The determinants of livelihood diversification among small-scale rural farmers in Alfred Nzo and King Cetshwayo District, South Africa

Mbewana, Vusi; Kaseeram, Irrshad Article The determinants of livelihood diversification among small-scale rural farmers in Alfred Nzo and King Cetshwayo District, South Africa Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Mbewana, Vusi; Kaseeram, Irrshad (2024) : The determinants of livelihood diversification among small-scale rural farmers in Alfred Nzo and King Cetshwayo District, South Africa, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-16, https://doi.org/10.1080/23322039.2024.2368901 This Version is available at: https://hdl.handle.net/10419/321522 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 The determinants of livelihood diversification among small-scale rural farmers in Alfred Nzo and King Cetshwayo District, South Africa Vusi Mbewana & Irrshad Kaseeram To cite this article: Vusi Mbewana & Irrshad Kaseeram (2024) The determinants of livelihood diversification among small-scale rural farmers in Alfred Nzo and King Cetshwayo District, South Africa, Cogent Economics & Finance, 12:1, 2368901, DOI: 10.1080/23322039.2024.2368901 To link to this article: https://doi.org/10.1080/23322039.2024.2368901 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 08 Jul 2024. Submit your article to this journal Article views: 945 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE The determinants of livelihood diversification among small-scale rural farmers in Alfred Nzo and King Cetshwayo District, South Africa Vusi Mbewana and Irrshad Kaseeram Faculty of Commerce, Administration and Law, Department of Economics, University of Zululand, KwaDlangezwa, South Africa ABSTRACT The determinants of livelihood diversification have been studied by several researchers globally. However, these factors are not well understood in the ANDM and KCDM, because they are given little attention. The specific objective of the study was to examine the determinants of livelihood diversification in ANDM and KCDM. The cross-sectional dataset was collected from 268 and 264 participants who were randomly selected in ANDM and KCDM, respectively. A structured questionnaire was utilized to collect data on socio-economic and demographic factors among small-scale rural farmers in ANDM and KCDM. The data collection commenced in March to April 2022 in KCDM and started in August to September 2022 in ANDM. Stata version 14.0 was employed to estimate a Quantile regression. The results show that 66.04% of participants in ANDM were femaleheaded households, whereas 53.79% in KCDM were headed by males. The findings from a Quantile regression show that livelihood diversification was influenced by the household head’s gender, age, marital status, access to extension services, access to credit, employment status, food security, education, household size, farm size, poverty status, farm experience, and improved seeds. To promote livelihood diversification, policymakers should create policies that will target all factors that are significant in the study. IMPACT STATEMENT The determinants of livelihood diversification have been explored by researchers on a global scale. However, these factors are not well recognized in the ANDM and KCDM regions as they are given little attention. The primary focus of the study was to analyze the determinants of livelihood diversification in ANDM and KCDM. A Quantile regression show that livelihood diversification was influenced by the household head’s gender, age, marital status, access to extension services, access to credit, employment status, food security, education, household size, farm size, poverty status, farm experience, and improved seeds. To enhance livelihood diversification, policymakers should design policies that focus on all significant factors outlined in the study. ARTICLE HISTORY Received 27 February 2024 Revised 24 May 2024 Accepted 12 June 2024 KEYWORDS Livelihood diversification; Alfred Nzo; King Cetshwayo; small-scale farmers REVIEWING EDITOR Chris Jones, Aston University, United Kingdom SUBJECTS Economics; Environmental Economics; Economics and Development JEL Q10 1. Introduction The concept of livelihood diversification has been the subject of interest to researchers and has been widely accepted by several development theorists because of its theoretical assumption of reducing rural poverty and improving food security status (Bryceson, 2000; Ellis, 1998). Arguments related to the livelihood approach have predominantly targeted rural areas, since most households in those areas are involved in farming (Krantz, 2001). It has been postulated that a household’s livelihood is sustained if it can adequately manage and elude strain and pressure while maintaining its resources and capacities (Ellis, 1998; Goldman et al., 2000). To secure their livelihoods, households adopt multiple approaches, including securing permanent positions that pay salaries which in turn assists them in supporting their families and participating in livestock rearing and crop cultivation (Chambers & Conway, 