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Background The livelihood of rural households in Ethiopia, like in most developing countries, largely depends on land resource. However, nowadays most rural households are denied access to arable land in the highland of Ethiopia due to high population growth and shortage of arable land. Landlessness is, therefore, becoming a serious social and economic problem in the rural highland areas of Ethiopia in general and Tigrai region in particular. This study, there- fore, intends to explore the choice of livelihood strategies of landless rural households and assess the challenges and opportunities of the livelihoods of landless rural households in selected districts of Tigrai region. Methods This study is conducted in three randomly selected districts of Tigrai region, namely, Kilte-Awlaelo, Degua- Tembien, and Hintalo-Wajerat districts. For the purpose of this study, two Tabias were randomly chosen from each districts. Then, afterward, both primary and secondary data sources were consulted to address the specific objectives of this study. The primary data were collected from 258 randomly selected households and six focus group discus- sions. This study used Multivariate Probit and Negative Binomial Regression to analyze factors influencing the choice of livelihood strategies and the number of livelihood options adopted by the landless rural households, respectively. Results This study finds that the livelihood sources of the landless rural households in the study area include farm (90%), non-farm (72%), and off-farm (41%) economic activities. The result of the Multivariate Probit regression indicates that household head characteristics, human capital, social capital, physical capital, financial capital, and institution-related factors were significantly influencing the choice of livelihood strategies of the landless rural house - holds. The results of the negative binomial regression model, on the other hand, assert that household head-related factors, social capital, and institution-related factors were significantly influencing the number of livelihood options adopted by the landless rural households. This study affirms that stone or sand selling, dairy farming, poultry produc- tion, animal fattening, and bee keeping are the major opportunities to improve the livelihood of the landless rural households. Moreover, this study also identifies that shortage of arable land, youth unemployment, lack of access to infrastructure, poor land administration, and lack of access to financial capital were the major challenges facing the landless rural households. Concluding remarks This study concludes that all stakeholders efforts to address the problem of landlessness need to be geared to enhance access of landless rural households to different livelihood capitals, such as human, social, financial, physical, and natural capitals. Moreover, rural township and village enterprises could enhance the access of landless rural households to market and job opportunities. *Correspondence: Teklay Negash teklaynegash@gmail.com; teklay.negash@mu.edu.et Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom- mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Negash et al. Agriculture & Food Security (2023) 12:6 Page 2 of 16 Keywords Landless rural households, Livelihood strategies, Multivariate probit model, Negative binomial regression, Tigrai region Background sufficient access to farm land to produce adequate food The livelihood strategies of rural population in devel - for their members. Thus, land as a safety net has been oping countries largely depend on natural resources, eroding and landlessness is emerging among the youth in particularly, land [1–3]. Nowadays, diversifying the live- most rural highlands areas of Ethiopia [12]. This has led lihood strategies has become common phenomenon as to rise rural unemployment in most parts of rural high- the carrying capacity of the agricultural sector to attain lands of the country [14]. Food insecurity, vulnerability, food and livelihood security is extremely declining from and land oriented poverty are the manifestation of the time to time [4]. This can be attributed to high popula - emergence of rural landlessness in Ethiopia [12]. tion growth rate, land fragmentation, soil erosion, low Similarly, in Tigrai region agriculture is the mainstay of soil fertility, and resulting low crop productivity. Some the rural population despite the sector is challenged by households diversify their livelihood strategies to reduce recurrent drought, erratic rainfall, and limited availability risk exposure, and maintain consumption requirements of farm land well below the national average. The share in the event of shocks, while others rely on one or few of agriculture in the Regional Gross Domestic Product activities as sources of livelihood [5–7]. Study by Ref. [8] (RGDP) was reported to be about 36.7% in 2018/19 [15]. conducted in Humla, a remote mountain district in west The sector is the source of employment opportunity for Nepal, reported that rural livelihood diversification and about 80% of the rural population [16]. The zonal distri - well-being can be achieved when households pull high bution of arable land in the region is reported to be une- return livelihood portfolio from among various economic ven with western and north western zones having larger activities available to them. In Ethiopia, agricultural sec- arable land per capita amounting to 3.36 and 1.36 hec- tor is the main economic pillar of the rural economy and tare, respectively. However, southern, eastern, and cen- the overall economic growth of the country is highly tral zones owned smaller arable land 0.82, 0.76, and 0.69 dependent on the success of this sector. It represents 42% hectare, respectively [16]. On top of this, the regional of the Gross Domestic Product (GDP), more than 90% of government of Tigrai had ceased re-distribution of ara- foreign exchange earnings, and about 85% of the popula- ble land since 1991 owing to limited availability of arable tion gaining their livelihood sources directly or indirectly land. On the contrary, in Tigrai region the growth rate from the sector [9]. u Th s, agriculture is still believed to of population was reported to be 2.5% per annum which remain a sector that plays an important role in stimulat- made the total rural population of the region around ing the overall economic development of the country in 3.847 million in 2017 [17]. This indicates that the demand the years to come [10]. for arable land is still increasing with escalating rural Rural residents in Ethiopia have been guaranteed population. The growing number of the rural landless access to land through a law that provides them a right households and the limited availability of cultivable land to obtain agricultural land for free. The constitution initiated the regional government to handle the scarcity of FDRE states that any citizen of the country who is of cultivable land through the re-distribution of commu- 18 years of age or above and wanted to engage in agri- nal land to the landless rural households. culture for living shall have the right to use rural land for However, little is known about the choice of the live- free [11]. Furthermore, children who lost their mother lihood strategies of landless rural households in Tigrai and father due to death or other situation shall