ABSTRACT Several models have been proposed to simulate the leaf area index (LAI) in soybean (Glycine max L.); however, these models do not directly account for the effect of branch growth. Because the increases in branches and branch node vary with plant density, the evaluation of branch growth is necessary for the application of the LAI model at various plant densities. In this study, we developed an LAI model for soybean, considering the branch growth and leaf elongation at each node. To simplify this model, we estimated the rate of branch and branch node increase based on the rate of main stem node increase. Branch growth was assumed to be restricted when the fraction of canopy radiation interception was increased. Moreover, we calculated the leaf area growth at each node based on leaf elongation at each leaflet. This LAI model was validated using the data of different years and plant densities for model calibration, and it estimated the LAI with a root mean square error of 0.76, which accounted for 92% of the variation in the data. Although further evaluation is needed, the LAI model proposed in this study reveals a high potential for accurate estimation of LAI at various plant densities. Graphical abstract

中文翻译：

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基于分枝生长和叶片伸长的大豆（Glycine max L.）叶面积发育建模

摘要 已经提出了几种模型来模拟大豆 (Glycine max L.) 的叶面积指数 (LAI)；然而，这些模型并没有直接考虑分支机构增长的影响。由于分枝和分枝节点的增加量随植株密度的变化而变化，因此对于LAI模型在不同植株密度下的应用，需要对分枝生长情况进行评估。在这项研究中，我们开发了大豆的 LAI 模型，考虑到每个节点的枝条生长和叶片伸长。为了简化这个模型，我们根据主干节点的增加率来估计分支和分支节点的增加率。当冠层辐射拦截的分数增加时，假定分支生长受到限制。此外，我们根据每个小叶的叶伸长率计算了每个节点的叶面积增长。该LAI模型使用不同年份和植物密度的数据进行模型校准，估计LAI的均方根误差为0.76，占数据变异的92%。尽管需要进一步评估，但本研究中提出的 LAI 模型显示了在各种植物密度下准确估计 LAI 的巨大潜力。图形概要