A dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputer

•Further acceleration of crop models and high-performance computing for large-scale crop modeling.•Combination of Bayesian inference and Bayesian model average to improve predictive accuracy.•Real-time simulation and prediction based on observational and scenario forces.•Risk analysis of yield losse...

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Published in:	Computers and electronics in agriculture Vol. 154; pp. 400 - 410
Main Authors:	Huang, Xiao, Yu, Chaoqing, Fang, Jiarui, Huang, Guorui, Ni, Shaoqiang, Hall, Jim, Zorn, Conrad, Huang, Xiaomeng, Zhang, Wenyuan
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.11.2018 Elsevier BV
Subjects:	Accuracy Agricultural management Agricultural production Atmospheric models Bayesian analysis Bayesian theory China Computer simulation computers corn Crop growth crop models crop yield denitrification Drought Dynamic prediction Impact prediction Optimization Parallel processing Parameters prediction Real time Risk analysis Soil water Statistical inference Supercomputer Supercomputers uncertainty Uncertainty analysis Zea mays China Supercomputer Accuracy Drought Risk analysis Dynamic prediction
ISSN:	0168-1699, 1872-7107
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Abstract	•Further acceleration of crop models and high-performance computing for large-scale crop modeling.•Combination of Bayesian inference and Bayesian model average to improve predictive accuracy.•Real-time simulation and prediction based on observational and scenario forces.•Risk analysis of yield losses in multiple scales and its spatial dependency. Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often restricted by available computing resource. This limits capacity to undertake uncertainty analysis and prohibits the use of models in real-time ensemble forecasting systems. This study addresses these concerns by presenting an integrated system for the dynamic prediction and assessment of agricultural yield using the top-ranked Sunway TaihuLight supercomputer platform. This system enables parallelization and acceleration for the existing AquaCrop, DNDC (DeNitrification and DeComposition) and SWAP (Soil Water Atmosphere Plant) models, thus facilitating multi-model ensemble and parameter optimization and subsequent drought risk analysis in multiple regions and at multiple scales. The high computing capability also opens up the possibility of real-time simulation during droughts, providing the basis for more effective drought management. Initial testing with varying core group numbers shows that computation time can be reduced by between 2.6 and 3.6 times. Based on the powerful computing capacity, a county-level model parameter optimization (2043 counties for 1996–2007) by Bayesian inference and multi-model ensemble using BMA (Bayesian Model Average) method were performed, demonstrating the enhancements in predictive accuracy that can be achieved. An application of this system is presented predicting the impacts of the drought of May–July 2017 on maize yield in North and Northeast China. The spatial variability in yield losses is presented demonstrating new capability to provide high resolution information with associated uncertainty estimates.
AbstractList	•Further acceleration of crop models and high-performance computing for large-scale crop modeling.•Combination of Bayesian inference and Bayesian model average to improve predictive accuracy.•Real-time simulation and prediction based on observational and scenario forces.•Risk analysis of yield losses in multiple scales and its spatial dependency. Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often restricted by available computing resource. This limits capacity to undertake uncertainty analysis and prohibits the use of models in real-time ensemble forecasting systems. This study addresses these concerns by presenting an integrated system for the dynamic prediction and assessment of agricultural yield using the top-ranked Sunway TaihuLight supercomputer platform. This system enables parallelization and acceleration for the existing AquaCrop, DNDC (DeNitrification and DeComposition) and SWAP (Soil Water Atmosphere Plant) models, thus facilitating multi-model ensemble and parameter optimization and subsequent drought risk analysis in multiple regions and at multiple scales. The high computing capability also opens up the possibility of real-time simulation during droughts, providing the basis for more effective drought management. Initial testing with varying core group numbers shows that computation time can be reduced by between 2.6 and 3.6 times. Based on the powerful computing capacity, a county-level model parameter optimization (2043 counties for 1996–2007) by Bayesian inference and multi-model ensemble using BMA (Bayesian Model Average) method were performed, demonstrating the enhancements in predictive accuracy that can be achieved. An application of this system is presented predicting the impacts of the drought of May–July 2017 on maize yield in North and Northeast China. The spatial variability in yield losses is presented demonstrating new capability to provide high resolution information with associated uncertainty estimates. Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often restricted by available computing resource. This limits capacity to undertake uncertainty analysis and prohibits the use of models in real-time ensemble forecasting systems. This study addresses these concerns by presenting an integrated system for the dynamic prediction and assessment of agricultural yield using the top-ranked Sunway TaihuLight supercomputer platform. This system enables parallelization and acceleration for the existing AquaCrop, DNDC (DeNitrification and DeComposition) and SWAP (Soil Water Atmosphere Plant) models, thus facilitating multi-model ensemble and parameter optimization and subsequent drought risk analysis in multiple regions and at multiple scales. The high computing capability also opens up the possibility of real-time simulation during droughts, providing the basis for more effective drought management. Initial testing with varying core group numbers shows that computation time can be reduced by between 2.6 and 3.6 times. Based on the powerful computing capacity, a county-level model parameter optimization (2043 counties for 1996–2007) by Bayesian inference and multi-model ensemble using BMA (Bayesian Model Average) method were performed, demonstrating the enhancements in predictive accuracy that can be achieved. An application of this system is presented predicting the impacts of the drought of May–July 2017 on maize yield in North and Northeast China. The spatial variability in yield losses is presented demonstrating new capability to provide high resolution information with associated uncertainty estimates.
Author	Zorn, Conrad Zhang, Wenyuan Huang, Xiao Fang, Jiarui Yu, Chaoqing Huang, Guorui Hall, Jim Huang, Xiaomeng Ni, Shaoqiang
Author_xml	– sequence: 1 givenname: Xiao surname: Huang fullname: Huang, Xiao organization: Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China – sequence: 2 givenname: Chaoqing surname: Yu fullname: Yu, Chaoqing email: chaoqingyu@yahoo.com organization: Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China – sequence: 3 givenname: Jiarui surname: Fang fullname: Fang, Jiarui organization: Department of Computer Science and Technology, Tsinghua University, Beijing, China – sequence: 4 givenname: Guorui surname: Huang fullname: Huang, Guorui organization: Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China – sequence: 5 givenname: Shaoqiang surname: Ni fullname: Ni, Shaoqiang organization: Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China – sequence: 6 givenname: Jim orcidid: 0000-0002-2024-9191 surname: Hall fullname: Hall, Jim organization: Environmental Change Institute, University of Oxford, Oxford, UK – sequence: 7 givenname: Conrad surname: Zorn fullname: Zorn, Conrad organization: Environmental Change Institute, University of Oxford, Oxford, UK – sequence: 8 givenname: Xiaomeng surname: Huang fullname: Huang, Xiaomeng organization: Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China – sequence: 9 givenname: Wenyuan surname: Zhang fullname: Zhang, Wenyuan organization: Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
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Snippet	•Further acceleration of crop models and high-performance computing for large-scale crop modeling.•Combination of Bayesian inference and Bayesian model average... Crop models are widely used to evaluate the response of crop growth to drought. However, over large geographic regions, the most advanced models are often...
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SubjectTerms	Accuracy Agricultural management Agricultural production Atmospheric models Bayesian analysis Bayesian theory China Computer simulation computers corn Crop growth crop models crop yield denitrification Drought Dynamic prediction Impact prediction Optimization Parallel processing Parameters prediction Real time Risk analysis Soil water Statistical inference Supercomputer Supercomputers uncertainty Uncertainty analysis Zea mays
Title	A dynamic agricultural prediction system for large-scale drought assessment on the Sunway TaihuLight supercomputer
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