Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain |
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| Affiliation: | 1. Key Lab. of Computational Geodynamics, University of Chinese Academy of Sciences, Beijing 100049, China;2. Institute of Eco-Environment and Agro-meteorology, Chinese Academy of Meteorological Sciences, Beijing 100081, China;3. Key Lab. of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;4. College of Geoscience, Yangtze University, Wuhan 430100, China;1. School of Remote Sensing and Information Engineering, Wuhan University, 430079, China;2. Lab for Remote Sensing of Crop Phenotyping, Wuhan University, 430079, China;3. College of Life Sciences, Wuhan University, 430072, China;4. School of Resources and Environmental Science, Hubei University, Wuhan, 430062, China;5. Division of Mathematical Sciences, Wuhan Institute of Physics and Mathematics of Chinese Academy of Sciences, Wuhan, 430071, China;6. The 29th Research Institute of China Electronics Technology Group Corporation, Chengdu, China, 610036;1. School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China;2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China;3. Lanzhou Institute of Arid Meteorology, China Meteorological Administration, Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Key Open Laboratory of Arid Climate Change and Disaster Reduction of China Meteorological Administration, Lanzhou 730000, China;1. UPR SCA, CIRAD, Station de Ligne-Paradis, 7 chemin de l’Irat, FR-97410 Saint-Pierre, La Réunion, France;2. UMR TETIS, CIRAD, 500 rue Jean-François Breton, FR-34093 Montpellier, France;3. UPR SCA, CIRAD, Station de la Bretagne, 40 chemin de Grand Canal, FR-97743 Saint-Denis, La Réunion, France;4. UMR TETIS, CIRAD, Station de Ligne-Paradis, 7 chemin de l’Irat, FR-97410 Saint-Pierre, La Réunion, France;5. IRD, 911 avenue Agropolis, FR-34394 Montpellier Cedex 05, France;1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China;2. National Center of Efficient Irrigation Engineering and Technology Research-Beijing, Beijing, 100048, China;1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Future Earth Research Institute, Beijing Normal University, Beijing 100875/Zhuhai 519087, China;2. National Research Council, Institute for Agricultural and Forest Systems, 80040 Ercolano, Napoli, Italy;3. Wageningen UR, Alterra, Earth System Science and Climate Change Group, PO Box 47, 6700 AA Wageningen, The Netherlands;4. Department of Agroecology, Aarhus University, DK-8830 Tjele, Denmark |
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| Abstract: | Climate change significantly impact on agriculture in recent year, the accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C4 crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS–P–YEC (Remote-Sensing–Photosynthesis–Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS–P–YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002–2011. The 111 statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (p < 0.01) between the simulated yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002 to 2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale. |
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| Keywords: | Process-based model Maize Crop yield Remote sensing Northeast China Plain |
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