have the despite very few studies in some parts of the country. For right to use rural land through legal guardians until they instance, Ref. [18] accessed the diversification and liveli - attain 18 years of age [11]. Conversely, Ethiopia currently hood sustainability in a semi-arid environment of south- faces severe land scarcity in the highland part of the ern Ethiopia. Ref. [4] identified the livelihood strategies nation where population density has become very high and assessed the factors that influenced households’ and the per capita holding of farm lands has become very decision to choose among the alternative livelihood strat- small [12]. For instance, study by Ref. [13] reported that egies in Wolaita zone of Ethiopia. Ref. [19] assessed the about 43% of the people in the rural areas of Ethiopia are livelihood strategies among the Borana pastoralists in landless, and nearly 60% of the households do not have southern Ethiopia and Ref. [20] looked at the livelihood diversification of rural households to supplement their small-scale agricultural activities in east Gojjam zone. Similarly, study by Ref. [21] investigated households Federal Democratic Republic of Ethiopia. Negash et al. Agriculture & Food Security (2023) 12:6 Page 3 of 16 Fig. 1 Administrative map of study districts livelihood diversification options and analyzed the deter - three districts of Tigrai region, northern Ethiopia. More minants of livelihood diversification strategies in east - specifically, this study is designed to assess the livelihood ern Tigrai Region of Ethiopia. However, none of these sources of landless rural households, analyze factors studies investigated the livelihood options of landless influencing the choice of livelihood strategies, examine rural households in Tigrai region. On top of this, the the major determinants affecting the number of liveli - problem of rural landlessness and unemployment are hood strategies adopted by the landless rural households, crucial policy issues which need critical examination to and identify the major challenges and opportunities fac- alleviate rural poverty and food insecurity. Our study ing the livelihood of rural landless households in the is unique from previous studies in two perspectives. study sites. First, this study focuses on the livelihood sources of the landless rural households in land scarce Tigrai region. Data and methods Second, methodologically previous studies used Multi- Description of the study area nomial logit model to estimate the correlates of choice This research was conducted in three randomly selected of livelihood options ignoring the fact that the choice districts of Tigrai region, northern Ethiopia. The study of livelihood options could be simultaneously modeled. areas include Kilte-Awlaelo, Degua-Tembien, and Hin- However, this study used Multivariate Probit model to talo-Wajerat districts (Fig. 1). Afterward, two Tabias estimate factors affecting the livelihood choice of landless were randomly selected from each district. Accordingly, rural households appreciating simultaneity of the liveli- Kihen and May-Kuiha from Kilte-Awlaelo, Debre-Nazret hood options adopted by the landless rural households in the study districts. This paper, therefore, aims at exploring the choice of Tabia is the smallest local government administrative unit in the rural set- livelihood strategies among rural landless households in ting of Tigrai region. Negash et al. Agriculture & Food Security (2023) 12:6 Page 4 of 16 and Mizan-Birhan from Degua-Tembien, and Mesanu Table 1 Distribution of sample landless rural households in the selected Tabias and Alem-segeda from Hintalo-Wajerat were randomly selected for the purpose of this study. Geographically, Districts Tabia Total landless Sample Hintalo-Wajerat is delimited by the Emba-Alage district Households selected on the south, Seharti Samre district on the west, Enderta Kilte-Awlaelo May-Kuha 141 50 district on the north, and the Afar Region on the east. Kihen 118 42 Degua-Tembien is bordered on the south by the Seharti Hintalo-Wajerat Mesanu 107 38 Samre district, on the west by Abergele district, on the Alem-Segeda 124 44 northwest by Kola-Tembien, on the north by Hawzen dis- Degua-Tembien Debre-Nazret 110 39 trict, on the northeast by Kilte-Awlaelo, and on the east Mizan-Birhan 127 45 by Enderta. Furthermore, Kilte-Awlaelo is encircled on Total 727 258 the south by Enderta district, on the west by the Hawzen district, on the north and northeast by Saesi Tsaedaemba, and on the east by Atsbi-Wenberta. Mixed crop and live- administration to supplement the household survey and stock farming systems are the major economic activi- focus group discussion. ties practiced in the study districts. The dominant cereal crops grown in Degua-Tembien district include wheat, Sampling technique and sample size barley, teff, and pulses. Wheat, barley, teff, and pulses are This study employed multi-stage sampling technique. the most staple crops grown in Hintalo-Wajerat districts. In the first stage, three districts, namely, Kilte-Awlaelo, Similarly, wheat and barley are the major crops grown in Hintalo-Wajerat, and Degua-Tembien, were randomly Kilte-Awlaelo district. The major livestock populations in selected from Tigrai region, northern Ethiopia. In the the study districts include cattle, goat, sheep, and donkey. second stage, two Tabias from each district was also ran- domly selected. In the third stage, sampling frame was prepared for each Tabia. The study applied [22] standard Data sources and collection methods formula to estimate representative sample size as shown This study used primary and secondary data sources. in Eq. 1. In the fourth stage, the representative sample The primary data were collected from randomly selected size was distributed among the study Tabias in propor- landless rural households using structured survey ques- tion to their representation of target population. Finally, tionnaire. The structured survey questionnaire was pre - respondents were randomly selected from the list of pared to collect relevant information on demographic households obtained from respective local government characteristics, livelihood sources, capital assets owner- administrations. The lists of study districts and Tabias ship, like human, natural, financial, physical, and social along with the sample drawn are presented in Table 1. capital, institutional factors, challenges, and opportuni- ties of the landless rural households. Pre-testing of the n = questionnaire was undertaken in a randomly selected vil- (1) 1 + N (e) lage within the study area to enhance the relevance and reliability of the data collection tool. where N stands for the total landless rural households Focus Group Discussion (FGD) was also conducted to found in six study Tabias (727) and e is the tolerable explore concepts, generate ideas, determine differences magnitude of error at 95% level of significance which is in opinion among stakeholders, and perceive challenges equal to (e = 0.05) and n stands for representative sample and opportunities of the livelihood strategies of landless size (258). rural households of each districts. Checklist was pre- pared to guide focus group discussions. The checklist was Conceptual framework of the study designed in a manner that was able to generate relevant This study adopted sustainable livelihood approach which information regarding the