Analyzing techno-economic production efficiency with machine learning: the relationship between plant suitability and food security in Sub-Saharan Africa

Introduction In light of growing concerns about global food security, this study investigates how techno-economic indicators derived from production yield, market prices, and harvested area can be used to assess crop suitability in Sub-Saharan Africa. Methods Using open-access statistical data from the Food and Agriculture Organization for the period 2000–2023, we apply machine learning (ML) models—Random Forest (RF) and multilayer perceptrons (MLP)—to predict a suitability proxy (Yield × log(Price)). In addition, we perform profitability-based clustering and trend analysis for five representative countries. In contrast to traditional ecological approaches, crop suitability is interpreted here in terms of economic viability. This perspective is particularly relevant in regions that are highly vulnerable to climate change. Results The results indicate that yield is the most important predictor of both profitability and suitability, highlighting the critical role of agronomic performance in ensuring food security. Discussion Our framework may also serve as a reference for evaluating high-tech agricultural solutions, such as IoT-based precision farming and remote sensing-based monitoring systems, thereby supporting evidencebased policy design in environments with limited technological capacity.