 6.3

19-03-2012 34.5 16.3 0.0 1.5 83.0 25.0 5.2

20-03-2012 37.0 20.0 0.0 3.9 77.0 22.0 8.0

99

21-03-2012 33.5 12.2 0.0 7.4 68.0 24.0 2.7

22-03-2012 25.5 13.7 0.0 12.7 50.0 32.0 6.7

23-03-2012 26.5 13.2 0.0 10.7 50.0 19.0 8.0

24-03-2012 30.0 15.4 0.0 10.4 62.0 17.0 8.6

25-03-2012 32.8 16.0 0.0 8.5 66.0 13.0 8.4

26-03-2012 33.3 14.6 0.0 5.0 90.0 27.0 5.7

27-03-2012 35.2 16.4 0.0 2.3 76.0 34.0 5.0

28-03-2012 33.5 16.0 0.0 9.0 83.0 24.0 2.0

29-03-2012 33.5 18.8 0.0 8.7 60.0 28.0 6.4

30-03-2012 32.5 16.0 0.0 6.4 63.0 27.0 9.7

31-03-2012 34.2 14.7 0.0 5.5 72.0 62.0 10.0

01-04-2012 35.0 18.3 0.0 3.7 62.0 25.0 8.9

02-04-2012 36.0 18.4 0.0 3.0 63.0 30.0 7.1

03-04-2012 37.0 21.2 0.0 3.7 67.0 34.0 5.6

04-04-2012 37.0 19.3 0.0 4.6 67.0 28.0 6.6

05-04-2012 37.5 19.4 0.0 4.7 64.0 28.0 9.0

06-04-2012 37.5 18.2 0.0 4.9 62.0 33.0 9.2

07-04-2012 36.0 18.0 0.0 3.8 62.0 31.0 9.6

08-04-2012 36.5 18.7 0.0 3.3 70.0 27.0 8.5

09-04-2012 37.0 23.0 0.0 6.1 57.0 32.0 8.9

10-04-2012 39.0 18.8 Tr. 3.6 62.0 43.0 8.0

Fig. 1: Weekly observed and normal minimum (min) and maximum (max)

temperature during rabi season 2011-12 at IARI, New Delhi

Fig. 2 Weekly Observed and normal rainfall during rabi season 2011-12 at IARI,

New Delhi

Fig. 3 Observed and normal bright sunshine hours (BSS) during rabi season 2011-12 at

IARI, New Delhi.

Fig. 4 Weekly observed and normal evaporation during rabi season 2011-12 at

IARI, New Delhi

Fig.5 Observed and normal wind speed during rabi season 2011-12 at IARI, New Delhi

Fig.6 Observed and normal minimum (min) and maximum (max) Relative

humidity during rabi season 2011-12 at IARI, New Delhi

Fig. 7 Daily Net Radiation (MJ/m2/day) estimated at experimental site.

Fig. 8 Daily Reference Evapotranspiration (mm/day) estimated at experimental site.

Fig. 11 Height of Mustard at Different Varieties at Variable Weather Conditions

Fig. 13 Adjusted Basal Crop Coefficient at Experimental Site.

Fig. 12 Adjusted Single Crop Coefficient at Experimental Site.

Fig.10 LAI of different varities of Mustard under variable weather conditions

(a)

0

2

4

6

8

10

First Sowing Second Sowing Third Sowing

WU

E (k

g/h

a/m

m)

Pusa Gold Pusa Jaikisan Pusa Bold

(b)

0

2

4

6

8

First Sowing Second Sowing Third Sowing

WU

E(kg

/ha/

mm

)

Pusa Gold Pusa Jaikisan Pusa Bold

(C)

0

2

4

6

8

10

First Sowing Second Sowing Third Sowing

WU

E (k

g/ha

/mm

)