perception of discussants on provides insight about the complex and comprehensive the available livelihood strategies, sustenance of each understanding of how rural household struggle to survive livelihood strategy, as well as the challenges faced and in resource deficient rural setting. The concept of liveli - possible opportunities of the landless rural households in hood can be seen from capabilities and entitlement per- each Tabia. Six FGD consisted of 8–10 participants each spectives [23–26]. The sustainable livelihood approach were conducted. By doing so, the authenticity of data col- also considers natural resource management and use, and lected using the household survey was verified and tri - comprehensive capital ownership of rural households. angulated. Secondary data were gathered from reports The livelihood strategies practiced by the rural house - and documents of the regional and local government holds are based on the five capital asset ownership [5, Negash et al. Agriculture & Food Security (2023) 12:6 Page 5 of 16 Human Capital Financial Capital Natural Capital Social Capital Livelihood Options of Landless Rural Household Institutional Physical Factors Capital Household Head Characteristics Fig. 2 Conceptual framework for choice of livelihood options of the landless rural household 27, 28]. These include human, natural, financial, social, rural household in Tigrai region, northern Ethiopia. The and physical capital assets of households. Access to and detailed conceptual framework of the study is presented ownership of these capital assets determine the choice of in Fig. 2. livelihood strategies adopted by the landless rural house- holds [29]. Moreover, household head-related and access Methods of data analyses to institutional factors also significantly determine the This research paper used both descriptive statistics and livelihood choice of landless rural households. Livelihood econometric techniques of data analyses. This study used strategies comprise the range and combination of activi- descriptive statistics to summarize the demographic and ties and choices that people make in order to achieve socioeconomic attributes of the surveyed landless rural their livelihood goals. In the rural setting of developing households. Particularly, frequency and percentage were countries, the livelihood strategies of households can be also used to summarize the challenges and opportuni- categorized into farm, non-farm, and off-farm activities. ties of the landless rural households in the study area. To The case is not unique in Ethiopia at large and in Tigrai analyze the major factors affecting choice of livelihood region, in particular, where more than 80% of the popu- strategies and examine the major determinants influenc - lation rely on the agriculture. The concern of this study ing the number of livelihood options adopted by the rural is, therefore, to investigate on how the ownership of and landless households, this study used Multivariate Probit access to different livelihood capitals along with house - (MVP) and Negative Binomial Regression (NBR) models, hold head and institutional factors influence the choice respectively. of livelihood strategies of landless rural households in the randomly selected districts of Tigrai region of Ethi- Multivariate probit model (MVP) opia. Moreover, this study also attempted to investigate The livelihood options available to the landless rural whether or not the livelihood capital assets, institutional households might not necessarily be mutually exclusive; factors, and household-related factors influence the rather a household can simultaneously adopt more than number of livelihood options adopted by the landless one option as the means of livelihood sources. That is, the Negash et al. Agriculture & Food Security (2023) 12:6 Page 6 of 16 landless rural households may choose any one or combi- (like private house ownership for residence and live- nation of the available livelihood options to support their stock holding in TLU ), financial capital-related variables livings. For this reason, a multivariate modeling frame- (credit access, membership of Equb, and remittance), work is needed to account for the interdependence and social capital -related factors (such as membership in 7 8 possibly simultaneous decision making characteristics of Edir, and traditional labor sharing ), and institutional the landless rural households in the study districts. factors (like participation in safety net program, distance Methodologically, MVP applies when the probability of to all-weather road, and distance to nearest market) of choosing more than one option is simultaneously mod- the rural landless households were included as explana- eled against the explanatory variables [30, 31]. Therefore, tory variables in the MVP (Table 2). this study used MVP to estimate factors affecting choice of livelihood options of the landless rural households. A Negative binomial regression system of simultaneous Multivariate probit model for the The number of livelihood options adopted by the land - livelihood options of landless rural households was con- less rural household can be considered as a count vari- structed as follows [32]: able. This observed count variable basically refers to the number of livelihood options adopted by the landless y ∗= β X + ε , y = 1 if y ∗ > 0 and 0 otherwise. im m im im im im rural households. The dependent variable of the model (2) (that is, number of livelihood options of the landless rural Equation 2 is based on the assumption that a rational households) assumes non-zero positive integer values ith landless rural households has a latent variable y im in which both Truncated Poisson Regression and Nega- which captures unobserved preferences associated with tive Binomial Regression (NBR) are possible candidates the mth choice of livelihood option (m = 3; available to estimate the major factors influencing the number of livelihood options in the study districts); βm is the set of livelihood options of the landless rural households. The parameters that reflect the impact of changes in the vec - Truncated Poisson Model assumes equi-dispersion of tor of explanatory variables on the landless rural house- the mean and variance of the dependent variable which hold’s preference toward the mth livelihood option; x im is commonly violated in most applied researches. On the represents the vector of observed explanatory variables other hand, the Negative Binomial Model integrates the that are expected to explain the choice of each type of the problem of over-dispersion into account while estimat- livelihood option; and ε represents error terms of the im ing the parameters of the model. To select which model model. is appropriate to apply between the two models, it is per- The dependent variables of the model are the liveli - tinent to conduct a Z test following Refs. [32, 35] given in hood options adopted by the landless rural households in Eq. 3: the study districts. The dependent variable assumes the −1 −1 −1 values of Y = 1, if household is engaged in crop produc- Ŵ(α + y) α pr(Y = y|, α) = tion and/or animal rearing activities on gifted, rented, −1 −1 −1 Ŵ(α )Ŵ(y + 1) α + + α or shared in land; Y = 2, if the choice lies on non-farm (3) activities which include petty trade, sale of handicraft, where and α are parameters in which α is the variance sale of beverage, animals trading, stone