Ranking genotype, environment, management effects on the optimum nitrogen rate for maize: A cropping system modeling analysis

Abstract

Ranking the contribution of genotype, environment, and management (G × E × M) on maize's economic optimum nitrogen fertilizer rate (EONR) variability could improve understanding and predictability of EONR. We performed a simulation experiment using the Agricultural Production Systems sIMulator model with the objectives to (1) rank the effects of 24 individual G × E × M factors on the magnitude and interannual variability of the EONR across the US Midwest and (2) investigate the impact of G × M factors on the EONR variability under present and future climate scenarios. Results indicate that genetics (27%), management (31%), and environmental conditions (41%) each influence the EONR variability. Within these broad categories, the top three individual factors impacting the EONR were interannual weather variability, crop radiation use efficiency, and the soil inorganic N carryover from the previous year. The G × E × M factors influenced the yield response to N fertilizer in different ways. Soil-related factors (e.g., organic matter and residual nitrate) influenced grain yields at the low N rates, while management factors (e.g., planting date and density) influenced yield at all N rates. Combining increases in plant density and changes in genetics synergistically increased the EONR by 15% from baseline. Future climate scenarios without adaptation decreased the EONR and yield loss, but crop adaptation was buffered against the negative climate change impacts. We concluded that 59% of the annual EONR variability is manageable (due to genetics and management) and that G × M factors could buffer climate change's negative effects on crop production. Present results can inform experimental research on N fertilizer and N rate decisions.