Water productivity of irrigated crops in Sirsa district, India

173 Pages·2003·2.18 MB·English

Water productivity of irrigated crops in Sirsa district, India
Integration of remote sensing, crop and soil models
and geographical information systems
J.C. van Dam and R.S. Malik (editors)
2003
CHAUDHARY CHARAN SINGH
HARYANA AGRICULTURAL UNIVERSITY

Overview of scientists who participated in the WATPRO project
Chaudhary Charan Singh Haryana Agricultural University
Dr. R.S. Malik, chief scientist
Prof. R. Kumar, soil and water engineering
Prof. J. Singh, soil and water engineering
Dr. R.K. Jhorar, soil and water engineering
Dr. A.S. Dhindwal, agronomy
Dr. H. Singh, agronomy
Dr. M.S. Bhatto, agronomy
Dr. B.S. Jhorar, soil science CHAUDHARY CHARAN SINGH
Dr. D.S. Dabas, soil science HARYANA AGRICULTURAL UNIVERSITY
Mr. Devender Singh, MSc., research associate
Mr. Udaivur Singh, MSc., research associate
Mr. Jhaber Mal, MSc., research associate
Mr. Sher Singh, MSc., research associate
International Water Management Institute
Dr. P. Droogers, water resources
Dr. H. Murray-Rust, water resources
Wageningen University and Research centre
Dr. J.J.E. Bessembinder, plant production systems
Dr. P.A. Leffelaar, plant production systems
Mr. T. Ponsioen, MSc., plant production systems
Mr. J. Wolf, MSc., plant production systems
Mr. J.G. Kroes, MSc., regional water analysis
Mr. H.W. ter Maat, MSc., regional water analysis
Mr. W. Immerzeel, MSc., geographical information systems
Prof. R.A. Feddes, water resources
Dr. J.C. van Dam, water resources
Mr. R.S. Khatri, MSc., water resources
Mr. A. Roelevink, BSc., water resources
WaterWatch
Dr. W.G.M. Bastiaanssen, remote sensing
Dr. H. Pelgrum, remote sensing
Mr. S.J. Zwart, MSc., remote sensing

