Optimizing genotype-environment-management interactions for maize farmers to adapt to climate change in different...

Crop growth conditions are being altered by ongoing climate change and the agronomic management practices should be adjusted accordingly and timely. In Chinese Maize Belt, climate change impacts are always compounded by agronomic management and the regional differences have yet to be well understood. How local farmers adapt to climate change is a big challenge and related adaptive strategies are urgently required. Based on detailed field experiments performed for >15 years, we applied the CERES-Maize model to disentangle the impacts of individual climate variables, quantify the contributions of three low-cost measures (cultivar, sowing date, and planting density) to yield variations, and design effective adaptation options in each zone. We found the patterns and impacts of climate change varied among the cultivated areas: yield increased by 0.39% per year in Northeast China (NEC) and 0.78% in the northwestern arid area (NWA) but decreased by 1.13% in the North China Plain (NCP). The results highlighted the considerable impacts of increased minimum temperature and decreased solar radiation on the changes of maize yield. CERES-Maize model reproduced the phenology and yield well with <9% bias and >81% yield explanation ability. The simulation results suggested that an appropriate delay in sowing date could mitigate climatic negative effects and enhance maize yields significantly. Planting cultivars of Nongda108 in NEC, Zhengdan958 in the NCP, and Shendan10 in the NWA substantially increased yield compared with planting the cultivars most widely used by farmers. The optimal planting density were 11.4, 12.3, and 12.7 plants/m² respectively, which were generally higher than the local common levels. By optimizing genotype (G)-environment (E)-management (M) interactions, maize yield can be enhanced by at least 10%, especially in the NWA, implying that efforts to increase food production should be made in low-yielding zones. This study illustrated the patterns of climate change in different zones, and demonstrated an effective approach to develop sustainable intensification options and improve yield and stability with fewer economic-environmental costs by optimizing G × E × M interactions in the future.