sale, sand sale, indicators. carpenter, masonry, guarding, and sale of firewood or The result of the Z test in Table 3 confirmed that there charcoal; and Y = 3, if the choice lies on off-farm activi - is statistically significant difference between the mean and ties, such as land preparation, plow, weeding, harvesting, and threshing among others to earn farm wages. Following Ref. [33] and the study area context, house- It is an aggregation of livestock from various species and age as per con- hold head-related characteristics (like age, sex, and vention factors given by Ref. [34]. It is given by calf = 0.5, Heifer = 0.75, cow = 1, Ox = 1, horse = 1.1,goat/sheep = 0.13, donkey = 0.70, camel = 1, and marital status of the household head), human capital- chicken = 0.013. related variables (such as educational status of house- Refers to traditional and informal way of saving practices where by each hold head and labor force), physical capital-related asset member can take credit based on the share he or she has contributed to it. So, this is considered as social capital in this study. Social capital refers to institutions, relationships, and norms that shape the quality and quantity of a society’s social interactions. Refers to informal self-help group in which each member gets different In this study, the definition of non-farm and off-farm economic activities is benefits during mourning, wedding, and other social ceremonies. This is based on Ref. [3]. Accordingly, the non-farm activities involve earnings from counted as social capital as it may improve the social network among mem- permanent and self-generated economic activities. However, off-farm activi - bers. ties are defined as economic activities mostly relying on selling labor to other farmers. Off-farm activities are, therefore, seasonal farm works by their very This refers to labor sharing practice in building house, weeding, harvest - nature. ing, and threshing activities. Negash et al. Agriculture & Food Security (2023) 12:6 Page 7 of 16 Table 2 Explanatory variables included in the multivariate probit and negative binomial regression models Variables Descriptions Dependent variables of multivariate probit (MVP) Household livelihood Options 1. If the choice of household is farm activities 2. If the choice of household is off-farm activities 3. If the choice of household is non-farm activities Dependent variable of negative binomial regression (NBR) Number of Livelihood options adopted by the rural landless households Variable code Description of the explanatory variables Nature Expected sign in NBR Family size Size of household Continuous Age Age of household head Continuous ± EduStatu 1 = if the educational status of household head is literate Dummy + Gender 1 = if the gender of household head is male Dummy + Marital Status 1 = if the marital status of household head is married Dummy Labor force Number of active labor forces of household (age between15- 64) Continuous DISMKT Distance to nearest market in kilometer Continuous − DISdistrict Distance to district center in kilometer Continuous − DISFTC Distance to farmers’ training center in kilometer Continuous TLU Number of livestock holding in Tropical Livestock Unit Continuous + Farm size Farm size of the household during the survey year in Tsimad Continuous Migrated 1 = if any household member is migrated Dummy PSNP 1 = if the household participated in safety net program Dummy − Residence 1 = if the household owned private house Dummy Edir 1 = if the household participated in Edir Dummy + Traditional Labor share 1 = if the household has shared labor in weeding, harvesting and other Dummy + activities Equb 1 = if the household participate in Equb Dummy + Credit 1 = if the household has credit access Dummy + Remittance 1 = if the household received remittance during the survey year Dummy It is an aggregation of livestock from various species and age as per convention factors. It is given by calf 0.5, Heifer 0.75, cow 1, Ox 1, horse 1.1, goat/sheep 0.13, donkey 0.70, camel 1, and chicken 0.013 [34] 1 tsimad is equivalent to 0.25 hectare of arable land Table 3 Z test of equi-dispersion of variance and mean Regress zhat lambda, noconstant noheader Zhat Coef Std. Err T P > t [95% Conf. interval] Lambda − 0.0711 0.0232 − 3.06 0.002 − 0.1168 − 0.0254 variance value as the p-value of the coefficient that corre - µ = exp(ln(t ) + β X + β X + ... + β X ), i i 1 1i 2 2i k ki (4) spond the lambda is 0.0002. This implies that there exists problem of over-dispersion which indicates that Negative where β are unknown parameters to be estimated from Binomial Model instead of Truncated Poisson Model is the data set, t represents the exposure time, and X stands recommended for the purpose of this analysis. for the explanatory variables included in the model The mathematical representation of the Negative Bino - (Table 2). mial Regression is given in Eq. 4: Negash et al. Agriculture & Food Security (2023) 12:6 Page 8 of 16 Table 4 Summary statistics of demographic and socioeconomic administration believes that they [non-beneficiary] are features of respondents relatively young and able to work elsewhere in the urban or sub-urban areas to smooth out consumption and Variable Mean Standard Minimum Maximum deviation ensure their food security. On top of this, respondents were asked about their perception regarding the depend- Gender 0.80 0.40 – – ency syndrome on safety net program. Accordingly, Age 32.80 9.20 19 68 about 40% of the respondents reported that the safety net Edustatu 0.61 0.49 – – program created dependency syndrome due to entitle- MartalSta 0.81 0.40 – – ment for extended years. The participants of focus group Family size 4.23 1.80 1 11 discussion further confirmed that safety net program Labor 2.33 1.11 1 8 harms the self-reliance and work habit of the beneficiar - Farm size 2.79 1.98 0 10.25 ies of the program. For instance, one of the respondent Dismrod 4.03 4.37 0.17 20 revealed that there are few number of safety net benefi - Dismkt 4.98 3.44 0.10 15 ciaries who always under report their earnings and went DisTFC 2.28 3.28 0.17 22 to the extent of selling their basic asset like livestock so TLU 2.19 1.89 0 9.2 that they remained entitled in the program. This result Residence 0.62 0.49 – – is in contrast to the empirical finding of Ref. [36] who PSNP 0.30 0.46 – – reported that there were no indications that participation PSNPdep 0.40 0.49 – – in productive safety net program-induced households to Migrated 0.28 0.69 – – disinvest in livestock production or tree plantation. Remittance 0.10 0.30 – – This study found out that 27% of the surveyed house - Livelihood options 2.88 1.62 1 9 holds reported to have any member of their household migrated abroad looking for employment opportunity. However, this study finds out that only about 10% of the Results and discussion households received remittance. The result of the this Demographic and socioeconomic profiles of the surveyed study is similar with the empirical finding of Ref. [19] households who reported that remittance is not an important source The results of descriptive analyses of demographic and of household income in the Borana area, southeastern socioeconomic characteristics of the surveyed house- zone of Oromia, Ethiopia. Table 4 also reveals that, on holds are presented in Table 4. The result of this study average, the surveyed households relied on 3 livelihood