Accurate model of nitrogen accumulation in transplanted rice under different nutrient distribution ratios

The purpose of this study were to explore an accurate model of nitrogen accumulation factoring in temperature and light meteorological conditions from 2018 to 2019, as well as the pattern of nutrient absorption and utilization in transplanted Yongyou No. 15 rice under different conditions of nutrient application, i.e., different ratios of nitrogen, phosphorus and potassium.Using Yongyou No. 15 rice as the experimental material, five groups of samples that received different fertilization treatments and one group of samples that was not treated with fertilizer (N0P0K0) were established under field conditions for two sequential years to study the dynamic changes in rice yield and nitrogen accumulation, as well as the influence of efficiency of fertilizer used under different ratios of nitrogen, phosphorus and potassium. The principal results is that the dynamic change in nitrogen accumulation under optimal fertilization conditions could be expressed by the Gompertz model: y = 178.60 *exp(-exp[2.05–0.0884x]) (R 2 =0.985, x represents the number of days after rice transplanting, and y represents nitrogen accumulation). The OPT demonstrated superiority in nitrogen accumulation in Yongyou No. 15 rice, and the days of gradual growth stage (21.78 d) and rapid growth stage (21.75 d) were all higher than those of other treatments. The Gompertz model is suitable for simulating the dynamics of nitrogen accumulation in rice under different nutrient distribution ratios In addition, the analytical method of the accurate model was used to quantitatively describe the dynamic variation of total nitrogen accumulation (TNA) in Yongyou No. 15 rice and explain the mechanism of optimal treatment (OPT) for high yields. The Gompertz model can simulate all the dynamic characteristics of the variation of nitrogen accumulation in rice plants after transplanting. The parameters i.e., N accumulation amount and the precise time to reach it, determined by the models provide a basis to accurately nitrogen regulation in the period of gradual increase and rapid increase in nutrient absorption, which is especially beneficial for the nutrient management in digital agriculture.