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Optimizing genotype-environment-management interactions to enhance productivity and eco-efficiency for wheat-maize
1Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
Abstract:
Agricultural production is facing unprecedented challenges to ensure food security by increasing productivity and in the meantime lowering environmental risk, especially in China. To enhance productivity and eco-efficiency of the typical winter wheat-summer maize rotation simultaneously in the North China Plain (NCP), we optimized the Genotype (G) × Environment (E) × Management (M) interactions to propose the optimal agronomic management practices and cultivars for four representative sites, with the Agricultural Production Systems sIMulator (APSIM) model and detailed field trial data. The results showed that an appropriate delay in sowing date could mitigate climatic negative effects and a proper increase in sowing density could increase yield. The optimal nitrogen application rate could be 180 kg N ha-1 year-1 for maize. For the cropping system, 240 mm of irrigation for wheat and 330-390 kg N ha-1 year-1 of nitrogen application rate (150-210 kg N ha-1 year-1 for wheat and 180 kg N ha-1 year-1 for maize) were suitable to sustain high yield, resource use efficiency, and lower N2O emissions. These recommended levels were, respectively, 40% less than the current irrigation and N application rate commonly used by local farmers. The recommended management practices could increase groundwater recharge while reducing nitrogen leaching and N2O emissions without reducing yield. The maize cultivars with a long growth duration, large grain number and grain-filling rate are desirable. The desirable wheat cultivars are characterized with a medium vernalization sensitivity and high grain filling rate. The present study demonstrated an effective approach to develop sustainable intensification options for producing more with less environmental costs through optimizing G × E × M interactions.
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