Which crop and which drop,and the scope for improvement of water productivity |
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| Institution: | 1. Plant Production Systems, Wageningen University and Research Centre, P.O. Box 430, 6700 AK Wageningen, The Netherlands;2. Chaudhary Charan Singh Haryana Agricultural University, Department of Soil Science, Hisar 125004, Haryana, India;3. Agricultural Economics and Rural Policy Group, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, The Netherlands;1. CSS Haryana Agricultural University, Hisar, Haryana, India;2. Borlaug Institute for South Asia, (BISA), CIMMYT, Ludhiana, Punjab, India;3. International Maize and Wheat Improvement Centre (CIMMYT), NASC Complex, Pusa, New Delhi, India;4. Sri Karan Narendra Agriculture University, Jobner, Rajasthan, India;5. ICAR-Central Soil Salinity Research Institute (CSSRI), Karnal, Haryana, India;1. Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Yangzhou University, Yangzhou, Jiangsu, China;2. School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong, China;1. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling, Shaanxi, 712100, China;2. Institute of Water Saving Agriculture in Arid Regions (IWSA), Northwest A&F University, Yangling, Shaanxi, 712100, China;3. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, Shaanxi, 712100, China;4. Oklahoma State University, Stillwater, OK, United States;5. National Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, China;6. School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan;7. The UWA Institute of Agriculture, The University of Western Australia, Perth, WA, 6001, Australia;1. College of Agriculture, Shanxi Agriculture University, No.1, Minxian south road, Taigu, 030801, Shanxi, China;2. College of Agronomy, Northwest Agriculture & Forestry University, Yangling, 712100 Shaanxi, China;1. International Maize and Wheat Improvement Center, Dhaka, Bangladesh;2. CSIRO Agriculture and Food, Brisbane, Australia;3. Uttar Banga Krishi Vishwavidyalaya (UBKV), Pundibari, Coochbehar, West Bengal, India;4. Bihar Agricultural University, Sabour, Bhagalpur, Bihar, India;5. Nepal Agricultural Research Council, Kathmandu, Nepal;6. Bangladesh Agricultural Research Institute, Gazipur, Bangladesh;7. RDRS, Rangpur, Bangladesh;8. Bangladesh Wheat and Maize Research Institute, Dinajpur, Bangladesh;9. ICAR-RCER, Patna, Bihar, India;10. International Maize and Wheat Improvement Center, El Batan, Texcoco, Mexico City, Mexico;11. Soils and Environment Research Group, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Victoria 3010, Australia |
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| Abstract: | The information provided in publications on water-related agronomic trials and irrigation interventions is often too limited to compare values of water productivity (WP), i.e. the ratio between produced plant biomass and the amount of water used for that production, from different years, regions, etc. in a meaningful way. In this article, we show with the help of simulation models how WP-values are affected by different definitions of the numerator and denominator, environmental circumstances, such as climate, year and sowing date, and crop characteristics. In many cases, this resulted in 10–25% change in the WP-values, and sometimes even more. A minimum dataset is formulated that will make normalization and comparison of different WP-values easier. Most of these data are known by those who execute experiments, and we recommend strongly that these are reported in the future. Simulation models are excellent tools to explore the limitations and opportunities for increasing WP, provided they are well calibrated and validated for biomass, soil water availability, and ET. Such a balanced estimation of the “crop” and the “drop” requires an improved cooperation between hydrologists and agronomists. Comparison of actual WP(E)T and simulated maximum WP(E)T for the same environmental conditions does show the scope for increasing WP(E)T and other WP-values. Since WP-values are ratios, the production level on a hectare basis should be given besides WP. When we try to find an optimum combination of production per hectare and production per m3 irrigation water, we will be able to produce “more food with less water”. |
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