Food security is one of the major world agenda in 2018 in several contexts. Worldwide, in 2017 about 124 million people in 51 countries faced food security crisis (FSIN, 2018). According to FSIN (2018), conflict and insecurity are the major drivers of food insecurity in eighteen countries, and the number of food-insecure people across the world has been increasing over time. Likewise, food security situation in Ethiopia deteriorated sharply in 2017. In Ethiopia, the number of food-insecure population was increased from 5.6 million in December 2016 to 8.5 million in August 2017 (ACAPS, 2018). An  estimated  3.6 million children and women in Ethiopia were acutely malnourished in 2017 (IFRC, 2018).

The main causes of food insecurity in Ethiopia are prolonged drought, conflict and insecurity, crop disease, etc. According to FAO (2018), in Ethiopia, prolonged drought conditions are severely affecting the livelihoods in most southern and southeastern pastoral and agro-pastoral areas of SNNPR, southern Oromia and southeastern Somali Regions, where cumulative seasonal rainfall was up to 60 percent below average. In these areas,  pasture  and  water  availability  have  declined  to extremely low levels, severely affecting crop production and livestock conditions, leading to large scale animal deaths. More than one million people are displaced in Ethiopia, most of whom have been displaced by conflict starting in September 2017 and many of whom are displaced along the Oromia-Somali regional border (FEWS, NET and WFP, 2018). In the near-time, this displacement has disrupted households’ ability to engage in their typical livelihoods activities, such as seasonal cultivation and raising of livestock, and has resulted in food security crisis in the region where conflict has been reported to be most severe. Another factor driving the food security crisis in Ethiopia is the fall armyworm outbreak, which affects large parts of the country; especially maize-producing parts of SNNPR, Western Oromia, Amhara, Gambela, and Benshangul Gumuz (ACAPS, 2018; FEWS, NET and WFP, 2018). According to ACAPS (2018), food security situation in Ethiopia remain acute in 2018 with the reduced output of 2017 harvests, decreased food access as a result of poor purchasing power, and the exhaustion of coping mechanisms.

The problem of food insecurity greatly varies among households residing in the same country.   In Ethiopia, some households frequently face the problem of food insecurity, even in areas where there are no aforementioned drivers of food insecurity. Although a number of efforts have been done to achieve food security at the household level in the rural areas of Ethiopia, it has remained as a challenging goal even today. In Ethiopia, the poor performance of food security at household level is associated with poor institutional forms and dependency on rain-fed agriculture, which is highly vulnerable to drought which leads to loss of rural household’s lives and livelihoods in every three years (Abduselam, 2017).

Most of previous studies have focused on wider level. For instance, study conducted by Lemesa et al. (2017) reviewed literature to seek an answer for the question “why does food insecurity persist in Ethiopia?” They found that macro-economic challenges like increasing food prices and unemployment determine the prospect of food security in the country. Therefore, according to them, there is an urgent need to transform access to agricultural technology by farmers and employment opportunity. However, interrelated causes of household food insecurity require an analysis at a household level. Therefore, this study aims to examine determinants of food insecurity among rural households of South Western Ethiopia by taking sample households from Woliso district.

Food insecurity is an evolving concept. There are many definitions of food insecurity, which is a clear indication of differing views and approaches to the problem. FAO defined food insecurity as “a situation that exists when people lack secure access to sufficient amounts of safe and nutritious food for normal growth and development and an active and healthy life” (FAO, 2008; Marion, 2011).According to this definition, factors that may lead to a situation of food insecurity include non-availability of food lack of access, improper utilization and instability over a certain time period.  In other words, food availability, access, stability and utilization form the four pillars of food security. The four pillars must be fulfilled simultaneously in order to realize food security objectives. Determinants of each pillar are as shown in Figure 1.

In general, the reviews of empirical studies of household food security/insecurity in Ethiopia show the existence of difference in findings, which are associated with difference in study areas. Therefore, their findings may not apply in different contexts. Moreover, most of previous empirical studies done on the problem were stick to similar methods. This study is an attempt to fill the aforementioned gaps using different methods, focusing on determinants of food insecurity among rural households of Woliso district, South Western Ethiopia.

