Livelihood diversification strategies and food security in the Weaving-based livelihood system of Gamo Zone, Southern Ethiopia

doi:10.21203/rs.3.rs-3937867/v1

This study investigates livelihood diversity, income, and association with food security status of weaving-based households in the Chencha district of Southern Ethiopia. Quantitative and qualitative data were collected from 303 randomly selected households using household surveys, key informant interviews, focus group discussions, and observation. Data was analyzed by using descriptive statistics and the Herfindahl-Hirschman Index. The result revealed that subsistence farming (52.1%) and weaving (38.6%) activities were reported as the primary and secondary main income sources of households. A majority of households (90.7%) diversified their livelihood. Based on income share, non-farm, on-farm, and off-farm income sources contributed, 72.96%, 25.3%, and 1.74% of total household income, respectively. Formal employment, weaving, barber/hairdresser, cattle production and sale, cereal production, and vegetable production (Potato) were highly paying livelihood activities in order. Households who combined on-farm, off-farm, and non-farm have gained far larger mean income followed by on-farm and non-farm strategies. Ordered Probit model results showed that landholding size, livestock holding size, and access to farmer training center (FTC) services have positively affected the extent of livelihood diversification. Positive association was found between livelihood diversification and the food security status of households. Thus, the majority of weaving-based livelihood groups diversify their livelihood activities to on-farm, off-farm, and non-farm activities. Hence, supporting the activity, increasing other livelihood options, and modernizing the system with appropriate training, education, and market linkage seems inevitable.

Food security

Herfindahl-Hirschman Index (HHI)

livelihood diversification

weaving

Chencha

While income diversification and migration play important roles in the poor's livelihoods, policymakers have ignored the off-farm and non-farm sectors in favour of agriculture [1] . Despite the fact that rural households' livelihoods in most developing countries are inextricably linked to the agricultural sector, agriculture has failed to become the primary source of livelihood for rural households in Sub-Saharan Africa (SSA) countries [2]. This is due to increased vulnerability to poverty and food insecurity as a result of rapid population growth and associated decline in landholding size [3] . Agriculture in SSA countries is rain-fed, with decreasing land size, low output per farm, and is subsistence-based [4] (Jirsttrom et al., 2011). Overdependence and overexploitation of nature result in environmental degradation, declining production, and food insecurity, forcing households to seek alternative livelihoods [5] (Reynolds et al, 2007). Conventionally it is believed that the livelihood system in rural areas is purely agricultural; with crop and livestock production [6] . Nevertheless, off-farm and non-farm activities play an important role in the economy, poverty reduction, and food security [7] (Lanjouw et al; 2013). In rural Africa, off-farm income accounts for 40 to 45% of average household income and is positively related to well-being [8] (Barrett et al., 2006). In Ethiopia, out-of-farm income contributes 42% of household income [9]. However, its role is not supported by policy focus.

Agriculture, as in other Sub-Saharan African countries, is the mainstay of the Ethiopian economy [10]. It accounts for 41% of GDP, 90% of exports, 85% of employment, and is the primary source of food security. However, 95% of it is done on a small scale, on less than 0.5 hectares, with the majority used for home consumption [11, 12] Arega et al., 2013). It is rain-fed, vulnerable to recurrent drought that occurs every three years, crop and animal pests and disease, land degradation, and seasonal fluctuation [13,14] . This has resulted in a 10% decrease in agricultural production and a 10% increase in food insecurity [15]. As a result, achieving food security and coping with recurring risks will be impossible unless the livelihood system is diversified to include off-farm and non-farm activities [16]. Increasing household capacity to withstand livelihood shocks and stresses improves livelihood sustainability and food security [17, 18, 19] (Farrington et al., 2002; Dai et al., 2020).

Ethiopia has a diverse topography and agro-ecology, making it suitable for a wide range of animal and crop adaptations. However, Ethiopia is one of the poorest agrarian countries in the world, with the majority of its population residing in the highlands [20]. The country is one of the poorest in Sub-Saharan Africa [21], owing to rapid population growth [22,23]. Food insecurity is a problem in Ethiopia's highlands, where population growth has resulted in smaller landholdings [23]. Rural livelihood diversification is the process of constructing a diverse portfolio of activities and social support capabilities to maintain or improve living and managing risk [24] (Carswell et al., 2000) [1]. It can also be articulated as an act of expanding the rural households’ income activities to different sectors [25,26]. Categorization of diversification can differ per different scholars as; on-farm, off-farm, and on-farm sources (Ellis, 1998); agricultural intensification, livelihood diversification, and migration by Barrett et al. (2001) [8]; and farm or non-farm by sector, wage employment or self-employment by function, on-farm or off-farm using the location of activity by [25,26].

However, farm households diversify their income sources for various reasons like; pull (voluntary) and push factors (involuntary). In the former case, households diversify for asset accumulation, increasing income, and using available economic advantages. In the later case, the purpose is risk management, reducing vulnerability, and enhancing resilience to shocks [27,28]. In rural Ethiopia, the poor need to diversify their livelihood activity to off-farm and non-farm activities both to mitigate risks of rain-fed agriculture and to improve livelihood conditions [29]. Thus, diversification is indicated as the main way to fill the gap in the agricultural sector [30,31].

In the Southern Nation Nationalities People’s Regional (SNNPR) state of Ethiopia, a large proportion of the people about 90 percent located in the highland agroecology holds 66 percent of the farmland [32]. Our study area, Gamo Highland in Broad and Chencha area particularly is one of the erosion-affected areas in the SNNPR. Chencha Woreda¹ Agricultural and Rural Development Office (CWARDO) report indicates that 38 percent of the total area is affected by soil erosion and more than 65% of agricultural land is prone to sheet, rill, and gully erosion due to continuous cultivation for centuries [33]. As a panacea for soil erosion, the government-led and donor agencies supported agricultural intensification, and livelihood diversification through agricultural technology adoption plays an important role. For instance; the adoption of a newly introduced new potato variety achieved a relatively higher yield of 80 Quintals per hectare than other crop yields in the Chencha area [34] which reaches in food deficit season of June to August. However, it is below the expected potential due to possible reasons for declining soil fertility due to soil erosion, lack of training, and high vulnerability to disease.

