针对目前已有群体叶面积指数（LAI）模拟模型形式多样、参数较多以及应用性不强等问题，对春玉米、水稻和冬小麦的LAI及出苗至成熟天数进行归一化处理，分别将最大LAI和出苗至成熟天数定为1，以相对LAI (0~1)和相对时间(0~1)为参数进行LAI动态模拟，筛选、建立了一个适用于这3种作物的相对化LAI动态模拟模型y= (a+bx) / (1+cx+dx2)。其中，春玉米y= (0.0134+0.3234x) / (1－2.774x+2.4178x2)，r=0.9859**；水稻y= (0.0777+0.0205x) / (1－2.73744x+2.0484x2)，r=0.9865**；冬小麦y= (0.0131+0.0035x) / (1－2.4515x+1.5273x2)，r=0.9719**。利用该模型，自拔节期起就能够较准确地进行LAI的动态预测，其在春玉米、水稻和冬小麦上的准确度（以k表示）分别为1.050、1.0357和1.1168，精确度（以R2表示）分别为0.8728、0.9270和0.9254。3种作物整个生育期内模型的模拟值与测量值的精确度均在0.98以上，准确度达0.86以上，表明相对化LAI动态模型能够准确地反映作物群体动态变化。不仅可以计算出作物生育期间的平均LAI、总光合势，还能计算任一时刻的LAI以及任一时段的光合势。结合田间调查还可得到作物生长期间的平均净同化率和平均作物生长率等产量相关的重要生理参数。根据作物群体中各光合生理参数与产量的关系，提出了3种作物进一步增产的可能途径。
Leaf Area Index (LAI) simulation models is an indispensable tool for supporting crop scientific research, population and community growth analysis, and crop management. However, LAI is very difficult to be quantified properly, owing to ecological and varietal factors and temporal variability. Many methods have been developed to quantify LAI and yielded good results, but most of them have different formats with many parameters, which hamper their application. In this paper, an extensively suitable LAI simulation model, y= (a+bx) / (1+cx+dx2), was developed with normalized LAI and crop growth duration data. And by means of which, the specific normalized models y= (0.0134+0.3234x) / (1－2.774x+2.4178x2) for spring maize (r=0.9858**), y= (0.0777+0.0205x) / (1－2.7374x+2.0484x2) for rice (r=0.9865**), and y= (0.0131+0.0035x) / (1－2.4515x+1.5273x2) for winter wheat (r=0.9719**) were established. Compared with other LAI simulation models, normalized LAI dynamic model can not only generate reliable prediction for the evolution of LAI, but also estimate leaf area duration (LAD) and LAI in any period. Once the crop life span and a total leaf area of a crop population in any moment are acquired, the crop LAI and LAD can be estimated easily. From the time of jointing stage, the model could make a good pre-estimation of LAI dynamics with the accuracy of 1.050, 1.0357, and 1.1168 and the precision (R2) were 0.8728, 0.9270, and 0.9254 for spring maize, rice and winter wheat, respectively. Compared with the measured values, the simulated LAI had a linear coefficient (k) over 0.98, with R2 above 0.86. The results showed that normalized LAI dynamic model provided a better description for crop population dynamics. Combined with dry matter accumulation, the average LAI, NAR (net assimilation rate), and CGR (crop growth rate) can be calculated. By means of which, the relationships between the physiological parameters and crop yield were analyzed, and the possible ways for higher crop yield were put forward accordingly.
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