1992). CONTACT Vusi Mbewana [email protected] Faculty of Commerce, Administration and Law, Department of Economics, University of Zululand, KwaDlangezwa, South Africa ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2368901 https://doi.org/10.1080/23322039.2024.2368901 Research has demonstrated that income acquired from non-agricultural sources supplements farm income, which in turn enhances the standard of living of marginalized and rural poor households (Reardon, 1997). Additionally, residents in rural areas have devised multiple strategies to sustain their way of living. Those with little access to resources face obstacles such as food insecurity and livelihood uncertainty (Purvis & Smith, 2006; Smith, 2012). Diversifying one’s livelihood is generally seen as an adaptive measure in times of hardship (Ghosh & Bharadwaj, 1992). The pull and push factors play a significant role in determining whether households should expand their income sources. Echebiri et al. (2017) noted that pull factors can be perceived as opportunities that provide individuals or households with the potential to extend their income sources. Steady demand for products and services, as well as the potential for significantly higher returns, has been regarded as the primary factor that drives households to undertake non-agricultural activities (Khapayi & Celliers, 2016). Considering the potential of generating extra income through alternate sources, households are becoming significantly more engaged in various income-generating activities beyond agriculture (Cervantes-Godoy & Dewbre, 2010). Push factors are classified as circumstances in which households are compelled, by their situation, to take up multiple livelihood activities for them to survive (Barrett et al., 2001). Households that struggle financially are obliged to invest in resources with low returns because they are unable to obtain the necessary assets due to a lack of funds (Ellis, 1998,2000). Thus, the poverty status could serve as one of the factors that may push small-scale farmers to engage in various livelihood activities to gain a living. Among the studies that investigated the determinants of livelihood diversification, Habib et al. (2023) found that livelihood diversification was positively influenced by the level of education, family labour, and social connections. Conversely, there was a negative relationship between livelihood diversification and farm organizations, access to new farming equipment, credit accessibility, and natural disasters. Onuwa et al. (2022) found that education level, household size, credit access, and productive assets have a positive impact on livelihood activities. However, the age of the household head was found to have a negative effect. Kumar and Umesh (2020) assert that extension services and membership in farming organizations have a positive effect on livelihood diversification. Workie (2023) indicated that onfarm and off-farm livelihoods are positively influenced by the gender of the household head, while household head age, and livestock ownership, had a negative impact. The determinants of livelihood diversification have been investigated extensively around the globe. However, the determinants of livelihood diversification have been given little attention in the Alfred Nzo District Municipality (ANMD) and King Cetshwayo District Municipality (KCDM). Additionally, most studies did not include poverty status as one of the predictor variables that could explain the participation in multiple livelihood activities. To the researchers’knowledge, this is the first study to incorporate poverty status as an independent variable and this is the original contribution to the existing literature. The primary objective of the study is to examine the determinants of livelihood diversification in ANDM and KCDM. This will shed light on policymakers to understand the factors that require an urgent response to enhance livelihood diversification among small-scale rural farmers in the study areas. The determinants of livelihood diversification may vary based on different household characteristics and geographical areas. This is one of the reasons it was important to conduct this investigation in the ANDM and KCDM. 2. Literature review This section provides a summary of previous empirical research on the determinants of livelihood diversification. Dinku (2018) investigated the determinants of livelihood diversification using a multinomial regression. In the first model, the age of the household head, farm input, and livestock extension contact were found to be positively associated with on-farm and off-farm livelihood activities. The second model revealed that the age of the household head, farm input, livestock extension contacts, and access to remittances, were positively associated with a combination of on-farm and non-farm livelihood activities. In the third model, the ownership of cattle, farm input, and market distance were found to have a positive relationship, with a combination of on-farm, off-farm, and non-farm livelihood activities. Conversely, credit access was