The interactions between genotype,management and environment in regional crop modelling |
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| Institution: | 1. The New Zealand Institute for Plant & Food Research Limited, Private Bag 4704, Christchurch, New Zealand;2. Institute of Crop Science and Resource Conservation, University of Bonn, Germany;3. Landcare Research, Private Bag 11052, Palmerston North, New Zealand;1. Crop Science Group, INRES, University of Bonn, Katzenburgweg 5, 53115 Bonn, DE, Germany;2. Institute of Agrophysics, Polish Academy of Sciences, ul. Doświadczalna 4, 20-290 Lublin, PL, Poland;3. Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374, Müncheberg, DE, Germany;1. Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Gatton Campus, QLD 4343, Australia;2. Advanta Seeds Pty Ltd, Toowoomba, QLD 4350, Australia;3. Department of Agriculture and Fisheries, Kingaroy, QLD 4610, Australia;4. International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, United Nations Avenu-Gigiri, Kenya;1. Tasmanian Institute of Agriculture (TIA), University of Tasmania, Tasmania, Australia;2. Institute for Agricultural Research, Ahmadu Bello University, Zaria, Nigeria;1. Agricultural & Biological Engineering Department, University of Florida, Gainesville, FL 32611, United States;2. Natural Resources Institute Finland (Luke), FI-00790 Helsinki, Finland;3. Institute of Crop Science and Resource Conservation (INRES), Universität Bonn, 53115, Germany;4. National Institute for Agricultural Research (INRA), UMR1248 Agrosystèmes et développement territorial, 31326 Castanet-Tolosan Cedex, France;5. INRA, UMR1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), F-63 100 Clermont-Ferrand, France;6. Blaise Pascal University, UMR1095 GDEC, F-63 170 Aubière, France;7. National Laboratory for Agriculture and Environment, Ames, IA 50011, United States;8. National Aeronautics and Space Administration (NASA), Goddard Institute for Space Studies, New York, NY 10025, United States;9. Commonwealth Scientific and Industrial Research Organization (CSIRO), Ecosystem Sciences, Dutton Park QLD 4102, Australia;10. Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany;11. CGIAR Research Program on Climate Change, Agriculture and Food Security, CIMMYT, New Delhi 110012, India;12. Department of Geological Sciences and Kellogg Biological Station, Michigan State University, East Lansing, MI, United States;13. National Institute for Agricultural Research (INRA), US1116 AgroClim, F- 84 914 Avignon, France;14. Institute of Soil Ecology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, D-85764, Germany;15. National Institute for Agricultural Research (INRA), UMR0211 Agronomie, F-78750 Thiverval-Grignon, France;p. AgroParisTech, UMR0211 Agronomie, F-78750 Thiverval-Grignon, France;q. Institute for Climate and Atmospheric Science, School of Earth and Environment, University of Leeds, Leeds LS29JT, United Kingdom;r. CGIAR-ESSP Program on Climate Change, Agriculture and Food Security, International Centre for Tropical Agriculture (CIAT), A.A. 6713, Cali, Colombia;s. Cantabrian Agricultural Research and Training Centre (CIFA), 39600 Muriedas, Spain;t. Water & Earth System Science Competence Cluster, c/o University of Tübingen, 72074 Tübingen, Germany;u. International Atomic Energy Agency, 1400 Vienna, Austria;v. School of Agriculture, Policy and Development, University of Reading, RG6 6AR, United Kingdom;w. Joint Research Center, via Enrico Fermi, 2749 Ispra, 21027 Italy;x. Department of Plant Agriculture, University of Guelph, Guelph, Ontario, N1G 2W1, Canada;y. Institute of Soil Science and Land Evaluation, Universität Hohenheim, 70599 Stuttgart, Germany;z. Dept. of Geographical Sciences, Univ. of Maryland, College Park, MD 20742, United States;1. Texas A&M AgriLife Research and Extension Center, Texas A&M Univ., Temple, TX 76502, United States;2. Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany;3. Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi 110 012, India;4. Landscape & Water Sciences, Department of Economic Development, Jobs, Transport and Resources, Horsham, Victoria 3400, Australia;5. Department of Agroecology, Aarhus University, 8830, Tjele, Denmark;6. National Centre for Atmospheric Science, Department of Meteorology, University of Reading, RG6 6BB, United Kingdom;7. Computational and Systems Biology Department, Rothamsted Research, Harpenden, Herts, AL5 2JQ, United Kingdom;8. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy;9. Biological Systems Engineering, Washington State University, Pullman, WA 99164-6120, United States;10. Water Sytems and Global Change Group, Wageningen University, The Netherlands;11. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China;12. Institute for Climate and Water, INTA-CIRN, 1712 Castelar, Argentina;13. USDA ARS, ALARC, Maricopa, AZ, USA;14. Plant Production Systems, Wageningen University, 6700AA Wageningen 37, The Netherlands;15. Department of Crop Sciences, Georg-August-University Goettingen, Germany;P. Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany |
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| Abstract: | Biophysical models to simulate crop yield are increasingly applied in regional climate impact assessments. When performing large-area simulations, there is often a paucity of data to spatially represent changes in genotype (G) and management (M) across different environments (E). The importance of this uncertainty source in simulation results is currently unclear. In this study, we used a variance-based sensitivity analysis to quantify the relative contribution of maize hybrid (i.e. G) and sowing date (i.e. M) to the variability in biomass yield (YT, total above-ground biomass) and harvest index (HI, fraction of grain in total yield) of irrigated silage maize, across the extent of arable lands in New Zealand (i.e. E). Using a locally calibrated crop model (APSIM-maize), 25 G x M scenarios were simulated at a 5 arc minute resolution (∼5 km grid cell) using 30 years of historical weather data. Our results indicate that the impact of limited knowledge on G and M parameters depends on E and differs between model outputs. Specifically, the sensitivity of YT and HI to genotype and sowing date combinations showed different patterns across locations. The absolute impact of G and M factors was consistently greater in the colder southern regions of New Zealand. However, the relative share of total variability explained by each factor, the sensitivity index (Si), showed distinct spatial patterns for the two output variables. The YT was more sensitive than HI in the warmer northern regions where absolute variability was the smallest. These patterns were characterised by a systematic response of Si to environmental drivers. For example, the sensitivity of YT and HI to hybrid maturity consistently increased with temperature. For the irrigated conditions assumed in our study, inter-annual weather conditions explained a higher share of total variability in the southern colder regions. Our results suggest that the development of methods and datasets to more accurately represent spatio-temporal G and M variability can reduce uncertainty in regional modelling assessments at different degrees, depending on prevailing environmental conditions and the output variable of interest. |
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| Keywords: | Apsim Corn Clustering Spatial modelling Uncertainty Sensitivity |
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