Highland areas in southern Ethiopia in general and Chencha district in particular encounter both seasonal and chronic food shortages due to small land holding size and high population density. A strong food shortage occurs in the Belg² season (from April to May) and September to mid-November (Tadesse et al., 2019) [35]. As a result, people are forced to adopt diverse activities like; intensification through technology utilization, Ensete production, trade, weaving, and out-migration [36]. Chencha is one of the highland areas of the Gamo zone which is the origin of year-round flowing rivers like; Kulffo, Hare, and Baso, but they do not provide any economic value in the highlands except washing away the fertile soil [34].

In the Gamo highlands including the Chencha area, land is transferred from parents to sons; which causes exponentially declining landholding shares for the coming generations (Abera et al, 2014) [23]. Accordingly, this forces households to face food shortages to feed year-round and practice undesirable livelihood diversification and rural-urban migration. In history, Gamo highlands had not been threatened by extreme famines and food insecurity problems the country had passed through [37]. The area has been safeguarded from such serious food shortage problems for centuries due to the availability of multifaceted economic and ecological root crops of Enset (Ensete Ventricosum) and Qoltso (Arisaema schimperianum). Currently, cultivation of these crops declining from time to time and they are replaced by other crop types like; cereals, fruit trees (Apple), and newly intervened potato crops [23].

As a result, recently people in the area facing food shortages due to high vulnerability to climate change-related challenges like; drought, crop pests and disease, rainfall fluctuation, livestock death, and so on. For this reason, the majority of food-insecure people are supported by the government-launched Productive Safety Net Program (PSNP) to sustain life. As a result, running multiple livelihood options both within agriculture and outside of agriculture is expected to be the way out of food insecurity and poverty thereby building household resilience to food insecurity. For this reason, this study is designed to examine the availability of livelihood options, the level of diversification, and its effect on the food security status of households.

¹ Woreda is a smaller admin unit next to Kebele (ward) and below zone in Ethiopia since 1991.

² Belg refers to a spring season which includes months March, April & May.

2.1. Study area and sampling

The study has been conducted in Chencha District, Gamo zone, Southern Ethiopia. Chencha is one of the ten woredas found in the Gamo zone of Southern Ethiopia, 474 kilometers away from Addis Ababa and 37 kilometers North of Arba Minch, the zonal town. Chencha has a longitude and latitude: of 6°14′ 60.00” N and Longitude: of 37°33′ 59.99” E and an elevation of 2732 meters above the sea. It has a total area of 373.5 km² shared by 33 kebeles³ and a human population density of 388 persons per square kilometer [38]. This makes the area one among densely populated areas in southern Ethiopia. Chencha has an agro-ecological category of highland (>2500 m asl) account 82%, and midland (2000-2500 meters above sea level) of 18%. The area receives biannual rainfall in two cropping seasons of Belg (March to May) and Meher (June to October) following these two rainy seasons with an average rainfall of 1172 millimeters. It has an average annual temperature of 16.5⁰C. The livelihood system of the people mainly depends on small-scale but intensive subsistence farming and; is supported with some off-farm and non-farm livelihood strategies; like weaving, wage labour, formal employment, out-migration to town areas for the search for a better life, trade, and so on. As a result, this study is designed to examine the extent of livelihood diversity in the area and its effects on household income and the food security status of households.

Figure 1 Research Area Map

To this end, a multistage sampling procedure was employed to select the study district and the sample households. In the first stage, Chencha woreda was selected purposively where weaving activity is most dominantly practiced in the zone. Besides this, so far livelihood diversification, food security status, and determinants have not been studied in the area. In the second stage, four kebeles were selected purposively based on preliminary information considering the dominancy of weaving practices in the area; namely, Doko Danbo, Doko Loosha, Lakana Maldo, and Setena Borcha. In the third stage, the sample respondents were been selected by using systematic random sampling techniques. Finally, the sample size of the respondents was determined by using the formula suggested by [39] as follows;

where N refers to total population size, n refers to sample size and e-refers to the level of precision. In this study, N=2485 and e=0.05 n=303. Then, a representative sample respondent was identified based on probability proportion to a population from four kebeles using a systematic random sampling method per each 8-household interval.

2.2. Data Collection

The study applied mixed data collection techniques which combined both quantitative and qualitative data collection techniques. Data used for this study were both primary and secondary which are quantitative and qualitative. To this end, household surveys, Focused Group Discussion (FGD), Key informant Interviews, field observations, and secondary sources analysis.

Questionnaire survey: A household survey has been conducted using semi-structured questionnaires. It has addressed the demographic, socio-economic, livelihood strategies, food security-related issues, and challenges that affect the livelihood conditions of the sample households. The survey process has been managed by using trained enumerators under close follow-up of the researchers.

Focused Group Discussion (FGD): To support the data gathered through the household survey 4 Focus Group Discussions (FGD) one in each kebele have been carried out from March 10-20 2022. It has included 6-10 members who know well about the livelihood conditions and associated challenges in the area. The members included in the group discussion were elders, women, and youth considering the heterogeneity of the group and managing the participation of all members. In that, the FGD has addressed the issues related to livelihood options, challenges, and food security conditions in the area.

