EXPERIMENTAL ASSESSMENT OF CROP COEFFICIENTS FOR JUDICIOUS IRRIGATION SCHEDULING व कप र ण स च ई क मय न र णरर ह त फ ल ग र क क प रय ग त मक आ कल

Transcription

1 EXPERIMENTAL ASSESSMENT OF CROP COEFFICIENTS FOR JUDICIOUS IRRIGATION SCHEDULING व कप र ण स च ई क मय न र णरर ह त फ ल ग र क क प रय ग त मक आ कल AJITA GUPTA DIVISION OF AGRICULTURAL ENGINEERING INDIAN AGRICULTURAL RESEARCH INSTITUTE NEW DELHI

2 EXPERIMENTAL ASSESSMENT OF CROP COEFFICIENTS FOR JUDICIOUS IRRIGATION SCHEDULING BY AJITA GUPTA A Thesis 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 Technology In AGRICULTURAL ENGINEERING 2015 Approved by the Advisory committee Chairperson Dr. A. Sarangi Co-Chairperson Dr. D.K. Singh Members Dr. S.S. Parihar Dr. Cini Varghese

3 Dr. A. Sarangi (Principal Scientist) CERTIFICATE Water Technology Centre ICAR-Indian Agricultural Research Institute New Delhi , India This is to certify that the thesis entitled Experimental assessment of crop coefficients for judicious irrigation scheduling submitted to the Post Graduate School, ICAR-Indian Agricultural Research Institute (IARI), New Delhi in partial fulfillment of the requirements for the award of the degree of Master of Technology in Agricultural Engineering embodies the results of bona fide research work carried out by AJITA GUPTA under my supervision and guidance, and that no part of the thesis has been submitted by her for any other degree or diploma. It is further certified that any help or information that has been availed during the course of investigation have been duly acknowledged by her. Date: 25 th June, 2015 Place: New Delhi Dr. A. Sarangi Chairman

4 Dedicated to My Chairman & family members

5 ACKNOWLEDGEMENT Euripides (484 BC 406 BC) once told that, The best and safest thing to keep a balance in your life, acknowledge the great powers around us and in us. If you can do that, and live that way, you are really a wise man. So it is essential that I acknowledge the great powers, who paved the way on which I have walked so far. As a prelude to my thanksgiving, at first I wish to thank the almighty for giving me power to complete my entire course and research program after all He is the greatest. I wish to express my deepest sense of gratitude and indebtedness to Chairman of my Advisory Committee, Dr. A. Sarangi, Principal Scientist, Water Technology Centre, ICAR- Indian Agricultural Research Institute (IARI), New Delhi for his invaluable guidance, constant encouragement, cooperative attitude and peerless criticisms during the course of investigation and preparation of the thesis. He has always been a fountain of inspiration to me. My gratitude is again to my sir. It is a great privilege for me to express my esteem and profound sense of gratitude to Dr. D.K. Singh Co-chairman of my Advisory Committee and Principal Scientist, Water Technology Centre, ICAR-Indian Agricultural Research Institute, New Delhi for his esteemed guidance, endurance, constructive and valuable suggestions, useful discussion and immense patience during the whole course of investigation and preparation of the thesis. He was always there in all my needs and helped his best whenever I seek for it. I feel immense pleasure to convey my heartfelt thanks to my advisory committee members, Dr. S.S. Parihar, Principal Scientist, Water Technology Centre (WTC), Indian Agricultural Research Institute, New Delhi and Dr. Cini Varghese, Principal Scientist, ICAR-Indian Agricultural Statistics Research Institute, for their encouragement and invaluable suggestions endowed during the course of my research work. I extend my sincere thanks to Dr. I.M. Mishra, Head and Professor, Division of Agricultural Engineering for providing me excellent tips, encouragement and facilities during my course work and research programme.

6 My sincere thanks are due to Dr. A. K. Mishra, Dr. M. Hassan, Dr. Neelam Patel and Mr. Ashok Kumar for their support, enthusiastic and constant inspirational help during my research work. My hearty gratitude to all other scientists of Division of Agricultural Engineering for their constant encouragement and kind concern throughout the study. I would also like to convey my heartfelt gratitude to Saqib sir, Muskan di and Om Shukla sir who helped me a lot during my work. I wish to acknowledge the facility provided by Indian Meteorological Department, Pune for using lysimeter facility in the evapotranspiration observatory at WTC, farm, ICAR- IARI. Besides this the climatic parameter for the experiment seasons acquired from the Automatic Weather Station located at WTC observatory is duly acknowledged. It gives me immense pleasure to mention names of Chanchala di, Yukti, Abhinav, Rajeev sir, Veeranna, Bholu and Mukhtar whose help and collective efforts have been reflected in the completion of this venture. The unceasing affection and support of seniors Chetan sir, Mukesh Sir, Sagar sir, Jeetendra Sir, Pawan Jeet sir, Manmohan sir, Ravindra sir, Bikram sir, Manish sir, Laulina di, Jaya di, Himani di would be in my memory for all the time. I extend my heartfelt thanks to my juniors like Alka, Shradhdha and Arti for their help and support. The endless love, affection, sacrifice and constant inspiration from Maa, Papa and other family members have enabled me to reach the footsteps of my long cherished aspiration. I am in dearth of words to express my love to my Pari for her innocence and beautiful smile which help in strengthening my will and boosting my morale, which is beyond the level of acknowledgement. My sincere thanks to the Dean and Director of Indian Agricultural Research Institute, New Delhi. Finally, the financial assistance provided by the I.C.A.R. in the form of Junior Research Fellowship during the tenure is gratefully acknowledged. Place: New Delhi Dated: (Ajita Gupta)

7 CONTENTS S. No. Chapter Page No. 1. INTRODUCTION BACKGROUND MATERIALS AND METHODS RESULTS AND DISCUSSION SUMMARY AND CONCLUSIONS SUGGESTIONS FOR FUTURE WORK ABSTRACT i-ii 8. BIBLIOGRAPHY i-vi 9. APPENDIX i-xvi

8 LIST OF FIGURES Fig. No. TITLE 3.1 Location map of field experiment site at Water Technology Center (WTC), ICAR-Indian Agricultural Research Institute,New Delhi, India ( Source: Weekly meteorological data during the crop growth period for rabi Page No Weekly meteorological data during the crop growth period for rabi Layout of the experimental field at WTC farm, ICAR-IARI, New Delhi Picture showing the field preparation for sowing of mustard on (A) 20 th 26 November, 2013 and (B) 12 th November, A view of lysimeter with 50% deficit irrigation (A) and the lysimeter with 26 full irrigation (B) at experimental site of WTC farm 3.7 A view of experimental field covered with mustard (A) and the lysimeter without crop to estimate the evaporation from bare soil (B) View of the experiment with three lysimeters (A, B & C) located at WTC farm, ICAR-IARI New Delhi Data recording from lysimeters for estimation of daily evapotranspiration (A) and lysimeters with mustard crop (B) Criterion used for irrigation scheduling of the field experiment Major modules and data base of the developed software The system architecture of the developed software for estimation of crop coefficient and irrigation scheduling 4.1 Variation of LAI, plant height and root depth at different days after sowing (DAS) during rabi Variation of LAI, Plant height and Root depth at different days after sowing (DAS) during rabi Variation of daily evapotranspiration, evaporation and transpiration components during rabi Variation of daily evapotranspiration, evaporation and transpiration components during rabi Variation of crop coefficient (k c ), basal crop coefficient (K cb ) and evaporation component of crop coefficient (K e ) during rabi

9 4.6 Variation of crop coefficient (K c ), basal crop coefficient (K cb ) and evaporation component of crop coefficient (K e ) during rabi Comparison of single crop coefficient (K c ) of mustard during rabi with FAO reported K c values 4.8 Comparison of single crop coefficient (K c ) of mustard during rabi with FAO reported K c values Relationship between the leaf area index (LAI) and basal crop coefficient (K cb ) of mustard rabi Relationship between the leaf area index (LAI) and basal crop coefficient ( K cb ) of mustard rabi Comparison between the lysimeter measured and FAO-56 estimated evapotranspiration during rabi Comparison between the lysimeter measured and FAO-56 estimated evapotranspiration during rabi Variation of basal crop coefficient (K cb ) with Growing Degree Days (GDD) during rabi Variation of basal crop coefficient (K cb ) with Growing Degree Days (GDD) during rabi Screen captured window of the front page of the software Screen captured window of the crop coefficient estimator interface module of the software for wheat 4.17 Screen captured window of the irrigation scheduler interface module of the software for wheat when K c single is known 4.18 Screen captured window of the irrigation scheduler interface module of the software for mustard under known value of dual K c 4.19 Screen captured window of the irrigation scheduler interface module of the software for wheat under known values of soil moisture content 4.20 Screen captured window of the irrigation scheduler interface module of the software for maize 4.21 Screen captured window of the irrigation scheduler interface module of the software for soybean

10 LIST OF TABLES S. No. TITLE Page no. 3.1 Physical and chemical properties of the soil of experimental field 3.2 Field operation activity schedule undertaken during the experiment in rabi and Monthly climatic parameters of the study area during mustard growth period 4.2 Rainfall, effective rainfall, irrigation depths, reference evapotranspiration (ET0) and crop evapotranspiration (ET c ) of mustard during rabi and Crop growth period of wheat, maize, soybean and mustard divided under different stages of development for estimation of crop coefficient 4.4 Grain and biomass yield, water productivity (WP) and harvest index (HI) of mustard during rabi and

11 LIST OF ABBREVIATIONS c : Degree Celsius AI : Aridity Index AIW : Applied Irrigation Water Bd : Bulk Density CC : Canopy Cover cm : Centimeter DAS : Days After Sowing DCC : Dual Crop Coefficient DI : Deficit Irrigation DRZ : Depth of Root Zone DSS : Decision Support System E : Evaporation ET : Evapotranspiration et al : and others ET 0 : Reference Evapotranspiration ETc : Crop Evapotranspiration FAO : Food and Agriculture Organization FC : Field Capacity FGC : Full Ground Cover FI : Full Irrigation Fig. : Figure GDD : Grow Degree Day GUI : Graphical User Interface ha : Hectare HI : Harvest Index i.e. : That is IARI : Indian Agricultural Research Institute ICAR : Indian Council for Agricultural Research ICRISAT : International Crop Research Institute for Semi -Arid Tropics ICT : Information and Communication Technology IW : Irrigation Water

12 K : Potash K c : Crop Coefficient Kcb : Basal Crop Coefficient Ke : Evaporative component of crop coefficient kg : Kilogram Km : kilo-meter Ks : Saturated Hydraulic Conductivity LAI : leaf Area Index M : Million MAD : Manageable allow Depletion MC : Moisture Content mg : Milligram MJ : Mega Joule Mm : Millimeter N : Nitrogen P : Phosphorous Pa : Pascal PET : Potential Evapotranspiration PWP : Permanent Wilting Point RMSE : Root Mean Square Error SMD : Soil Moisture Deficit t : tons T : Transpiration TDR : Time Domain Reflectometer Tp : Transpiration UK : United Kingdom viz : namely WB : Water Balance WIC : Water Impact Calculator WMO : World Meteorological Organization WP : Water Productivity WTC : Water Technology Centre yr : Year

