Agricultural intensification and forest cover change in South Asia: A panel econometric and ridge analysis

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Type of the article: Research Article

Abstract
In the context of growing pressure on forest ecosystems arising from agricultural area expansion and intensification, expanding population, and climate variability, this study aims to identify and quantify the impacts of these changes on forest coverage in the South Asian region. Using a balanced panel dataset for 1990–2023, the analysis employs a regularized fixed-effects estimation to identify the key drivers of forest area change and assess variable importance.
The findings show that a 1% increase in agricultural value added is achieved at the cost of a 0.32% decrease in forest area, making it the most significant driver of forest loss. Use of inorganic fertilizer also exerts a strong negative influence, as forest cover is reduced by 0.18% for every additional percentage usage of fertilizer. Irrigation expansion has a similarly adverse effect, contributing to a 0.21% decline per 1% increase in irrigated area. Population density growth intensifies pressure on forests, with each additional 10 persons per km² corresponding to a 0.05% decrease in forest area. However, pasture share exhibits a positive association: a 1-percentage-point increase corresponds to a 0.14% rise in forest area, and cattle density also shows a modest but positive effect. The results indicate the presence of mixed livestock–forest systems and early forest-transition dynamics in some countries.
Overall, the findings demonstrate that the pattern of agricultural practices determines forest trajectories in South Asia, and achieving sustainability will require country-specific strategies that balance productivity growth with integrated land-use planning and long-term conservation goals.

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    • Figure 1. Forest percentage over years in select south Asian countries
    • Figure 2. Accumulated local effects plot
    • Figure 3. Country-specific feature contribution
    • Table 1. Descriptive statistics of variables
    • Table 2. Model selection in panel data analysis
    • Table 3. Variance Inflation Factors (VIF) for FE model predictors
    • Table 4. Ridge regression results for forest area determinants
    • Conceptualization
      Bidyut Kumar Ghosh, Suvayan Neogi
    • Data curation
      Bidyut Kumar Ghosh, Suvayan Neogi
    • Methodology
      Bidyut Kumar Ghosh, Suvayan Neogi
    • Software
      Bidyut Kumar Ghosh, Suvayan Neogi
    • Supervision
      Bidyut Kumar Ghosh
    • Validation
      Bidyut Kumar Ghosh, Suvayan Neogi
    • Writing – original draft
      Bidyut Kumar Ghosh, Suvayan Neogi
    • Writing – review & editing
      Bidyut Kumar Ghosh, Suvayan Neogi
    • Investigation
      Suvayan Neogi
    • Visualization
      Suvayan Neogi