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Estimating potential yield of wheat production in China based on cross-scale data-model fusion 被引量:8

Estimating potential yield of wheat production in China based on cross-scale data-model fusion
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摘要 The response of the agro-ecological system to the environment includes the response of individual crop's physiologic process and the adaption of the crop commu- nity to the environment. Observation and simulation at the single scale level cannot fully explain the above process. It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales. In this research, two typical agro- ecological models, the Decision Support System for Agro- technology Transfer (DSSAT) model and the Agro- ecological Zone (AEZ) model, are employed, and a framework for effective cross-scale data-model fusion is proposed and illustrated. The national observed data from 36 different agricultural observation stations and historical weather stations (1962-1999) are employed to estimate average crop productivity. Comparison of the two models' estimations are consistent, which would indicate the possibility ofcross-scale crop model fusion. The response of the agro-ecological system to the environment includes the response of individual crop's physiologic process and the adaption of the crop commu- nity to the environment. Observation and simulation at the single scale level cannot fully explain the above process. It is necessary to develop cross-scale agro-ecological models and study the interaction of agro-ecological processes across different scales. In this research, two typical agro- ecological models, the Decision Support System for Agro- technology Transfer (DSSAT) model and the Agro- ecological Zone (AEZ) model, are employed, and a framework for effective cross-scale data-model fusion is proposed and illustrated. The national observed data from 36 different agricultural observation stations and historical weather stations (1962-1999) are employed to estimate average crop productivity. Comparison of the two models' estimations are consistent, which would indicate the possibility ofcross-scale crop model fusion.
出处 《Frontiers of Earth Science》 SCIE CAS CSCD 2012年第4期364-372,共9页 地球科学前沿(英文版)
关键词 DSSAT model AEZ model data-model fusion agro-ecological system DSSAT model, AEZ model, data-model fusion, agro-ecological system
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  • 1Bannayan M, Crout N M J, Hoogenboom G (2003). Application of the CERES-Wheat model for within-season prediction of winter wheat yield in the United Kingdom. Agron J, 95(1): 114-125.
  • 2Batjes N H (2009). Harmonized soil profile data for applications at global and continental scales: updates to the WISE database. Soil Use Manage, 25(2): 124-127.
  • 3Baumer O W, Rice J W (1988). Methods to predict soil input data for DRAINMOD. ASAE Paper No. 88-2564.
  • 4Beven K, Binley A (1992). The future of distributed models: model calibration and uncertainty prediction. Hydrol Processes, 6(3): 279 - 298.
  • 5Blasone R S, Vrugt J A, Madsen H, Rosbjerg D, Robinson B A, Zyvoloski G A (2008). Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov chain Monte Carlo sampling. Adv Water Resour, 31 (4): 630-648.
  • 6Brooks S (1998). Markov chain Monte Carlo method and its application. Journal of the Royal Statistical Society (D): The Statistician, 47(1): 69-100.
  • 7Campbell E P, Fox D R, Bates B C (1999). A Bayesian Approach to parameter estimation and pooling in nonlinear flood event models. Water Resour Res, 35(1): 211-220.
  • 8Dente L, Satalino G, MaRia F, Rinaldi M (2008). Assimilation of leaf area index derived from ASAR and MERIS data into CERES-Wheat model to map wheat yield. Remote Sens Environ, 112(4): 1395-1407.
  • 9Fang H L, Liang S L, Hoogenboom G, Teasdalec J, Cavigellic M (2008). Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model. Int J Remote Sens, 29(10): 3011--3032.
  • 10FAO/IIASA/ISRIC/ISSCAS/JRC (2009). Harmonized World Soil Data- base (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria.

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