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14 1 Chapter I Introduction Water is the elixir of life and essential for socio-economic development of any region. Yet, water resources have been taken for granted as a free resource to be used at will without paying heed to the long term consequences of its improper management. Agriculture sector is the major consumer of water resources in India. However, increasing competition for water for industrial, domestic and power sectors necessitates urgent need for development of water saving technologies to ensure more crops per drop in agriculture. Efficient water management in agriculture is the major challenge to ensure sustainable food production in India. Due to escalating demand of water in arid and semi-arid regions, the irrigation water availability will decline in coming years. Therefore, it is imperative to develop technologies for judicious utilization of available water in agriculture to ensure future food security of the country. It is estimated that by 2050 in India, about 22 % of the geographic area and 17 % of the population will be under absolute water scarcity. The per capita availability of water which was about 1704 cubic meter in 2010 is projected to be 1235 cubic meter in 2050 (Anonymous, 2011). Infrastructure to increase the command area and minimize the gap of irrigation potential created and utilized is the dominant component in the overall investment in agriculture (Anonymous, 2011). Besides this, judicious agricultural water management assist in ensuring good returns through use of high yielding crop varieties. Above all, water will remain as a critical input for attaining sustainability in agricultural production. Irrigated agriculture is the biggest water consumer in the world and often competes with industrial and urban sectors. Estimates indicate that more than 56 % of the total food grain comes from irrigated ecosystem while rainfed agriculture contributes to 44 % food grain production. It is envisaged that in future the major gain in food production by at least 50 % of the incremental food requirement by 2025 would come from irrigated agriculture. In semi-arid environments with limited and irregular rainfall, the use of supplemental irrigation is necessary for enhancing crop production and productivity. Moreover, enhancing water productivity (WP) of different crops is one of the major activities to ensure water saving in agriculture sector. The misuse of

15 2 water due to improper scheduling of irrigation and low irrigation water use efficiency would result in higher production costs and adverse environmental impacts. Matching the water supply and demand are essential for enhancing productivity and attaining sustainability under irrigated environment. Moreover, knowledge of crop-water requirements is crucial for management and planning of water resources in order to improve water productivity (Hamdy and Lacirignola, 1999; Katerji and Rana, 2008). Judicious irrigation scheduling besides other crop management practices plays a vital role in enhancing the water productivity in agriculture. Scheduling the time and quantity of irrigation water application is primarily governed by the crop evapotranspiration. Evapotranspiration (ET) is considered to be the dominant component of the hydrologic cycle due to the fact that about 60% of annual precipitation falling over the land surface is returned to atmosphere as ET (FAO, 2003). Estimation of ET is not only used for crop water demand assessment but also for management of water resources under changing climate scenarios. In agriculture, accurate quantification of ET is important for effective and efficient irrigation water management (Irmak, 2009). ET is dependent not only on the meteorological elements but also on factors related to the crop, soil environment and management parameters (Abu-Zeid and Hamdy, 2002).Therefore, determination of daily crop evapotranspiration and computation of crop coefficients at different crop growth stages assist in proper irrigation scheduling and judicious water management in agriculture (Martins et al., 2013). India is one of the largest mustard growing countries in the world which occupies the first position in terms of area of cultivation and second position in its production after China (Shekhawat et al., 2012). Mustard (Brassica Juncea) is the second most important edible oilseed crop in India after groundnut and accounts for about 30 % of the total oilseeds production (Kumar et al., 2005). Globally, mustard is grown in 34.2 Mha area with a production of about 63 Mt and average yield of 1.85 t ha -1. India s share in mustard is about 19.3 % of area with about 11 % of global production. There exists a scope of enhancing the production of mustard in India through judicious water management technologies. Precise irrigation scheduling in mustard necessitates reliable estimation of crop coefficient values at different crop growth stages. Moreover, the crop coefficients are estimated by quantifying the actual and potential crop evapotranspiration during different crop growth stages.

16 3 Evapotranspiration accounts for the evaporation of water from soil and plant surfaces besides transpiration from the stomata of plants, which account for about 98 % of crop water use. Crop coefficients (Kc) are multiplied with reference evapotranspiration (ET0) to estimate the actual crop evapotranspiration (ETc). Crop coefficient estimation can be accomplished by using either the single or the dual crop coefficient approaches. In single crop coefficient approach, the effect of both crop transpiration and soil evaporation are integrated into a single crop coefficient (Allen et al., 1998). Whereas, in dual crop coefficient approach, the basal crop coefficient (Kcb) and the soil evaporation coefficient (Ke) are estimated separately. Multiplication of Kcb with ET0 represents primarily the transpiration component of ETc. The soil evaporation coefficient (Ke) represents the evaporation component of ETc. The dual crop coefficient is used in crop modeling studies, estimation of real time irrigation scheduling for frequent water application such as daily irrigation using micro irrigation methods, supplementary irrigation and detailed soil and hydrologic water balance investigations. Dual crop coefficients are preferred over single crop coefficients for cash crops, crop with incomplete soil cover and for high frequency irrigation (Allen and Pereira, 2009). However, there are limited studies on the estimation of dual crop coefficient for field crops including oil seeds because of the complexity involved in the computation process, which require weighing type field lysimeters, daily meteorological data and different soil specific parameters.weighing lysimeter is developed to provide a direct measurement of ET. A lysimeter is a device with a tank or container used to define the water movement across a boundary having weighing mechanism to measure its weight on daily basis. A weighing type field lysimeter with crop is used to estimate ETc directly by considering the dynamic mass balance of the water concept as contrasted to a nonweighing type lysimeter which indirectly determines ETc using the volume balance approach (Howell et al., 1991). Recent development in Information and Communication Technology (ICT) assist the user to acquire information and generation of alternate scenarios for solving complex problems that are beyond the limitations of expert systems. This is accomplished by development of information systems or decision support systems (DSS) using primary and secondary data and production functions developed using process based models pertaining to any given problem domain. Moreover, softwares for soil water balance and irrigation scheduling are developed to assist in deciding on

17 4 when to apply and how much water to apply for different crops around the globe. Some of the softwares viz. IrriSatSMS (Australia), ISS-ITAP (Albacete, Spain), BEWARE (Crete, Greece), Anglia river Basin (UK), IRRISA (France), IrriSat (Campania Region, Italy) CROPWAT, ISAREG, PILOTE or more recently the SIMDualKc are being used for irrigation scheduling of different crops. However, the online software on irrigation water management developed by Abourached et al., (2007) and Hillyer and Sayde (2010) are only a few softwares that employ the dual Kc method for irrigation scheduling of crops. Moreover, the communication between the developed interfaces and DSS on irrigation scheduling and stakeholders is becoming a key topic on the implementation of the decisions on scheduling of irrigation. It is observed form literature that majority of irrigation scheduling or irrigation water management simulation models use the single Kc approach to compute ETc, which provides satisfactory results for the estimation of daily evapotranspiration with adequate precision for most applications. However for high frequency irrigation using micro irrigation systems for crops that only cover part of the soil, and when the soil is often wetted by rain or irrigation, the dual Kc approach can lead to more accurate estimates of ETc. Dual Kc approach permits estimation of the transpiration and evaporation components of evapotranspiration more accurately and capturing impacts of irrigation frequency and soil management on total water use during the crop growing season. The crop coefficient (Kc) value represents crop-specific water use and is required for accurate estimation of irrigation requirements of different crops in the command area. Crop coefficient values for a number of crops grown under different climatic conditions have been suggested by Doorenbos and Pruitt (1977). Moreover, the database of crop coefficients of different crops in varying agro climatic regions of India is not available and the Kc values given by FAO are generally used for irrigation scheduling of different crops. It is also suggested in FAO-56 that the regional crop coefficient (Kc) values for different crops need to be estimated using the weighing type field lysimeter for more accurate irrigation scheduling (Allen et al., 1998). Therefore, development of interfaces, DSS and softwares for soil water budgeting, moisture dynamics in the crop root zone and irrigation scheduling using the regional crop coefficient values acquired from weighing type lysimeters are more relevant these days which assist the user to generate quick and accurate decision on irrigation water management. Keeping in view of accomplished research activities, the present study

18 5 was targeted at quantification of both single and dual crop coefficients for mustard using the weighing type lysimeters in semi-arid climatic condition prevailing in IARI farm and development of software for estimation of crop coefficients and subsequent irrigation scheduling of mustard crop using both experimental and available secondary data. The study was undertaken with these specific objectives: To assess single and dual crop coefficients of mustard in weighing type lysimeter. To develop a software for assessment of crop coefficients using both experimental and secondary data.

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20 7 Chapter II Review of Literature Agriculture sector is the major consumer of water resources in India. Therefore, it becomes imperative to develop water saving technologies for ensuring more crops per drop leading to increase in water productivity and attainment of sustainability in agriculture. Moreover, in semi-arid regions with limited and irregular rainfall, irrigation is must for enhancing crop production and productivity. Thus, enhancing water productivity of different crops is one of the major activities to ensure water saving in agriculture sector. To accomplish this, determination of crop coefficients and daily evapotranspiration are considered as basic parameters for estimating crop water requirements to achieve better irrigation scheduling and water management in different crops. Crop water requirements vary during the crop growing period, mainly due to variation in crop canopy and climatic conditions and related to both cropping technique and irrigation methods. About 99 % of the water uptake by plants from soil is lost as evapotranspiration (ET) and thus the measurement of actual crop evapotranspiration (ETc) on daily basis for the entire crop cycle is equal to the water requirement of the given crop. The concept of Kc was introduced by Jensen (1968) and further developed by other researchers (viz. Doorenbos and Pruitt, 1975 and 1977; Burman et al., 1980; Allen et al., 1998). The crop coefficient is the ratio of the actual crop evapotranspiration (ETc) to reference crop evapotranspiration (ET0).The FAO (Food and Agricultural Organization) and WMO (World Meteorological Organization) experts have given the Kc value corresponding to the different crop development and growth stages. It is suggested in FAO-56 (Allen et al., 1998) that the regional crop coefficient (Kc) values for different crops need to be estimated using the weighing type field lysimeter for more accurate irrigation scheduling at regional settings. Therefore, judicious irrigation scheduling for different crops at regional scales require the availability of accurate, quick and easy to use tools developed using the data of daily evapotranspiration acquired from weighing type field lysimeters. This chapter presents a review of published information on single and dual crop coefficient and softwares developed for irrigation scheduling. The information is presented under these three major headings viz.:

21 8 a. Determination of single crop coefficient b. Determination of dual crop coefficient c. Development of software for crop coefficient estimation and irrigation scheduling 2.1 Determination of single crop coefficient Crop coefficient estimation can be accomplished by using either the single or the dual crop coefficient approaches. In single crop coefficient approach, the effect of both crop transpiration and soil evaporation are integrated into a single crop coefficient (Allen et al. 1998). Single crop coefficient is used for irrigation planning and design, irrigation management, irrigation scheduling and real-time irrigation timing for less frequent water applications. The single crop coefficient method is much simpler and more convenient than the dual crop coefficient method and is still in practice. For most agricultural crops, a relationship can be established between evapotranspiration and climatic parameters by introduction of the crop coefficient, which is the ratio of crop evapotranspiration (ETc) to reference crop evapotranspiration (ET0) (Doorenbos and Pruitt, 1977). Several reports on the estimation on of Kc are available (viz. Doorenbos and Pruitt, 1977; Synder et al., 1987). Doorenbos and Kassam (1979) and Jensen et al. (1990) have reported crop coefficients for several crops. Tyagi et al. (2000) conducted an experiment on rice and sunflower in a set of two electronic weighing type lysimeters to measure the hourly evapotranspiration and to determine crop coefficients of these crops from 1994 to 1995 at the research farm of ICAR-CSSRI, Karnal, India. The estimated values of crop coefficient for rice at the four crop growth stages (viz. initial, crop development, reproductive and maturity) were 1.15, 1.23, 1.14 and 1.02and corresponding K c values for sunflower were 0.52, 1.1, 1.32 and 0.41, respectively. It was reported that in case of rice the estimated values were in line with FAO K c values, whereas, K c values of sunflower was % higher than those suggested by FAO. It was also observed that the ET0 estimated using Pan Evaporation, FAO-corrected Penman, Blaney-Criddle and Hargreaves methods resulted in higher ET0 than the Penman-Monteith method. It was suggested that crop coefficients need to be estimated using weighing type field lysimeters for judicious irrigation scheduling. Kashyap et al. (2001) evaluated the evapotranspiration estimation methods and developed crop coefficients for potato crop using weighing type field lysimeters located