Pusa Gold Pusa Jaikisan Pusa Bold

Fig.21 Water use efficiency of different varieties of mustard estimated by (a)

single crop coefficient (b) dual crop coefficient and (c) soil water balance under

variable weather conditions

Fig .17 Biomass of different varities of mustard sown under variable weather

conditions

Fig .14 Variation in Soil Evaporation Coefficient (Ke) with crop growing period

Fig.16 Calculated Crop Evapotranspiration through Dual Crop Coefficient

under variable weather conditions in different varieties of mustard

Fig.15 Calculated Crop Evapotranspiration through Single Crop Coefficient

under variable weather conditions in different varieties of mustard

y = 0.6741x + 0.5252

R2 = 0.9112

0

1

2

3

4

5

6

7

8

0 2 4 6 8 10

Observed Pan evaporation

Estim

ated

ET

0

Fig 9 Relation between reference evapotranspiration estimated through Penman-

Monteith equation and Pan Evaporation

First Sowing

y = 12.312x3 - 91.46x

2 + 285.41x - 193.94

R2 = 0.9898

0

200

400

600

800

1000

793

1042

1134

1258

1345

1679

GDD

Bio

mass (

kg

/ha

)

Second Sowing

y = 10.031x3 - 86.076x

2 + 313x - 248.85

R2 = 0.9723

-100

0

100

200

300

400

500

600

700

800

476 724 816 940 1027 1362

GDD

Bio

mass (

kg

/ha)

Third Sowing

y = -3.012x3 + 55.688x

2 - 138.04x + 91.798

R2 = 1

-50

0

50

100

150

200

250

300

350

400

450

450 542 611 753 1088

GDD

Bio

mass (

kg

/ha)

Fig.18 Thermal response curve of biomass for Pusa Gold under variable weather

conditions

First Sowing

y = 20.543x3 - 130.97x

2 + 401.41x - 289.63

R2 = 0.9711

0

400

800

1200

1600

2000

793 1042 1134 1258 1345 1679

GDD

Bio

mass (

kg

/ha

)

Second Sowing

y = -26.806x3 + 302.81x

2 - 718.17x + 463

R2 = 0.9971

0

200

400

600

800

1000

1200

1400

476 724 816 940 1027 1362

GDD

Bio

mass (

kg

/ha)

Third Sowing

y = 2.4739x3 + 27.045x

2 - 71.766x + 47.086

R2 = 0.997

0

100

200

300

400

500

600

700

800

450 542 542 753 1088

GDD

Bio

mas

s (k

g/ha

)

Fig.19 Thermal response curve of biomass for Pusa Jaikisan under variable weather

conditions

First Sowing

y = 42.454x3 - 354x

2 + 990.89x - 669.27

R2 = 0.9862

0

500

1000

1500

2000

793 1042 1134 1258 1345 1679

GDD

Bio

ma

ss

(k

g/h

a)

Second Sowing

y = -18.543x3 + 241.49x

2 - 643.24x + 454.75

R2 = 0.9812

0

400

800

1200

1600

476 724 816 940 1027 1362

GDD

Bio

ma

ss

(k

g/h

a)

Third Sowing

y = 16.32x3 - 93.851x

2 + 214.41x - 130.73

R2 = 0.9866

0

100

200

300

400

500

600

700

450 542 542 753 1088

GDD

Bio

mass (

kg

/ha

)

Fig 20. Thermal response curve of biomass for Pusa Bold under variable weather

conditions

i

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APPENDIX

Weather Table

xiv

Date T MAX.

(oC)

T MIM.

