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Spatial Patterns of Productivity and Human Development Potentials for Pinus pinea L.
Pinus pinea (stone pine), a Mediterranean species, is valued for its highly nutritious pine nuts and its ability to adapt to different environmental conditions. The species has been increasingly planted in Chile, where its main ecological requirements are met across a vast area. However, new plantations are established without considering social dimensions. Policymakers can regulate private decisions on tree planting through the appropriate design of economic incentives to foster social well-being. The objective of this work was to describe spatial patterns of potential areas for the cultivation of the exotic nut-bearing conifer P. pinea in central Chile and the possible correlation of those patterns with human development indices. Spatial data layers of the municipality development index (MDI), elevation, edaphoclimatic variables, and stone pine nut’s productive potential were overlapped at the municipality scale along 1225 km in central Chile. A spatial principal component analysis (sPCA) was used to integrate multiple dimensions, summarizing covariation structures, and identifying spatial patterns in the study area. Key results showed that spatial patterns of the potential productive index (PPI) were strongly regulated by the spatial pattern of climate and soil variables, whereas the spatial pattern of MDI showed a cryptic pattern and that the three dimensions of MDI—welfare, economy, and education—showed a different spatial movement, especially education and welfare. The results allow us to recommend that public policies boost municipality development through the promotion of P. pinea plantations and should target areas with a high productive potential and low MDI to generate socio-economic improvements. These findings are useful for the strategic spatial planning of the species cropping in Chile.
Spatial Patterns of Productivity and Human Development Potentials for Pinus pinea L.
Pinus pinea (stone pine), a Mediterranean species, is valued for its highly nutritious pine nuts and its ability to adapt to different environmental conditions. The species has been increasingly planted in Chile, where its main ecological requirements are met across a vast area. However, new plantations are established without considering social dimensions. Policymakers can regulate private decisions on tree planting through the appropriate design of economic incentives to foster social well-being. The objective of this work was to describe spatial patterns of potential areas for the cultivation of the exotic nut-bearing conifer P. pinea in central Chile and the possible correlation of those patterns with human development indices. Spatial data layers of the municipality development index (MDI), elevation, edaphoclimatic variables, and stone pine nut’s productive potential were overlapped at the municipality scale along 1225 km in central Chile. A spatial principal component analysis (sPCA) was used to integrate multiple dimensions, summarizing covariation structures, and identifying spatial patterns in the study area. Key results showed that spatial patterns of the potential productive index (PPI) were strongly regulated by the spatial pattern of climate and soil variables, whereas the spatial pattern of MDI showed a cryptic pattern and that the three dimensions of MDI—welfare, economy, and education—showed a different spatial movement, especially education and welfare. The results allow us to recommend that public policies boost municipality development through the promotion of P. pinea plantations and should target areas with a high productive potential and low MDI to generate socio-economic improvements. These findings are useful for the strategic spatial planning of the species cropping in Chile.
Spatial Patterns of Productivity and Human Development Potentials for Pinus pinea L.
Verónica Loewe-Muñoz (author) / Rodrigo Del Río (author) / Claudia Delard (author) / Ricardo González (author) / Mónica Balzarini (author)
2024
Article (Journal)
Electronic Resource
Unknown
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