negatively related to a combination of livelihood activities such as onfarm þoff-farm þnon-farm. 2 V. MBEWANA AND I. KASEERAM Etuk et al. (2018) found that household size has a negative impact on livelihood diversification, meaning that larger households tend to have lower levels of diversification. However, the study also found that marital status, farm size, and total household income have a positive influence on small-scale farmers’engagement in various livelihood activities. Tyenjana and Taruvinga (2019) discovered a positive correlation between livelihood diversification and education level, livestock ownership, and household size. They also observed that the gender of the household head negatively affects participation in different livelihood activities. Aweke et al. (2023) conducted a study and found a positive correlation between off-farm work and factors such as soil erosion, livestock ownership, and proximity to markets. On the other hand, they found a negative association between livelihood diversification and total household income, maleheaded households, education level, and training. Similarly, Guite et al. (2022) discovered that proximity to markets, affiliation with farming and non-farming organizations, size of operational wetlands, and availability of forest resources positively influence livelihood diversification. However, they found a negative association between average educational level, participation in multiple income-generating activities, and operational cash crop land. Roy and Basu (2020) found that diversifying livelihoods was associated with higher government donations, increased household members with income, and greater involvement of social workers. Alemu (2023)discovered that various factors significantly influenced the extent of livelihood diversification, including household head age and gender, level of education, farm size, land quality, soil conservation methods, access to extension services, distance to markets, exposure to shocks, and availability of infrastructure. Ayana et al. (2021) found that the level of education of the household head, dependency ratio, access to irrigation, credit availability, and urban connectivity significantly impacted livelihood diversification. Abera et al. (2021) showed that the combination of agricultural and non-farm activities in livelihoods was influenced by factors such as the age and gender of the household head, household size, education status, livestock ownership, land size, credit access, distance to markets, and income level. Another model revealed that agriculture supplemented with off-farm work was influenced by variables like the age of the household head, household size, education attainment, livestock ownership, land size, and proximity to markets. The third model demonstrated that engagement in agriculture, non-farm, and off-farm activities in livelihoods was influenced by factors including the gender and age of the household head, household size, education level, livestock ownership, land size, credit access, and distance to markets. According to Washo et al. (2021), access to credit, ownership of livestock, and household size had a positive impact on combining agriculture with off-farm activities. However, a second model revealed that household size had a negative influence on combining agriculture with non-farm activities. Additionally, a third model indicated that combining agriculture, off-farm, and non-farm activities in livelihoods was negatively affected by the age of the household head. The study also found that marital status, level of education, land ownership, livestock holding, and access to credit positively influence a combination of agriculture, off-farm, and non-farm livelihood activities. A research study conducted by Akyoo (2021) used a multinomial logit to examine the predictor variables that impact the livelihood diversification of small-scale farmers, in two different areas of Tanzania: Kilombero Sugar Company Limited (KSCL) and Kilombero Plantation Limited (KPL). The findings from KSCL, indicated that household size and land size had a negative impact on on-farm and non-farm livelihood activities, while marital status showed a positive correlation. Furthermore, there was a negative relationship between livelihood diversification (on-farm and off-farm) and household size, land size, and access to credits. The second model revealed that household size, land size, and credit access were negatively associated with a combination of on-farm, off-farm, and non-farm livelihood activities. The third model, from KPL, demonstrated that the combination of on-farm, off-farm, and non-farm livelihoods was positively related to land size and total household income. The literature review indicates a lack of studies that have incorporated poverty as an independent variable in the regression model, highlighting a gap in the existing research that requires attention. 