Quantitative design of yield components to simulate yield formation for maize in China

Maize(Zea mays L.) stands prominently as one of the major cereal crops in China as well as in the rest of the world. Therefore, predicting the growth and yield of maize for large areas through yield components under high-yielding environments will help in understanding the process of yield formation and yield potential under different environmental conditions. This accurate early assessment of yield requires accuracy in the formation process of yield components as well. In order to formulate the quantitative design for high yields of maize in China, yield performance parameters of quantitative design for high grain yields were evaluated in this study, by utilizing the yield performance equation with normalization of planting density. Planting density was evaluated by parameters including the maximum leaf area index and the maximum leaf area per plant. Results showed that the variation of the maximum leaf area per plant with varying plant density conformed to the Reciprocal Model, which proved to have excellent prediction with root mean square error(RMSE) value of 5.95%. Yield model estimation depicted that the best optimal maximum leaf area per plant was 0.63 times the potential maximum leaf area per plant of hybrids. Yield performance parameters for different yield levels were quantitatively designed based on the yield performance equation. Through validation of the yield performance model by simulating high yields of spring maize in the Inner Mongolia Autonomous Region and Jilin Province, China, and summer maize in Shandong Province, the yield performance equation showed excellent prediction with the satisfactory mean RMSE value(7.72%) of all the parameters. The present study provides theoretical support for the formulation of quantitative design for sustainable high yield of maize in China, through consideration of planting density normalization in the yield prediction process, providing there is no water and nutrient limitation.