Simulating the adaptive measures of soybean production to climate change in China: based on cross-scale model coupling

Fan, D., Ding, Q., Tian, Z., Sun, L., & Fischer, G. (2015). Simulating the adaptive measures of soybean production to climate change in China: based on cross-scale model coupling. In: Emerging Economies, Risk and Development, and Intelligent Technology: Proceedings of the 5th International Conference on Risk Analysis and Crisis Response, June 1-3, 2015, Tangier, Morocco.

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Soybean is an important source of protein for humans and livestock. The steadily increasing demand fo soybean and global climate change both have brought great concern on risks and uncertainty in soybean supply. While fieldwork reports suggest that soybean farmers are aware of the risks posed by climate change and are adopting adaptive and mitigating measures, few studies have simulated and quantitatively assessed the effects of these adaptive techniques. In this study, we established a model coupling procedure between Decision Support System for Agrotechnology Transfer (DSSAT) and the China Agro-ecological Zone model (AEZ-China) to simulate soybean production based on observation records of soybean growth at 13 agro-meteorological observation stations in northeasten China and the Huang-Huai-Hai Plain over 1981-2011. The coupling procedure takes the advantage of DSSAT in its ability to calibrate the eco-physiological parameters based on simulating the dynamic bio-physiological processes of crop growth in daily step and the advantage of AEZ in rapidly evaluating the effects of shifts in planting day and changes in the length of growth cycle. Results indicate that climate change would result in beneficial effects in Northeastern China and cause losses in the Huang-Huai-Hai Plain in 2050s, the simulations using adaptive planting dates and cultivars with adaptive length of growth-cycle show that the losses can be reduced obviously.

Item Type: Conference or Workshop Item (Paper)
Research Programs: Water (WAT)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 08:54
Last Modified: 27 Aug 2021 17:25

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