22 9 at the research farm of IIT, Kharagpur, India, during One lysimeter was planted with the rey grass to measure reference evapotranspiration and compare this value with a total of 10 climatological methods used for estimation of reference evapotranspiration on daily basis. It was observed that the Penman-Monteith method produced the best result amongst all, followed by 1982-Kimberly-Penman, FAO- Penman, Truc-Radiation, FAO-Bleney-Criddle and Priestley-Taylor, with RMSE ranging from to The other lysimeter was used for estimation of crop coefficient of potato crop, and the measured values were 0.42, 0.85, 1.27 and 0.57 at initial, development, mid-season and late season stages, respectively. It was reported that Kc values recommended by Allen et al. (1998) were found to be higher than the measured values, whereas it was in close agreement with K c values recommended by Doorenboss and Pruitt (1977). They suggested that the K c values would be useful for irrigation planning and decision making at farm and regional level for potato under subhumid climatic condition of Asiatic regions having high evaporative demand. Liu et al. (2002) conducted field experiment during 1995 to 2000 at Luancheng Station in the Northern China plain region to obtain the daily evapotranspiration and average crop coefficient of wheat and maize using a large scale weighing type lysimeter and micro lysimeters to determine the soil evaporation. The average water consumption was estimated to be 453 and 423 mm for winter wheat and maize, respectively and average crop coefficients were 0.93 for winter wheat and 1.1 for maize during five year growing seasons. The ratio of soil evaporation to total evapotranspiration was estimated, which could be used to improve the field water utilization efficiency. It was found that evaporation from the soil surface consumed 29.7 and 30.3 % of the total ET for winter wheat and maize, respectively equaling an annual loss of about 250mm water. It was suggested that reducing the soil evaporation could be one of the most important water saving measures in the water deficit regions. Fronza et al. (2003) estimated the crop water requirement and crop coefficients of estiva (Stevia rebaudiana) at different growth stages. The experiment was conducted at San Piero a Grado, Pisa, Italy during 2000 using two constant water table micro lysimeters by maintaining water table level at 35 cm depth below the ground surface. Crop evapotranspiration for the total cycle (80 days) was estimated to be 464 mm with average evapotranspiration of 5.44 mm day- 1.The crop coefficient values were 1.45 for initial stage (0-25 days), 1.14 for mid-season (26 to 50 days), and 1.16 for end season

23 10 (51 to 80 days). It was reported that the crop coefficient values can be used in the stevia grown region for irrigation scheduling and water management practice because the Kc values are not reported in FAO literature. Kang et al. (2003) evaluated the crop coefficient and ratio of transpiration (Tp) to evapotranspiration (ETc) of winter wheat and summer maize based on lysimeter data during in the semi-arid region of North-West China. The relationship between Kc and days after sowing (DAS), Kc and leaf area index (LAI), ratio of Tp and ETc (Tp/ETc) and Kc were also analysed. The average seasonal ETc was estimated to be and mm, of which transpiration accounted for 67 and 74 % of total ETc for wheat and maize, respectively. The average, minimum and maximum values of Kc were 0.92, 1.33 and 0.42, respectively for winter wheat and 1.04, 1.43 and 0.45, respectively for maize. It was reported that the value of Tp/ETc ratio started from 0 at sowing and reached to its maximum of about 0.9 at the mid-season stage. They concluded that the result would assist in precise planning and efficient management of irrigation of wheat and maize crop in the study region. Bryla et al. (2006) highlighted the importance of weighing type lysimeter to estimate crop evapotranspiration and developing crop coefficients of broccoli, iceberg lettuce, bell pepper, and garlic. Data acquired from two lysimeters located at the research farm of central California during were analysed for the crop evapotranspiration and reference evapotranspiration values. Hourly ETc and ET0 values were determined and then summed up to calculate the daily Kc. It was reported that the values of Kc were found to be in line with the FAO recommended values, except during the mid-season stage of crop. A linear relationship between crop coefficients and ground cover was observed. However, it was reported that the correlation between climate driven ET0 and lysimeter ET0 was poor at night and suggested for weather based estimation of ET0. They concluded that the developed crop coefficients could become an efficient tool to facilitate irrigation scheduling in the crops and help to achieve full yield potential without over irrigation for both the developed and other region having similar climatic condition. Petillo et al. (2007) evaluated the actual evapotranspiration using water balance method of mature Valencia orange trees (Citrus sinensis (L.)) under both drip-irrigated and non-irrigated condition during 1997 to 2004 at San José, Uruguay. The annual ETc was observed to be 24% higher in irrigated trees (i.e.767 mm) than that from non-

24 11 irrigated trees (620 mm). A higher K c was also reported in fully irrigated trees as compared to restricted irrigations with the average value of It was reported that though the water balance (WB) method for estimation of evapotranspiration is simplest among all methods, but the precision obtained using the WB method is lower than that obtained using a weighting lysimeter. It was recommended that Kc values can provide a useful base for the design and operation of micro irrigation systems for mature citrus trees in Uruguay. Er-Raki et al. (2009) evaluated the performance of the FAO 56 (Allen et al., 1998) approach for citrus orchard under two different irrigation methods (i.e. drip and surface irrigation) in Morocco. Estimated Kc values were lower than the FAO-56 reported values by about 20 %. The results suggested that the single crop coefficient approach can be used to derive a good estimate of water consumption of citrus orchards irrigated by the surface irrigation method with less frequent water applications, while the dual Kc approach can be used for real-time irrigation scheduling with frequent water applications as in the case of the drip irrigated citrus orchards. Bhandari (2012) estimated the potential evapotranspiration (PET) and crop coefficient (K c ) for Wheat cultivar BL3235 using a lysimeter installed in the research farm of Bhairahawa, Nepal. It was reported that the plant height and total growing season influenced crop coefficient values. Kc values increased with increase in the number of growing days and reached maximum with the highest plant height. The PET was estimated using water balance approach in lysimeter and the Blaney-Criddle formula was used to estimate the ET0 and subsequent estimation of Kc values for wheat. The estimated values of Kc were 0.34, 0.67, 0.73 and 0.06 at initial, development, midseason and late-season stages, respectively, whereas the total PET was 278 mm. They also evaluated the AI (Aridity Index) during wheat growing season which was found to be 0.39, and based on the AI value, the region was classified as a semi-arid region. Shankar et al. (2012) conducted lysimeter experiment at Roorkee, India, during 2006 to develop crop coefficients K c for Maize and Indian mustard and to compute daily crop evapotranspiration from reference crop evapotranspiration (ET0) and crop coefficients. The K c for maize for the four crop growth stages (viz. initial, development, mid-season, and late season) were 0.55, 1.08, 1.25, and 0.75, respectively and for Indian mustard were 0.3, 0.6, 1.12, and 0.35, respectively. They developed irrigation schedules based on daily crop evapotranspiration and the observed values were in line with the

25 12 simulated soil moisture status prevalent during crop growing season. They also observed significant differences between the estimated Kc values and values reported by FAO in all stage except the late season growth stage of both maize and mustard crop and recommended to use the estimated regional crop coefficients for irrigation scheduling. Amoah et al. (2013) determined the water requirement and crop coefficient of hot pepper during different growth stages using an irrigation interval of two days through conduction of two field experiments in the research farm of the University of Cape Coast, Ghana, during The estimated values of the crop coefficients were , , and for the initial, developmental, midseason and late-season growth stages, respectively with total water requirement of 320mm and 432mm. It was reported that there was good symmetry between experimented and FAO recommended Kc values. They found that the plant height and total growing season influenced crop coefficient values and at maximum plant height and for long growing season, the crop coefficient values were maximum. Shenkut et al. (2013) conducted an experiment to determine growth stage specific Kc and crop water use for sorghum (Sorghum bicolor L.) cultivar Gambella at the Melkassa Agricultural Research Centre in Ethiopia during The calculated Kc values for the crop were 0.45, 0.83, 1.18 and 0.78 during the initial, development, mid-season and late-season stages; respectively with total seasonal value was mm. The yield was obtained to be 5.3 t ha -1 and the measured LAI were 0.2, 4.2, 4.9 and 1.6 at the initial, development, midseason and late season stages, respectively. The Kc values were reported to be on higher side initially due to a high evaporation from the wetted topsoil in a semi-arid environment. It was reported that the obtained Kc values were greater than FAO reported values, which emphasizes the need for developing site-specific Kc values for proper irrigation water management. They concluded that the result can be used for judicious irrigation schedule for sorghum under both full and deficit irrigation regimes. Garg et al. (2014) estimated crop coefficients of Jatropha and Pongamia using water balance approach at ICRISAT research farm, India during 2007 and They collected temporal data of soil moisture at different depths in block plantations of Jatropha and Pongamia at 15 days interval and analyzed using one dimensional water balance model (viz. Water Impact Calculator (WIC) developed at ICRISAT). The agreement between the simulated and observed soil moisture for both Jatropha and

26 13 Pongamia cultivated fields was found comparable with RMSE (<10%). The average annual water requirement of Jatropha and Pongamia was estimated to be 750 mm and 950 mm, respectively. The values of crop coefficient were estimated using the inverse optimization technique and was found to be varying from 0.10 to 0.95 and from 0.30 to 1.10 for Jatropha and Pongamia, respectively. It was concluded that Pongamia utilized the stored soil moisture more effectively than Jatropha and can be used to remove water from deeper soil layers even at high levels of soil moisture suction, whereas estimated crop coefficients can be utilized to assess technical feasibility for growing Jatropha and Pongamia crops in the study region. 2.2 Determination of dual crop coefficient The dual crop coefficient consists of two coefficients i.e. a basal crop coefficient Kc band a soil evaporation coefficient Ke. These coefficients are estimated separately using the plant and soil parameters (Paco et al., 2006). Accurate quantification of water lost by direct soil evaporation necessitates a partitioning of total evapotranspiration into its soil evaporation and plant transpiration components. Therefore, a separate and direct measurements of transpiration and soil evaporation components are desirable through sap flow or isotope measurements techniques (Williams et al., 2004; Rana et al., 2005). However, due to the complexity in estimation of dual crop coefficient, it is generally used for real-time irrigation scheduling for highly frequent water applications, supplemental irrigation besides detailed soil and hydrologic water balance investigations (Allen et al. 1998). Urrea et al. (2009) determined water requirement of onion under sprinkler irrigation system using both the single and dual crop coefficients estimated from weighing type lysimeter. The experiment was conducted during 2005 at Las Tiesas farm, located in Albacete (Central Spain). The estimated values of Kc were 0.65, 1.20 and 0.75 and of Kcb were 0.60, 1.10 and 0.65 during initial, mid and late season stages, respectively. The seasonal ETc ( mm) measured in the lysimeter was found to be higher than the seasonal ETc ( mm) calculated by the FAO method. A linear relationship between ground cover and Kcb was observed with coefficient of determination (R 2 ) value It was found that the evaporative component was high during the growing season due to the high frequency of irrigation and the fact that the onion crop does not completely cover the ground.

