A summer maize dressing decision-making model based on effective accumulated temperature

Sound judgments on crop dressing time are important technical measures?to improve the utilization efficiency of fertilizers and increase crop yield. Traditional methods of predicting crop dressing time take continuous stock of crop growth processes. These methods are, however, inaccurate and depend largely on observer experiences. Growth and nutrient accumulation processes of summer maize are closely related with accumulated temperature, light intensity, precipitation and other meteorological factors. It is therefore an effective method to build a meteorological model that precisely defines the dressing times of different crops. Field trials were conducted to study the response of crop growth and development processes to temperature, light and other meteorological factors. In this paper, effective accumulated temperature (a critical factor of crop growth and development processes) replaced growing day and was used as the step unit of Logistics model that described the levels of nutrients accumulation in summer maize. Based on the characteristics of Logistics model, three key times of summer maize nutrients accumulation were defined, which were beginning full period, full period and end full period. The decision point of dressing was then determined. The field experiments and analyses on nutrients showed that nutrient accumulation by summer maize was efficiently simulated with the established Logistics model. Comparisons suggested that the key times of beginning full period, full period and end full period of maize nutrients accumulation predicted by the accumulated temperature model was more accurate than those by the growth-day model. Validation analysis further suggested that the accumulated temperature model was innovative and practical. The growth-day model failed to sufficiently capture the full range of variability in dressing time due to regional and varietal differences. Due to difference in areas, crop variety, the established model according to a certain area was not universal. To meet this problem, the model was adjusted according to the characters, meaning of model parameters and farming practices. The modified accumulated temperature model with 11.9% n-RMSE was adaptable to any region of the world and any variety of crop to efficiently predict the optimal dressing time of farm crops. The results provided the scientific basis for rational fertilizer application with significant improvements in fertilizer utilization rates.