ArticleFrontiers in plant science2025
Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
- Projected spatiotemporal shifts in forest community suitability in the northeastern Qinghai-Tibet Plateau under climate change.Frontiers in plant science · 2026Article
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6 authors.
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Abstract
Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. A total of 33 predictors related to climate, topography, and soil were analyzed, and model hyperparameters were optimized through grid search combined with blocked cross-validation to mitigate spatial autocorrelation. The RF models exhibited strong predictive performance, with the BP model achieving the highest
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