27 14 Liu et al. (2010) evaluated the dual crop coefficient (DCC) method proposed in FAO-56 and its applicability for estimation of actual daily evapotranspiration of winter wheat and summer maize grown in the North China Plain (NCP). The experiment was conducted from 1998 to 2005 in weighing type lysimeter installed at the Yucheng Comprehensive Experimental Station (YCES) of the Chinese Academy of Sciences (CAS). It was observed that the daily crop evapotranspiration using the DCC method proposed by FAO was in close agreement with the lysimeter based estimations for winter wheat, but failed to match well for summer maize. The DCC method estimated the seasonal ETc with better accuracy than the ETc during different crop developmental stages which may be attributed to the underestimation of the crop evapotranspiration at the initial stages and overestimation at the late season stages in the DCC method. It was concluded that the performance of DCC method is better for the winter wheat than for summer maize and was preferred for simulating the total evapotranspiration during the crop growth period. Abyaneh et al. (2011) determined the irrigation water requirement, single and dual crop coefficient of garlic using lysimeter data of located at Bu-Ali Sina University, Hamedan, Iran. It was reported that the evapotranspiration of garlic were mm and mm during the growing season of 2008 and 2009, respectively. Reference evapotranspiration (ET0) was estimated using artificial neural network (ANN) method during the garlic growing season. Single crop coefficient values (K c ) of initial, middle and final stages were observed to be 0.53, 1.4 and 0.3, respectively. Moreover, the comparison of single and dual crop coefficients using FAO-56 (Allen et al., 1998) procedure revealed maximum differences of ETC estimated from single and dual Kc values for initial and final growth stages of garlic. This study also showed that the dual crop coefficient approach for estimating irrigation water requirement was more precise than the single crop coefficient approach. Yarami et al. (2011) determined the potential evapotranspiration, single and dual crop coefficients for saffron using three field lysimeters during 2007 and 2008 in Badjgah region, Shiraz, Iran. Potential evapotranspiration values of saffron were estimated to be 523 and 640 mm during 2007 and 2008, respectively. Crop coefficient (K c ) values for the initial, mid and late season growth stages were , and during both years, respectively. Basal crop coefficient (Kcb) values for the initial, mid and late season growth stages were , and0.15

28 in both years, respectively. Results showed that the evaporation coefficient during the saffron growing season were high because of the narrow leaves of saffron, which indicated that the evaporation component is more important in saffron crop than the transpiration component. It was recommended to use dual crop coefficient i.e. Kc and Kcb values for proper irrigation scheduling in saffron crop. Lopez-Urrea et al. (2012) quantified the water use and determined the single crop coefficient (Kc) and dual crop coefficient values and obtained the relationship between the basal coefficient (Kcb) and the canopy cover (CC) of grape grown for wine production during three growing seasons ( ) in Albacete (Central Spain). Grape was planted under drip irrigation in three lysimeters. It was concluded that the irrigation method and its frequency affected the evaporation component of ET c and suggested that the measurement of canopy cover is a reliable approach to estimate Kcb (basal crop coefficient) values in grapevines. Results were compared with other similar studies on grapevine and it was concluded that the variation among the reported crop coefficients was because of production systems, cultivar types and climatic differences. It was suggested to carry out local or regional adjustments of grapevine crop coefficients to improve irrigation efficiencies. Majnooni-Heris et al. (2012) determined the single crop coefficient (Kc), basal crop coefficient (Kcb) and the ratio of transpiration to evapotranspiration of canola (Brassica napus L.) using weighing type lysimeter located at Tabriz University, Iran, during the growing season of 2010 and Relationships of the crop coefficients Kc and Kcb and parameters viz. days after planting (DAP), growing degree days (GDD), leaf area index (LAI), ground cover percentage (GC %) and the ratio of transpiration (Tp) to evapotranspiration (ETc) with LAI and GC % was investigated. They found that the seasonal transpiration accounted for 80 % and 75 % of evapotranspiration during 2010 and 2011, respectively. The variation of ETc was mainly controlled by transpiration, because evaporation from the soil only included a small part of evapotranspiration and decreased with increasing DAP, LAI and GC %. The average, maximum, and minimum values of K c were 1.03, 1.47 and 0.57 and of K cb were 0.76, 1.37 and 0.36 in 2010 and 0.90, 1.24 and 0.41 for Kc and0.64, 1.06 and 0.0 for K cb, respectively, during Such variation might be due to changing local climatic condition in experimental site during these two years. It was reported that estimated crop coefficients were higher than the recommended values by FAO for oil seed crops

29 16 because of higher soil bulk density, higher plant density, different crop cultivars and local climatic parameters. Silva et al. (2012) conducted an experiment during in the field of a commercial distillery located on Paraiba state, Brazil, to evaluate the applicability of the dual crop coefficient method for irrigation scheduling in sugarcane grown in tropical region. Besides this, the estimated ET by the single and dual crop coefficients were compared with the ET measured using field water balance approach. The estimated values of crop coefficient derived from soil water balance method during the initial, mid-season and late-season stages for sugarcane were 0.56, 1.43 and 1.32, respectively. It was found that there was symmetry between ET c measured and ETc calculated using dual K c approach. However, the values of ETc obtained from single K c approach underestimated by 36 %. It was also reported that the ETc and K c were linearly related to leaf area index with R 2 value It was suggested that the dual crop coefficient provided accurate estimates of ET c at both daily and seasonal time scales when appropriate instrumentation (Lysimeter) is not available. Parekh et al. (2013) determined the crop water requirement of cauliflower (Brassica Oleraces L.) having crop growing period of 90 days and irrigated by drip irrigation system, using single and dual crop coefficient approach. The experiment was conducted in the research farm of Water Resources Engineering Management Institute, Vadodara, India. It was estimated that the crop water requirement (ETc) of Cauliflower were 256 mm and 237 mm using single and dual crop coefficient approaches, respectively. The maximum differences between ET c of single and dual K c values were observed at the initial stage of crop growth. The study showed that the dual crop coefficient approach is best suited for high frequency irrigation systems such as drip irrigation systems. Lopez-Urrea et al. (2014) conducted an experiment during 2009 and 2011 in the Las Tiesas research farm located near Albacete (Central Spain) using weighing type field lysimeters with electronic data logger to quantify the consumptive water use and crop coefficients of irrigated sunflower. It was found that the seasonal ET c was 619 mm in 2009 and 576 mm in 2011 and the higher ET c value in 2009 was due to early planting and a longer growing season. Estimated values of K cb for sunflower at four different crop growth stages (viz.initial, development, mid-season, and late season)

30 17 were 0.12, 0.63, 1.08, and 0.60, respectively during 2009 and 0.12, 0.65, 1.18, and 0.62, respectively during It was recommended that the duration of crop growth stages and the corresponding K c values as proposed by FAO-56 need to be adjusted based on local weather conditions and crop duration. 2.3 Development of software for crop coefficient estimation and irrigation scheduling Computer based information systems have changed the way the research is being carried out and disseminated to the stake holders. Software and hardware tools these days have enhanced our ability to identify and solve problems and to perform tasks that are beyond our physical and mental capability. Software s are considered as advancement of management information systems, which assist human beings to solve complex problems, and provide data that can lead to non-predetermined solutions that are beyond the limitations of expert systems or heuristics. The application of the software in irrigation scheduling works as a remedy for conversion of statistical formula or complex data in digital format for easy and accurate calculation which are manually tedious and cumber some. Nowadays, some irrigation scheduling services are focusing on the implementation of decisions support systems (DSS)for irrigation scheduling viz. IRRINET service (Mannini et al., 2013; IrriSatSMS (Australia) John et al., 2009; BEWARE (Crete, Greece) Chartzoulakis et al., 2008; IRRISA (France) Boyer et al.,1996; IrriSat (Campania Region, Italy) Urso et al. 2013; CROPWAT smith and Martin,1991; ISAREG Teixeira et al., 1992; PILOTE Khaledian et al., 2009 and SIMDual Kc Rolim et al., The irrigation management- online software by Abourached et al. (2007) and Hillyer and Sayde (2010) are irrigation scheduling programs that employs the dual K c method. The communication between DSS and farmers is becoming a key topic on the implementation of irrigation scheduling softwares. Gorgea et al. (2000) developed an irrigation scheduling model (ISM) using a daily water balance approach consisting of a database management system for storing and retrieval of data along with a user-friendly graphical user interface. Climate, crop and soil data base were used as input with options for choosing different root growth functions, crop stress function, different ET0 estimation method and irrigation scheduling criteria (i.e fixed depth, fixed interval, variable depth and variable interval).

31 18 The developed software was tested using the field data and the CROPWAT model. The model-predicted soil moisture contents were compared with the field measured data for both single and multiple field sites. It was observed that for the single site, measured soil moisture was less than simulated soil moisture whereas for the multiple field sites, the measured soil moisture was higher than simulated moisture with mean absolute relative error in soil moisture varying from 4 to 15 %.Moreover the model predicted soil moisture value was slightly higher than that CROPWAT predicted values with R 2 value It was concluded that flexible and user-friendly irrigation scheduling software scan be used for efficient irrigation water management activities. Rolim et al. (2007) developed SIMDualK c software to compute crop evapotranspiration and irrigation scheduling using the dual crop coefficient approach (Kcb + Ke) which was mainly focused for the partially covered crops, including vegetables and orchard crops, and highly frequent irrigating crops. Soil, climate, crop and irrigation system data were used as input parameters. Software was developed to perform the soil water balance at field level for daily time step. The software was tested by comparing the observed and simulated soil moisture using regression analysis for cotton and winter wheat in Uzbekistan, Central Asia. It was observed that there was a good agreement between simulated and observed soil moisture content with the regression coefficient close to 1.0 and coefficients of determination (R 2 ) between 0.88 and 0.93during all crop growth stages. It was concluded that SIMDualKc provided good results obtained for the Mediterranean and Central Asia regions which can be used not only to predict crop evapotranspiration and irrigation requirements but also for planning and environmental studies based on farmers requirement. Rolim et al. (2008) developed a soil water balance simulation model IrrigRotation based on the dual Kc methodology. The software was developed within the GIS environment using Geo media 6.0 tool for a sequence of crops by accounting the soil moisture stored in the soil profile during the off-season period, thus enabling to simulate the water requirements of the cropping system. The IrrigRotation software was tested for sugar beet-maize-tomato-wheat cropping system in Beja, Alentejo, South Portugal. It was observed that there was high correlation between observed and software simulated data on water requirement with R 2 value of It was concluded that this software could be used to compute crop water requirements and irrigation

32 19 scheduling under cropping systems and to assess the impacts of the climate change on the irrigated agriculture and water productivity of cropping systems at regional scale. Lashari et al. (2010) developed a simple and user friendly irrigation scheduling software named as Mehran Model to compute crop evapotranspiration (ETc) using dual crop coefficients approach for sixty six types of crops. The software was designed for real time irrigation schedule for crops using daily weather data of a reference site and the irrigation supply schedule prevalent in the region. Software was validated for predicting the irrigation schedules for cotton and wheat crops in Lower Indus Basin of Pakistan. The seasonal ET c of cotton was observed to be 486 mm and those computed by the model was 504mm. Similarly, the observed seasonal ET c of wheat was 363mm and those computed was 383 mm. Statistical analysis showed that the model overestimated seasonal ETc of cotton and wheat crop by 2.41 % and 4.31 %, respectively. It was concluded that the software was capable of providing alternative scenarios of irrigation schedules under different irrigation water supply situations based on quarries by users. Judicious use of water for enhancing crop productivity is of prime importance in arid and semiarid regions of the world. Further, the increasing demand for water in agriculture coupled with decline of the ground water levels necessitates development of proper irrigation schedules for different crops and cropping systems. Since agriculture sector is the major consumer of water resources in India, it is imperative to determine crop coefficients and daily evapotranspiration for estimating crop water requirements to achieve better irrigation scheduling and water management in different crops. Actual crop evapotranspiration (ETc) is calculated by multiplying the reference evapotranspiration by the crop coefficient. Two types of crop coefficient approaches are adopted to estimate the crop evapotranspiration. Moreover, the single crop coefficient is used for irrigation planning and design, irrigation management, basic and real-time irrigation scheduling of less frequent water applications. Whereas the dual coefficient approach is best suited for real time irrigation scheduling, soil water balance computations and for research investigations under day-to-day variations in soil surface wetness, soil moisture dynamics in the crop root zone under more frequent micro irrigation systems. The latter approach requires more numerical calculations than that required for estimation of single crop coefficient values. However, in the dual crop coefficient approach, the effects of crop transpiration and soil evaporation are

33 20 determined separately which can also be used in water driven crop models. Moreover, user friendly and flexible softwares of irrigation scheduling when coupled with real field data source possesses a great potential for integrating irrigation based research into information and communication technologies (ICT) for dissemination to stakeholders.