shows that about 80% of the landless rural households options with minimum of 1 and maximum of 9 liveli- were male headed and 81% of the surveyed household hood sources. This implies that most of the landless rural heads were married. The mean age of the landless rural households did not diversify their livelihood options to household heads was about 32.8 years old. Furthermore, mitigate their vulnerability to various risks and shocks. the mean household size, dependency ratio, and labor Participation of landless rural households only on lim- force of the surveyed landless rural households were also ited number of livelihood sources might influence their found to be 4.23, 1.90, and 2.33, respectively. With regard food security status adversely. That is, households who to educational status of household heads, about 61% of have limited livelihood sources might suffer from critical the household heads were literate. Concerning to live- food shortage and hence food insecure. However, those stock holding of the households, on average, the surveyed who diversified their livelihood sources are more likely landless rural households owned 2.2 TLU. to be food secure. Moreover, the focus group discussant About 62% of the surveyed landless rural households affirmed that households who diversified their livelihood have private residential house, while the remaining live sources are linked with food security status. in rented houses, depending on their parents’ house or in relatives houses. Concerning access to public insti- Factors affecting landless rural households’ choice tutions, the mean distance to all-weather road, near- of livelihood strategies est market, and farmers’ training center were 4.03 km, Prior to running MVP regression, predicted probabili- 4.98 km, and 2.28 km, respectively. Furthermore, about ties and correlation matrix of the households’ liveli- 29% of the sampled landless rural households were bene- hood options were conducted to look at the overall ficiaries of the safety net program either in food for work significance of the model. The estimation result shows or direct support programs. A significant number of the that the probability of choosing among the alternative landless rural households were not beneficiaries of the livelihood sources by the landless rural households’ was safety net program. This is because the local government 90%, 72%, and 41% for farm, non-farm, and off-farm Negash et al. Agriculture & Food Security (2023) 12:6 Page 9 of 16 Table 5 Predicted probabilities and correlation matrix of labor force from agricultural sector, particularly, the livelihood strategies landless rural youths. The correlation matrix revealed that there is positive Off‑farm Non‑farm Farm and statistically significant interdependence between off- Variables farm and non-farm activities as livelihood sources at 5% Predicted Probability 0.41 0.72 0.90 significance level. This indicates that the landless rural Estimated correlation matrix households simultaneously choose both off-farm and Off-farm 1.00 non-farm sources of livelihood. In other words, this study Non-farm 0.14(0.03)** 1.00 asserts that off-farm and non-farm sources of livelihoods Farm 0.03(0.65) 0.01(0.87) 1.00 are found to be complementary to each other. That is, ** Denotes for 5% significance level and the values in the parenthesis are p-value landless rural households tend to engage in off-farm and non-farm sources of income simultaneously to reduce the risk of falling into food insecurity and poverty. The Wald test (Wald chi2 (45) = 135.28, Prob > chi2 = 0.0000) indicated that over all the model activities, respectively (Table 5). That is, on average, fits very well to the data set. That is, the explanatory about 90% of the surveyed landless rural households variables included in the model significantly explained were engaged in crop and livestock farming activities. the livelihood choice of the landless rural households. Most of them were involved in crop production activi- Table 6 indicates that out of fifteen explanatory variables ties on gifted, rented, and shared in land even if they included in the MVP, four variables were significantly did not own private farm land. Similarly, about 72% affecting the choice of farm activity as means of liveli - of the surveyed households also participated on non- hood; three variables were significantly influencing the farm activities, such as petty trade, sale of handcraft, off-farm income generating activities as source of liveli - sale of beverage, animals trading, daily laborer, car- hood; and eight variables were significantly determining penter, masonry, stone sale, sand sale, guarding, and in non-farm activities as source of livelihood of the landless rare cases sale of firewood or charcoal. In this regard rural households at different significance levels (Table 6). study by Ref. [37] indicated that non-farm income sig- The next paragraphs present detailed discussions of the nificantly contributed toward reducing the incidence, statistically significant variables into six basic categories, depth, and severity of household poverty in rural Kedah namely, household head-related characteristics, human of Malaysia. This indicated that non-farm economic capital, physical capital, social capital, financial capital, activities played a vital role in the livelihood of rural and institution-related variables. areas of developing countries. Ref. [21] also indicated that in the context of food inadequacy and drought, non-farm income played a significant contribution in Household head‑related factors eastern Tigrai region of Ethiopia. Similar results have Sex, age, and marital status of household head were sig- also been reported by Ref. [12] in north west Ethio- nificantly influencing the choice of the livelihood options pia that next to crop farming, livestock rearing, daily of the landless rural households in the study districts. labor, selling wood, and charcoal were the major liveli- Specifically, this study confirms that sex of household hood options and strategies practiced by landless rural head is positively and significantly influencing the like - households. Furthermore, almost 41% of the sampled lihood of choosing non-farm activities as the source of landless rural households were participating in differ - livelihood for the landless rural households at 5% signifi - ent off-farm activities, like plow, weeding, harvest - cance level. Table 6 indicates that landless rural house- ing, and threshing in others’ farm land. Moreover, our holds are more likely to rely on non-farm activities than study result is consistent with empirical findings of female-headed households in the study area. The possi - Refs. [38, 39] documented that landless people often ble reason for such finding may be derived from the fact rely on food aid, sharecropping, petty trading, and that males could have more spare time to move away daily labor for survival or they migrate to urban areas from their residence to work and engage in different in Loess Plateau, China, and west Bengal, respectively. income generating activities than female counterparts. This indicated that the only way to pursue an increase Besides, females, household head, have extra work bur- in households income is to enhance the employability dens at home, such as food preparation and taking care of landless people in non-farm and off-farm job oppor - of children