(oC)

Rain

(mm)

Avg. Wind

Speed

(kmph)

RH

mornin

g (%)

RH

evening

(%)

BSS

(hours)

14.10.2011 34.9 19.6 0.0 4.5 69.0 28.0 6.8

15.10.2011 34.0 18.2 0.0 6.1 74.0 26.0 8.0

16-10-2011 34.0 15.7 0.0 4.4 80.0 21.0 9.5

17-10-2011 34.0 15.5 0.0 2.7 77.0 27.0 9.5

18-10-2011 32.5 15.2 0.0 2.3 82.0 27.0 8.3

19-10-2011 31.9 15.0 0.0 4.0 81.0 20.0 7.0

20-10-2011 32.8 15.0 0.0 3.0 90.0 35.0 8.7

21-10-2011 32.2 16.2 0.0 0.6 81.0 38.0 3.3

22-10-2011 31.0 15.9 0.0 0.5 86.0 29.0 0.5

23-10-2011 32.5 15.4 0.0 1.9 84.0 33.0 4.0

24-10-2011 33.0 16.4 0.0 2.5 88.0 34.0 6.9

25-10-2011 31.8 14.6 0.0 1.7 82.0 34.0 4.4

26-10-2011 30.8 14.4 0.0 1.5 89.0 34.0 1.8

27-10-2011 30.4 14.3 0.0 1.9 72.0 25.0 3.5

28-10-2011 29.8 11.9 0.0 4.0 84.0 29.0 7.5

29-10-2011 29.6 12.6 0.0 2.4 85.0 35.0 7.0

30-10-2011 29.4 14.8 0.0 1.5 85.0 31.0 5.8

31-10-2011 30.4 13.0 0.0 2.1 89.0 29.0 3.5

01-11-2011 31.0 14.7 0.0 0.8 93.0 26.0 1.7

02-11-2011 32.0 13.0 0.0 2.1 79.0 25.0 5.6

03-11-2011 32.0 13.0 0.0 2.8 76.0 22.0 6.6

04-11-2011 31.6 12.5 0.0 2.3 89.0 28.0 7.0

05-11-2011 31.5 14.3 0.0 0.5 94.0 35.0 2.3

06-11-2011 31.0 16.7 0.0 2.8 74.0 31.0 1.4

07-11-2011 30.0 13.0 0.0 4.9 87.0 30.0 6.5

08-11-2011 29.5 11.0 0.0 2.5 90.0 24.0 7.7

09-11-2011 30.0 13.5 0.0 1.7 89.0 35.0 4.2

10-11-2011 30.5 18.2 0.0 4.7 69.0 38.0 5.6

11-11-2011 31.0 15.9 0.0 5.6 81.0 52.0 7.6

12-11-2011 28.8 16.4 0.0 7.0 77.0 30.0 6.0

13-11-2011 29.2 11.7 0.0 4.6 86.0 32.0 8.3

14-11-2011 29.6 11.8 0.0 1.7 88.0 32.0 7.9

15-11-2011 29.0 12.2 0.0 2.1 89.0 27.0 5.0

16-11-2011 29.3 13.2 0.0 2.8 84.0 35.0 6.3

17-11-2011 28.8 12.6 0.0 3.9 89.0 29.0 6.0

18-11-2011 29.0 11.6 0.0 4.0 88.0 30.0 6.7

19-11-2011 29.8 13.0 0.0 2.7 93.0 58.0 6.9

20-11-2011 26.0 11.3 0.0 2.5 96.0 51.0 2.2

21-11-2011 26.8 12.4 0.0 1.6 95.0 51.0 2.4

APPENDIX

Weather Table

xv

22-11-2011 27.6 12.5 0.0 1.4 93.0 55.0 0.0

23-11-2011 27.0 12.8 0.0 0.8 89.0 49.0 0.0

24-11-2011 26.5 11.4 0.0 1.2 82.0 44.0 0.0

25-11-2011 27.5 10.3 0.0 1.2 88.0 28.0 1.0

