Achieving rice self-sufficiency in West Africa will require an expansion of the irrigated rice area under water-scarce conditions. However, little is known about how much area can be irrigated and where and when water-saving practices could be used. The objective of this study was to assess potentially irrigable lands for irrigated rice cultivation under water-saving technology in Burkina Faso. A two-step, spatially explicit approach was developed and implemented. Firstly, machine learning models, namely Random Forest (RF) and Maximum Entropy (MaxEnt) were deployed in ecological niche modeling (ENM) approach to assess the land suitability for irrigated rice cultivation. Spatial datasets on topography, soil characteristics, climate parameters, land use, and water were used along with the current distribution of irrigated rice locations in Burkina Faso to drive ENMs. Secondly, the climatic suitability for alternate wetting and drying (AWD), an irrigation management method for saving water in rice cultivation in irrigated systems, was assessed by using a simple water balance model for the two main growing seasons (February to June and July to November) on a dekadal time scale. The evaluation metrics of the ENMs such as the area under the curve and percentage correctly classified showed values higher than 80% for both RF and MaxEnt. The top four predictors of land suitability for irrigated rice cultivation were exchangeable sodium percentage, exchangeable potassium, depth to the groundwater table, and distance to stream networks and rivers. Potentially suitable lands for rice cultivation in Burkina Faso were estimated at 21.1 × 10⁵ ha. The whole dry season was found suitable for AWD implementation against 25–100% of the wet season. Soil percolation was the main driver of the variation in irrigated land suitability for AWD in the wet season. The integrated modeling and water balance assessment approach used in this study can be applied to other West African countries to guide investment in irrigated rice area expansion while adapting to climate change.

要在西非实现稻米的自给自足，就需要在缺水的情况下扩大稻米的灌溉面积。但是，对于多少灌溉面积以及何时何地可以节水的做法知之甚少。这项研究的目的是评估在布基纳法索采用节水技术进行水稻种植的潜在灌溉土地。开发并实施了两步，空间明确的方法。首先，在生态位建模（ENM）方法中部署了机器学习模型，即随机森林（RF）和最大熵（MaxEnt），以评估土地对灌溉水稻种植的适宜性。有关地形，土壤特征，气候参数，土地利用，使用水和水以及布基纳法索灌溉稻田的当前分布情况来驱动ENM。其次，通过使用简单的水平衡模型对两个主要生长期（2月至6月和7月至6月至7月）进行气候适应性评估，这是一种灌溉管理方法，可在灌溉系统的水稻种植中节水，这是一种灌溉管理方法。 11月）以十进制表示。ENM的评估指标，例如曲线下的面积和正确分类的百分比，显示RF和MaxEnt的值均高于80％。水稻可灌溉土地适宜性的前四项预测指标是可交换的钠百分比，可交换的钾离子，到地下水位的深度以及与河流网络和河流的距离。⁵公顷 整个干旱季节被发现适合AWD的实施，而潮湿季节则占25-100％。在渗水季节，土壤渗滤是灌溉用地对AWD适宜性变化的主要驱动力。本研究中使用的综合建模和水平衡评估方法可以应用于其他西非国家，以指导在适应气候变化的同时扩大灌溉水稻面积的投资。