3. Conceptual framework This research paper utilized the sustainable livelihood framework (see Figure 1) as a theoretical foundation to support the study’s findings and conclusions. Maintaining a sustainable livelihood requires the COGENT ECONOMICS & FINANCE 3 capacity to effectively adapt to external shocks or pressures without compromising the productivity of natural resources or harming others (Sneddon, 2000). The framework has five livelihood assets which include human capital, social, financial, natural, and physical capital. These livelihood assets directly affect the livelihood strategies and could be used to address the vulnerability context such as external shocks, seasonality, and trends. The sustainable livelihood framework has institutions that are responsible for implementing policies and laws that could be used as a responsive major when sudden shocks take place. This would protect the livelihood strategies which could lead to a positive livelihood outcome. Being involved in various income-generating activities can help small-scale farmers increase their overall household income. This could ultimately lead to an enhanced food security status and improved dietary intake for rural smallholder farmers. The Sustainable Livelihoods Framework was employed in this research to rationalize the various livelihood activities undertaken by rural small-scale farmers in both ANDM and KCDM. 4. Materials and methods 4.1. Description and justification of study areas Alfred Nzo District Municipality (ANDM) is situated in the north-eastern part of the Eastern Cape Province and has four local municipalities: Mbizana, Ntabankulu, Umzimvubu, and Matatiela (ANDM [Alfred Nzo District Municipality], 2017). This is the smallest district, covering an area of 10,731 square kilometers, which accounts for 6% of the geographical area in this region. Almost 70% of the population and households in the ANDM are situated in rural areas. This municipality has a population of 867,864 with 195,975 households. The said district is dominated by maize small-scale farmers who have organized projects which are registered with the Department of Agriculture and more than 1000 cooperatives deliver their maize to the Dyifani milling plant every year (Miti, 2017). Small-scale farmers in the ANDM who participated in commercial farming accounted for 1.4% of the total. This was the lowest percentage compared with other regions in the Eastern Cape Province (Stats SA [Statistics South Africa], 2020). The Eastern Cape is the reference where poverty and unemployment rates are among the highest in the country since this province is characterized as the second poorest in South Africa (NDA [National Development Agency], 2014). Additionally, the Alfred Nzo District Municipality (ANDM) is declared as worse off compared to other districts within the Eastern Cape province (ANDM, 2020). This is what makes the ANDM to be unique in the Eastern Cape and targeted as a study area. The findings of the study would inform policymakers about the determinants that promote or hinder the participation of Figure 1. Sustainable livelihood framework. Source: Ellis (2000). 4 V. MBEWANA AND I. KASEERAM small-scale farmers in different livelihood activities. Thus, the appropriate majors would be taken to address the poverty issues that prevail in the district which will encourage the participation of smallscale farmers in multiple income-generating activities. King Cetshwayo District Municipality is situated in the northern part of KZN, with its administrative headquarters in Richards Bay. This district covers an area of 8213 square kilometers. It has five local municipalities: uMhlathuze, Mthonjane, uMlalazi, uMfolozi, and Nkandla. Approximately 80% of King Cetshwayo District households and the population are regarded as rural. This district has a total population of 982,726, with an estimated 222,000 households, with an average of 3.95 persons per household (Stats SA, 2016). According to Statistics South Africa (Stats SA, 2020), the number of commercial farms in the King Cetshwayo District constituted 5.1% of the total farmers in the province. KwaZulu-Natal (KZN) is described as the third poverty-stricken area that follows the Eastern Cape (Stats SA, 2019). Despite the efforts that have been put in place by the government such as ‘One Home One Garden’to fight the incidence of hunger it has never worked out because food shortages still exist in rural KZN (Ngema et al., 2018). The KZN is dominated by households whose livelihoods are derived from agricultural activities (Hornby et al., 2018). This was particularly observed in the northern part of the region, where the King Cetshwayo District Municipality (KCDM) was identified to have more farming potential than the other districts within the province. Given this background, the KCDM was purposively chosen in KZN as the study area. 4.2. Ethical considerations On the 19th of January 2022, the Research Ethics Committee of the University of Zululand issued the ethical clearance certificate with reference number: UZREC 171110-030 PGD 2021/65. The participants were informed that they may stop answering the questions at any time, without any consequences. 