34 21 Chapter III Materials and Methods The study was undertaken to estimate single and dual crop coefficient of mustard for the study region and to develop a software for crop coefficient estimation and irrigation scheduling of wheat, maize, soybean and mustard crops. To accomplish these specific objectives, activities pertaining to conduction of field experiment in weighing type field lysimeters during rabi and , acquisition and analysis of primary and secondary data besides coding of the software were taken up. This chapter deals with description of the study area, analysis of climate data, design of the field experiment in weighing type lysimeters, data analysis and development of a software for crop coefficient estimation and irrigation scheduling of selected crops. 3.1 Study area The experiment for the estimation of single and dual crop coefficient of mustard crop was undertaken at the Water Technology Centre (WTC) research farm of ICAR- Indian Agricultural Research Institute, New Delhi during rabi seasons of and The WTC experimental farm is located between to N latitude and ' 45'' to ' 24'' E longitudes with an average elevation of 230 m above mean sea level. The experiment was undertaken in 0.1ha (50m 20m) area enclosing three weighing type field lysimeters of size 1.2m X 1.2m at WTC-01 research farm of ICAR-IARI during both rabi and The location map of the experimental farm is shown in Fig Climate Study area located in the ICAR-IARI campus, New Delhi falls under the agroclimate region (ACR) VI of Trans Gangetic plains. Summer months i.e. May and June are the hottest months with the maximum temperature varying from 41 C to 46 C while temperature falls to its lowest during January with minimum temperature ranging between 4 C to 7 C, which increases gradually from the months of February till June, then it gets reduced with the advent of south-west monsoon. The mean open pan evaporation reaches as high as mm per day during the month of June, however it is as low as 0.6 mm per day during January and evaporation rate follows the same pattern as that of temperature during this period. Average annual rainfall of Delhi is

35 22 about 611 mm, 74% of which is received during active south-west monsoon months viz. July, August and September. The wettest months in a year are July and August with sporadic winter rains during September to February having varying rainfall depth, intensity and duration. The mean wind velocity varies from a minimum of 3.5 km hr -1 during October to 6.4 km hr -1 during April. Storms with high wind speed are generally associated with winter showers. The mean relative humidity (RH) reaches its maximum during the monsoon season and the minimum of 40 to 45 % during summer months. Mean annual temperature recorded at an observatory adjacent to the experiment site was 24 C with June being the hottest month (i.e. mean maximum temperature of 45 C) and January was the coldest (i.e. mean minimum temperature of 7 C). The mean annual rainfall based on 100 years record ( ) was 619 mm. About 80 per cent of the annual rainfall was received during monsoon (June-September) and the rest during winter, with occasional summer rain accompanied by hail storms. Humidity is high during the monsoon months. Wind speeds are low during the post monsoon and winter months and high during the summer and monsoon months. The meteorological observations during mustard growth periods used in the data analysis were acquired from the Water Technology Centre (WTC) observatory, located at a distance of 100 meter from the field experiment site. The climatic data pertaining to mustard growing period for the experimental year of and are presented in Figs. 3.2 & 3.3, respectively. India Fig. 3.1 Location map of field experiment site at Water Technology Center (WTC), ICAR-Indian Agricultural Research Institute (IARI), New Delhi, India (Source:

36 Max Temp( C), Min Temp ( C), Evaporation(mm), Sunshine hrs (h), Mean RH (%) Rainfall (mm) Max Temp( C), Min Temp ( C), Evaporation(mm), Sunshine hrs(h), Mean RH (%) Rainfall (mm) 23 rabi Week Rainfall (mm) Max Temp ( C) Min Temp ( C) Mean RH (%) Sunshine hrs (h) Mean Evaporation (mm) Fig.3.2 Weekly meteorological data during the crop growth period for rabi rabi Week Rainfall(mm) Max Temp ( C) Min Temp ( C) Mean RH (%) Sunshine hrs (h) Mean Evaporation (mm) Fig.3.3 Weekly meteorological data during the crop growth period for rabi Soil The soil texture of experimental field was sandy loam up to 30cm soil depth (Table 3.1). Data of different soil physical and chemical parameters of the experimental site are presented in Table 3.1. The field capacity of the soil ranged from to 33% (w/w) dry basis and permanent wilting point ranged from 9.5 to 15 % (w/w) dry basis.

37 24 The soil was moderate in organic carbon, low in nitrogen and moderate in available phosphorous and potassium. 3.4 Weighing type field lysimeters Three ASIA brand dormant type steelyard weighing type field lysimeters (model-ds) were used in the present study, located at 3 meter distance from each other. They comprised of two rectangular tanks with one at inner side and the other at outer periphery. The dimensions of the inner tanks were 1.2 m X 1.2 m X 1m. The outer tank was 1.25 m X 1.25 m X 1.25 m size. The effective surface area of lysimeter available for growing plants is 1.44 m 2. The total suspended weight of the lysimeter including tank, soil and water was about 2000 kg capacity. The major division on the weighing scale of the lysimeter was 0 to 2000 kg X 50 kg (i.e. major weighing scale is divided into 50 kg interval) and minor divisions in the scale was 0 to50 kg X 200 gm. Daily evapotranspiration (ET c ) depth values were calculated as the difference in weight of the lysimeter between two consecutive days, which provides the information about the amount of water lost and lysimeter mass gains from precipitation, irrigation during this period and the soil inside the lysimeter was same as the surrounding soil of the research farm. 3.5 Experimental details Experiment was conducted on a set of three weighing-type field lysimeters of 1.2 m 1.2 m surface area and 1 m depth, with mustard cultivar Pusa Vijay (NPJ-93) sown on 22 nd Nov 2013 and harvested on 28 th March 2014 during rabi and the sowing was on 14 th Nov 2014 and harvested on 20 th March 2015 during rabi to measure the daily evapotranspiration of mustard Crop (Figs. 3.5 & 3.9). First lysimeter was kept bare to estimate bare soil evaporation, whereas second and third lysimeter were sown with mustard. There were two levels of irrigation with third lysimeter under full irrigation treatment and the second lysimeter was under 50% deficit irrigation (Figs. 3.6 & 3.7). The adjoining area surrounding the lysimeter was sown with similar variety of mustard under non limiting water condition (Figs. 3.7 & 3.8).

38 25 Fig Layout of the experimental field at WTC farm, ICAR-IARI, New Delhi Table 3.1 Physical and chemical properties of the soil of experimental field Soil depth Soil Properties Sand (%) Silt (%) Clay (%) Soil texture Sandy loam Sandy loam Loam Loam Clay loam FC (w/w) PWP (w/w) Ks (cm d -1 ) Bd (gcm -3 ) EC (dsm -1 ) PH Organic matter (%) N (ppm) P (ppm) K (ppm) Bd: Bulk Density, Ks: Saturated Hydraulic Conductivity, FC: Field Capacity, PWP: Permanent Wilting Point, EC: Electric Conductivity

39 26 (A) (B) Fig. 3.5 Picture showing the field preparation for sowing of mustard on (A) 20 th November, 2013 and (B) on 12 th November, 2014 (A) (B) Fig. 3.6 A view of lysimeter with 50% deficit irrigation (A) and the lysimeter with full irrigation (B) at the experiment site of WTC farm (A) (B) Fig. 3.7A view of the experimental field covered with mustard (A) and the lysimeter without crop to estimate the evaporation from bare soil (B)

40 27 Fig. 3.8 View of the experiment with three lysimeters (A, B &C) located at WTC farm, ICAR-IARI New Delhi (A) (B) Fig. 3.9 Data recording from lysimeters for estimation of daily evapotranspiration (A) and view of lysimeters with mustard crop (B) 3.6 Experimented crops and varieties The experiment was conducted to estimate the single and dual crop coefficients of mustard crop during rabi and cropping seasons. Further, the generated experimental data of mustard during these mentioned years besides the data of wheat, maize and soybean crops acquired from the same experiment during previous years were used to develop a software for irrigation scheduling of mustard, wheat, maize and soybean crops and varieties such as: Maize (HQPM-1): This maize variety was released by CCS HAU, Hisar, Haryana in It is a quality protein maize hybrid having late maturity and recommended for

41 28 cultivation across the country. It is resistant to MLB (Maydis Leaf Blight) reaction and rust having an average grain yield of 55 q ha -1. Soybean (Pusa 9712/DS 9712): This variety of soybean is released by IARI, New Delhi (2005). It is recommended for cultivation in National Capital Region of Delhi and resistant to yellow mosaic virus, soybean mosaic virus, bacterial pustule, charcoal rot, Myrothecium leaf spot and stem fly. It is reported to be of early maturity type cultivar of about 116 days with an average seed yield 20.5 q ha -1. Pusa Vijay (NPJ-93): This mustard variety was released by IARI, New Delhi (2008). This variety is suitable for timely sown irrigated conditions and recommended for cultivation in National Capital Region (NCR) of Delhi. It is tolerant to abiotic stresses and a bold seeded variety with % oil content having an average seed yield of 25.0 q ha -1. Wheat (HD 2894): This hybrid wheat cultivar was released by IARI, New Delhi (2008) and is suitable for the agro-climatic conditions prevailing in Northern India. This variety is of medium dwarf type (average plant height of 80 cm) with maturity period varying from 118 to 146 days. The potential yield of this cultivar is about 52 q ha -1 with 11% protein content. 3.7 Cultivation practices The land was ploughed with tractor mounted cultivator before sowing mustard seed to provide proper aeration which facilitate subsequent crop growth. The sowing was carried out with manual seed drill and the field was enclosed by appropriate field bunds to eliminate loss of surface runoff from the field. Recommended doses of nitrogen (120 kg ha -1 ), phosphates (40 kg ha -1 ) and potassium (30 kg ha -1 ) fertilizers and 1/3 rd of N fertilizer in form of urea was applied as basal dose before sowing of mustard. The remaining N fertilizer was applied in two split applications during vegetative growth stage, the first at germination stage and the second at the flowering stage, respectively, at 28 and 60 days after sowing (DAS). Field operation schedule undertaken in mustard is presented in Table 3.2. Soil moisture inside the lysimeter and of the adjoining field was taken every alternate day using TDR (Time Domain Reflectometer) sensor and crop parameters viz. plant height, root depth, leaf area index were recorded at 15 days interval during rabi and at 10 days interval during rabi , respectively. Pre-sowing irrigation was applied three days before sowing

42 29 to prepare the lysimeter and adjoining field for proper germination of mustard seeds. Two irrigations were applied during rabi and four irrigations were applied during rabi Mustard seeds were sown at a spacing of 30cm and covered with soil and inter cultural operations were undertaken to ensure proper weed free and disease free environment during the crop growth period. 3.8 Scheduling of irrigation in the experiment Soil moisture content up to the crop root zone were monitored periodically using TDR (Time-domain reflectometer) sensor for irrigation scheduling i.e. to decide the date and quantity of irrigation water during the crop growth period. This technique is easy to use and allows fast, accurate and non-destructive soil moisture reading on volumetric basis. TDR soil moisture sensor reading was calibrated against the standard gravimetric method to ensure accurate measurement of soil moisture. The date of irrigation was decided when the soil moisture in the root zone got depleted to 50% of the difference between the permanent wilting point (PWP) and the field capacity (FC) i.e. the total available water (TAW) in the root zone depth of soil profile (Fig. 3.10). Table 3.2 Field operation activity schedule undertaken during the experiment in rabi and rabi Dates of operation S. No. Activity rabi rabi Land preparation Layout and field bunding Sowing of crop Application of fertilizer a) Basal doses of N,P,K b) Top dressing of nitrogen i ii Weed and insect management Weeding Pesticide application Irrigation application a) Pre-sowing irrigation b) Post sowing irrigation 1 st irrigation nd irrigation rd irrigation NA Harvesting