26-11-2011 27.2 9.5 0.0 1.7 90.0 32.0 4.3

27-11-2011 26.0 9.4 0.0 1.9 93.0 35.0 0.2

28-11-2011 25.8 9.4 0.0 1.8 95.0 27.0 0.6

29-11-2011 24.5 12.0 0.0 5.1 82.0 36.0 2.2

30-11-2011 25.4 9.2 0.0 6.9 78.0 27.0 3.7

01-12-2011 25.4 8.8 0.0 3.0 85.0 34.0 6.8

02-12-2011 25.0 10.6 0.0 2.0 88.0 47.0 1.9

03-12-2011 27.0 11.4 0.0 1.0 93.0 46.0 3.2

04-12-2011 26.5 9.3 0.0 2.9 88.0 34.0 4.0

05-12-2011 26.5 8.0 0.0 2.6 92.0 30.0 5.4

06-12-2011 28.5 11.6 0.0 1.1 88.0 42.0 5.5

07-12-2011 27.1 13.8 0.0 4.2 98.0 48.0 4.7

08-12-2011 26.0 13.9 0.0 3.2 94.0 47.0 3.4

09-12-2011 28.0 15.3 0.0 4.5 92.0 77.0 4.4

10-12-2011 22.0 12.8 0.0 5.3 93.0 46.0 0.0

11-12-2011 23.0 7.5 0.0 5.3 81.0 40.0 2.5

12-12-2011 22.3 5.2 0.0 2.8 94.0 34.0 4.9

13-12-2011 22.4 4.2 0.0 1.7 97.0 38.0 4.9

14-12-2011 22.0 4.0 0.0 2.8 97.0 29.0 4.1

15-12-2011 21.2 3.7 0.0 2.9 90.0 32.0 3.8

16-12-2011 21.2 2.4 0.0 2.3 84.0 31.0 4.4

17-12-2011 20.8 2.3 0.0 2.3 100.0 33.0 2.8

18-12-2011 20.5 2.2 0.0 2.7 90.0 25.0 3.1

19-12-2011 22.0 1.3 0.0 1.2 96.0 43.0 1.7

20-12-2011 21.0 3.0 0.0 1.5 91.0 47.0 0.0

21-12-2011 19.4 4.8 0.0 2.9 94.0 40.0 3.3

22-12-2011 22.5 3.2 0.0 0.5 90.0 42.0 3.5

23-12-2011 20.5 2.8 0.0 2.0 93.0 42.0 2.1

24-12-2011 18.5 0.0 0.0 2.8 83.0 43.0 3.2

25-12-2011 18.2 0.0 0.0 2.8 82.0 37.0 3.9

26-12-2011 20.0 0.2 0.0 1.4 83.0 37.0 4.2

27-12-2011 20.0 0.9 0.0 0.8 93.0 45.0 3.8

28-12-2011 20.5 2.3 0.0 0.4 97.0 50.0 0.0

29-12-2011 22.5 3.6 0.0 0.7 97.0 62.0 0.4

30-12-2011 21.5 3.0 0.0 1.1 97.0 70.0 0.0

31-12-2011 19.5 3.7 0.0 0.7 97.0 58.0 0.0

01-01-2012 22.0 9.0 0.0 1.8 93.0 96.0 0.0

APPENDIX

Weather Table

xvi

02-01-2012 18.5 10.7 0.0 1.0 100.0 74.0 0.0

03-01-2012 19.5 5.8 0.0 1.1 97.0 57.0 0.0

04-01-2012 19.6 5.3 0.0 1.9 97.0 49.0 0.1

05-01-2012 21.0 7.8 0.0 0.8 95.0 75.0 0.0

06-01-2012 19.0 11.3 0.0 3.7 98.0 82.0 0.0

07-01-2012 17.0 11.5 6.6 5.3 100.0 80.0 0.0

08-01-2012 18.0 10.4 0.0 3.8 85.0 66.0 0.0

09-01-2012 17.0 7.3 0.0 5.2 87.0 47.0 0.0

10-01-2012 16.5 1.3 0.0 3.0 89.0 36.0 3.7

