Cay, TayfunTokat, SezaiSatilmis, Ramazan Yoldas2026-04-102026-04-1020260039-62651752-2706https://hdl.handle.net/20.500.13091/13241https://doi.org/10.1080/00396265.2025.2607074This study evaluates the economic impact of land consolidation by predicting profitability changes using machine learning techniques. Research was conducted in the Kızılcabölük neighborhood of Denizli, Turkey, using field-based data on parcel structure and farm inputs. Several algorithms - artificial neural networks, decision trees, and linear regression - were tested. Linear regression achieved the best performance (RMSE: 0.0043 validation, 0.0031 testing). Sensitivity analysis showed parcel area, parcel number, and labour as the most influential variables. The results demonstrate that machine learning can reliably estimate post-consolidation profitability using only pre-consolidation data, providing a practical decision-support tool for land consolidation planning.eninfo:eu-repo/semantics/closedAccessMachine LearningEconomic AnalysisLand ConsolidationArticle10.1080/00396265.2025.2607074