氮素对温室黄瓜开花后干物质分配和产量影响的模拟研究

了解氮素对干物质分配和产量的定量影响是实现温室黄瓜氮肥优化管理的前提.该研究通过黄瓜雌性无限生长型品种"戴多星"(Cucumis sativas.'Deltastar')不同定植期和开花后不同氮素处理试验,定量分析了氮素施用水平对该类型黄瓜开花后干物质分配和产量的影响,并建立了开花后分配指数和采收指数与盛果期叶片氮浓度的关系方程.在此基础上,建立了氮素对黄瓜开花后干物质分配和产量影响的预测模型,并用独立的试验数据对模型进行了检验.结果表明,模型对茎干物质量、叶干物质量和果实干物质量及黄瓜产量(鲜质量)预测值与实测值之间基于1:1直线之间的决定系数R2分别为0.945、0.943、0.990、0.955;相对预测误差RE分别为13.0%、12.3%、9.2%、16.8%.本模型可预测不同氮素水平下温室黄瓜地上部各器官干物质量和产量,可以为中国温室黄瓜生产的氮肥优化管理决策提供参考.

Simulation of the effects of nitrogen on after anthesis dry matter partitioning and yield of greenhouse cucumber

Quantifying the effects of nitrogen (N) on dry matter partitioning and yield of greenhouse cucumber is essential for the optimization of nitrogen management in greenhouse cucumber production. For this purpose, experiments with different planting dates and levels of nitrogen application rate after anthesis for greenhouse cucumber (Cucumis sativus) were conducted in a greenhouse in Shanghai during 2005. First, the seasonal time course of the partitioning indices of shoot dry matter to stem, leaf and fruit were derived using the experimental data. The relative decreasing rate of leaf partitioning index and the relative increasing rate of fruit partitioning index after anthesis were found, respectively, to be an exponential function of leaf nitrogen concentration at middle fruit harvest stage (NL). The seasonal time course of the harvest index, defined as the ratio of the accumulated dry weight of the harvested fruit to the accumulated total fruit dry weight, was also derived from the experimental data. The relative increasing rate of harvest index was also found to be an exponential function of NL. Based on these quantitative relationships, a model was developed to predict the effects of nitrogen on after anthesis dry matter partitioning and yield of greenhouse cucumber. Independent experimental data were used to validate the model. The coefficient of determination (R2) between the predicted and the measured dry weight of stem, leaf, fruit and the yield (fresh weight) based on the 1:1 line were 0.945, 0.943, 0.990, 0.955, respectively, and the relative prediction error (RE) were 13.0%, 12.3%, 9.2%, 16.8%, respectively. The results showed that the model developed in this study can give satisfactory prediction of dry matter partitioning and yield of greenhouse cucumber under different nitrogen application level, hence, can be used to support decision-making for nitrogen management in greenhouse cucumber production.