中文摘要：

依据温室黄瓜（Cucumis sativus）叶片生长与温度和辐射的关系，构建了适合我国种植技术的黄瓜叶面积模拟模型，并利用不同品种、播期的试验资料对模型进行了检验。结果表明，该模型比传统的积温法和比叶面积法更准确地模拟温室水果黄瓜的叶面积。该模型对黄瓜叶面积指数的模拟结果与1：1直线之间的决定系数R^2和回归估计标准误差RMSE分别为0．8792和0．3980，比用积温法和比叶面积法模拟叶面积指数的精度分别提高了37％和74％。

英文摘要：

Background and Aims Leaf area index （LAI） is one of the most important crop parameters in photosynthesis driven crop growth simulation models. Temperature and radiation are important climate factors affecting crop leaf growth. The aim of this study is to quantitatively investigate the effects of both temperature and photosynthetically active radiation （PAR） on the leaf growth of greenhouse cucumber （ Cucumis sativus ）. Methods Experiments with different cultivars and sowing dates were conducted in greenhouses of Shanghai Academy of Agricultural Sciences during August, 2003 and July, 2004. We used the following quantitative relationships based on experimental data to model the relationship between the product of thermal effectiveness and PAR （TEP） ： accumulated TEP to the number of unfolding leaves per plant and the number of old leaves removed per plant, leaf position relative to the rate of increase in leaf length and the maximum leaf length; and the ratio of leaf area to leaf length. Based on these quantitative relationships, a leaf area simulation model for greenhouse cucumber was developed. Independent experimental data were used to validate the model. The simulated results of the model developed in this study were compared with those of the traditional GDD based model （which predict leaf area based on growing degree days） and SLA based model （which predict leaf area based on the specific leaf area and leaf dry weight）. Key Results The coefficient of determination （ R^2） and the root mean squared error （RMSE） between the simulated and the measured leaf area index （LAI） based on the 1 ： 1 line are 0. 879 2 and 0. 398 0, respectively. The prediction accuracy of this model is 37% and 74% higher, respectively, than that of the traditional GDD and SLA based models. Conclusions The model developed in this study can predict LAI satisfactorily using air temperature, radiation, date of the first leaf unfolding and planting density. The simple model input makes the model use