To effectively plan and manage the use of agricultural land, it is crucial to identify and evaluate the multiple human and environmental factors that influence it. In this study, we propose a model framework to identify the factors potentially explaining the use of agricultural land for wheat, maize, and olive grove plantations at the regional level. By developing a machine-learning model coupled with a model-agnostic approach, we provide global and local interpretations of the most influential factors. We collected nearly 140 variables related to biophysical, bioclimatic, and agricultural socioeconomic conditions. Overall, the results indicated that biophysical and bioclimatic conditions were more influential than socioeconomic conditions. At the global interpretation level, the proposed model identified a strong contribution of conditions related to drainage density, slope, and soil type. In contrast, the local interpretation level indicated that socioeconomic conditions such as the degree of mechanisation could be influential in specific parcels of wheat. As demonstrated, the proposed analytical approach has the potential to serve as a decision-making tool instrument to better plan and control the use of agricultural land.

为了有效地规划和管理农业用地的使用，识别和评估影响它的多种人类和环境因素至关重要。在这项研究中，我们提出了一个模型框架，以确定在区域层面上可能解释小麦、玉米和橄榄林种植园使用农业用地的因素。通过开发机器学习模型以及与模型无关的方法，我们提供了对最有影响力的因素的全局和局部解释。我们收集了近 140 个与生物物理、生物气候和农业社会经济条件相关的变量。总体而言，结果表明生物物理和生物气候条件比社会经济条件更具影响力。在全球解读层面，提议的模型确定了与排水密度、坡度和土壤类型相关的条件的重要贡献。相比之下，当地的解释水平表明，机械化程度等社会经济条件可能会影响特定的小麦地块。正如所证明的那样，所提出的分析方法有可能作为决策工具来更好地规划和控制农业用地的使用。