Tuesday, November 14, 2006 - 2:30 PM

Modelling Grain Protein Formation in Relation to Nitrogen Uptake and Remobilization in Rice Plant.

Weixing Cao, Yan Zhu, Weiguo Li, and Hongbao Ye. Nanjing Agricultural Univ, 1 Weigang Road, Nanjing, 210095, China

Protein concentration of grain is an important quality index of rice product, and formation of grain protein largely depends on pre-anthesis nitrogen assimilation and post-anthesis nitrogen remobilization in rice plant.  The primary objective of this study was to develop a simplified process model for simulating nitrogen accumulation and remobilization in plant and protein formation in grain of rice on the basis of an established rice growth model.  Six field experiments involving different years, eco-sites, varieties, nitrogen rates and irrigation regimes were conducted to obtain the necessary data for model building, genotypic parameter determination and model validation.  Using physiological development time (PDT) as general time scale of development progress and cultivar-specific grain protein concentration as genotypic parameter, the dynamic relationships of plant nitrogen accumulation and translocation to environmental and genetic factors were quantified and synthesized in the present model.  The pre-anthesis nitrogen uptake rate by plant changed with the PDT in a negative exponential pattern, and post-anthesis nitrogen uptake rate changed with LAI in an exponential equation.  Post-anthesis nitrogen translocation rate depended on the plant nitrogen concentration and dry weight at anthesis as well as residue nitrogen concentration of plant at maturity.  The nitrogen for protein synthesis in grain came from two sources: the nitrogen pre-stored in leaves, stem and sheath before anthesis and then remobilized after anthesis, and the nitrogen absorbed directly by plant after anthesis.  The implemented model was tested using the data sets of different years, eco-sites, varieties, N fertilization and irrigation conditions with the RMSEs of 0.22%~0.26% for grain protein concentrations, indicating the general and reliable features of the model.  It is hoped that by properly integrating with the existing rice growth models, the present model can be used for predicting grain protein concentration and grain protein yield of rice under various environments and genotypes.