To be useful for silvicultural and forest management practices, the models of Site Index (SI) should be based on accessible predictor variables. In this study, we used spatially explicit data obtained from digital elevation models and climate data to develop SI prediction models with high local precision. Predicting tree growth and yield is a key component to sustainable forest management and depends on accurate measures of site quality. The aim of this study was to develop both empirical models to predict site index (SI) from biophysical variables and a dynamic model of top height growth for plantations of Pinus elliottii Engelm. in Cordoba, Argentina. Site productivity described by SI was related to environmental characteristics, including topographic and climatic variables. Separate models were created from only topographic data and the combination of topographic and climate data. Although SI can be adequately predicted through both types of models, the best results were obtained when combining topographic and climate variables (R2 = 0.83, RMSE% = 7.02%, for the best-fitting model). The key factors affecting site productivity were the landscape position and the mean precipitation of the last 5 years before the reference age, both related to the amount of plant-available water in the soils. Furthermore, the top height growth models developed are fairly accurate, considering the proportion of variance explained (R2 = 98%) and the precision of the estimates (RMSE% < 8%). The models developed here are likely to have considerable application in forestry, since they are based on accessible predictor variables, which make them useful for silvicultural and forest management practices, particularly for non-forest areas and for the young or uneven-aged stands.

中文翻译：

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使用阿根廷中部松树种植园的气候数据和地形图像模拟林地生产力

为了对造林和森林管理实践有用，场地指数 (SI) 模型应基于可访问的预测变量。在这项研究中，我们使用从数字高程模型和气候数据中获得的空间显性数据来开发具有高局部精度的 SI 预测模型。预测树木生长和产量是可持续森林管理的关键组成部分，取决于对场地质量的准确测量。本研究的目的是开发两个经验模型来预测生物物理变量的立地指数 (SI) 和种植园顶高生长的动态模型。在阿根廷科尔多瓦。SI 描述的场地生产力与环境特征有关，包括地形和气候变量。单独的模型仅根据地形数据以及地形和气候数据的组合创建。尽管可以通过两种类型的模型充分预测 SI，但在结合地形和气候变量时获得了最佳结果（对于最佳拟合模型，R2 = 0.83，RMSE% = 7.02%）。影响立地生产力的关键因素是参考年龄前最近5年的景观位置和平均降水量，两者都与土壤中植物可利用水量有关。此外，考虑到解释的方差比例 (R2 = 98%) 和估计的精度 (RMSE% < 8%)，开发的顶部高度增长模型相当准确。这里开发的模型可能在林业中有相当大的应用，因为它们基于可访问的预测变量，