Key Informant Interview: In addition to the above, 20 in-depth Key Informant Interviews (KII) were held with household heads, kebele leaders, experts of agricultural extension, Natural Resource Management experts, health extension, food security and early warning, water supply and job creation and enterprise at kebele, woreda, and zone level. The KII has raised diverse issues for individuals and profession-specific questions for different offices. The issues elaborated include; livelihood income sources in the area, food supply and adequacy, shocks and risks, Vulnerability contexts, access and availability of basic livelihood options and services, and challenges.

Field Observations: Field observations have been made by transect walk throughout the research areas to observe the livelihood patterns of the people and the prevailing opportunities and challenges. Secondary data were collected through analysis of various published and unpublished secondary documents. Researchers and trained enumerators administrated the household survey.

2.3. Data Analysis

Data collected through a combination of different techniques were analyzed using appropriate data analysis methods as per the nature of the data. Descriptive statistics such as mean, standard deviation, percentage, ratio, and range were used to examine the socioeconomic status of respondents. Qualitative data collected through key informant interviews, focus group discussions, and field observation were qualitatively analyzed using describing and narrating. Livelihood income diversification was determined by using the Herfindahl-Herschman Index (HHI) [40] (Admasu et al., 2022). On the other hand, the association between socio-economic characteristics and the food security status of households was examined by adopting the bivariate Pearson correlation analysis technique. Data analysis and management were carried out using SPSS (Statistical Software Package for Social Science version 20). Whereas; determinants of household livelihood diversification status were identified by applying the ordered Probit model. The details of analysis techniques were briefed as follows;

2.3.1 Herfindahl–Hirschman Index

Authors use a variety of indices and measures to determine the livelihood diversification status of households such as counting the number of household income sources, Simpson’s Diversity Index, Herfindahl–Hirschman Index, Ogive index, Entropy index, Modified Entropy index, and Composite Entropy index [41]. Hence, this study used the Herfindahl–Hirschman Index (HHI) to examine the income diversification status of households for its common use and suitability to apply in household livelihood diversification analysis [42,43]. HHI is calculated by taking the sum squares of each household income share ‘Yi’ in the total household income ‘Y’. Household livelihood diversification is determined by using the inverse of income diversification (Herfindahl-Hirschman Index). Thus, the Herfindahl-Hirschman Index which is independently developed by two authors [44,45] expressed as follows;

Where Si refers to the income share of economic activity i in the total household income, Yi is the household income from specific activity i, and Y means the total household income from all livelihood activities for the household i. However, the level of livelihood diversification is determined by computing the inverse of the squared sum of the proportion of household income (HHI) from each economic activity to the total household income i.e. the inverse of income diversification ( Olubunmi et al, 2017) [46]. It is expressed as;

The value of livelihood diversification (D=1) is equal to 1 when there is a complete specialization or no livelihood diversification, moderately diversified (1<D<2), and highly Diversified (D≥2) (Admasu et al., 2022) [40].

2.3.2 Determinants of Household Livelihood Diversification Status

This part deals with identifying the determinant factors affecting household livelihood diversification. In this case, our dependent variable used is household livelihood diversification status having three ordered outcome alternatives; i.e., ‘Low diversified’, ‘Moderately Diversified’, and ‘Highly Diversified’.

Based on the experiences of previous studies [43] (Admasu et al., 2022) [40], the households are classified into three classes of diversification levels namely; ‘Low diversified’, ‘Moderately Diversified’, and ‘Highly Diversified’. Since, the dependent variable has more than two outcomes, and three ordered outcomes, the ordered probit regression model was the most commonly recommended technique used to examine the determinant factors affecting the probability of the household either into a higher or lower category. Then, the differences across these categories can be affected by different explanatory variables at different levels.

Y*_j = X_j¹β+U_1j………………………..…………………. Equation (4)

Y=1 if Y*=1

Y=2 if 1<Y* <2

Y=3 if Y* ≥ 2.0

Where Y refers to the level of livelihood diversification with ordered outcomes of Y1= No Diversification, Y2= Moderately Diversified, and Y3= Highly Diversified. The X_ijare the explanatory variables that are hypothesized to determine the Income diversification status of households. The explanatory variables used in this model consist of dummy and continuous forms in their nature. And, β represents the parameters estimated and U_ijis the disturbance term. The dependent and independent variables used in this model are described as follows.

2.3.3 Definition of variables and hypotheses

Following the clear discussion of analytical techniques used for investigating the determinants of household livelihood diversification status, it is imperative to describe the dependent variable and the potential explanatory variables used in the model (Table 1). To this end, the identification of the dependent variable and its potential explanatory variables was done based on previous literature and authors’ experiences regarding livelihood diversification analysis. The descriptions of dependent and explanatory variables are shown in the table below.

³ Kebele refers to the lowest administrative unit in Ethiopia since 1991.

3.1. Socio-demographic characteristics of respondents

Table 2 presents households’ socio-economic characteristics. The sample respondents have an average age of 46.49 years and the majority about 91.7% of the household heads were male-headed. About 85.9% were married, and 71.6% of the respondents had access to formal education having 5.54 years of schooling.

Access to quality education and productive assets like land and livestock are major potential determinant factors affecting household livelihood diversification. Accordingly, the sample households have highly fragmented or minimal average landing holding size of 1.11 hectares and average livestock holding of 2.95 in TLU. Due to the small per capita landholding size and resulting very low agricultural production, the households in Chencha district practice off-farm and non-farm livelihood diversification. As a result, 47.9% of the respondent households reported out-of-farm livelihood activities as their main livelihood income sources. However, only 52.1% of the households responded that they used subsistence farming as the main source of their living. The households in the area faced the problem of food shortage for 3-6 months. As a result, about 13.5% of randomly selected respondents were beneficiaries of Productive Safety Net (PSNP) support in the form of members on food for work and free access to aid.

