The Population Optimal Design And Nutrient Evaluation Method Of Winter Wheat In North China Plain

To build an optimum population growth process and quantify the nutrient effect on dry matter production at different growth stage in winter wheat, field experiments were designed to obtain different growth and nutrient uptake populations in winter wheat growing seasons from2009to2011,2012to2013varied by sowing date, basic seedlings, irrigation and fertilization in shimai15cultivar. After measuring the related meteorological data and other population index, the dry matter accumulation, leaf area index dynamics and nutrient absorption characteristic parameters were studied, and the yield formation model was established based on the population growth character parameters. Then a method of winter wheat optimum dynamic process was researched using yield as objective function. Besides, under the difference of nutrient uptake and dry matter production, a model of evaluating the nutrient effect on dry matter production based on BLUP method was established. The main research results were as follows:Logistic model can well describe the process of dry matter and nutrient dynamic change according to relative accumulated temperature. The parameters represented the different growth processes. The dry matter maximum increased rate appeared during booting to anthesis, and the rapid accumulation period was from jointing to20days after anthesis. The maximum uptake rate of nitrogen, phosphorus, potassium appeared during jointing to booting, jointing to anthesis, and jointing to booting stage, respectively. The rapid nutrient accumulation period were regreening to anthesid, setting to middle of grain filling stage, setting to heading stage, respectively. The Rational model can well describe the leaf area index dynamic process and deformation of the model can reflect the law of new and old leaves growth.The grain yield of winter wheat was closely related to characteristic parameters such as dry matter maximum accumulation rate, inflection points in the curve for dry matter accumulation rate in grain filling stage and leaf area index character parameters. The determimation coefficient was inproved to0.9. The relationship between spike number, grain number per spike and1000grain weight were influence gradually with growth. The spike number was the square of total stem and tiller numbers before wintering and the same relationship between spike number and grain numbers per spike.1000grain weight was determined by the parameters reflecting grain filling rate and duration. The yield components were all related to the dry matter accumulation parameters.Based on the yield formation model, a method of population optimal design was established. In the process of parameter design, the constraint conditions were constituted by the reasonable scope of dry matter accumulation and LAI in different growing periods, parameters range and yield formation model. According to the nonlinear optimization theory, the population growth was designed on the optimum yield target9826kg ha-1. The results were with the spike number543×104ha-1, the grain number per spike39, and1000grain weight46.5gram, basic seedlings450×104ha-1, total stem and tillers before wintering1010×104ha-1. The dynamic process of dry matter and leaf area was also described. Besides, under the climate condition of2010-2011and2012-2013, the dynamic processes were designed with the optimum yield target10087kg ha-1and8066kg ha-1respectively. The method can provide theoretical guidance for management decision.Besides, a model of evaluating the nutrient effect on dry matter production was established. In the model, four impacts at a specific stage (Pi) were concerned:leaf nutrient impact at Pi-1, non-leaf nutrient impact at Pi-1, leaf nutrient impact at Pi, and non-leaf nutrient impact at Pi. The determination coefficient were all more than0.8. The effect parameters such as the growth stage effect, character effect and environment effect parameters reflected the function of plant organs and the mechanism of growth. The nutrient effect can be quantified and the impact mechanism can be analyzed. The model can provide theoretical guidance for nutrient management.