Liu Tie-Mei, Wang Yan, Zou Wei, Sun Dong-Fa, Tang Liang, Cao Wei-Xing
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
Ying Yong Sheng Tai Xue Bao. 2010 Jan;21(1):121-8.
To simulate leaf area index (LAI) accurately is the key for the prediction of crop growth and yield in a crop growth model. Based on the analysis of the dynamic changes in the LAI of high-yielding barley cultivars in Wuhan and Yangzhou, a simulation model of barley LAI was established, in which, the LAI was the function of expansion coefficient of LAI for cultivar genetic property, climatic factors such as daily air temperature difference, sunshine hours, and accumulation of photosynthetic available radiation after sowing (sigma PAR), and limitation indices of water and nutrients. It was indicated that the maximum LAI and optimal LAI at the stages of booting and heading were not the same conception, but differed significantly. The model was tested by the field experiments with different barley cultivars under different sowing dates and nitrogen application rates in Yangzhou, Nanjing, and Kunming. The results showed that this model gave the good predictions of LAI at different development stages, with the RMSE values ranged in 0.742 and 2.865, and averaged 1.348. The simulated and observed LAI values were significantly positively correlated, and the correlation coefficient from y = x regression analysis was between 0.511 and 0.954.
准确模拟叶面积指数(LAI)是作物生长模型预测作物生长和产量的关键。通过对武汉和扬州高产大麦品种LAI动态变化的分析,建立了大麦LAI模拟模型,其中LAI是品种遗传特性的LAI扩展系数、日气温差、日照时数和播种后光合有效辐射积累量(σPAR)等气候因子以及水分和养分限制指数的函数。结果表明,孕穗期和抽穗期的最大LAI和最佳LAI不是同一概念,且差异显著。该模型在扬州、南京和昆明不同播种期和施氮量条件下,对不同大麦品种进行了田间试验验证。结果表明,该模型对不同发育阶段的LAI预测效果良好,RMSE值在0.742至2.865之间,平均为1.348。模拟和观测的LAI值显著正相关,y = x回归分析的相关系数在0.511至0.954之间。