43 30 TAW FC Saturation Field Capacity (FC) FC MAD (50 %) PWP Soil moisture content (%) MAD-50% Under stress PWP Fig Criterion used for irrigation scheduling of the field experiment The quantity of irrigation water applied to each lysimeter was calculated based on the soil moisture content before irrigation and the root zone depth of the plant using the Eq. (3.1). SMD = (θ Fc θ i ) D Bd f (3.1) Where: SMD: Soil moisture deficit (mm), θfc: Soil water content at field capacity, θi: Soil water content before irrigation (weight percent basis), D: Depth of root development (mm), Bd: Bulk density of the particular soil layer as per root depth (g cm -3 ). f: coefficient for each irrigation treatment levels in the experiment. The coefficient of each treatment f = 1 (full irrigation up to FC without any deficit), f = 0.5 (50% of FC) were used for different treatments to estimate the quantity of irrigation water. First and third lysimeters were kept under full irrigation treatment, whereas the second lysimeter was under 50% deficit irrigation treatment. In the irrigation treatments of second lysimeter (50% of FC), water was applied on the same day as that of the fully irrigated lysimeter but the irrigation depths were reduced to 50% of the first and third lysimeter which were under full irrigation treatment. 3.9 Estimation of reference evapotranspiration FAO Penman-Monteith method is defined as evapotranspiration from the reference crop such as alfa-alfa grass with an assumed height of 0.12 m, with a surface resistance of 70 Sm -1 and an albedo of 0.23, actively growing in large area and adequate

44 31 water all the time. Reference evapotranspiration (ET0) was estimated using CROPWAT software version 4.2 developed by FAO (Martin et al., 2013). Daily measurements of air temperature, wind speed, solar radiation and relative humidity for estimating ET0 as well as rainfall were collected from automatic weather station (AWS) located near the experimental site. CROPWAT software uses daily sunshine hours, air temperature (maximum and minimum), relative humidity and wind speed at 2 meter height to calculate daily ET0 using the Penman-Monteith- method (Allen et al., 1998). The Penman-Monteith equation used for estimation of reference evapotranspiration (ET0) is given by: Where: ET 0 = (R 900 n G) + γ T U 2 (e a e d ) + γ( U 2 ) ET0 = Reference evapotranspiration (mm day -1 ); Rn= Net radiation at the crop surface (MJ m -2 day -1 ); G= Soil heat flux density (MJ m -2 day -1 ); T = Mean daily air temperature at 2 m height ( o C); U2 = Wind speed at 2 m height (m s -1 ); ea = Saturation vapor pressure (kpa); ed =actual vapor pressure (kpa); (ea - ed)= Saturation vapor pressure deficit (kpa); Δ = Slope vapor pressure curve (kpa o C -1 ); γ = Psychometric constant (kpa o C -1 ). (3.2) 3.10 Estimation of single crop coefficient Crop coefficient is defined as the ratio of actual crop evapotranspiration to the reference evapotranspiration. In single crop coefficient approach, the effect of both actual crop transpiration and soil evaporation are integrated into a single crop coefficient. Daily reference evapotranspiration values were calculated using CROPWAT software and the daily evapotranspiration values were estimated from lysimeter readings. For determining the crop coefficients, the crop development is basically partitioned to four stages, such as: 1. Initial stages (1-30 DAS): Germination and early crop growth stage when the soil surface is not fully covered by the crop. 2. Crop development stages (31-70 DAS): From the end of the initial stages to attainment of effective full ground cover.

45 32 3. Mid - season stages ( DAS): From attainment of effective full ground cover to time of start of maturity. 4. Late season stage ( DAS): From end of mid-season stage until full maturity or harvest of crop. Evapotranspiration rates were obtained from the change in weight of lysimeter on daily basis using the following water balance equation: ET = Rainfall + Irrigation Percolation ± Change in soil moisture Further, Kc values were calculated using equation 3.3 Kc= ET c ET 0 (3.3) 3.11 Estimation of dual crop coefficient In dual crop coefficient approach, the basal crop coefficient (K cb ) and the soil evaporation coefficient (K e ) were estimated separately. The basal crop coefficient (K cb ) is defined as the ratio of crop evapotranspiration to reference evapotranspiration (i.e. ET c /ET0) when the soil surface is dry, but transpiration is occurring at a potential rate and water is not a limiting factor in the root zone. (FAO 56, Allen et al., 1998) Therefore, multiplication of Kcb with ET0 represents primarily the transpiration component of ET c. The soil evaporation coefficient (K e ) represents the evaporation component of ET c eq (3.4). Moreover, due to segregation of evaporation and transpiration components and estimation of crop coefficients at different crop growth stages with varying dominancy of evaporation and transpiration, the dual crop coefficient approach would provide more accurate irrigation scheduling of crops. The dual crop coefficient is used in crop modelling studies, estimation of real time irrigation scheduling for frequent water application such as daily irrigation, supplementary irrigation and detailed soil and hydrologic water balance investigations under varying crops and water supply situations. In dual crop coefficient approach, the K c is the sum of K cb amd K e given by: Kc = Kcb + Ke (3.4)

46 Estimation of crop parameters Crop parameters viz. mean root depth, plant height were measured during different growth stages at an interval of 10 days. Besides these parameters, the dates of planting, 10% crop cover, full ground cover, maturity, and leaf area index values were also recorded. The grain and biomass yield were estimated after harvesting of crop Development of software for crop coefficient estimation and irrigation scheduling A software was developed using data acquired from field experiments for mustard during rabi and besides the data of wheat, maize and soybean crops acquired from previous years to estimate crop coefficient and subsequent irrigation scheduling. This software is developed based on the water budgeting protocol using the crop evapotranspiration, soil moisture, crop growth parameter, single and dual K c values and the climatic data of the study region to estimate the irrigation scheduling and seasonal soil water balance for selected crops. The input data for operation of software were soil parameters, crop parameters, climatic parameters and irrigation method. Different wetness fractions were used for each irrigation method at different crop growth stages for evaporation estimation. Irrigation scheduling option based on field capacity and current moisture content were also included in this software. The system architecture of developed software is presented in Fig Data requirement of the software and output information generated by the software is detailed in Fig The software was coded in JAVA language using NetBeans IDE software tool within the eclipse environment. This software is comprised of a computational module, graphic user interface (GUI) and back ground databases. The system architecture of software is shown in Fig The computational module was developed in a modular manner having flexibility for populating with additional data base and for subsequent integration with crop models. The back ground database include information about daily crop evapotranspiration, soil parameter, crop and irrigation method. Mathematical module include calculation methods for estimation of crop coefficient values and preparation of irrigation schedules.

47 34 Fig Major modules and data base of the developed software Fig The system architecture of the developed software for estimation of crop coefficient and irrigation scheduling

48 Input data used for development of the software The input data used for development of the software were meteorological parameters, crop data, soil data and irrigation method (Fig. 3.12). Meteorological parameter comprised of the minimum and maximum temperature, average relative humidity, wind speed and sunshine hours which were used for computing the reference evapotranspiration. Besides this, the precipitation data was also used for estimation of effective rainfall depths. Crop data consisted of crop type and cultivar, plant height, full ground cover (FGC), root depth and growth stage of crop. Irrigation methods considered in the software were viz. surface irrigation method, drip method and sprinkler method. Using these input parameters, the single and dual K c was estimated using Eqs 3.3 and 3.4. Methods of irrigation also affect the ET c, particularly with regard to the fraction of the field surface wetted during irrigation (fw), the frequency of wetting and the depth of water applied during each irrigation event. The wetness fraction (fw) under different methods of irrigation generally influences the soil evaporation component of crop evapotranspiration (ET c ). The fw of different irrigation methods at different growth stages were used to adjust the calculated K c values. Moreover, the standard value of fw was used from the reported information in FAO-56 (Allen et al., 1998). However, for single K c, the adjustment was done only for initial and late season growth stage where the soil evaporation component dominate the transpiration component (Eq 3.5) whereas for dual K c, the adjustment was done only in the soil evaporation component (Eq 3.6) Kc ini or Kc end = fw Kc single (3.5) Kc dual adj = (fw Ke) + Kcb (3.6) Besides this, the parameters as mentioned in Fig were used for estimation of irrigation schedules of different crops. Irrigation scheduling was programmed using three methods namely based on single K c, dual K c and soil moisture deficit protocol. User can choose any method based on the availability of data to obtain the information on irrigation scheduling. Case-1: When the single K c is known, the user can calculate ET0 using the CROPWAT activation button of the software. Then the software adjust the K c value based on the selected irrigation method and growth stage of the crop. Further, the adjusted value of

49 36 K c is multiplied with ET0 to obtained ET c and then the depth of irrigation water and irrigation interval is calculated by the software using Eqs 3.7 to 3.9. Net amount of water applied = ETc (Effective rainfall) (3.7) Gross irrigation = irrigation interval = Net irrigation irr.efficiency Depth of irrigation peak period moisture use rate of crop (3.8) (3.9) Case-II: When the dual K c is known, then the user inputs these values and the software computes evaporation and transpiration separately (Eq. 3.4). Further, the computed value was adjusted according to selected crop growth stage and irrigation methods by the software and irrigation scheduling is displayed by using similar procedure as depicted for single K c (Case-I). Case III: When the field moisture content is known, the database contained information on moisture content at field capacity (FC) and permanent wilting point (PWP) besides the bulk density (BD) of different type of soil. After inputting the present soil moisture content (MC) and root zone depth (RZD) of crop, the depth of irrigation was calculated using Eq and irrigation scheduling is done by using previous methods. Depth of irrigation = (FC MC) 100 RZD BD (3.10)

50 37 Chapter IV Results and Discussion 4.1 General Experiment was conducted to estimate both single and dual crop coefficients of mustard crop using weighing type field lysimeters during rabi and Besides this, a software was developed for estimation of crop coefficients and scheduling of irrigation for wheat, maize, soybean and mustard crops. A set of three weighing type field lysimeters with 1.2 m 1.2 m surface area and 1 m soil depth with mustard cultivar Pusa Vijay (NPJ-93) were used in the experiment. Irrigation regimes with full and 50% deficit levels were maintained in two lysimeters and one lysimeter was experimented without crop to estimate the evaporation from soil surface. Actual evapotranspiration (ETc) of mustard crop were calculated from daily weight readings of lysimeters. Soil moisture content up to the crop root zone were monitored periodically using TDR (Time Domain Reflectometer) sensor for irrigation scheduling. Further, crop parameters viz. plant height, root depth, leaf area index were also recorded periodically. Nonetheless, a software for estimation of crop coefficient and irrigation scheduling was coded in JAVA programming language using the experimental data and established equations. The results obtained from experiment were analysed and presented in this chapter. 4.2 Meteorological parameters Monthly meteorological parameters during mustard growing seasons of two consecutive years of experiment i.e. rabi and is presented in Table 4.1. It is observed from the meteorological data that moderate low to high temperature, large diurnal variation of humidity and sunshine hours for a period from one-eighth to one fourth of a day prevailed during November to March. Mean temperature was C and C during and growing seasons, respectively. The mean relative humidity was 74.3% in and 74.1% in Lowest sunshine hours observed during the month of January was due to cloudy weather and atmospheric haze for long hours during the day time.

