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Decision-makers need readily accessible tools to understand the potential impacts of alternative policies on forest cover and greenhouse gas (GHG) emissions and to develop effective policies to meet national and international targets for biodiversity conservation, sustainable development and climate change mitigation. Land change modelling can support policy decisions by demonstrating potential impacts of policies on future deforestation and GHG emissions. We modelled land change to explore the potential impacts of expert-informed scenarios on deforestation and GHG emissions, specifically CO 2 emissions, in the Ankeniheny-Zahamena Corridor in eastern Madagascar. We considered four scenarios: business as usual; effective conservation of protected areas; investment in infrastructure; and agricultural intensification. Our results highlight that effective forest conservation could deliver substantial emissions reductions, while infrastructure development will likely cause forest loss in new areas. Agricultural intensification could prevent additional forest loss if it reduced the need to clear more land while improving food security. Our study demonstrates how available land change modelling tools and scenario analyses can inform land-use policies, helping countries reconcile economic development with forest conservation and climate change mitigation commitments.

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Summary Soil organic carbon (SOC) is an important carbon pool in terrestrial ecosystems. Prediction of SOC based on soil properties and environmental factors helps to describe the spatial and vertical distribution in SOC; however, the effectiveness and accuracy of various prediction methods, including classical and recently developed model approaches, need to be tested for tropical soil and environments. In this study, random forest (RF) and linear mixed effects model (LMM) approaches were tested to predict the spatial and vertical variation of SOC stocks in Eastern Madagascar. Topography, climate, soil types and vegetation‐based variables were used as predictor variables for modelling SOC stocks at different soil depths to 1 m. The LMM was the most accurate method for predicting SOC stocks for different depth ranges; altitude, soil clay content, land use and precipitation were identified as the most relevant factors for prediction. The accuracy of prediction in SOC modelling decreased with increasing soil depth, resulting in a root mean square prediction error (RMSE) that ranged from 1.98 Mg ha−1 (90–100‐cm depth) to 5.54 Mg ha−1 (10–20‐cm depth) for LMM, which resolved 43–68% of the variation in SOC stocks. Explanatory variables, which contributed to the fixed effect of the model, explained from 2.6 to 28.2% of the total variance, whereas the random effect contributed from 21.7 to 35.0%. This study emphasizes the strength of LMM for predicting SOC stocks in tropical soil taking into account the random effect related to sampling. These results could be used to improve SOC mapping in Madagascar. Highlights Prediction accuracy of vertical variation in SOC stocks was tested with RF and LMM approaches. Linear mixed effects model provides the most accurate predictions of SOC stocks. Altitude, clay content, climate and land use were identified as relevant predictor variables of SOC. The LMM can be used to improve SOC mapping of tropical soil.