About 82.5% of the respondents were within the productive age category (15-64 years) with maximum age ranges of 17 and 107 years which is a similar result to Dersseh et al. (2016) [47]. The majority of weaving-based households about 90.75% have participated in more than one economic activity as their household income source and the remaining 8.25% have specialized only in one livelihood income source. The study result indicated that the respondent households have adopted an average of 4.53 number of income sources. Regarding the dependency ratio, the study revealed a 0.223 or 22.3 percent dependency ratio in the family.

3.2. Characteristics of Livelihood Diversification and Annual Income Shares

This part provides evidence of the diversity of income sources used in the area with the respective total participation, mean income, total income, and their share of the overall income of the whole household. In this case, mean income has been computed as the total income gained by several households participating in the corresponding income activity.

Due to challenges of declining landholding size, diminishing farm productivity, and decreasing capacity of agriculture; rural households diversify their productive labour both into non-farm and off-farm sectors to get their livelihood needs [48,49]. More particularly, the average per capita landholding size of the study area was one of the lowest; 1.11 hectares as per the current survey, which is slightly greater than the national and regional (SNNPR) averages of 0.84 and 0.52 hectares per household [50]. According to the CSA report, about 84% of the households in the area have 0.5 hectares and below landholding size [51]. As a result, people in the district are forced and opt to derive their household income from diverse sources.

As shown in the Table 4, the study result indicated that about 93.39%, 81.85 %, and 21.13% of respondents have participated in non-farm, on-farm, and off-farm activities, respectively. Like other rural areas, the economy of Chencha district is dominantly characterized by subsistence farming. However, the non-farm income accounts for 72.96% of the total overall income of households which is 47.67% by far advance than the on-farm income share. The income shares of on-farm income sources accounts for only 25.29% of the overall income of the respondents. This result is in line with the study conducted in the Himalayas [52].

Specifically, vegetable production (mainly Potato) was 61.72%, cereal production was 52.14%, cattle sales was 27.72%, sheep and goat production and sale were 26.73%, and fruit production 21.12% activities had relatively higher household participation recorded among on-farm activities. But, cattle sales (6,950 ETB), Cereal production (5,738.18 ETB), and vegetable production (4512 ETB) have relatively higher average annual income. While; cereal production, vegetable production (Potato and Cabbage), and cattle sales have 6.73%, 6.26%, and 4.33% shares on the overall income of the respondents, respectively.

However, off-farm income source has relatively lower levels of household participation and lower income contribution to the total household income. This could be due to high population density, very minimal landholding size, and the subsistence nature of agriculture in the area. This may limit off-farm livelihood options as an alternative employment sector for needy groups of society. Among off-farm income sources, annual income from grain trade (5650 ETB), Wage labor (4690.9 ETB), and petty trade (3706.6 ETB) has higher mean income. However, off-farm activity has an overall income share of only 1.74% of the annual income of the whole respondents. This result indicates the shortage of off-farm livelihood alternatives and low repaying capacity in the area.

Non-farm activity plays a significant role in the livelihood system of the rural households in the area. Based on non-farm income activity participation, weaving, and spinning activity account for 81.18%, remittance 17.49%, and formal employment (governmental or non-governmental) accounts for 10.89%. However, based on the average annual income return for the corresponding participants; formal employment, weaving, and Barber or hairdresser service have higher repayment capacities of (75,025.66 ETB), (38,162 ETB) and (24,500 ETB), respectively. This result is similar to the study by Israr et al. (2014) and Gautam and Andersen (2016) which reported a larger income contribution of non-farm activity and formal employment to total household income and general well-being than on-farm activities. Also, the findings of Yizengaw et al. (2015) [53] revealed similar results in which non-farm income contributed 60 percent to household income in Debre Elias woreda East Gojjam zone, Ethiopia. The study by IFAD also supports that households in developing countries drive more than 50 percent of their household income from non-farm livelihood activities [54]. Hence, giving due attention to education and strengthening non-farm livelihood diversification is inevitable in the area.

3.3. Livelihood Diversification Strategies

As shown in Table 3 above, the respondent households have adopted an average of 4.53 diverse livelihood options. Based on the survey result, 90.76% of the households got their income from more than one livelihood source. Similarly, people in highly populated and fragmented landholding conditions, diversify their sources of household income (Gebru et al., 2018) [55].

Despite the most frequent adoption of on-farm, off-farm, and non-farm concepts in the livelihood classification, many argue for inextricability and difficulty in putting clear demarcation among this classification. However, this study has adopted the conceptualization provided by [56]. Accordingly, on-farm activities consist of farming and agricultural production-related activities that include; all activities of farming (crop production and livestock rearing) carried out on the farm of the household or occur at the beginning of the value chain. On the other hand, off-farm income encompasses all agriculture-related activities that occur beyond the farm or in the “middle” and “end” of the process which include; processing, packaging, storage, transporting, and retail sale. So, off-farm activities include all processes carried outside own farm up to final consumption. Whereas; non-farm activity refers to sectors that exist outside of agricultural market systems like; construction, healthcare, hospitality, formal employment, education, mining, tourism, and artisans.

Based on the livelihood activity participation of the households, livelihood activities identified in the area were categorized into seven livelihood strategies;

Group 1: Only on-farm livelihood activities
Group 2: Only off-farm livelihood activities
Group 3: Only non-farm livelihood activities
Group 4: On-farm and off-farm combination of livelihood activities
Group 5: On-farm and non-farm livelihood activities
Group 6: Off-farm and non-farm livelihood activities
Group 7: On-farm and off-farm and non-farm livelihood activities

Different authors; [31, 43] (Admasu et al., 2022) [40] classify households based on the use of on-farm, off-farm, non-farm, and a combination of one or more of them as their livelihood income source. Based on these experiences, the respondent households were categorized into seven (7) livelihood groups (Table 5). Accordingly, the majority of the respondents 179(59.08%) and 52(17.16%) were getting their livelihood means from ‘on-farm and non-farm’, and ‘on-farm, off-farm, and non-farm’ economic activities, respectively. Following the two, ‘non-farm only’ employed 45(14.85%) of the households.

