Machine learning and species distribution models for crops: A review

The agricultural sector is facing enormous challenges to sustain food security, with rising pressures from population growth, climate change, water scarcity and land degradation. In this regard, Agricultural Land Suitability Analysis (ALSA) serves as a fundamental tool in identifying the most appropriate areas for crop cultivation and optimizing land use planning and spatial crop allocation. Recent advances in Artificial Intelligence (AI), particularly Machine Learning (ML) and Species Distribution Models (SDMs), have significantly enhanced ALSA by improving prediction robustness and accuracy. In this regard, this scoping review aims to provide an overview of current trends of the most applied applications in ALSA concerning the most relevant crops for human subsistence and prosperity, in order to identify possible future development and research needs. This research focuses on feature space-based machine learning application to infer cash crop suitability. Following PRISMA guidelines and the Population Concept Context (PCC) framework, a qualitative-quantitative analysis of 113 peerreviewed articles was performed to provide a comprehensive overview of the theme, showing a significant increase in interest in this area since 2021. Among 55 studied crops, rice, coffee, and wheat were the most frequently analyzed. The most commonly used model was Maximum Entropy (MaxEnt), followed by Random Forest (RF), with limited applications of modelling ensemble approaches. Environmental variables considered in studies are mostly bioclimatic, followed by topographic and pedological factors, with limited socio-economic thematic consideration. Climate change scenarios were incorporated in 63.7% of studies, with Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP) scenarios being considered in 50.6% and 43.3%, respectively. Findings highlight the growing interest for Artificial Intelligence in ALSA, and emphasize the need to integrate a larger spectrum of variables, especially socioeconomic ones, and multi-model ensemble approaches to enhance model robustness and ensure more reliable assessments for a wider range of crops.