PREDICTION OF DRY MATTER AND YIELD OF SPRING MAIZE (ZEA MAYS L.) IN NORTHWEST CHINA BASED ON LOGISTIC MODEL

Abstract

Water and nitrogen are vital factors limiting dry matter accumulation (DMA) and yield formation of spring maize. Simulation of DMA in spring maize under different irrigation and nitrogen application levels is conducive to better field management. Logistic model is a simple and effective measure to simulate the DMA. However, the coupling effect of water amount and nitrogen rate on logistic model parameters for spring maize in Northwest China has received little attention thus far. In this study, regression equations between logistic model parameters, water amount (i.e. irrigation plus rainfall), and nitrogen application rate were established and validated using independent data sets from three experiment sites (Wuwei in Gansu Province, Guyuan in Ningxia Province and Bayannur in Inner Mongolia Autonomous Region). Similarly, the empirical model of harvest index was established and validated. The results showed that these regression equations can accurately predict the dry matter accumulation and yield of spring maize. Therefore, this study can provide an alternative simple method to predict DMA and yield of spring maize under various water and nitrogen treatment in this area.