Determinants of food security through statistical and fuzzy mathematical synergy

Ensuring household food security and fighting hunger are global concerns. This research highlights factors affecting food security and solutions by utilizing a nexus of statistical and fuzzy mathematical models. A cross-sectional study was conducted in district Torghar, Northern Khyber Pakhtunkhwa, Pakistan, among 379 households through a structured interview schedule. Further, the data were coded into SPSS for analysis, i.e., descriptive, and inferential statistics. In a binary logistic regression model, the study explored that agricultural productivity lag, population growth, and climate change all had a negative impact on household food security (P = 0.000). In addition, these results were further cross-checked through spherical fuzzy mathematics. The fuzzy results revealed that population growth is the most powerful factor deteriorating the existing equilibrium of household food security in Torghar. The study concluded that climate change, population explosion, and agricultural production were the major factors behind the hunger in the study area. Thus, designing a synergy based on taking all these aforementioned realities into consideration is the need of the hour. If extended, a comprehensive model of addressing these anomalies with a solely objective-based approach may work as a panacea for all ills. Lastly, this study contributes to the food security literature by introducing a new approach that is more robust and flexible than existing methods.