Data collection

This study depended mainly on primary data, which was collected from sample farm households using questionnaire. The questionnaire was administered to heads of farm households through interviews by trained enumerators. In the absence of the head of the household, other members of the household such as grown up child who can provide the required information was interviewed. For the interview, trained enumerators were deployed, and continuous supervision was made by the researcher to correct possible errors in the interview. The primary data needed for the study mainly focused on households’ demographic and socio-economic characteristics, their food consumption and expenditure, asset ownership and crop production, technology adoption and access to different services, and households’ perception toward food insecurity. It is known that households do not keep records of data on different aspects. So, the household survey entirely depended on recall method.

The target population of this study is farm households living in Woliso district, South Western Ethiopia. There are 22, 888 farm households in Woliso district (SWSZANRO, 2018). The study used a simplified  formula  provided  by  Yamane (1967) to determine the sample size at 95% confidence level and 5% degree of variability (Israel, 2002). In addition, 5% level of precision is used in order to get the sample size which represents a true population. According to Hussey and Hussey (1997), no survey can ever be believed to be free from error or provide 100% surety and error limits of less than 10% and confidence levels of higher than 90% can be regarded as acceptable. The sample size determination formula provided by Yamane (1967) is as follows (Israel, 2002).

where n is the sample size, N  is the total number of farm households in Woliso district, which is 22,888.  is the level of precision or sampling error, which assumed to be 9% for this study.

Therefore, this study randomly selected 122 farm households from the target population, and systematic random sampling based on a given interval between houses was employed during household selection.

Data analysis

The data analysis was started with the conversion of monthly, weekly and daily consumption data into kilocalorie (kcal) in order to realize objective of the study. The converted data was further changed into household adult equivalent (AE). Following this, the amount of energy in kcal for sampled households was recorded. Next, kcal of each sample household was compared with the minimum subsistence requirement per AE per day. The government of Ethiopia has set the minimum subsistence food requirement per AE per day at 2200 kcal (MoFED, 2010/2011). Households which consume below this minimum requirement were grouped as food insecure whereas those which consume above the threshold were grouped as food secure. Thus, the response variable (food insecurity status) is a dichotomous, which takes a value of y = 0 if a household is food secure and the value y = 1 if the household is food insecure. Here, we are interested in estimating the probability that a household is food insecure, given the explanatory variables. This probability can be expressed by logistic distribution function as (Gujarati, 2004):

After specification of the logit model, diagnostic tests were conducted to detect the problem of hetroscedasticity and multicollinearity. Moreover, to assist the regression analysis, two-group percentage of food insecurity-comparison test was done for discrete variables with two categories, such as sex, land owning, fertilizer usage, improved seed usage, pesticide usage, herbicide usage, road access, market access, credit access and ceremonial practice. 

Descriptive analysis

For examining the food security status of sample households, calorie acquisition by households is taken as an indicator. The amount of energy attained by a household is compared with the minimum subsistence requirement per AE per day (that is, 2200 kcal).  Accordingly, the  result shows that 25.4% (31) and 74.6% (91) of the sampled households were found to be food insecure and food secure, respectively.

Table 1 shows that the mean energy intake of food insecure households is 2,027, whereas that of food secure household is 3,541. The mean comparison test (t=2.9992***) confirms that there is significant difference between food insecure and food secure households with respect to calorie intake. Socio-economic characteristics of farm households are important determinants of their food security or insecurity status (Babatunde et al., 2007). 

Table 2 compares the percentage of food insecure households between two categories of discrete variable under consideration. This comparative analysis helps us to identify which category is more vulnerable to food insecurity in the study area. Table 2 shows that of female headed households 57% are food insecure, whereas only 18% are food insecure for male headed households and the difference (39%) is significant at 1% level of significance. This implies female headed households are more vulnerable to food insecurity as compared male headed households. This difference is associated with customary rules and practices that often have restrictive effects for women through limiting their access to resources and their roles in food production, preparation, processing, distribution, and marketing activities, which generally affects women’s food security and nutrition. Zakari et al. (2014) also found that male-headed households are more food secure compared to female headed households, which is consistent with result shown in Table 2.   

