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Abstract
Pinus arizonica is a widely distributed tree species growing in temperate forests of Northwest Mexico where it is utilized through different regeneration harvest methods. Yet, management models based on estimations of its productive potential are sorely lacking. In this study, a procedure to create a productive map using site index (SI) equations and Geographic Information Systems (GIS) was developed. A SI model for P. arizonica was created for the study area and used to classify a group of randomly sampled plots on three productivity categories (High, Medium, and Low) for management purposes. Climatic, topographic and edaphic variables were determined on the sampled plots. Then, a statistically-based analysis was performed to identify the climatic, topographic and edaphic variables significantly influencing the productivity levels. Based on the values of these significant variables, a map of productive potential was elaborated for the whole study area. Sites with the highest productivity were those with slopes ≤12°, soil depths ≥0.46 m, minimum and maximum mean annual temperatures of 5 °C and 18 °C respectively, and precipitation ≥900 mm. This methodology could be considered for similar species/conditions where productivity models do not exist or to update old models rendered obsolete by climate change.
Highlights
Forest ecosystems play an important role in biodiversity conservation and the provision of ecosystem services, such as timber production and carbon sequestration [1]
The maximum height registered from the trees was 25.6 m at an age of 90 years, with diameters of 33.1 ± 6.1 cm, which may indicate the existence of high variability in the productivity of the study area
Our results showed an adequate growth prediction for all tree ages, which ratifies the effectiveness of this procedure in our study area
Summary
Forest ecosystems play an important role in biodiversity conservation and the provision of ecosystem services, such as timber production and carbon sequestration [1]. Forest productivity estimations are necessary for both effective forest management decisions and the assessment of site conditions for ecological studies [10,11]. This knowledge greatly improves the ability to manage forest resources at a specific site, which in turn will help in making cost-effective decisions on the use of silvicultural soil [12]. Assessing forest productivity is essential to understand sustainability in forest management because timber extraction should be based on the capacity for recovery of a forest area [15]
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