An Investigative Study of Fuzzy Cognitive Maps for Strategic Farm Management
Keywords:
Agricultural systems, Farming systems, Fuzzy cognitive maps, Strategic planning, Yield predictionAbstract
Strategic farm management is increasingly challenged by the growing complexity of socio-economic dynamics, environmental uncertainties, climate variability, and the rapid integration of precision agriculture technologies. In this context, effective decision-support tools are essential for managing interconnected agricultural systems. This review article investigates the application of Fuzzy Cognitive Maps (FCMs) as a robust and flexible modeling approach for strategic decision-making in agriculture. FCMs uniquely bridge the gap between qualitative expert knowledge and quantitative computational modeling by representing complex causal relationships among system variables in an intuitive and transparent manner. Through a structured analysis of recent literature, this study identifies key applications of FCMs in areas such as crop yield prediction, sustainability and resource-use assessment, risk and resilience evaluation, and agricultural policy analysis. The findings indicate that FCMs offer superior interpretability and explainability compared to traditional “black-box” machine learning models, making them particularly suitable for participatory farm management and stakeholder-driven planning. Nevertheless, challenges remain, including sensitivity to weight assignment, scalability, and the need for systematic methods to achieve expert consensus. Overall, this review highlights current advances in FCM-based agricultural research and outlines future directions for developing more resilient, adaptive, and data-informed farm management systems.
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