4.3. Data collection instrument and sampling method A questionnaire was used as the data collection instrument because it is easy to analyse, provides anonymity to the participant, and produces a comparable result. Each copy of the questionnaire was accompanied by informed consent approved by the University of Zululand Research Ethics Committee. In KCDM, data collection commenced on the first week of March to 30 April 2022, whereas in ANDM it started on the first week of August to 30 September 2022. Systematic random sampling was used to select participants because it is the easiest method to draw a sample from a larger population. One starts by selecting a starting point from the sampling frame, and consistently maintaining the same interval while skipping households or individuals (Mkonda et al., 2018). 4.4. Instrument design In preparation for the face-to-face interview, open-ended and closed-ended questions were developed. Respondents were asked to identify their sources of income and specify the amount generated from each livelihood strategy through the open-ended questions. Conversely, participants were given only two options with the closed-ended questions, requiring them to choose either ’yes’or ’no’. Before gathering data, a pilot study was conducted with 20 randomly selected farmers to determine if they would understand the questions. 4.5. Sample size The data collection process for this study was conducted in two districts, which required the sample size to be calculated separately. Krejcie and Morgan’s sampling formula was used to determine the appropriate sample size for this study. JUSTIFY s¼x2NPð1−PÞ d2N−1 ðÞ þx2Pð1−PÞ COGENT ECONOMICS & FINANCE 5 4.5.1. The sample size for the ANDM and KCDM According to a study conducted by Sifundza (2019), it was found that a total of 787 households from the KwaMkhwanazi community were engaged in farming and delivered their sugarcane produce to Felixton Mill. Therefore, the calculation showed that the sample size for KCDM was 258. A total of 300 questionnaires were printed and delivered to fieldworkers in the KCDM. After data were collected in the KCDM, 264 questionnaires were completed by the enumerators. The response rate was calculated by dividing the questionnaires that were returned by fieldworkers against the number that was printed out. For the KCDM the response rate was 88% which was very close to 100%. The first author approached the Department of Agriculture in Mbizana which is situated in ANDM to request access to the database to get the total number of small-scale farmers in the area. The officials convened a meeting in February 2022, where all representatives from different maize projects were present. The meeting aimed to request access to their information and the permission was granted. Then after, one of the officials printed the Excel spreadsheets with details of the farmers which also revealed the total number of registered small-scale farmers. This spreadsheet revealed a total of 1457 farmers which assisted in a sample size determination of 304. In the ANDM, 350 questionnaires were delivered to field workers. After data were collected in the ANDM, 268 questionnaires were returned by field workers. The response rate in ANDM was 77%. This validates the findings of the study because the response rate was more than 50% in both districts. 4.6. Analytical approach: quantile regression model The objective of this study was to assess the determinants of livelihood diversification in ANDM and KCDM. The authors wanted to explore how the dependent variable (livelihood diversification) was influenced by the predictor variables at different levels of quantiles. Thus, the Ordinary Least Squares technique was not applicable in this situation because it provides only one coefficient for each variable. However, the objective of the study can only be achieved if a quantile regression model is utilized. This model was first introduced by Koenker and Bassett (1978) as an alternative to overcome ordinary least squares (OLS) shortfalls. Quantile Regression (QR) is used to predict the median rather than the mean, which is normally estimated by OLS. QR is an extension of OLS, and it is used when the normality and homoscedastic assumptions are violated. QR can be expressed as follows: yt¼x0 tbt where x0 trepresents the vector of household characteristics of the sampled population, and b t denotes the parameters that should be estimated in the QR model. The QR equation minimizes: XtqjetjþXtð1−qÞjetj Where qje t jrepresents under-prediction and (1 −q)je t jdenotes over-prediction. The estimator of the qth predicted bminimizes the following objective function: minbeRKXteRðt:ytx0 tbÞqy t−x0 tbq   þXteRðt:yt<x0 tbÞð1−qÞyt−x0 tbq    hi where 0 <q<1. The standard conditional quantile can be expressed in the following linear form: QqðytxtÞ¼x0 tbq   For the Kth predicted coefficient, the marginal effect can be expressed as: ǝQqðytjxtÞ ǝxk ¼bqk 4.7. Model specification This study employed Simpson’s Diversity Index (SDI) as the dependent variable, which is a proxy for livelihood diversification. The Quantile Regression model was specified as: 6 V. MBEWANA AND I. KASEERAM SDI ¼b0þb1x1þb2x2þbkxkþui where b 0 denotes the slope of the regression model; b 1 ,b 2 , and b k represent the coefficients that will be estimated; x 1 ,x 2 , and x k represent the characteristics of the sampled households; and u i signifies the error term. 4.8. Measurement of the dependent variable The Simpson’s diversity index (SDI) was calculated using the formula derived by Warwick et al. (2008). The SDI equation is expressed as: SDI ¼1−Pnðn−1Þ NðN−1Þ where the small letter n represents individual income (i.e. pension or remittances, farm income, salaries from employer), while the capital letter Nrepresents the sum of all individual income, from different livelihood strategies. If the household head has only one source of income, the SDI is equal to zero. Conversely, if a household head has several sources of income, the SDI will be closer to or equal to one, suggesting that a particular smallholder farmer, in the surveyed population has multiple sources of income. This implies that the dependent variable ranges from 0 to 1. 4.9. Measurement of independent variables The measurement scales for the independent variables depicted in Table 1 were utilized by the previous researchers which examined the determinants of livelihood diversification in different countries or regions. For example, Tyenjana and Taruvinga (2019) used a binary measurement scale to code the variable for the household head gender (1 ¼male and 0 female), access to credit (1 ¼yes and 0 ¼no), and access to extension services (1 ¼yes and 0 ¼no). Mudzielwana et al. (2022) used a binary measurement scale in education status (1 ¼formal and 0 ¼no formal education), and marital status (1 ¼married and 0 ¼single), while household head age was measured as continuous variable. Etuk et al. (2018) measured family size as the number of persons living in the same household, farm size was measured as the number of hectares, and farm experience was measured as the number of years spent in farming. Abera et al. (2021) measured the variable for improved seeds as a binary (1 ¼yes and 0 ¼0). The employment status was coded as a binary variable (1 ¼employed and 0 ¼unemployed). Table 1. Measurement of independent variables. Variables Type Description HHG Dummy 1, Male-headed 0, Female-headed HHMS Dummy 1, Married 0, Otherwise HHEXT Dummy 1, Access to extension services 0, Otherwise CREDACC Dummy 1, Access to credits 0, Otherwise HHEMP Dummy 1, Employed, self-employed, and part-time 0, Otherwise FSEC Dummy 1, Food secure 0, Otherwise HHED Dummy 1, Formal education 0, Otherwise HHAG Continuous Age in years HHFEXP Continuous Experience in years HS Discrete Number of family members living together IMSEEDS Dummy 1, Utilising improved seed 0, Otherwise FARMS Continuous Number of hectares POVST Dummy 1, non-poor 0, Otherwise COGENT ECONOMICS & FINANCE 7 About the author Vusi Mbewana is a part-time lecturer at the University of Zululand, Department of Consumer Sciences. Dr. Mbewana holds a Ph.D. in Economics from the University of Zululand. My expertise lies in Microeconomics, and I have a strong background in survey data cleaning, interpretation, and analysis. ORCID Vusi Mbewana http://orcid.org/0009-0009-8380-2319 Data availability statement The first author can provide the data upon request. Please contact Vusi Mbewana at [email protected] if you need access to the data. References Abebe, T., Chalchisa, T., & Eneyew, A. (2021). The impact of rural livelihood diversification on household poverty: Evidence from Jimma Zone, Oromia National Regional State, Southwest Ethiopia. The Scientific World Journal, 2021,1–11. https://doi.org/10.1155/2021/3894610 Abera, A., Yirgu, T., & Uncha, A. (2021). Determinants of rural livelihood diversification strategies among Chewaka resettlers’communities of southwestern Ethiopia. Agriculture & Food Security,10(1), 1–19. https://doi.org/10.1186/ s40066-021-00305-w Adem, M., & Tesafa, F. (2020). Intensity of income diversification among small-holder farmers in Asayita Woreda, Afar Region, Ethiopia. Cogent Economics & Finance,8(1), 1759394. https://doi.org/10.1080/23322039.2020.1759394 Akyoo, E. P. (2021). Determinants of livelihood diversification strategies in communities adjacent to large scale agricultural investment in Kilombero Valley, Tanzania. Tanzania Institute of Accountancy. Alemu, F. M. (2023). Measuring the intensity of rural livelihood diversification strategies, and Its impacts on rural households’welfare: Evidence from South Gondar zone, Amahara Regional State, Ethiopia. MethodsX,10, 102191. https://doi.org/10.1016/j.mex.2023.102191 ANDM [Alfred Nzo District Municipality]. (2017). Alfred Nzo district municipality socio-economic review and outlook. Eastern Cape socio-economic consultative council. ANDM [Alfred Nzo District Municipality]. (2020). Alfred Nzo district municipality profile and analysis: District development model. Cooperative governance and Traditional affairs. Aweke, A., Tefera, T., Gezahegn, M., & Sileshi, M. (2023). Determinants of household choice of livelihood diversification strategies in selected drought prone areas of the southern nations