A cross-scale model coupling approach to simulate the risk-reduction effect of natural adaptation on soybean production under climate change

Fan, D., Ding, Q., Tian, Z., Sun, L., & Fischer, G. (2017). A cross-scale model coupling approach to simulate the risk-reduction effect of natural adaptation on soybean production under climate change. Human and Ecological Risk Assessment: An International Journal 23 (3) 426-440. 10.1080/10807039.2016.1221308.

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Abstract

This study establishes a procedure to couple Decision Support System for Agrotechnology Transfer (DSSAT) and China Agroecological Zone model (AEZ-China). This procedure enables us to quantify the effects of two natural adaptation measures on soybean production in China, concern on which has been growing owing to the rapidly rising demand for soybean and the foreseen global climate change. The parameters calibration and mode verification are based on the observation records of soybean growth at 13 agro-meteorological observation stations in Northeast China and Huang-Huai-Hai Plain over 1981–2011. The calibration of eco-physiological parameters is based on the algorithms of DSSAT that simulate the dynamic bio-physiological processes of crop growth in daily time-step. The effects of shifts in planting day and changes in the length of growth cycle (LGC) are evaluated by the speedy algorithms of AEZ. Results indicate that without adaptation, climate change from the baseline 1961–1990 to the climate of 2050s as specified in the Providing Regional Climate for Impacts Studies-A1B would decrease the potential yield of soybean. By contrast, simulations of DSSAT using AEZ-recommended cultivars with adaptive LGC and also the corresponding adaptive planting dates show that the risk of yield loss could be fully or partially mitigated across majority of grid cells in the major soybean-growing areas.

Item Type:	Article
Uncontrolled Keywords:	climate change adaptation, soybean production, model coupling, China
Research Programs:	Water (WAT)
Depositing User:	Michaela Rossini
Date Deposited:	02 Dec 2016 10:06
Last Modified:	27 Aug 2021 17:28
URI:	https://pure.iiasa.ac.at/14056

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