and elderly. This is perhaps also related with tunities. Typically, the construction sector, including some cultural barriers, which still keep women and aged large infrastructure works, private housing, and urban household members engage in agricultural activities. infrastructure development, is capable of attracting Negash et al. Agriculture & Food Security (2023) 12:6 Page 10 of 16 Table 6 Estimation results of multivariate probit on factors affecting landless rural households’ choice of livelihood options Explanatory variables Livelihood options of rural landless households Farm Off‑farm Non‑farm marginal effect (SE) marginal effect (SE) marginal effect (SE) Age 0.0037 (0.03030) − 0.0074 (0.0157) − 0.05757 (0.0175)*** Gender(male = 1) − 0.6752 (0.5514) − 0.2025 (0.3160) 0.8498 (0.3435) ** Marital status (married = 1) 1.8132 (0.5715)*** 0.2635 (0.3423) − 0.2264 (0.3779) Labor force 0.1406 (0.2141) − 0.0498 (0.1142) 0.2726 (0.1209)** Educational status (literate = 1) 0.5100 (0.4038) − 0.6199 (0.1996)*** 0.2279 (0.2134) TLU 0.2815 (0.1668)* − 0.1129 (0.0572)** 0.0879 (0.0666) Private house ownership(yes = 1) 0.5674 (0.4033) 0.1184 (0.1973) 0.0886 (0.2159) Credit access (yes = 1) 0.6608 (0.3641)* − 0.0960 (0.1915) − 0.4621 (0.2140)** Remittance (yes = 1) − 0.6984 (0.5154) − 0.3822 (0.3242) − 0.5293 (0.3127)* Edir (yes = 1) − 0.1674 (0.3929) − 0.2034 (0.1848) 0.2304 (0.2081) Traditional labor sharing (yes) 0.9646 (0.3840)* 0.8662 (0.2074)*** 0.0730 (0.2166) Equb (yes = 1) − 0.3293 (0.5774) − 0.3332 (0.2736) 0.5443 (0.3259)* PSNP(yes = 1) 0.2468 (0.4499) − 0.2211 (0.2273) − 0.0929 (0.2451) Distance to road in km 0.0084 (0.0519) − 0.0097 (0.0259) − 0.0532 (0.0289)* Distance to market in km − 0.0115 (0.0409) − 0.0217 (0.0197) − 0.0563297 (0.0221) *** Model summary Number of observation = 258 Log likeli- hood = − 311.89149 Wald chi2(45) = 135.28 Prob > chi2 = 0.0000 *, **, and *** represent significance level at 10%, 5%, and 1%, respectively. SE stands for standard errors in parentheses Furthermore, age of household head was negatively sources of livelihood than others (divorced, widowed, but significantly influencing the likelihood of choosing separated, and single). non-farm activities of the landless rural households at 1% significance level. Holding other factors constant, as Human capital‑related factors the age of the household head increases by one year, the Educational status of household head and labor force of likelihood of choosing non-farm activities declines by households were significantly influencing off-farm and 6%. Similar finding has been reported by Ref. [19] who non-farm livelihood sources of the landless rural house- studied about determinants of livelihood diversification hold, respectively. Educational status of household head strategies in Borana pastoralist communities of Ethio- was negatively but significantly related with the likeli - pia and found out that the age of household head was hood of choosing off-farm activity as the source of liveli - negatively but significantly affecting the of pastoralist hood of the landless rural households at 1% significance choice of pastoral and off-farm combination, and pas - level. This implies households with literate heads are less toral and non-farm combination as livelihood sources. likely to engage in off-farm activities as the major liveli - However, our result is against the empirical findings of hood source of the landless rural households than house- Ref. [40] who reported that age of the household head holds with illiterate heads in the study districts. complemented with the non-farm income generating Moreover, labor force was positively and significantly activities. This is to mean as the age of the household influencing households’ likelihood of choosing non-farm head increases, the participation of household in non- activities as major livelihood sources at 5% significance farm income generating activities tend to rise. Moreo- level. Households with large number of labor force tend ver, the marital status of household head was also found to engage in non-farm activities as major source of liveli- to be positively and significantly related with the likeli - hood, holding other factors constant. This is perhaps due hood of choosing farm activities as source of livelihood to the fact that households with large labor force could at 1% significance level (Table 6). That is, married land - have more extra labor force so that they can engage in less rural households were more likely to choose farm- different non-farm activities to support their livings. ing activities on gifted, rented, and shared in land as the The finding of this study is congruent with the empiri - cal findings of Ref. [21] who underscored that productive Negash et al. Agriculture & Food Security (2023) 12:6 Page 11 of 16 family size adds significantly to the share of total income human capital for coping with natural hazards and cli- received from farming by participating in different non- mate change. This literature supports our empirical result farm income diversification strategies. This result is also in the sense that social capital contributes to the liveli- consistent with the empirical findings of Ref. [38] who hood choice of landless rural households. asserted that household size is complement with non- farm income generating activities on top of farming Financial capital‑related factors activities. This study used credit access, Equb membership, and remittance to capture the effects of financial capital on the choice of livelihood options of the rural landless Social capital‑related factors households. The result of this study shows that credit Membership in Edir and participation in traditional labor access was significantly determining the choice of non- sharing were included in the model to account for the farm and farm activities of the landless rural households. effect of social capital-related factors on the choice of Specifically, credit access was positively and significantly livelihoods landless rural households. Accordingly, this influencing the choice of farm activities of the landless study finds that participation in traditional labor sharing rural households at 10% significance level. This means was positively and significantly influencing the decision credit access contributes more to the farming activities of of landless rural households to choose farm and off-farm the rural landless households to produce crop on rented activities. Specifically, participation in traditional labor and shared in land. The reason could be credit services sharing was positively and significantly related with the are available mostly for farming activities, such as pur- likelihood of choosing off-farm activities as sources of chase of farm inputs, daily production, production of livelihood at 1% significance level. This result is in line shoat, and poultry production. Similar, results have been with the prior expectation of this study. Traditional labor reported by Refs. [43, 44] who found that households sharing may help households create linkage and network with credit access focused on agricultural intensification with which they can enhance their likelihood of getting to enhance productivity instead of diversifying their live- off-farm job opportunities in their localities. lihood sources. This implies that households tend to con - Moreover, traditional labor sharing was positively