Abstract:
This study sets out to investigate the status of food insecurity, vulnerability and resilience among farm households and their determinants in North Shewa zone of Amhara region using cross-sectional data collected from 382 sample households. The outcome of food insecurity was estimated based on households’ calorie consumption. Logistic regression model was used to identify factors that influence food insecurity status of households. Accordingly, the results of the study show that majority (56.28%) of the sample households were food insecure. In addition, results revealed that age, literacy status, cultivated land, soil fertility, oxen owned and irrigation water were the major factors affecting (negatively) households’ food insecurity status. In contrast, sex, household size, distance to market and rainfall variability have increased the probability of being food insecure. Similarly, the vulnerability of households to food insecurity was estimated using vulnerability as expected poverty approach. The factors which influenced vulnerability to food insecurity were analysed using logistic regression model. Accordingly, the results of the study indicate that 56.28% and 59.95% of sample households suffered from current and future food insecurity, respectively. Considering both the current and future food insecurity, the study found that about 43.72% suffered from chronic food insecurity, 12.57% from transient food insecurity, and 16.23% suffered from transient food security. In addition, logistic regression model results revealed that extension service, early warning information, agricultural technology, and crop diversity were the major factors affecting (negatively) households’ vulnerability to food insecurity. On the other hand, sex, rainfall variability and drought have increased the probability of being vulnerable to food insecurity. Furthermore, households’ resilience status to food insecurity was estimated using a two-stage process. In the first stage, each latent variable used for resilience estimation was estimated directly from observable variables. In the second stage, the estimated values of the latent variables were used to estimate the resilience index for each household. Ordinary least squares model was used to identify factors that influence the status of households’ resilience to food insecurity. Accordingly, the results of the study indicate that 56.28% of the sample households were resilient to the current food insecurity and 53.72% of the sample households were non-resilient to the current food insecurity. When considering both the current food insecurity and non-resilience status, the study found that about 42.67% suffered from chronic food insecurity, 13.61% from transient food insecurity, and 10.73% suffered from transient food security. In addition, fertilizer, pesticide, veterinary service, cultivated land, livestock owned, early warning information, frequency of assistance, household dietary diversity score, and per capita income were the major factors affecting (positively) households resilience to food insecurity. On the other hand, dependency ratio, job lost and household food insecurity access scale have increased the probability of being non-resilient to food insecurity. Therefore, the findings of this study reveal the need for implementing appropriate policies and strategies that separately target and address the specific determinants of households food insecurity, vulnerability and resilience