Based on average annual income per participant; households who combined the three livelihood sources ‘on-farm, off-farm, and non-farm activities’ have the highest average annual income of 55,366 ETB, followed by ‘on-farm and non-farm’, ‘on-farm and off-farm’, and ‘non-farm only’ income sources. The least remunerative livelihood group is the ‘only off-farm’ activity. This implies that households who combine different income sources will get higher annual income and thereby minimize risk and enhance their capacity to withstand natural and economic uncertainties.

3.4. Extent of Livelihood Diversification in the Area

People diversify their livelihood income sources to increase their household income and thereby improve the well-being and food security status of their household members. Accordingly, this study attempted to determine the status of livelihood diversification and its association with the food security status of households. Following the experiences precedents [42, 43] (Admasu et al., 2022) [40] the status of livelihood diversification is identified by using the Herfindahl-Hirschman Index (HHI).

Table 6 presents the extent of household livelihood diversification using (HHI) techniques. Based on the thorough analysis of income distribution of households using HHI results, 14.52% of the respondents specialized only on one income source, or adopted ‘No livelihood diversification’ whereas, 52.15% practiced ‘Moderate level’ of livelihood diversification status. Surprisingly, 33.33 percent of the respondents ‘highly diversified’ their livelihood income sources. The mean HHI score is 2.68 which indicates ‘highly diversified’ livelihood sources result across all households. Using similar techniques, other researchers have also shown similar results [42]. 27% and 37% of the respondents in the Coastal Community of Bangladesh have ‘poor’ and ‘medium’ levels of livelihood diversification, respectively.

3.5. Determinants of Household Livelihood Diversification

Table 7 presents determinants of livelihood diversification. The ordered Probit model was used to detect the determinants of the livelihood diversification status. Here it is mainly focused on carrying out the data analysis and identifying explanatory factors (continuous and discrete) that affect livelihood diversification status. Before running the data analysis, the existence of bad correlation (multi-collinearity) among potential explanatory variables was tested using Variance Inflation Factor (VIF) and Contingency coefficient values for continuous and discrete variables, respectively. Then, the test result revealed that there is no strong correlation among independent variables. Accordingly, the VIF values for all continuous variables were found to be small (i.e., VIF<10) which is below the cutting threshold value of 10. In the same way, the multi-Collinearity test result for discrete explanatory variables revealed a contingency coefficient value of less than 0.75 which confirmed the existence of no strong association.

To determine the determinants of the livelihood diversification status of households ordered probit analysis model is used with a 95% confidence interval (CI) or p<0.05 value. The model fitting information shows that the model has high predictive power with Nagelkerke pseudo-R-square value 0.562 which indicates the model fits the data well with 56.2% of the variance in the dependent variable (i.e., livelihood diversification status) explained by the modeled explanatory variables. Whereas; the difference between the two log-likelihoods the chi-square has shown a significance level of less than 0.001.

Parameter Estimates

In the following part, the parameter results with significant influence on the household livelihood diversification status were interpreted. Among 17 explanatory variables, 7 variables were found to significantly determine the likelihood of household livelihood diversification from lower to higher or vice versa.

Family size of the household: the sample respondents have a relatively larger average family of 6.81 which is even greater than the regional average of 5.47 members in the Southern Nations Nationalities people’s region of Ethiopia [14]. It is expected that an increased family size can increase the likelihood of increasing livelihood income sources. However, the model result revealed that family size has negatively and significantly affected the probability of household livelihood diversification at a less than 5% significant level. Accordingly, a unit increase in the family size decreases the likelihood of income diversification to a higher level by 0.0681 units when other factors are kept constant. This may imply, that an extended family member may become a burden in the rural contexts where there is a shortage of land resources and other off-farm and non-farm job opportunities.

Landholding size: Here, livelihood diversification is expected to be happening both within the agricultural sector and outside of the agricultural sector, in turn, could have a positive impact on household general well-being and food security (Onunka et al., 2017) [46]. In this regard, as expected land holding size has positively influenced the likelihood of household livelihood diversification at less than a 5% significant level. A unit increase in the landholding size of a household increases the likelihood of attaining a higher livelihood diversification category by 0.225 units if other factors were kept constant. Consequently, increased landholding size gives opportunities to expand income sources into growing several crops and vegetables, and rearing large variety and size of livestock. Therefore, it is advisable to improve mechanisms of accessing land for rural households to enhance household food self-sufficiency and productivity through livelihood diversification.

Total Livestock holding size: Although a minimal average livestock holding size (2.95 Tropical Livestock units) is reported in the study, income gained from livestock and livestock products is confirmed as the top-ranked among on-farm income sources. As expected, the model result indicated that the size of total livestock holding became one of the important determinant livelihood diversification factors that positively affected at less than a 1% significance level. A unit increase in livestock holding size in TLU increases the likelihood of households achieving higher diversification levels by 0.178 times when other factors are kept constant. This may be explained as increased diversity and size of livestock holding could mean expanded sources of food and income for households in the form of livestock and livestock products. Thus, in the highly fragmented landholding areas like; the Chencha district; rearing diversified livestock in an intensive and home-managed way with necessary expertise and support services seems more recommendable.

Access to Farmer Training Center (FTC) services: Access to agricultural production-improving technologies and services like Farmer training services is one of the important factors that are expected to enrich farmers with the necessary knowledge and skills to increase production and productivity through adopting diverse income activities. As expected, access to FTC services was found to positively determine the likelihood of household livelihood diversification. Accordingly, a household that had access to FTC services has an increased likelihood of falling into the higher livelihood diversification category by 0.388 units more than non-users at less than 10% significance status. Increased access to FTC enhances livelihood diversifications of households by exposing them to new experiences, knowledge, and skills to practice in their livelihood system. Thus, strengthening FTC service provision in the area would have a desirable effect on livelihood diversification and the food security status of rural households.