and centrate their production decision on selected economic significantly affecting landless rural households decision activities instead of engaging in various activities. to engage in farm activities at 10% significance level. This However, credit access was negatively but significantly is might be due to the fact that the social network could affecting the choice of non-farm source of income as help them access land either in the form of rented and means of survival at 5% significance level. This is perhaps shared in land. This is an instrumental solution to access due to the fact that the amount of credit offered by the land for the land poor and to engage in crop production financial institution might not enough to engage in non- in land scarce Tigrai region. This result is similar with farm income generating activities, such as petty trade and the empirical finding of Ref. [41] who revealed that the animal trading among others. The other reason could be relationship between livelihood diversification and mem - landless rural households could not access credit for non- bership of a cooperative society was found to be positive farm due to lack of collateral. Furthermore, membership and statistically significant. This in turn indicated that in Equb was also significantly and positively affecting the social capital has significant contribution toward liveli - choice of non-farm income generating activities by the hood diversification. Similarly, Ref. [42] reported that landless rural households at 10% significance level. That neighborhood attachment as social capital has a positive is, the money they got from Equb could be important effect on household confidence in coping with food and seed capital to start up small business, like petty trade income insecurity in the face of climate change. Thus, and animal trading. Surprisingly, remittance was nega- the empirical finding confirmed that rural households tively but significantly determining the choice of non- are more likely to rely on bonding social capital to cope farm economic activities as income generating livelihood with economic stress. Households who have a close con- options at 10% significance level. nection with their neighborhoods can mobilize resources from their neighbors in order to cope with economic and non-economic challenges. Furthermore, the study by Physical capital‑related factors Ref. [43] also asserted that membership in cooperative Livestock ownership (TLU) was significantly influencing promotes household livelihood diversification into off- landless rural households choice of farm and non-farm farm and non-farm economic activities. Moreover, Ref. livelihood options. That is, households with large number [40] asserted that social capital is as important as other of livestock were positively and significantly related with livelihood capital assets physical, natural, financial, and choice of farm activities at 10% significance level. This Negash et al. Agriculture & Food Security (2023) 12:6 Page 12 of 16 Table 7 Estimation result of negative binomial regression on factors influencing the number of livelihood options adopted by landless rural households Livelihood options IRR Robust Std. Err P‑ value Gender of household head (male = 1)*** 1.4448 0.1665 0.001 Age square of household head*** 0.9998 0.0001 0.009 Educational status of household head (literate = 1) 0.9003 0.0739 0.200 Family size of the households 1.0306 0.0297 0.297 Edir (yes = 1) * 1.1605 0.0933 0.064 Distance to road in km 0.9996 0.0093 0.964 Distance to district in km*** 0.9806 0.0052 0.000 Credit access (yes = 1) 0.9288 0.0782 0.380 Distance to farmers training center in Km 1.0138 0.0112 0.213 Livestock holding in TLU 0.9868 0.0223 0.555 Safety net Participation(yes = 1) 0.9164 0.0932 0.391 Constant term 3.2629 0.5052 0.000 /lnalpha − 53.6532 Alpha 5.00e−24 Model summary Negative binomial regression Number of obs = 258 Dispersion = mean LR chi2(11) = 50.35 Prob > chi2 = 0.000 Log pseudo-likelihood = −449.2308 Pseudo R2 = 0.053 Similarly, this study confirms that distance to nearest is due to the fact that livestock is important farm input market was also negatively but significantly affecting the required to engage in farm activities, like providing draft choice of non-farm livelihood option at 10% significance power and hauling services. However, households with level. This means the further the distance from the resi large number of livestock were negatively but signifi - - cantly influencing the choice of off-farm economic activi - dence area of the landless rural households to the market, ties by the landless rural households at 5% significance the lesser the likelihood of choosing non-farm livelihood level. The results of this study is in line with the empirical sources. Furthermore, similar finding has been reported findings of Ref. [4] who asserted that the livestock owner - by Ref. [22] who confirmed that diversifying livelihood beyond the agricultural practice is likely to reduce as the ship was negatively and significantly affecting the diversi - distance to nearest market place increases from their res fication of livelihood sources into non-farm and off-farm - in Wolaita Zone of southern Ethiopia. idence. This result is consistent with the findings of Ref. [31] who highlighted that labor markets offer non-farm Institution‑related factors job opportunities for income generating activities of the Participation in safety net program, distance to near- rural households. est market, and all-weather road were included in the model to account for the effects of institutional factors Factors affecting the number of livelihood options on the choice of livelihood options of the landless rural adopted by landless rural households households. Consequently, distance to all-weather road The log likelihood ratio chi2(11) = 50.3 with (p = 0.000) was negatively but significantly influencing the likeli - is significant at the 1% significance level, which indicates hood of choosing non-farm activity as major sources of that the subset of all coefficients of the model is jointly livelihood at 10% significance level. This implies landless significantly explaining the dependent variable. uTh s, the explanatory power of the independent variables rural households whose residence is far from all-weather included in the model is overall satisfactory. Table 7 indi road have less likelihood of engaging in non-farm income - generating activities. This is perhaps due to the fact cates that out of eleven explanatory variables included in that access to information and job opportunity is highly the Negative Binomial Regression model, four explana- related with proximity to road where information about tory variables were significantly influencing the num - labor market and job opportunity are available. ber of livelihood sources adopted by the landless rural households. Negash et al. Agriculture & Food Security (2023) 12:6 Page 13 of 16 Particularly, gender of household head and member- and documented that direct or indirect experience moti- ship of the household in Edir were positively and signifi - vate farmers to engage in crop insurance market to cantly affecting the number of livelihood options adopted reduce risk and ensure food security during crop failure. by the landless rural household. Furthermore, age square Moreover, our finding is also inconsistent with the empir - of the household