51 38 Table 4.1 Monthly climatic parameters of the study area during mustard growth period. Months Temperature ( 0 C) Relative Humidity (%) Sunshine hours/day (Maximum) (Minimum) (Maximum) (Minimum) rabi November December January February March rabi November December January February March Rainfall and effective rainfall depth, irrigation scheduling, reference and actual evapotranspiration of mustard during cropping seasons Rainfall depths recorded during mustard growing seasons were mm in rabi and 234 mm during rabi It was observed that the rainfall events were distributed mainly during January to March months of both years. Moreover, using the soil moisture deficit approach i.e. applying irrigation water when 50% of soil moisture between the field capacity and permanent wilting point got depleted, it was observed that two and three irrigations were given during and growing seasons, respectively. Two irrigations of 30mm depth each amounting to a total depth of 60 mm were applied on 28 and 46 days after sowing (DAS) of mustard crop during rabi Moreover, three irrigations with total depth of 75mm were applied on 83, 96 and 105 DAS of the mustard crop, respectively during rabi It was observed that the number of irrigation and the total quantity of irrigation water applied was more during rabi as compared to rabi despite higher rainfall and effective rainfall depths. This may be attributable to the occurrence of 75 % of total rainfall in 1 st week of March (i.e mm out of 234 mm depth of rainfall) during the late season or harvesting stage of the crop when there was no need of

52 39 irrigation and all three irrigations were applied before 1 st week of March 2015 (Table 4.2). It was also observed that the effective rainfall depth during the late season stage was 9.1mm during rabi as compared to 112.2mm during rabi Therefore, during rabi , though there was higher amount of rainfall during 1 st week of March 2015, but this was not being used by the plant for its growth as the plant had already reached the fully developed growth stage. Reference evapotranspiration (ET0) estimated using modified Penman-Monteith formulae during the entire crop growth period was mm in rabi and mm in rabi (Table 4.2). It was observed that the ET0 was maximum during the mid-season growth stage of crop in which the crop was fully developed with canopy having full ground cover. Moreover, this period of highest ET0 was during the months of February to March coinciding with the high values of net radiation and mean air temperature during the entire crop growing period. Table 4.2. Rainfall, effective rainfall, irrigation depths, reference evapotranspiration (ET0) and crop evapotranspiration (ETc) of mustard during rabi and Growth Stages Rainfall (mm) Effective Rainfall (mm) Irrigation (mm) Av. Daily ET 0 (mm) Growth stages ET c (mm) Av. Daily Growth Stages rabi season Initial (1-30 DAS) (28 DAS) Development (31-70 DAS) (46 DAS) Mid-season ( DAS) Late season ( DAS) Total rabi season Initial (1-30 DAS) Development (31-70 DAS) Mid-season ( DAS) (83 DAS) 30 (96 DAS) 15 (105 DAS) Late season ( DAS) Total

53 Variation in different crop growth parameters during both years of experiment The crop growth period of mustard divided under different growth stages during both years of experiment were observed to be 30, 40, 40 and 20 days for initial, crop development, mid-season and late season stages, respectively with a total growing period length of 130 days (Table 4.3). Further, the length of growing period under different growth stages of wheat, maize, soybean crops used for development of the software to estimate crop coefficient and irrigation scheduling are shown in Table 4.3. The plant height, root depth and leaf area index (LAI) for mustard was recorded once in two weeks interval during the entire cropping season and presented in Fig. 4.1 and Fig.4.2 for rabi and , respectively. It was observed from Fig. 4.1 that during rabi the LAI increased upto 82 DAS i.e. the fully developed stage and after that the LAI value decreased gradually. Maximum value of LAI was obtained to be 3.88 during mid-season stage. Whereas during the LAI increased upto 89 DAS with maximum value of LAI was obtained to be 4.14 during mid-season stage. Such variation during two years of experiment may be attributable to the change in climatic parameters which would have affected the crop growth. Similar to LAI, the plant height also increased consistently from initial to the development and mid-season stages which was attained at DAS and DAS, respectively. Moreover, at the late-season stage of plant development, LAI started to decrease gradually, whereas the plant height remained relatively constant for the rest of the growing period upto harvest of the crop. The decrease in LAI was primarily due to the maturity of the crop associated with leaf ageing and senescence. Table 4.3 Crop growth period of wheat, maize, soybean and mustard divided under different stages of development for estimation of crop coefficient. Crop Total growing period (days) Initial stage (days) Development stage (days) Midseason stage (days) Late season stage (days) Wheat Maize Mustard Soybean

54 Leaf area index Plant height (cm), Root depth (cm) Leaf area index Plant height (cm), Root depth (cm) rabi DAS LAI Plant Height (cm) Root Depth (cm) Fig. 4.1 Variation of leaf area index, plant height and root depth at different days after sowing (DAS) during rabi rabi LAI Plant Height (cm) Root Depth (cm) DAS Fig. 4.2 Variation of leaf area index, plant height and root depth at different days after sowing (DAS) during rabi

55 Variation of evapotranspiration, transpiration and soil evaporation components of mustard during cropping seasons Observed mean values of actual crop evapotranspiration (ET c ) were 1.23 mm/day and 1.08 mm/day during rabi and , respectively. The total value of ET c during the mustard growth period were mm and mm and the reference evapotranspiration (ET0) during same seasons were mm and 207.9mm during rabi and , respectively. The maximum value of daily crop evapotranspiration (ET c ) was estimated to be 2.81 mm/day and 2.76 mm/day which occurred at 102 and 101 DAS during rabi and , respectively. Variation in the actual crop evapotranspiration besides the transpiration and evaporation components of mustard during two cropping seasons are presented in Figs. 4.3 & 4.4, respectively. It was observed from these figures that there was rapid decline of ET c from the end of mid-season stage (i.e. 105 DAS) onwards which might be due to cessation of leaf growth and completion of grain formation and filling thereby limiting transpiration and subsequent decrease in water demand. Moreover, continuous fluctuations in the crop evapotranspiration as depicted in Figs. 4.3 & 4.4 can be attributed to the effect of daily weather parameters besides application of irrigation during the crop growth period. It was also observed that the transpiration component was high and dominant during both years of experiment as compared to the soil evaporation. The ratio of transpiration to evapotranspiration was observed to be 0.75, which implies that the soil evaporation component was only 25% whereas the transpiration component was 75%. It was observed that the actual crop evapotranspiration (ET c ) exceeded the reference evapotranspiration (ET0) from 60 to 90 DAS during both years which was in the mid-season stage of crop growth. It can be interpreted from this result that the crop water demand was high during the mid season stage due to flowering, grains formation and filling. Therefore, presence of adequate soil moisture during mid season stage need to be ensured to avoid stress in plant and create an environment for enhancing the grain yield.

56 Daily ET (mm), E (mm), T (mm) Daily ET (mm), E (mm), T (mm) rabi Days After Sowing Evapotranspiration (mm) Transpiration (mm) Evaporation (mm) Fig. 4.3 Variation of daily evapotranspiration, evaporation and transpiration components during rabi rabi Days After Sowing Evapotranspiration (mm) Transpiration (mm) Evaporation (mm) Fig. 4.4 Variation of daily evapotranspiration, evaporation and transpiration components during rabi Single and dual crop coefficients of mustard during rabi and Weather parameters acquired from the automatic weather station located near lysimeter facility was used for estimation of reference evapotranspiration using modified Penman-Monteith formulae. Single crop coefficient (K c ) for mustard cultivar Pusa Vijay (NPJ-93) during rabi was estimated to be 0.39, 0.72, 1.02 and 0.5,

57 44 for initial (0-30DAS), development (31-70), mid ( DAS), late ( DAS) stages, respectively and for dual K c (K cb +K e ) the value of basal crop coefficient (K cb ) was 0.19, 0.55, 0.91 and 0.24 and the soil evaporation component (K e ) was 0.20, 0.17, 0.11 and 0.26 for initial, development, mid and late stages, respectively (Fig. 4.5). Similarly, during rabi , the single K c was estimated to be 0.36, 0.63, 1.04 and 0.44 and for dual K c (K cb +K e ) the value of basal crop coefficient (K cb ) was 0.17, 0.46, 0.91 and 0.23 and the soil evaporation component (K e ) was 0.19, 0.17, 0.13 and 0.21 for initial, development, mid and late stages, respectively (Fig. 4.6). Moreover, the K c values obtained for ten days period termed as decadal K c was observed to increase from the initial to development stages and reached its highest at the end of development stage and remained almost constant upto the midseason stage. However, after the midseason stage, there was a rapid decline of K c during the late season stage. Further, for crops viz. wheat, maize and soybean grown in the lysimeter during 2009 to 2013, the crop coefficient values are presented in Figs. A-a to Fig. A-c of Appendix A, which were used in development of the software for estimation of crop coefficient and subsequent irrigation scheduling. It was observed from Fig. A-a that for soybean, the value of estimated single crop coefficient was 0.41, 0.80, 1.17 and 0.58, for initial (0-20 DAS), development (21-50), mid season (51-98 DAS) and late season ( DAS) stages, respectively. Similarly, the single crop coefficient of wheat was estimated to be 0.85, 1.28, 1.37 and 0.38, for initial (0-30 DAS), development (31-60), mid-season ( DAS) and late season ( DAS) stages, respectively (Fig. A-b). Moreover, for Maize crop the estimated single crop coefficient was 0.55, 1.35, 1.23 and 0.75, for initial (0-20 DAS), development (21-45), mid-season (46-80 DAS) and late season ( DAS) stages, respectively (Fig. A-c). However, it was observed that single K c values of mustard during rabi and were more or less constant and relatively low during the initial growth stage and after this stage there was gradual increase of daily K c values which attained its maximum apropos to maximum LAI during mid-season stage. Further, the K c values remained constant during the midseason stage and in the late season stage, there was a gradual decline in daily K c values of mustard. In case of the dual K c of mustard, it was observed that the soil evaporation component exceeded the plant transpiration during initial growth stages of the crop upto

58 Kc, Kcb, Ke and 27 DAS during and , respectively. After this period, the plant transpiration component was more than the soil evaporation component and the difference between these two components increased upto maximum LAI value i.e. upto 82 and 89 DAS during rabi and , respectively (Figs. 4.5 & 4.6). Further the basal crop coefficient representing the transpiration component remained almost constant up to 97 DAS and after that there was gradual decline of this parameter (Figs. 4.5 & 4.6). Further, it was observed form these figures that the soil evaporation component increases marginally over the plant transpiration rate towards the end of late season or during the harvesting stage for both years of experiment. Such variation may be attributable to the dominance of soil evaporation over plant transpiration due to the crop maturity and leaf senescence at the harvesting stage rabi Day After Sowing Kc Kcb Ke Fig. 4.5 Variation of crop coefficient (Kc), basal crop coefficient (Kcb) and evaporation component of crop coefficient (Ke) during rabi

59 Kc, Kcb, Ke rabi Kc Kcb Ke Days After Sowing Fig. 4.6 Variation of crop coefficient (K c ), basal crop coefficient (K cb ) and evaporation component of crop coefficient (K e ) during rabi Comparison of estimated regional Kc with FAO reported values for mustard The estimated crop coefficient value at different growth stage of mustard during two growing seasons were compared with K c values reported by FAO (Allen et al., 1998). The FAO suggested K c values for mustard crop were 0.35, 0.6, 1.15 and 0.35 during initial (0-30 DAS), development (31-70), mid (71-110) and late stages ( ), respectively. It was observed that measured K c values exceeded the FAO values by about 10 %, 17% and 30% during rabi (Fig. 4.7) and by 3%, 5% and 21% in rabi (Fig. 4.8) during initial, development and late-season stages, respectively. However, the K c value corresponding to mid-season growth stage was observed to be less than the K c value given by FAO-56 by about 11% and 9% during rabi and rabi , respectively (Figs. 4.7 & 4.8). Overall, it was observed that there was an overestimation of K c values during the entire growing season excluding the mid-season stage for the study region by about 19% and 10% as compared to FAO reported K c values during rabi and rabi , respectively.