Access to Transfers of Payment: In the study, households use a variety of cash and kind transfers of support to sustain their living. Alternative transfers reported include; remittances from their family members from urban centers of the country and abroad the country, PSNP support, food insecurity relief aids during food deficit seasons, and so on. As expected, ceteris paribus, the lack of access to transfers of payment significantly decreases the probability of households falling into the higher livelihood diversification category by 0.586 units at less than a 5% significant level.

On-farm participation: In rural areas, on-farm income sources are the most important livelihood diversification sources, in the form of livestock and crop production. The probit model result indicated that participation in on-farm activity highly contributed to the livelihood diversification status of the respondent households at a 1% significant level. Keeping other factors constant, those who did not participate in on-farm activity have a 2.207 units lower likelihood of falling into a higher (moderate or highly diversified) income category than those who have participated in on-farm income activity. This implies, that participation in various on-farm activities enhances improved livelihood diversification status and in turn, improves food security and household resilience to food insecurity.

Off-farm participation: This study has revealed options for off-farm livelihood sources such as; wage labour, firewood collection and charcoal production and sale, animal feed collection and sale, grain and livestock trades, petty trade, furniture and woodwork, and farm tool production and sale. In the econometric model analysis, access and participation in off-farm activities significantly influenced the livelihood diversification status of households at less than a 5% significant level. Considering other factors constant, households who had not participated in or accessed incomes from off-farm activity have decreased probability of attaining higher livelihood by 0.392 units than those who had access to off-farm income participation.

3.6. Socio-economic characteristics and their associations with food security status

Livelihood diversification is a common practice followed by rural farming households in Africa which involves increasing economic activities within and out of farming activities as a means to escape from the effects of poverty and food insecurity problems (John Afodu et al., 2020) [57]. As can be in Table 7, the associations between different socioeconomic variables and the food security status of weaving-based households were observed using Pearson Correlation Analysis [42]. Accordingly, age of the respondent (AGE), educational status in years (EDU), family size (FAMSZ), number of productive labour (PROLABR), total landholding size (LAND), livestock holding size, total income from all sources (TOTINCOM), Livelihood diversification status using HHI (LD), food security status (FCS) and Dependency Ratio (DEPNDC) were used.

As shown in the Table 8, the result revealed that Pearson correlation of household age and Food security status (FCS) was found to be very low negative and statistically significant (r=0.199, p<0.01). This shows that an increase in household head age leads to a decreased Food Consumption Score in the household. Educational status of the household head (EDU) and Food Consumption Score (FCS) were found low positive and significantly correlated (r=0.322, p<0.01). This implies that increased educational attainment leads to enhanced food security status. The association of other variables like; family size, landholding size, total livestock holding size, and total household income have a very low positive and significant correlation at p<0.01 with the Food security of households. However, the Pearson correlation of livelihood diversification status (HHI) has a high positive (r=0.720, p<0.05) and significantly correlated with food security status (FCS).

This implies that an increase in household livelihood diversification has a positive association with the improved food security status of the households. This result is similar to the research output of Onunka et al. (2017) [47] who found a positive association between increased livelihood diversification and improved food security status among households of Udi local government area, Enugu state, Nigeria. Another study by [58] has also found a positive effect of livelihood diversification on the food security status of farming households. Therefore, expanding and creating necessary opportunities for weaving-based households to diversify their income sources within and out of farming activities is advisable to improve the well-being and food security status of their households.

4.1. Conclusions

Subsistence farming (crop and livestock production), weaving and spinning, formal employment, and trade are the main livelihood income sources used in Chencha district of South Ethiopia. Agriculture and weaving are the primary and secondary income sources of employment and income source, respectively. Small landholding size, high population density, out-migration, weaving, and Enset-based livelihood system characterize the study area. The major livelihood challenges in the area include; fragmented landholding and associated food shortage, soil erosion, lack of infrastructure (road), and resulting poor market linkage and food shortage. Most people in the area diversified in ‘farm only’, ‘non-farm only’, ‘on-farm and off-farm’, ‘on-farm and non-farm’, ‘off-farm and Non-farm’, ‘on-farm, off-farm, and non-farm’ livelihood strategies. Even though subsistence farming was reported as the primary income source by about 72.3% of the respondents, non-farm income contributed 72.96% share of the total household income. On-farm and off-farm income sources accounted for 25.3% and 1.74% of total income, respectively. However, based on mean per capita income from each economic activity for respective participants, formal employment, Weaving and Spinning, Barber/Hairdresser activity, cattle production and sale, cereal production, and vegetable (Potato) are identified as the most remunerating economic activities reported. Majority of the respondent households diversified their livelihood activities to more than one activity.

The livelihood strategy specific close examination revealed that, households who combined ‘on-farm, off-farm and non-farm’ gained far larger mean income followed by ‘on-farm and non-farm’, ‘on-farm and off-farm’, and ‘non-farm only’ livelihood strategies. Analysis of household livelihood diversification status indicated that the majority of the households in the study area have ‘moderately diversified’, ‘low diversified’, and ‘highly diversified’ in their respective order. Landholding size, TLU, and frequency of FTC services received positively contributed to the likelihood of household livelihood diversification to the higher levels. However, family size, lack of access to transfers, on-farm income participation, and lack of off-farm participation have negatively affected the household’s likelihood of attaining higher livelihood diversification. Besides this, education status, family size, land holding size, livestock holding size, total income, and livelihood diversification status have positive associations with the food security status of respondents. On the other hand, age and food security status have been found negative associations.

