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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。
The simulation model of starch accumulation in rice grain based on plant carbon flow dynamics was constructed by analyzing the dynamics of carbon accumulation and transport in Oryza sativa plants and its quantitative relationship with environmental factors and genotypes. The rate of starch accumulation in rice grains is dependent on the rate of starch accumulation at the library limit and the available carbon source at the source limit. The accumulation rate of starch under the restriction of the library is the result of the comprehensive effect of potential accumulation rate of starch and factors such as temperature, moisture, nitrogen, and starch synthesis ability. The available carbon source under the source restriction depends on the instant photosynthetic products and Storage of photosynthetic products by vegetative organs for transport to grain. The photosynthetic products of anthesis plants showed a logarithm of logarithm with anthesis after anthesis. Post-anthesis vegetative organs are transported to the grain storage photosynthate is divided into accumulation of carbon transport in leaves and stems. The dynamic model of grain starch accumulation was tested with independent field experiment data under different cultivation conditions. The average root mean square error between simulated and observed values of grain starch accumulation and content were 3.61% and 4.51%, respectively. The coefficients of determination were 0.994 and 0.959, respectively, indicating that this model has good predictability for starch accumulation and content of single grain under different cultivation conditions, which provides a quantitative tool for dynamic prediction and management regulation of grain starch in rice production.