Model validation and crop coefficients for irrigation scheduling in the North China plain |
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| Institution: | 1. China Institute for Water Resources and Hydro-electric Power Research, P.O. Box 366, Beijing, China;2. Instituto Superior de Agronomia, Tapada da Ajuda, 1399 Lisboa Codex, Portugal;3. Wangdu Agricultural Experiment Station, Wangdu, Hebei Province, China;1. Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Volcani Center, HaMaccabim Road 68, P.O.B 15159, Rishon LeZion 7528809, Israel;2. HIT- Holon Institute of Technology, POB 305, Holon 58102, Israel;1. UMR LEPSE, INRA, Montpellier SupAgro, 2 Place Viala, 34 060 Montpellier, France;2. Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL-32611, USA;3. Institute of Crop Science and Resource Conservation, University of Bonn, D-53 115 Bonn, Germany;4. Potsdam Institute for Climate Impact Research, D-14 473 Potsdam, Germany;5. Natural Resources Institute Finland (Luke), FI-01301 Vantaa, Finland;6. NASA Goddard Institute for Space Studies, New York, NY-10025, USA;7. Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts AL5 2JQ, UK;8. INRA, UMR 1248 Agrosystèmes et développement territorial, F-31 326, Castanet-Tolosan, France;9. CSIRO Agriculture, Black Mountain, ACT 2601, Australia;10. CIMMYT Int. AP 6-641, D.F. Mexico 06600, Mexico;11. Institute of Biochemical Plant Pathology, Helmholtz Zentrum München,German Research Center for Environmental Health, D-85764 Neuherberg, Germany;12. Department of Geological Sciences and W.K. Kellogg Biological Station, Michigan State University, East Lansing, MI-48 823, USA;13. Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK;14. CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Centre for Tropical Agriculture, A.A. 6713, Cali, Colombia;15. Cantabrian Agricultural Research and Training Centre, 39600 Muriedas, Spain;p. Institute of Soil Science and Land Evaluation, University of Hohenheim, D-70 599 Stuttgart, Germany;q. Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, D15 374 Müncheberg, Germany;r. USDA, Agricultural Research Service, US Arid-Land Agricultural Research Center, Maricopa, AZ 85138, USA;s. College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China;t. Grains Innovation Park, Department of Economic Development Jobs, Transport and Resources, Horsham 3400, Australia;u. Department of Agroecology, Aarhus University, 8830 Tjele, Denmark;v. The School of Plant Sciences, University of Arizona, Tucson, AZ 85721, USA;w. Institute of Soil Science and Land Evaluation, University of Hohenheim, D-70 599 Stuttgart, Germany;x. CSIRO Agriculture, 306 Carmody Road, St Lucia Queensland 4067, Australia;y. Center for Development Research (ZEF), Walter-Flex-Straße 3, 53113 Bonn, Germany;z. China Agricultural University, Beijing 100193, China;1. Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China;2. Met Office Hadley Centre, Exeter, UK;3. University of Chinese Academy of Sciences, Beijing, China |
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| Abstract: | ISAREG is a model for simulation and evaluation of irrigation scheduling. The model performs the soil water balance and evaluates impacts of water stress on yields for different crops. It is now being used to support a water saving irrigation scheduling program in a pilot area in the North China plain. This paper reports on the calibration and validation of the model using independent data sets relative to winter wheat and summer maize. Data are originated from the Wangdu experimental station and concern a set of drainage lysimeters where diverse irrigation treatments were applied representing different strategies of deficit irrigation. The calibration of the model was performed by deriving the crop coefficients adapted to the local climatic conditions, and considering the soil freezing during winter. The validation of the model was performed using different data sets. Results show that the relative errors to estimate the soil water content averaged 5.3% for summer maize and 7.3% for the winter wheat. These results support the use of the model in the practice. |
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