Econometric analysis 

The Logit model having eight explanatory variables was estimated, and the result of the regression is shown in Table 3. The explanatory variables are identified based on the previous empirical literatures. Some important variables that may affect household’s food security status, such as access to road, credit, herbicide and improved seed were omitted from the model because of the problem of multi-collineratity.

From Table 3, we see that out of eight explanatory variables included in the model, four of them were found to be significant. These are household’s education level, dependency ratio, amount of amount of land and amount of fertilizer. While the first three explanatory variables have the expected sign, the last one come up with unexpected sign. 

The regression result reveals that education level of household head has negative and significant effect on household food insecurity. The odds ratio in favor of food insecurity of illiterate households is 0.3% much higher than that of literate households, indicating that illiterate households are more food insecure than literate households. This reflects improvement in human capital contributes to food security significantly, which is consistent with our descriptive analysis. The possible explanation of this result is that literate households have more  chance   to   apply   their   knowledge  towards  the achievement of food security as compared to illiterate households. Similarly, Okyere et al. (2013) found negative and significant association between education level of household head and food insecurity. In their study, they showed that the odds of household heads having primary education are about 2.2 times more likely to be food secure than those with no formal education.

The maximum likelihood estimate shows that dependency ratio significantly influences food insecurity, and there is positive association between them. As it is observed from Table 3, the odds ratio in favor food insecurity will increase by a factor of 32.188 as the dependency ratio increases by one unit. In other words, the probability that a household becomes food insecure will increase as the number of dependent members of the household increases. This finding is obvious because the dependents contribute less labor and income to the family, which reduces the amount of food available to each of the family member. This reiterates the findings of others in which a household with large size, composed of mainly non-productive members is more likely to be food insecure due to high burden levied on active labor (Beyene and Muche, 2010). Moreover, the positive relation between dependency ratio and food insecurity implies that family planning policies, which have an impact in reducing dependent household size, will increase the probability of a household to be food secure. 

In  contrast  to  the   result   shown   in   the  descriptive analysis, amount of fertilizer is negatively correlated with food security. This finding contradicts previous findings, who stating that there is a positive association between the amount of fertilizer applied in farming and achievement of food security (Beyene and Muche, 2010; Stewart and Roberts, 2012). 

Majority of people in developing countries depend on agriculture as their subsistence of life. Therefore, access to land for rural people is essential for food security and economic development in developing countries. The logit model regression result points out that size of farm land holding is found to have a negative and significant impact on food insecurity. That is, the odds ratio in favor food insecurity will decrease by a factor of 0.04 as size of farm land increases by one hectare. Households with large farm land produce more for household consumption and for sale; thus have higher chance to be food secure than those having relatively small size of farm land. This finding supports result shown by Table 2, and is consistent with studies done by Beyene and Muche (2010), Muraoka et al. (2014), indicating a negative association between farm land size and food insecurity. 

This study has examined determinants of food security among rural households in Woliso district, South Western Ethiopia. Even though the descriptive result supports the importance of chemical fertilizer in enhancing food security, the econometric result invalidates the positive association between fertilizer use and improvement in food security. This indicates policies and strategies working on the use of chemical fertilizer should focus on long-run effect of chemical fertilizer on food security. On the other hand, the result supports the worth of human capital development in improving food security in the study area. Therefore, rural development policies should encourage access to basic education to improve the food security situation by revealing the strong association between education and food security. Moreover, the study found positive relation between dependency ratio and food insecurity, implying family planning policies should be enhanced in order to reduce food insecurity. Finally, like many empirical studies, this study also found out that there is significant relationship between size of farm land and achievement of improved food security. This implies that land markets should work efficiently to make land-constrained rural households to access additional land for cultivation. Generally, the results of this study suggest that attaining food security in the study area requires adoption of mixed policies and strategies, which can influence significant variables. Future research works should pay attention to the present empirical evidences in the study area, to investigate issues which have not been addressed by this research.  Moreover, it will better if the future research works examine the problem using panel data analysis.    

The author has not declared any conflict of interests.