doi:10.3390/agriculture15232440engJarne, AdriánUsón, AsunciónReiné, RamónData-Driven Management of Mountain Meadows in Central Spanish Pyrenees: Enhancing Productivity and Quality via Random Forests ModelsART-2025-147203Mountain meadows are key components of extensive livestock systems, yet their response to management practices remains poorly quantified. This study assessed the effects of cutting date, fertilization, and stocking rate on forage yield, quality (RFV), and protein yield across three meadow types (extensive, semi-extensive, and intensive) in the Central Spanish Pyrenees. Using Random Forest modeling and simulated scenarios, we evaluated how each factor influenced productivity and nutritive value. Cutting date was the most influential variable. Advancing the harvest improved forage quality (RFV) but reduced yield. Conversely, delaying the harvest increased biomass at the expense of RFV. Protein yield provided a more balanced metric: it remained stable or increased in intensive and extensive meadows but declined sharply in semi-extensive systems when cutting was delayed. Fertilization had a moderate effect, with semi-extensive meadows showing significant yield reductions when fertilizer input was halved, while other systems remained largely unaffected. Stocking rate had the least impact overall, although reduced grazing led to declines in protein yield in semi-extensive and extensive meadows. These findings suggest that cutting date should be prioritized in management decisions, while fertilization and grazing intensity require context-specific adjustments. Random Forest modeling effectively identified trade-offs and guided evidence-based strategies for sustainable mountain meadow management.2025http://zaguan.unizar.es/record/16536010.3390/agriculture15232440http://zaguan.unizar.es/record/165360oai:zaguan.unizar.es:165360Agriculture (Basel) 15, 23 (2025), 2440byhttps://creativecommons.org/licenses/by/4.0/deed.esinfo:eu-repo/semantics/openAccess