This study aimed to create a model to identify land suitable for growing sunn hemp (Crotalaria juncea) and pigeon pea (Cajanus cajan) in the Okavango River basin of the Kavango East region of Namibia. Advanced tree-based ensemble learning models, including Random Forest, Extra Trees, Gradient Boosting, XGBoost and multivariate regression analysis , were employed to enhance analytical accuracy. The Random Forest and XGboost models exhibited outstanding performance, as evidenced by their respective accuracy values of 0.97 and 0.96. In addition, this study proposed an innovative approach through the integration of subjective and objective analytical methods, which are independent of one another. The subjective component of the analysis employed a Multi-Criteria Decision Making-Analytic Hierarchy Process (MCDM-AHP). On the other hand, the objective component used a data-driven multivariate approach supported by tree-based learning algorithms. Twenty-two variables were considered, encompassing climatic conditions, hydro-geomorphologic features, soil characteristics, vegetation patterns, and socio-economic factors. These variables played a crucial role to identify the most suitable areas for growing the selected leguminous crops. The MCDM-AHP method utilised expert evaluations to rank the importance of variables, identifying water sources, slope, and soil properties as key factors. A suitability mapping analysis revealed that 17.63% of the area was highly suitable and 62.77% moderately suitable, while 10% was less suitable and 9.59% unsuitable for growing these two legumes. According to the data driven methodology, soil fertility and nitrogen content emerged as key determinants for land suitability. This is particularly relevant for nitrogen-fixing leguminous crops such as sunn hemp and pigeon pea, which play a central role in improving soil quality and ensuring food security.

中文翻译：

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评估奥卡万戈河流域豆科作物的土地适宜性：一种多标准和机器学习方法


本研究旨在创建一个模型，以确定在纳米比亚卡万戈东部地区的奥卡万戈河流域适合种植阳光大麻 （Crotalaria juncea） 和木豆 （Cajanus cajan） 的土地。采用先进的基于树的集成学习模型，包括随机森林、额外树、梯度提升、XGBoost 和多元回归分析，以提高分析准确性。Random Forest 和 XGboost 模型表现出出色的性能，它们各自的准确率值分别为 0.97 和 0.96。此外，本研究还提出了一种创新方法，通过整合彼此独立的主观和客观分析方法。分析的主观成分采用了多标准决策-分析层次结构过程 （MCDM-AHP）。另一方面，目标组件使用了基于树的学习算法支持的数据驱动的多变量方法。考虑了 22 个变量，包括气候条件、水文地貌特征、土壤特征、植被模式和社会经济因素。这些变量在确定最适合种植所选豆科作物的区域方面起着至关重要的作用。MCDM-AHP 方法利用专家评估对变量的重要性进行排名，将水源、坡度和土壤特性确定为关键因素。适宜性制图分析显示，17.63% 的面积高度适宜，62.77% 的面积适宜，10% 的面积不太适宜，9.59% 的面积不适合种植这两种豆科植物。根据数据驱动方法，土壤肥力和氮含量成为土地适宜性的关键决定因素。 这与固氮豆科作物（如阳光麻和木豆）尤其相关，它们在改善土壤质量和确保粮食安全方面发挥着核心作用。