基于植株碳流的水稻籽粒淀粉积累模拟模型

通过解析水稻(Oryza sativa)植株碳素积累和转运的动态规律及其与环境因子和基因型之间的定量关系, 构建基于植株碳流动态的水稻籽粒淀粉积累模拟模型。水稻籽粒中的淀粉积累速率取决于库限制下的淀粉积累速率和源限制下的可获取碳源。库限制下的淀粉积累速率是潜在淀粉积累速率及温度、水分、氮素、淀粉合成能力等因子综合影响的结果; 源限制下的可获取碳源取决于花后光合器官生产的即时光合产物和营养器官向籽粒转运的储存光合产物。花后植株即时光合产物随花后生长度日呈对数递减。花后营养器官向籽粒转运的储存光合产物又分为叶片和茎中积累碳素的转运。利用不同栽培条件下的独立田间试验资料对籽粒淀粉积累的动态模型进行了检验, 结果显示籽粒淀粉积累量和含量的模拟值和观测值之间的根均方差均值分别为3.61%和4.51%, 决定系数分别为0.994和0.959, 表明该模型对不同栽培条件下的水稻单籽粒淀粉积累量和含量具有较好的预测性, 为水稻生产中籽粒淀粉指标的动态预测和管理调控提供了量化工具。

Modeling rice grain starch accumulation based on plant carbon flow

Aims Our objective was to develop a simulation model of grain starch formation in rice plants by analyzing the dynamic patterns of carbon assimilation and translocation under varied environmental factors and genetic types. Methods We used field experiments involving different eco-sites, growing seasons, cultivar types and nitrogen rates in developing a model of grain starch accumulation. Important findings The model proposed that the rate of grain starch accumulation was determined by (a) carbon availability restricted by source and (b) carbon accumulation rate restricted by sink. Carbon accumulation rate restricted by sink was dependent on the potential starch accumulation rate and the interaction of influencing factors: temperature, water, nitrogen conditions within plants and the ability of carbon translation into starch. Carbon availability in grains restricted by source was the sum of carbon assimilation from the photosynthetic organs and remobilization from the vegetative organs after anthesis. Photosynthetic product transported to grain directly after anthesis exhibited a logarithmic relationship to post-anthesis growing degree days. Post-anthesis carbon remobilization from the vegetative organs included remobilization from leaves and stems. Testing of the model with independent datasets involving different years, eco-sites, varieties and nitrogen rates indicated values of RMSE of 3.61% and 4.51% and R² of 0.994 and 0.959 for starch accumulation and content, respectively. Results showed that the model could predict accumulation and content of grain starch in rice under different cultivated conditions, which provides a quantitative tool for quality prediction and regulation.