Soil-based assessment of site productivity for southern pine plantations in the coastal plain of the southeastern US: (II) slash pine

Non-industrial private forest landowners and foresters often rely on outdated site index models for forest productivity predictions, which hinders accurate assessment. To address this issue, 157 slash pine plots were established in the coastal plain of Georgia, USA, to collect tree and soil data. Using regression tree and linear regression models, we identified modified Cooperative Research in Forest Fertilization soil groups and surface soil extractable phosphorus as significant predictors of site index. The optimal regression tree model (R2 = 0.490, RMSE = 1.72 m) outperformed the linear regression model. Updated site index and simulated mean annual increments for slash pine were compared with long-term experimental study data, as well as with previous findings for loblolly pine in the same region (Zhao et al., 2024a). The new predictive models will help nonindustrial private forest landowners evaluate slash pine productivity in the Coastal Plain region, applicable across diverse sites.