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Predicting Wheat Grain Yield and Quality Based on Population Indexes and Nitrogen Nutrient Status

Author: XieZuo
Tutor: DaiTingBo
School: Nanjing Agricultural College
Course: Crop Cultivation and Farming System
Keywords: wheat population index nitrogen nutrient status grain yield protein content wet-gluten content prediction model
CLC: S512.1
Type: Master's thesis
Year: 2011
Downloads: 5
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Abstract


Accurately predicting grain yield and quality before harvest is important for wheat production management and utilization. This research based on field experiments under different cultivate conditions, studied the relationships between indexes of population, nitrogen nutrient status and wheat grain yield, protein and wet-gluten content to make clear the best indexes for predicting grain yield, protein and wet-gluten content. Build prediction models of different growth stages. So that predict wheat grain yield and quality rapidly and accurately will be realized. The main results are as follows:1. Made clear the predicting stage and best indexes of grain yield, protein and wet-gluten content. Comprehensively analyzed the relationships between the indexes, included dry matter accumulation, leaf area index, nitrogen content and accumulation, leaf SPAD value. LAI×LNC, LAI×LNA, LAI×TNA. LAI×SPAD and wheat grain yield, protein and wet-gluten content. showed that predict effect of single and complex indexes were not so good at jointing, but performed well from booting. And prediction accuracy of complex indexes were higher than single indexes. Best indexes for predicting grain yield were LAI×SPAD at booting, LAIxTNA at anthesis, DMA at filling. Best indexes for predicting grain protein content were LAI x LNC at booting, LAI x LNA at anthesis and filling. Best indexes for predicting wet-gluten content were LAI x LNC at booting, anthesis and filling.2. Wheat grain yield prediction model was build. LAIxSPAD at booting. LAI×TNA at anthesis. DMA at filling were used to build the prediction models. Tested the models with independent experiments data of different variety, places and years. RRMSE of models were 14.55%,14.90% and 13.20%, showed that models prediction accuracy were high.3. Wheat grain protein and wet-gluten content prediction model was build. LAI x LNC at booting. LAI x LNA at anthesis and filling were used to build the grain protein content prediction models. LAI x LNC at booting, anthesis and filling were used to build the wet-gluten content prediction models. Tested the models with independent experiment data of different variety in different places and years. RRMSE of grain protein content prediction models were 14.55%,14.90% and 13.20%, and RRMSE of wet-gluten content prediction models were 7.72%,5.83% and 6.04%, showed that models prediction accuracy were high.Results of this research provided dependable basis for wheat grain yield, protein and wet-gluten content prediction. And it had important directive significance for making wheat production management, grain yield and quality prediction, and utilization after harvest.

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