nationalities and peoples’region, Ethiopia. Agricultural Sciences,14(10), 1375–1392. https://doi.org/10.4236/as.2023.1410090 Ayana, G. F., Megento, T. L., & Kussa, F. G. (2021). The extent of livelihood diversification on the determinants of livelihood diversification in Assosa Wereda, Western Ethiopia. Geo Journal,87, 2525–2549. Barrett, C., Bezuneh, M., & Aboud, A. (2001). Income diversification poverty traps and policy shocks in Cote d’Ivore and Kenya. Food Policy,26(4), 367–384. https://doi.org/10.1016/S0306-9192(01)00017-3 Bryceson, D. (2000). Rural Africa at the crossroads: Livelihood practices and policies. Overseas Development Institute. Cervantes-Godoy, D., & Dewbre, J. (2010). Economic importance of agriculture for poverty reduction. OECD Food, Agriculture and Fisheries Working Papers, No. 23. OECD Publishing. https://doi.org/10.1787/5kmmv9s20944-en Chambers, R., & Conway, G. (1992). Sustainable rural livelihoods: Practical concepts for the 21st century. Institute of development studies. Danso-Abbeam, G., Dagunga, G., & Ehiakpor, D. S. (2020). Rural non-farm income diversification: implications on small-scale farmers’welfare and agricultural technology adoption in Ghana. Heliyon,6(11), e05393. https://doi.org/ 10.1016/j.heliyon.2020.e05393 Das, V. K., & Ganesh-Kumar, A. (2018). Farm size, livelihood diversification and farmer’s income in India. DECISION, 45(2), 185–201. https://doi.org/10.1007/s40622-018-0177-9 Dinku, A. M. (2018). Determinants of livelihood diversification strategies in Borena pastoralist communities of Oromia regional state, Ethiopia. Agriculture & Food Security,7(1), 1–8. https://doi.org/10.1186/s40066-018-0192-2 Echebiri, R. N., Onwusiribe, C. N., & Nwaogu, D. C. (2017). Effect of livelihood diversification on food security status of rural farm households in Abia State Nigeria. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development,17(1), 159–166. Ellis, F. (1998). Household strategies and rural livelihood diversification. Journal of Development Studies,35(1), 1–38. https://doi.org/10.1080/00220389808422553 Ellis, F. (2000). The determinants of rural livelihood diversification in developing countries. Journal of Agricultural Economics,51(2), 289–302. https://doi.org/10.1111/j.1477-9552.2000.tb01229.x 14 V. MBEWANA AND I. KASEERAM Etuk, E. A., Udoe, P. O., & Okon, I. I. (2018). Determinants of livelihood diversification among farm households in Akamkpa Local Government Area, Cross River state, Nigeria. Agrosearch,18(2), 101–112. Feliciano, D. (2019). A review on the contribution of crop diversification to Sustainable Development Goal 1 “No poverty”in different world regions. Sustainable Development,27(4), 795–808. https://doi.org/10.1002/sd.1923 Getahun, H., Smith, I., Trivedi, K., Paulin, S., & Balkhy, H. H. (2020). Tackling antimicrobial resistance in the COVID-19 pandemic. Bulletin of the World Health Organization,98(7), 442–442A. https://doi.org/10.2471/BLT.20.268573 Ghosh, J., & Bharadwaj, K. (1992). Poverty and employment in India. In H. Bernstein, B. Crow, & H. Johnson (Eds.), Rural livelihoods: Crises and responses. Oxford University Press and The Open University. Goldman, I., Carnegie, J., Marumo, D., Kela, E., Ntonga, S., & Mwale, E. (2000). Institutional support for sustainable rural livelihoods in Southern Africa: Framework and methodology. Natural Resources Perspectives,49,1–12. Guite, S., Sharma, H. I., & Thoudam, L. (2022). Determinants of livelihood diversification among the Thadou-Kukis of Manipur, India. Economic Affairs,67(1s), 79–86. https://doi.org/10.46852/0424-2513.1.2022.15 Habib, N., Rankin, P., Alauddin, M., & Cramb, R. (2023). Determinants of livelihood diversification in rural rain-fed region of Pakistan: Evidence from fractional multinomial logit (FMLOGIT) estimation. Environmental Science and Pollution Research International,30(5), 13185–13196. https://doi.org/10.1007/s11356-022-23040-6 Hornby, D., Nel, A., Chademana, S., & Khanyile, N. (2018). A slipping hold? Farm dweller in South Africa’s changing agrarian economy and climate. Land,7(2), 40. https://doi.org/10.3390/7020040 Khan, W., Jamshed, M., Fatima, S., & Dhamija, A. (2020). Determinants of income diversification of farm households in Uttar Pradesh, India. Forum for Social Economics,49(4), 465–483. https://doi.org/10.1080/07360932.2019.1666728 Khapayi, M., & Celliers, P. R. (2016). Factors limiting and preventing emerging farmers to progress to commercial agricultural farming in the King William’s Town area of the Eastern Cape Province, South Africa. South African Journal of Agricultural Extension,44(1), 25–41. Koenker, R., & Bassett, J. G. (1978). Regression quantiles. Econometrica,46(1), 33–50. https://doi.org/10.2307/1913643 Krantz, L. (2001). The sustainable livelihood approach to poverty reduction. SIDA. Division for Policy and SocioEconomic Analysis,44,1–38. Kumar, M. A., & Umesh, K. B. (2020). Extent and determinants of livelihood diversification in North and South Bengaluru: An interspatial analysis. Mysore Journal of Agricultural Sciences,54(1), 89–96. Mathebula, J., Molokomme, M., Jonas, S., & Nhemachena, C. (2017). Estimation of household income diversification in South Africa: A case study of three provinces. South African Journal of Science,113(1/2), 9. https://doi.org/10. 