4.2. Recommendation

Based on the findings of the study, the following suggestions were forwarded to the concerned stakeholders. Diversifying the income activity both within and outside of farming activities has been found highly economical in terms of increasing household income and contributing to food security. So, the government and other development agents should support households by creating awareness, providing skill training, and accessing financial sources. Besides efforts made to improve agricultural production and productivity, government bodies should give due focus to support the available non-farm and off-farm economic sectors in the area like; weaving, trade, beauty salon, quality education, and formal employment to counterbalance challenges of climate change and shortage of landholding. Creating work opportunities for youth by accessing credit and providing skill training to establish small-scale enterprises should be given due attention. Weaving and spinning activity in the area is reported as the second most important livelihood activity adopted by youth and productive adults next to agriculture. However, it is threatened by a lack of modernization, market linkage, and unfair price allocation from traders.

Thus, the government should give recognition for its economic role, make reasonable interventions to adopt technology-based production, create market linkages, and help and organize youth to add value to the weaving products. Adopting intensive agricultural practices (technology and irrigation substantiated) like; potato, fruit, and livestock could be suggested for the area. Furthermore, improving access to quality education, and training services and creating job opportunities for youth and college graduates should be rethought. The increasing trend of youth female participation should be motivated and institutionally supported.

Ethical Clearance: After reviewing the proposal in accordance with university academic standards, research tools, and inclusion of respondents' informed consent for the data collection process; this tudy has obtained Ethical clearance letter from Institutional Review Board (IRB) of Addis Ababa university with Ref.No. 029/01/2023. The study's participants were informed that their personal information would only be utilized for academic purposes and would not be disclosed. In this sense, participants' oral consent is acquired prior to data collection, and they indicate that they accept to have the material published in an open access journal. The Institutional Review Board (IRB) has three members: Amare Bantider (PhD), Adimasu Zerihun (PhD), and Teshome Tafese (PhD). The letter will be made available upon request.

Consent of publication: Consent was obtained from the study participants to publish their identifiable data in an online, open-access journal of BMC Nutrition Journal which is part of Springer Nature Journal.

Conflict of interest: The authors declare that they have no conflict of interest.

Availability of data and material: Supplementary information for this paper is available at: https://doi.org/10.20372/aau_rdm/ZS7MDH

Funding: authors declare that there is no funding source utilized and funding information to be displayed.

Author contributions: Authors contributed to this work as follows: Desta Dereje (conceptualization, questionnaire redesign, fund acquisition, project administration, methodology, investigation, data curation, formal analyses, original draft writing, visualization, writing—review, validation), Temesgen Tilahun (supervision, investigation, original draft writing, fund acquisition, project administration, validation), Teshome Yirgu (supervision, investigation, data curation, writing—review). All authors reviewed the manuscript.

Acknowledgements: We authors are very grateful to the College of Development Studies at Addis Ababa University for ethical clearance and necessary support. Desta Dereje, the corresponding author, give special thanks to all individuals who directly or indirectly supported data collection and data management through energy and knowledge and; provided critical comments and suggestions for the development of this paper.

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Table 1: Description of dependent and explanatory variables

Variable name	Description	Expected effect
Livelihood Diversification status	“Y=1” No diversification, “Y=2”, Moderately diversification, “Y=3” Highly diversified	(+, Positive, -, Negative)
Age of the household head	A continuous variable measured in years	+
Family size of the households	The number of families in the household	+
Landholding size	The landholding size of the household in hectares	+
Total livestock holding	Total number of livestock owned in Tropical Livestock Unit (TLU)	+
Access to Nearest local market	Access to the nearest local market within walking hours	-
Dependency Ratio	The ratio of dependent household members to total household members	_
Sex of the Household Head	Sex of household head (1=Male, 2=Female)	+/-
Access to Farmer Training Center (FTC)	Access to FTC services (0=No,1=Yes)	+
Access to Mobile services	Access to mobile services (0=No, 1=Yes)	+
Access to Safe water	Access to safe water (0=No, 1=Yes)	+
Household Head Read and Write	The ability of HH Read and Write (0=No, 1=Yes)	+
Access to formal employment	If the household member/s formal employed or not (0=No, 1=Yes)	+
Access to Transfers	If a household has got transfers (0=No, 1=Yes)	+
Access to Credit	If the household has used credit (0=No, 1=Yes)	+
Non-farm participation	If the household participated in a non-farm activity or not (0=No, 1=Yes)	+
On-farm participation	If the household participated in on-farm activity or not (0=No, 1=Yes)	+
Off-farm participation	If the household participated in an off-farm activity or not (0=No, 1=Yes)	+

Source: Own articulation, 2022

Table 2 Demographic characteristics of the respondents

Item Characteristics		Frequency	Percent
Agro-ecology	Highland	276	91.1
Agro-ecology	Midland	27	8.9
Sex of the HH	Male	278	91.7
Sex of the HH	Female	25	8.3
Marital status of the head	Single	18	5.9
	Married	260	85.9
	Divorced	7	2.3
	Widowed	18	5.9
Attended formal education	Yes	217	70.96
Attended formal education	No	88	29.04
Educational category	No formal education	88	29.04
	Primary education (1-6)	100	33
	Secondary education (7-10)	60	19.8
	Higher Secondary (11-12)	26	8.6
	Tertiary (College and Above)	29	9.57
Age category in years^{^[1]}	Early working age (15-24)	23	7.59
	Prime working age (25-54)	174	57.43
	Mature working age (55-64)	53	17.49
	Elderly (65 and above)	53	17.49
PSNP Membership	Member on food for work	34	11.2
	Member on free access to food	7	2.3
	Not member	262	86.5

