A Land-Based Caloric Framework for Assessing and Predicting Household Food Security in Rural Ethiopia

Food insecurity represents a critical human security in the global south, where recurrent climatic shocks, structural vulnerabilities, and limited anticipatory capacities expose rural households to severe livelihood risks. In protracted agrarian contexts such as Ethiopia, the inability to predict and respond to food insecurity in a timely manner can result in significant consequences for human wellbeing and survival. This study examines household food security using a calorie-based analytical framework that integrates own-farm production and income-based food access within a land size-stratified modelling approach. Quantitative data were collected from 420 randomly selected rural households, complemented by qualitative interviews with key informants. Household caloric availability was estimated by converting crop production and net income into kilocalorie equivalents and assessed using a modified Foster-Greer-Thorbecke index anchored to the Sphere minimum standard of 2,100 kcal per person per day. The findings indicate that 60.2% of households were food insecure during the September 2023-August 2024 reference period, with substantial caloric deficits among affected populations. Binary logistic regression results identify farmland size, household size, and livestock ownership as key determinants of food security, with land emerging as a central productive asset shaping household resilience. Stratified analysis reveals clear disparities in caloric adequacy across landholding categories, enabling identification of the most vulnerable groups. Building on these results, the study proposes a land-value-based caloric prediction framework capable of forecasting food insecurity under changing production, income, and price conditions, while providing a practical and data-efficient tool to support anticipatory food security interventions in Ethiopia. Significance of main findings This study demonstrates that food insecurity in rural Ethiopia is deeply rooted in structural inequalities, particularly landholding size and household composition, rather than solely climatic shocks. By integrating farm production and income into a unified caloric framework, it reveals substantial hidden deficits in household food access. The study's key contribution lies in the development of a land-based predictive model that enables forward-looking identification of vulnerable populations. This approach enhances analytical precision and provides a practical, scalable tool for strengthening early warning systems, improving targeting, and supporting anticipatory interventions in data-constrained humanitarian settings.