Contents
1 Introduction 11
1.1 Refocusing irrigation water management 11
1.2 General background of water productivity 14
1.3 Summary of earlier work in Northwest India 15
1.4 The toolbox 18
1.5 WATPRO objectives 19
2 Water management and crop production in Sirsa Irrigation Circle 21
2.1 Introduction 21
2.2 Climate and rainfall 21
2.3 Topography and soil types 22
2.4 Canal irrigation 22
2.4.1 Canal irrigation system 22
2.4.2 Canal irrigation system performance 23
2.5 Groundwater 25
2.5.1 Groundwater quality 25
2.5.2 Groundwater depths 25
2.5.3 Groundwater use 26
2.6 Crop production 26
2.6.1 Cropping pattern 26
2.6.2 Crop yields 27
2.7 Major issues related to water management and crop production 27
3 Measurement program and description database 29
3.1 Introduction 29
3.2 Farmer fields 29
3.2.1 Soil measurements 30
3.2.2 Irrigation water measurements 32
3.2.3 Crop measurements 32
3.2.4 Management practices 34
3.3 Field experiments at research stations 34
3.4 Regional data 36
3.4.1 Meteorological data 36
3.4.2 Land use 38
3.4.3 Soil information 38
3.4.4 Canal irrigation water 39
4 Water and salt balances at farmer fields 41
4.1 Introduction 41
4.2 SWAP model description 42
4.2.1 Water and salt balance 43
4.2.2 Soil water flow 43
4.2.3 Top boundary condition 44
4.2.4 Bottom boundary condition 46
4.2.5 Solute transport 46
4.3 Materials and methods 47
4.3.1 Monitoring of farmer fields 47
4.3.2 Input parameters of SWAP 48
4.3.3 Inverse modeling of soil hydraulic functions 49
4.3.4 Water management response indicators 50
4.4 Results and discussion 51
4.4.1 Soil hydraulic functions 51
4.4.2 Water and salt balances 54
4.5 Soil hydraulic parameters for regional scale 57
4.6 Conclusions 58
5 Analysis of crop growth 59
5.1 Introduction 59
5.1.1 Water productivity and simulation models 59
5.1.2 Research objectives 60
5.2 Calibration of SWAP/WOFOST for wheat, rice and cotton 60
5.2.1 The detailed crop module in SWAP 60
5.2.2 Methodology 61
5.2.3 Calibration for wheat 62
5.2.4 Calibration for rice 65
5.2.5 Calibration for cotton 66
5.3 Comparison of actual and simulated crop production and evapotranspiration 67
5.3.1 Methodology 67
5.3.2 Comparison for wheat 68
5.3.3 Comparison for rice 72
5.3.4 Comparison for cotton 73
5.4 Management options and water productivity 74
5.4.1 Definitions of water productivity 74
5.4.2 Levels of water productivity 74
5.4.3 Deficit irrigation 77
5.4.4 Variation between years 78
5.4.5 Sowing date 79
5.4.6 Soil type 80
5.4.7 Irrigation water quality 81
5.5 Discussion and conclusions 81
5.5.1 Methodology and recommendations for further research 81
5.5.2 Management options, water productivity and yield level 82
6 Remote sensing analysis 85
6.1 Introduction 85
6.2 Satellite images used 85
6.3 Land cover classification 87
6.4 The Surface Energy Balance Algorithm for Land (SEBAL) 90
6.5 SEBAL results Rabi season 2001-2002 91
6.6 SEBAL results Kharif season 2002 96
7 A regional approach to model water productivity 101
7.1 Introduction 101
7.2 Available regional datasets 102
7.2.1 Soils 102
7.2.2 Land use 102
7.2.3 Climate 103
7.2.4 Irrigation: groundwater 103
7.2.5 Irrigation: canal water 106
7.3 Methodology 107
7.3.1 Stratification 107
7.3.2 Parameterisation 109
7.3.3 Regional modeling 113
7.4 Results 114
7.4.1 Reference situation 114
7.4.2 Comparison with remote sensing 116
7.5 Conclusions and recommendations 118
8 Integration of remote sensing and simulation of crop growth, soil water and
solute transport at regional scale 121
8.1 Defining water productivity 121
8.2 An appraisal of the water productivity definitions 122
8.3 Calculation, aggregation and validation of water productivity 123
8.4 Application at investigated farmer fields 126
8.5 Proposed measures in Sirsa district 129
8.6 Concluding remarks 133
9 Future water management in Sirsa: options to improve water productivity 135
9.1 Introduction 135
9.2 Scale issues in water productivity 135
9.3 Water productivity under current conditions: the remote sensing approach 136
9.3.1 Linking remote sensing and models 136
9.3.2 Components of water productivity 137
9.3.3 Water balance 142
9.3.4 Water productivity of entire Sirsa Irrigation Circle 143
9.4 Options to increase water productivity: the modelling approach 146
9.4.1 Field scale scenarios 148
9.4.2 Regional scale scenarios 150
9.5 Overall conclusions and recommendations 153
9.5.1 Conclusions from the remote sensing analysis 153
9.5.2 Conclusions from the modelling analysis 155
9.5.3 Recommendations 155
10 References 157
Appendix A. Description of CD-ROM 165
Appendix B. SEBAL evapotranspiration 169