17159/sajs.2017/20160073 Miti, S. (2017). Local economic hubs already making a difference in EC. Vuk’uzenzele.https://www.vukuzenzele.gov. za/local-economic-hubs-already-making-difference-ec Mkonda, M. Y., He, X., & Festin, E. S. (2018). Comparing small-scale farmers’perception of climate change with meteorological data: experience from seven agroecological zones of Tanzania. Weather, Climate, and Society,10(3), 435–452. https://doi.org/10.1175/WCAS-D-17-0036.1 Morrissey, K., Reynolds, T., Tobin, D., & Isbell, C. (2023). Market engagement, crop diversity, dietary diversity, and food security: Evidence from small-scale agricultural households in Uganda. Food Security,16(1), 133–147. https:// doi.org/10.1007/s12571-023-01411-2 Mudzielwana, R., Mafongoya, P., & Mudhara, M. (2022). An analysis of the determinants of irrigation farmworkers’ food security status: A case of Tshiombo Irrigation Scheme, South Africa. Agriculture,12(7), 999. https://doi.org/10. 3390/agriculture12070999 Nabuuma, D., Reimers, C., Hoang, K. T., Stomph, T., Swaans, K., & Raneri, J. E. (2022). Impact of seed system interventions on food and nutrition security in low-and middle-income countries: A scoping review. Global Food Security, 33, 100638. https://doi.org/10.1016/j.gfs.2022.100638 NDA [National Development Agency]. (2014). State of poverty and its manifestation in the nine provinces of South Africa. Human Sciences Research Council. Ngema, P. Z., Sibanda, M., & Musemwa, L. (2018). Household food security status and its determinants in Maphumulo local municipality, South Africa. Sustainability,10(9), 3307. https://doi.org/10.3390/su10093307 Onuwa, G., Mailumo, S., Chizea, C., & Alamanjo, C. (2022). Socioeconomic determinants of livelihood diversification among arable crop farmers in Shendam, plateau state, Nigeria. Agricultural Socio-Economics Journal,22(4), 301– 309. https://doi.org/10.21776/ub.agrise.2022.022.4.7 Purvis, M., & Smith, R. (2006). Sustainable agriculture for the 21st century.InExploring sustainable development: Geographic perspectives. Earth-scan. Reardon, T. (1997). Using evidence of household income diversification to inform study of the rural non-farm labor market in Africa. World Development,25(5), 735–747. https://doi.org/10.1016/S0305-750X(96)00137-4 Roy, A., & Basu, S. (2020). Determinants of livelihood diversification under environmental change in coastal community of Bangladesh. Asia-Pacific Journal of Rural Development,30(1–2), 7–26. https://doi.org/10.1177/ 1018529120946159 Sifundza, S. B. (2019). Contract farming and access to formal credit in South Africa: A case of small-scale sugarcane growers in the Felixton Mill area of KwaZulu-Natal [Doctoral dissertation]. University of Pretoria. COGENT ECONOMICS & FINANCE 15 Smith, M. K. (2012). Dynamics affecting subsistence agricultural production: an exploration of a case study of subsistence crop production within a rural community in the Ingwe municipality of Southern KwaZulu-Natal [Master’s degree]. University of KwaZulu-Natal, South Africa. Sneddon, C. S. (2000). Sustainability in ecological economics, ecology, and livelihoods: A review. Progress in Human Geography,24(4), 521–549. https://doi.org/10.1191/030913200100189076 Stats SA [Statistics South Africa]. (2016). Community survey 2016, agricultural households, report no. 03-01-05. Statistic South Africa. Stats SA [Statistics South Africa]. (2019). Five facts about poverty in South Africa. Statistic South Africa. Stats SA [Statistics South Africa]. (2020). Census of commercial agriculture, 2017 KwaZulu-Natal: Financial and production statistics. Statistic South Africa. Teshager, A. M., Tsunekawa, A., Adgo, E., Haregeweyn, N., Nigussie, Z., Ayalew, Z., Elias, A., Molla, D., & Berihun, D. (2019). Exploring drivers of livelihood diversification and its effect on adoption of sustainable land management practices in the Upper Blue Nile Basin, Ethiopia. Sustainability,11(10), 2991. https://doi.org/10.3390/su11102991 Tyenjana, A., & Taruvinga, A. (2019). Determinants of rural on-farm livelihoods diversification: The case of Intsika Yethu Local Municipality, Eastern Cape, South Africa. Journal of Agribusiness and Rural Development,54(4), 373– 384. https://doi.org/10.17306/J.JARD.2019.01200 Warwick, R. M., Somerfield, P. J., & Clarke, K. R. (2008). Simpson index. Ecological indicators. Encyclopedia of Ecology, 4(5), 3252–3255. Washo, J. A., Tolosa, S. F., & Debsu, J. K. (2021). Determinants of rural households’livelihood diversification decision: The case of Didessa and Bedelle District, Bunno Bedelle Zone, Oromia Regional State, Ethiopia. African Journal of Agricultural Research,17(12), 1573–1580. Workie, D. M. (2023). Livelihood diversification strategies and determinants by small-scale farmers in the highland areas of North Shewa Ethiopia. Journal of Agribusiness and Rural Development,68(2), 217–228. https://doi.org/10. 17306/J.JARD.2023.01703 16 V. MBEWANA AND I. KASEERAM