Source: Own Survey, 2022

Table 3: Demographic Characteristics of Respondents by Continuous Variables

Demographic characteristics	Mean
Age of the respondents	46.49
Family size	6.81
Productive labour size	3.78
Dependency Ratio	0.223
Years of formal education	5.54
Total Landholding size	1.11
Livestock size in TLU	2.95
Number of income sources adopted	4.53
Mean annual income	82,041

Source: Own survey, 2022

Table 4 Diverse Income Sources and Shares in Total Annual Household Income

Income sources	No. of participants (%)	Mean income	Total income	% share in the total income
i) On-farm related activities
Cereal crop production	52.14	5,738.18	906,634	6.73
Vegetable production (potato, cabbage)	61.72	4,512	843,760	6.26
Fruit production	21.12	1,374	87,950	0.65
Livestock production	17.83	3,774	203,800	1.51
Leasing out land	2.31	1,728.57	12,100	0.09
Cattle sale	27.72	6,950	583,800	4.33
Goat and sheep production and sale	26.73	4,400	356,419	2.64
Donkey/Mule/Horse sale	2.64	4,037.5	32,300	0.24
Poultry production	19.80	1,802	108,120	0.80
Milk and milk products	17.16	2,495.2	129,750	0.96
Skin and hide	1.65	518	2590	0.02
Bee and hives	2.97	451	4,060	0.03
Planting of trees	11.88	3,832	137950	1.02
Total on-farm income		41,612.35	3,409,233	25.3
ii) Off-farm income activities
Wage labor	7.26	4,690.90	103,200	0.76
Firewood or charcoal sale	0.66	1,500.00	3000	0.02
Animal feed collection and sale	1.98	3,458.33	20,750	0.15
Grain trading	1.98	5,650.00	33,900	0.25
Livestock and product trade	2.31	2,642.85	18,500	0.14
Petty trade	4.95	3,706.66	55,600	0.41
Farm tool production and sale	0.66	500.00	1000	0.01
Furniture and woodwork	0.30	1000.00	1000	0.01
Total off-farm income		23,148.74	234,450	1.74%
iii) Non-farm income
Formal employment	10.89	75,025.66	2,475,846	18.37
Weaving and spinning	81.18	38,162.00	9,387,880	69.66
Cash transfer	5.28	3,799.00	60785	0.45
Military service	4.95	3,561.66	53,425	0.39
Pension	3.63	3,856.36	42420	0.31
Metalwork	0.66	200.00	400	0.003
Begging	1.32	230.00	920	0.01
Remittance	17.49	2,352.83	124,700	0.93
Blacksmith	0.30	2000.00	2000	0.015
Traditional healer	0.30	0.00	0	0
Barber or hairdresser	1.32	24,500.00	98,000	0.73
House Rent	6.93	2,933.33	61,600	0.46
Total non-farm income		156,620.84	9,832,130	72.96%

Source: Author’s compilation based on field survey, 2022.

Table 5 Choice of livelihood strategies adopted by respondent households

Author’s compilation from field survey, 2022

Table 6 Livelihood diversification status of households

Authors’ compilation from survey, 2022

Table 7: Determinants of household livelihood diversification status

Variables	Estimates	Std. Error	Wald	Sig.
HHI= 1 HHI=2	-0.863 0.991	0.0.547 0.548	2.483 3.268	0.115 0.071
Age	0.005	0.005	1.261	0.261
Family size	-0.068	0.031	4.633	0.031**
Landholding size	0.225	0.095	5.587	0.018**
Total livestock holding size (TLU)	0.178	0.040	19.215	0.000***
Access to Nearest local market (walking hours)	0.199	0.130	2.364	0.124
Dependency Ratio	0.400	0.336	1.422	0.233
Sex of the household Head (Yes=1)	-0.240	0.284	0.714	0.398
Access to FTC services (Yes=1)	0.388	0.212	3.368	0.066*
Access to Mobile (0=No)	-0.283	0.171	2.120	0.145
Access to Safe Water (0=No)	-0.090	0.165	0.299	0.585
Household Head Read and Write (0=No)	-0.022	0.161	0.018	0.893
Formal employment (0=No)	0.427	0.299	2.042	0.153
Access to Transfers (0=No)	-0.586	0.276	4.514	0.034**
Access to Credit (1=Yes)	0.046	0.226	0.042	0.837
Non-farm participation (0=No)	-0.188	0.304	0.381	0.537
On-farm participation (0=No)	-2.207	0.257	73.603	0.000***
Off-farm participation (0=N0)	-0.392	0.195	4.037	0.045**

LR χ2 (32) 596.093

Chi-Square 200.330

Number of observations 303

Prob>χ2 0.001

Pseudo R²0.562

***, ** and * stands for significant at less than 1%, 5% and 10% probability levels, respectively.

Source: Own survey, 2022.

Table 8 Socio-economic Variables and Food Security Associations

	AGE		EDU	FAMSZ	PRLABR	LAND	TLU	TOINCM	LD	FCS	DEPNDR
AGE		1
EDU		-.199^**	1
FAMSZ		.224^**	.129^*	1
PROLABR		.221^**	.068	.668^**	1
LAND		.067	.272^**	.333^**	.306^**	1
TLU		.189^**	.214^**	.535^**	.401^**	.468^**	1
TOTINCM		-.007	.222^**	.278^**	.193^**	.158^**	.101	1
LD		-.057	.060	.061	-.039	.173^**	.047	.057	1
FCS		-.018	.322^**	.176^**	.054	.197^**	.204^**	.151^**	0.720**	1
DEPNDC		.107	-.032	.107	-.313^**	.011	.017	.093	.110	.152^**	1

**Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed)

No competing interests reported.

Livelihood diversification strategies and food security in the Weaving-based livelihood system of Gamo Zone, Southern Ethiopia

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

3. Results and Discussion

4. Conclusion and Recommendations

Declarations

References

Tables

Additional Declarations

Status:

Version 1