Dam, J.C. van, and R.S. Malik (Eds.), 2003. Water productivity of irrigated crops in Sirsa district, India.
Integration of remote sensing, crop and soil models and geographical information systems. WATPRO
final report, including CD-ROM. ISBN 90-6464-864-6. 173 pp.
Abstract
Major issues with respect to water management in Sirsa district are waterlogging and salinization in
areas with saline groundwater and over-exploitation ofgroundwater in areas with fresh groundwater. The
present crop yield increase of the major crops in Sirsa district is marginal. Recent studies show that water
is the main limiting factor to increase the crop yields. In order to identify the main water losses, an
extensive WAter PROductivity study (WATPRO) has been performed in Sirsa district.
The WATPRO project focussed on (1) the integration and application of advanced research tools
(remote sensing, detailed crop and soil models, and GIS), (2) the upscaling from local field scale to
regional scale, (3) the application of recent concepts on water productivity at various scales, and (4) a
survey of the most viable scenario’s that improve water productivity in Sirsa district.
During the agricultural year 2001/02 extensive measurements were collected for the major crops
wheat, cotton and rice at both experimental fields and at farmer fields with various soil, irrigation and
management conditions. The water flow and salt transport model SWAP and the detailed crop growth
model WOFOST were calibrated in order to reproduce the measurements at the experimental and farmer
fields. The calibrated models have been used to analyse viable water management options at field level to
improve WP.
High and low resolution satellite images have been used to derive the cropping pattern, and in
conjunction with the remote sensing algorithm SEBAL to estimate evapotranspiration, biomass production
and water productivity as distributed over Sirsa district. The main conclusion from the remote sensing
analysis is that WP is good and rather uniform for wheat, and moderate for rice and cotton. The wider
range in WPfor rice suggests that by narrowing the variability and increasing the WPfor rice, productivity
if water resources in Sirsa can be improved substantially.
Available data sets of Sirsa district on meteorology, crops, soils, groundwater, canal and tubewell
water and cultivable command areas, have been integratedinto a GIS. The data were downscaled to a level
of 30x30 m to allow comparison with remote sensing data. The followed stratification resulted in a final
overlay with 2404 calculation units. For each unit the tuned SWAP/WOFOST combination was used to
simulate crop growth and water and salt balances. The results of the current regional analysis are such that
it can be used to compare regional scenario studies qualitatively. Improvement of both both instrument and
data sets may enable a more quantitative approach.
A theoretical framework is presented to analyse WP at crop, field and regional scale for fresh and
saline groundwater conditions. This methodology is applied to evaluate current proposals to increase and
maintain water productivity in Sirsa district. By far the highest increase of WP can be expected from
improved crop management (cultivation, fertilizer application, weed and pest control) and by replacing
paddy rice with dry rice or corn.
Finally a field scale modeling approach and a regional modelling approach were used to explore the
impact of different scenarios on yields, gross return and WP. Four scenarios have been explored indicating
that (i) climate change will have a positive effect, (ii) increased salinity levels will have negative impacts
on especially rice, (iii) proper irrigation scheduling is most important for wheat, and (iv) a further rise in
groundwater levels will have a detrimental effect in some areas. Key recommendations for future water
management in Sirsa, emerging from WATPRO, are the setup of an integrated agronomy-water
management program to enhance crop yields and WP,the construction of a drainage system in waterlogged
areas with saline groundwater, and enforced regulation of groundwater extraction.
The attached CD-ROM contains the collected data at experimental and farmer fields and of entire
Sirsa district, the input files for the calibrated SWAP and WOFOST models, the LANDSAT and NOAA
remote sensing images and the setup for the regional simulation.
Glossary
CCA Cultivable Command Area; area suitable for agriculture with attached water rights
CCS HAU Chaudhary Charan Singh Haryana Agricultural University
chak the watercourse unit, in which 20-100 farmers share the irrigation water from one outlet
CI confidence interval of optimized parameters
CV coefficient of variation (ratio of standard deviation and arithmetic mean)
DM weight of dry matter
DVS crop development stage
EC electrical conductivity
ET actual evapotranspiration
a
ET potential evapotranspiration
p
ET water limited evapotranspiration (optimal crop management, only water stress)
wl
FM weight of fresh plant material, which contains some moisture
GIS geographical information system
HI effective harvest index, ratio of fresh harvestable product and total dry matter production
HI dry harvest index, ratio of dry harvestable product and total dry matter production
dry
HID Haryana Irrigation Department
HSMITC Haryana State Minor Irrigation and Tubewell Corporation
HYPRESS European database of soil hydraulic functions, including pedotransfer functions
I irrigation amount
IWMI International Water Management Institute
kharif summer crop season (April – October)
LAI leaf area index
PTF pedotransfer functions, which relate soil hydraulic functions with basic soil properties
rabi winter crop season (October – April)
rostering rotation of water supply among distributary canals
RMSE root mean square error, measure for correspondence between observations and
simulations
RS remote sensing
SEBAL the Surface Energy Balance Algorithm for Land
SIC Sirsa Irrigation Circle
SO weight of storage organ
STD standard deviation
SWAP Soil-Water-Atmosphere-Plant model
T actual transpiration
a
T potential transpiration
p
TDM weight of total dry matter
TSUM temperature sum, which determines the length of crop growth phases
warabandi available canal water is spread to all farmers in proportion to their land holding
WMRI water management response indicator
WOFOST WOrld FOOd STudies, detailed plant growth model
WP water productivity based on transpiration water only
T
WP water productivity based on evapotranspiration water only
ET
WP water productivity based on the sum of evapotranspiration and percolated water
Leach
WP water productivity based on the sum of evapotranspiration, percolated water, distribution
Reg
and conveyance losses
WP water productivity based on total irrigation water supply
Irr
WP water productivity based on sum of irrigation water supply and rainfall
Supply
WUR Wageningen University and Research centre