60 Crop coefficient (K c ) Crop Coefficient (K c ) initial development mid season late season lysimeter FAO Fig. 4.7 Comparison of single crop coefficient (Kc) of mustard during rabi with FAO reported Kc values initial development mid season late season Lysimeter Kc FAO Kc Fig. 4.8 Comparison of single crop coefficient (Kc) of mustard during rabi with FAO reported Kc values 4.8 Relationship between basal crop coefficient (Kcb) and leaf area index (LAI) Leaf area index (LAI) values at different days after sowing were plotted against the basal crop coefficient and a regression equation was fitted which depicted a linear relationship with coefficient of determination (R 2 ) value of 0.89 and 0.84 during rabi and , respectively (Figs. 4.9 & 4.10). However, during rabi the LAI increased up to 82 DAS with maximum value of 3.88 and after that the LAI value decreased gradually. Whereas, during rabi the LAI increased upto 89 DAS with maximum value of 4.14 during mid-season stage. Similar trend was also

61 Kcb Kcb 48 found with basal crop coefficient (K cb ), which increased with LAI and reached to the maximum value 0.98 and 1.19 during rabi and , respectively at maximum LAI and then started decreasing gradually. Similar trend of K cb and its correlation with LAI was also reported by researchers for other crops (viz. Tyagi et al., 2000, López-Urrea et al., 2012, Shenkut et al., 2013). Such relationship would facilitate extrapolation of the results pertaining to estimation of K cb from LAI for other regions having similar climatic conditions Kcb = LAI R² = LAI Fig. 4.9 Relationship between the leaf area index (LAI) and basal crop coefficient (Kcb) of mustard during rabi Kcb = LAI R² = LAI Fig Relationship between the leaf area index (LAI) and basal crop coefficient (Kcb) of mustard during rabi

62 ET measured (mm) ) Comparison between ETc measured from lysimeters and ETc calculated using FAO reported Kc value of mustard Lysimeter measured crop evapotranspiration values were compared with estimated crop evapotranspiration using the FAO-56 method (Allen et al., 1998). Results showed that there existed a linear relation between the FAO measured and the estimated value obtained from the lysimeter experiment. The coefficient of determination of the fitted regression equation was found to be 0.88 and 0.82 during rabi and , respectively (Figs & 4.12). Similar results pertaining to the relationship between the measured and estimated ET c of mustard is reported by Shankar et al., ET measured = ET calculated R² = ET calculated (mm) Fig Comparison between the lysimeter measured and FAO-56 calculated evapotranspiration during rabi ET measured (mm) ET measured = 1.06 ET calculated R² = ET calculated (mm) Fig Comparison between the lysimeter measured and FAO-56 calculated evapotranspiration during rabi

63 Kcb Variation of Kcb with growing degree days (GDD) during both years of experiment To account for the climatic differences which would affect the transpiration rate, the K cb values were plotted as a function of growing degree days (GDD) during both years of experiment. GDD is a measure of the amount of heat needed for plants during their growth period and calculated using equation (4.1) given by: GDD = T Max T Min 2 T Base (4.1) Where, TMax and TMin are the maximum and minimum temperatures ( C) of a day and TBase is the base temperature taken as 5 C for mustard. For rabi , the K cb reached maximum values of 1.14 after about 724 GDD, whereas in rabi the maximum K cb values of about 1.21 was attained after 701 GDD and then decreased to their minimum values during harvest of crop at 1322 and 1307 GDD during rabi and , respectively. It was observed that during rabi , the K cb was higher by 0.07 and was attained before 23 GDD as compared to rabi Such difference in K cb and GDD might have resulted in higher grain yield during rabi GDD Fig Variation of basal crop coefficient (Kcb) with Growing Degree Days (GDD) during rabi

64 Kcb GDD Fig Variation of basal crop coefficient (K cb ) with Growing Degree Days (GDD) during rabi Grain and biomass yield of mustard during both years of experiment Grain yield was measured as weight of harvested grain with 15% grain moisture content in each lysimeter and adjoining field and converted to kg ha -1 unit. Biomass yield was determined by taking the weight of above ground plant parts without grain.. Water productivity was determined by dividing the marketable grain yield with the total water used (i.e. irrigation and effective rainfall) during the plant growth period. The harvest index (HI) was obtained by dividing the grain yield with the biological yield (i.e. biomass yield + grain yield). Estimated grain and biomass yield, water productivity and HI values are presented in Table 4.4. It was observed from Table 4.4 that the grain yield under full irrigation was 2.34 tha -1 and 2.89 t ha -1 and under 50% deficit irrigation was 1.85 t ha -1 and 2.77 t ha -1 during rabi and , respectively. The HI was observed to be 23 and 24 under full irrigation and 20 each under deficit irrigation during rabi and , respectively. The total water applied and water productivity was 157mm and 14.9 kg/ha.mm and 187mm and 15.4 kg/ha.mm during rabi and , respectively. Moreover, the highest water productivity (18.5 kg/ha.mm) was obtained under 50% deficit irrigation during rabi Grain and biomass yield of mustard under both full and 50% deficit irrigation regimes besides the adjoining field on the left and right side of lysimeters under full irrigation are presented in Appendix-B. It was observed that the total water applied (157mm) during rabi was almost same as that of the actual evapotranspiration (153mm) estimated using the lysimeter generated crop coefficient values. Therefore, it was

65 52 ascertained that the applied quantity of irrigation, which was based on soil moisture deficit protocol adopted in this study was in line with the total evapotranspiration of mustard crop. Hence, scheduling of irrigation should be based on soil moisture deficit approach to save water and enhance water productivity in irrigated agriculture. On the other hand, the total water applied (187mm) during rabi was more than the actual crop evapotranspiration (154mm) by 33mm. However, this difference of 33mm may be attributable as a major fraction of effective rainfall depth of 73.8mm (total rainfall depth of 175.8mm), which occurred during 1 st week of March 2015 during the harvesting stage of the crop which might not have taken up by the mustard crop to meet the evapotranspiration demand. The water stored in the soil profile due to this excess rainfall might have lost as evaporation component from soil surface through capillary rise phenomenon or percolated below the crop root zone depth. Table 4.4 Grain and biomass yield, water productivity (WP) and harvest index (HI) of mustard during rabi and Lysimeters rabi Full Irrigation (Lysimeter-3) Deficit Irrigation (50%) (Lysimeter-2) rabi Full Irrigation (Lysimeter-3) Deficit Irrigation (50%) (Lysimeter-2) Grain yield (t ha -1 ) Biomass yield (t ha -1 ) HI (%) Total water applied (mm) WP (kg/hamm) Operation of developed software The software was developed using JAVA programming language in NetBeans Integrated Development Environment (IDE) platform. Different modules of the software consisted of a computational module, a graphic user interface (GUI) and a database module (Fig.4.15). The software was linked with the execution file of CROPWAT by a command button to calculate the reference evapotranspiration and effective rainfall depths using different formulae. The back ground data base of the developed software was populated with crop coefficient values of wheat, maize,

66 53 soybean and mustard crops and the cell values were used in computation of crop coefficient and the irrigation schedule using appropriate formulae. The software was developed using the if..then..else programming construct with the back ground data base and computational modules pertaining to estimation of single and dual crop coefficient values to provide the information on scheduling of irrigation. The database is one of the key elements of this software along with different mathematical modules to estimate the crop coefficient and generate irrigation schedules based on user query. User query was inputted through the drop down menu options populated in different modules of the software. Developed software was validated under different input data scenarios to ascertain its bug free operation. Developed modules of the software besides the input data requirement and generated outputs and the operational procedure is presented in this section Front page interface of the developed software Coding in JAVA programming language for different module of the software was subjected to compilation and after successful and error free compilation of different programming lines (Appendix-C), the execution file was created. By activating the execution i.e. softwarekc.jar file, the front page of the interface gets activated. The screen captured window of the front page of the software after activation of the *.jar execution file is shown in Fig It is observed from the Fig that the main page contains two options under menu command button such as irrigation scheduler and the crop coefficient estimator. Besides the menu option, the front page also contains the File and Help command buttons (Fig. 4.15).

67 54 Fig Screen captured window of the front page of the developed software Crop coefficient estimator interface Activation of the crop coefficient estimation command button in the main frame of the software pops up a separate interface window named as crop coefficient estimator. The screen captured window of the interface with user provided data under different drop down menus are presented in Fig It is observed from Fig that with selection of mustard crop under name of crop drop down menu, selection of development stage under crop stage menu besides selection of options under irrigation methods, soil type, plant height and FGC drop down meus, the estimated value of ET c, ET0, evaporation (E) and transpiration (T) are displayed in appropriate boxes as shown in the captured screen (Fig. 4.16). Further, the single and dual crop coefficients pertaining to the input data for the desired crop growth stage were estimated and displayed in the decision box of the software interface (Fig. 4.16). However, different drop down menu as shown in the captured window consists of all data parameters acquired from the experiment of mustard crop besides the secondary data related to wheat, maize and soybean crops.

68 55 Fig Screen captured window of the crop coefficient estimator interface module of the software for wheat Irrigation scheduling interface module of the software Similarly, activation of irrigation scheduler command button in the front page of the software pops up irrigation scheduler interface window for estimation of time and depth of irrigation pertaining to different crops. The screen captured window of the interface is presented in Fig The input data for irrigation scheduler interface consisted of crop data, stage of crop growth, root zone depth, irrigation methods, soil texture, moisture content at field capacity, permanent wilting point moisture content, bulk density, moisture content at the time of irrigation, weather parameters, average daily evapotranspiration and effective rainfall depth. It was observed from the captured screen (Fig. 4.17) that for the wheat crop grown in loamy sand soil texture with the moisture content information at FC, PWP and during irrigation, the depth of irrigation water (54.563mm) and the irrigation interval (27 days) was displayed in the output display box of the software (Fig. 4.17).

69 56 Fig Screen captured window of the irrigation scheduler interface module of the software for wheat when the single K c is known. Similarly, the interface was operated for other crops using the desired input parameters. The results obtained while operating the interface for mustard crop is presented in the screen captured window as shown in Fig It is observed from Fig that by providing desired information pertaining for mustard crop pertaining to soil texture, crop growth stage and soil moisture content at FC, PWP and during irrigation besides the dual K c values etc. the software computed the depth of irrigation water to be105.5 mm and the irrigation interval was 52 days with respect to the input data provided by the user. Result of this run was displayed in the output display box of the software (Fig. 4.18). The operation of the software was with the dual K c values provided by the user. However, in cases where the single and dual K c values are not available with the user, the software can also be operated with the soil moisture content information for estimating the irrigation scheduling information. In such cases, the software is operated for wheat crop with selection of soil texture as loamy sand and all desired information were provides as shown in the screen captured window presented in Fig

70 57 Fig Screen captured window of the irrigation scheduler interface module of the software for mustard under known value of dual K c. It is observed from Fig that using the input data of soil moisture content at FC i.e. 15 %, at PWP i.e. 5% and moisture content at time of irrigation i.e. 9% besides the effective root zone depth of 30 cm, the software estimated the irrigation schedule based on the soil moisture deficit approach. It was displayed (Fig. 4.19) that the depth of irrigation water was 15 mm with irrigation interval of 8 days to be applied during the development stage of the crop. However, the irrigation schedule will change accordingly with occurrence of rainfall during the crop growth stage. Therefore, the user need to run the software with real time data of rainfall and soil moisture content to get more accurate irrigation schedules. Besides this, the software was operated for maize and soybean crops and the captured screen of the software window with input data and output information is presented in Fig and 4.21, respectively. It was observed from these figures that with the user provided data for maize, the depth of irrigation was estimated to be 19mm with an interval of 13 days during the initial growth stage of maize, whereas for soybean, the depth of irrigation estimated was 12mm with irrigation interval of 8 days during the late season growth stage of soybean.