11-01-2012 17.0 1.7 0.0 3.9 84.0 42.0 5.6

12-01-2012 17.3 0.7 0.0 3.4 89.0 38.0 4.2

13-01-2012 18.0 1.7 0.0 4.7 93.0 39.0 5.9

14-01-2012 18.9 2.7 0.0 3.5 94.0 40.0 6.0

15-01-2012 21.0 7.4 0.0 3.1 92.0 33.0 5.4

16-01-2012 24.6 12.3 8.2 5.9 91.0 72.0 4.9

17-01-2012 19.0 8.2 0.0 6.3 97.0 79.0 0.3

18-01-2012 13.6 3.2 0.0 3.8 97.0 65.0 0.0

19-01-2012 15.0 3.0 0.0 2.8 94.0 63.0 3.3

20-01-2012 16.2 0.7 0.0 2.4 97.0 79.0 0.0

21-01-2012 12.5 4.6 0.0 5.4 86.0 35.0 0.0

22-01-2012 17.5 4.7 0.0 6.0 83.0 41.0 8.1

23-01-2012 17.0 3.0 0.0 3.3 85.0 42.0 0.9

24-01-2012 21.5 5.5 0.0 3.5 81.0 32.0 7.8

25-01-2012 20.5 8.8 0.0 6.3 78.0 41.0 8.6

26-01-2012 20.5 8.4 0.0 5.5 76.0 42.0 7.0

27-01-2012 20.5 4.6 0.0 5.1 94.0 43.0 5.9

28-01-2012 19.9 2.0 0.0 8.7 97.0 42.0 7.4

29-01-2012 20.3 1.7 0.0 2.5 97.0 50.0 6.9

30-01-2012 19.9 3.4 0.0 3.9 88.0 43.0 8.9

31-01-2012 21.0 0.4 0.0 3.2 96.0 48.0 7.6

01-02-2012 20.4 5.0 0.0 3.5 97.0 49.0 6.6

02-02-2012 21.0 6.4 0.0 5.6 87.0 40.0 6.8

03-02-2012 22.0 7.8 0.0 4.3 78.0 24.0 7.4

04-02-2012 22.5 7.5 0.0 2.8 87.0 32.0 5.1

05-02-2012 23.0 9.6 0.0 5.0 88.0 40.0 1.4

06-02-2012 23.0 9.8 0.0 6.3 95.0 43.0 4.6

07-02-2012 21.0 6.7 0.0 6.9 64.0 35.0 6.4

08-02-2012 19.5 7.7 0.0 5.0 62.0 19.0 5.2

09-02-2012 18.8 0.7 0.0 5.9 81.0 31.0 8.2

10-02-2012 18.0 5.0 0.0 10.5 59.0 23.0 8.5

11-02-2012 19.6 4.6 0.0 2.9 91.0 36.0 6.2

APPENDIX

Weather Table

xvii

12-02-2012 20.6 9.0 0.0 3.5 76.0 43.0 4.0

13-02-2012 24.3 12.5 0.0 4.8 69.0 43.0 7.4

14-02-2012 23.2 9.0 0.0 4.0 88.0 42.0 5.9

15-02-2012 22.5 10.2 0.0 6.1 52.0 24.0 0.4

16-02-2012 20.5 5.0 0.0 6.5 65.0 31.0 8.1

17-02-2012 19.5 5.0 0.0 5.5 62.0 29.0 8.0

18-02-2012 21.5 6.8 0.0 6.1 86.0 25.0 4.0

19-02-2012 23.0 9.3 0.0 8.3 61.0 32.0 7.5

20-02-2012 23.5 9.4 0.0 7.7 70.0 35.0 7.8

21-02-2012 24.0 8.2 0.0 4.2 95.0 30.0 7.2

22-02-2012 28.2 11.0 0.0 2.7 84.0 37.0 7.6

23-02-2012 29.5 12.2 0.0 4.8 80.0 36.0 7.7

24-02-2012 26.8 9.1 0.0 5.1 80.0 37.0 7.4

25-02-2012 25.0 9.5 0.0 7.4 63.0 23.0 4.4

