Modeling forest site productivity using climate data and topographic imagery in Pinus elliottii plantations of central Argentina

Abstract

Key message: 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. Context: Predicting tree growth and yield is a key component to sustainable forest management and depends on accurate measures of site quality. Aims: 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 Córdoba, Argentina. Methods: 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. Results: 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%). Conclusion: 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.