26-02-2012 22.5 8.0 0.0 8.7 58.0 24.0 8.3

27-02-2012 23.2 8.2 0.0 9.5 65.0 21.0 9.5

28-02-2012 24.4 9.6 0.0 7.5 78.0 34.0 8.0

29-02-2012 25.2 7.7 0.0 5.7 92.0 24.0 8.2

01-03-2012 25.7 6.8 0.0 3.2 86.0 19.0 8.6

02-03-2012 28.5 8.8 0.0 2.8 85.0 16.0 7.8

03-03-2012 28.5 9.0 0.0 4.9 74.0 19.0 8.6

04-03-2012 28.5 11.2 0.0 4.6 73.0 32.0 6.8

05-03-2012 31.5 16.0 0.0 3.7 75.0 31.0 4.8

06-03-2012 31.7 18.3 0.0 5.6 81.0 28.0 6.4

07-03-2012 28.5 9.7 0.0 6.4 63.0 19.0 3.6

08-03-2012 28.5 9.5 0.0 3.3 83.0 27.0 8.3

09-03-2012 26.5 9.0 0.0 4.8 77.0 25.0 5.3

10-03-2012 24.4 5.4 0.0 4.0 83.0 20.0 7.4

11-03-2012 25.5 4.6 0.0 3.0 83.0 22.0 9.4

12-03-2012 27.3 6.8 0.0 2.1 87.0 31.0 6.8

13-03-2012 28.5 14.0 19.2 5.0 75.0 38.0 7.1

14-03-2012 26.2 10.8 0.0 4.7 84.0 31.0 6.7

15-03-2012 25.0 12.2 0.0 7.2 70.0 27.0 6.8

16-03-2012 27.5 13.0 0.0 6.8 78.0 31.0 7.7

17-03-2012 30.0 12.0 0.0 4.1 91.0 21.0 6.1

18-03-2012 33.5 13.5 0.0 1.9 92.0 24.0 6.3

19-03-2012 34.5 16.3 0.0 1.5 83.0 25.0 5.2

20-03-2012 37.0 20.0 0.0 3.9 77.0 22.0 8.0

21-03-2012 33.5 12.2 0.0 7.4 68.0 24.0 2.7

22-03-2012 25.5 13.7 0.0 12.7 50.0 32.0 6.7

23-03-2012 26.5 13.2 0.0 10.7 50.0 19.0 8.0

APPENDIX

Weather Table

xviii

24-03-2012 30.0 15.4 0.0 10.4 62.0 17.0 8.6

25-03-2012 32.8 16.0 0.0 8.5 66.0 13.0 8.4

26-03-2012 33.3 14.6 0.0 5.0 90.0 27.0 5.7

27-03-2012 35.2 16.4 0.0 2.3 76.0 34.0 5.0

28-03-2012 33.5 16.0 0.0 9.0 83.0 24.0 2.0

29-03-2012 33.5 18.8 0.0 8.7 60.0 28.0 6.4

30-03-2012 32.5 16.0 0.0 6.4 63.0 27.0 9.7

31-03-2012 34.2 14.7 0.0 5.5 72.0 62.0 10.0

01-04-2012 35.0 18.3 0.0 3.7 62.0 25.0 8.9

02-04-2012 36.0 18.4 0.0 3.0 63.0 30.0 7.1

03-04-2012 37.0 21.2 0.0 3.7 67.0 34.0 5.6

04-04-2012 37.0 19.3 0.0 4.6 67.0 28.0 6.6

05-04-2012 37.5 19.4 0.0 4.7 64.0 28.0 9.0

06-04-2012 37.5 18.2 0.0 4.9 62.0 33.0 9.2

07-04-2012 36.0 18.0 0.0 3.8 62.0 31.0 9.6

08-04-2012 36.5 18.7 0.0 3.3 70.0 27.0 8.5

09-04-2012 37.0 23.0 0.0 6.1 57.0 32.0 8.9

10-04-2012 39.0 18.8 Tr. 3.6 62.0 43.0 8.0