head and distance to the district center ical results of Ref. [40] who reported that households were negatively but significantly related with the number with experienced head are more likely to have the chance of livelihood options adopted by the landless rural house- to diversify their livelihood sources than those with rela- holds. The following paragraphs give detailed discussions tively younger or less experienced household head. of the statistically significant variables by grouping into The second household head-related factor signifi - household head, social capital, and institution-related cantly affecting the number of livelihood options was sex factors. of household head. Table 7 indicates that sex of house- hold head is positively and significantly influencing the number of livelihood choices adopted at 1% significance Household head‑related factors level. Male-headed households are expected to have a The first statistically significant household head-related rate of 1.45 times greater number of livelihood sources factor was age square of the household head. The age adopted than female-headed households, holding other square of the household head was included as an explan- factors constant. The result of this study implies that atory variable in the Negative Binomial Regression model male-headed households are more likely to increase their to account for the non-linear relationship between age livelihood sources than their female counter parts. This of the household head and the number of livelihood is perhaps due to the fact that male-headed households options adopted. The result of this study finds that there are more likely to have better opportunity to participate is non-linear relationship between age of household head in various non-farm and off-farm sources of income and the number of livelihood options adopted by house- than female-headed households. Besides, the cultural holds at 1% significance level. That is, at the early ages of and social burdens that women are faced with could also the household head, the number of livelihood options partly explained the lesser number of livelihood options adopted is expected to increase with the age of the house- adopted by female headed households. Similar findings hold heads. But after reaching a certain years of age, as has been documented by Ref. [4] who found that sex of the age of the household head increases, the number of the household is negatively and significantly affecting livelihood sources adopted is expected to decrease. the probability of diversifying livelihood sources into off- As shown in Table 7 at the later age of the household farm activities in Wolaita zone of south Ethiopia. head, as age increases by one more additional year, the number of livelihood options adopted by the household Social capital‑related factor is expected to decrease by a factor of 0.99, holding other Membership in Edir was included in the model to inves- factors constant. This is perhaps due to the fact that tigate whether it affects the number of livelihood options with age, the physical endurance and health status of the adopted by the landless rural households or not. This household heads will deteriorate, which adversely affects study found out that membership in Edir is positively and the decision and participation of landless rural house- significantly influencing the number of livelihood options holds to engage more on diversified sources of income. adopted by the landless rural households at 10% signifi - This result is consistent with the empirical findings of cance level. This result is in line with the prior expecta - Ref. [43] who reported that participation in economic tions of this study in the sense that social capital could activities declines with the age of the household head. increase the social link and network among households. Our finding is also consistent with Ref. [18] who docu - This in turn may enhance the opportunity to get access mented that younger households with literacy and more to various livelihood options in their localities. That is, exposure to the exchange system diversified more income membership in Edir is expected to raise the livelihood portfolios in southern Ethiopia. Ref. [44] also revealed sources as it significantly increases the social network of that age of the household head was negatively associated the rural landless household. with improved natural resources management practices. Holding other factors constant, this study found that That is, with increase in the age of the household head, households who participate in Edir are expected to the planning horizons will shrink, and thereby, the incen- increase their livelihood sources by the rate of 1.16 times tives to enhance future productivity will diminish. How- higher than those who did not participate in Edir. Simi- ever, the result of this study is somehow against to Ref. larly, Ref. [41] reported that neighborhood attachment [45] who investigated the relevance and impact of expe- as social capital has a positive effect on household con - rience in participation of Italian crop insurance markets fidence in coping with food and income insecurity in Negash et al. Agriculture & Food Security (2023) 12:6 Page 14 of 16 Table 8 Major challenges and opportunities of the landless rural household Frequency Percentage Major challenges Shortage of arable land 247 95.74 Lack of land for residence 98 37.98 Youth unemployment 186 72.09 Lack of awareness on alternative livelihood sources 67 25.97 Lack of financial capital 121 46.90 Poor land administration 156 60.47 Lack of access to infrastructure (water, electrification, and road) 173 67.05 Lack of market linkage 76 29.46 Conflict of interest on communal land use 78 30.23 Management problem of rural cooperatives 31 12.02 Major opportunities Bee keeping 127 49.25 Animal fattening 195 75.58 Dairy farming 186 72.09 Stone or sand selling 193 74.80 Poultry production 178 68.99 Hillside distribution program 82 31.78 the face of climate change or flooding. u Th s, the study to market place increases from their residence area. This indicates that rural households are more likely to rely is due to the fact that individuals who live near the mar- on bonding social capital to cope with economic stress. ket center have higher opportunity to engage in different That is, households having a close connection with their livelihood options. neighbors can mobilize resources from their neighbors Unfortunately, access to credit was also found to be to expand their range of livelihood options. This result is negatively related with the number of livelihood options also somehow similar with empirical findings of Ref. [40] adopted by landless rural households. That is, holding who revealed that the relationship between livelihood other factors constant, households with credit access are diversification and membership in cooperative society expected to have less number of livelihood sources by the was found to be positive and statistically significant. The rate 0.889 times compared to those who have not taken implication of the study result is that social capital has credit from any financial institutions. The result is statis - significant contribution toward livelihood diversification tically significant at 10% significance level. This result is of the landless rural households. in line with the empirical findings of Ref. [19] who stud - ied factors affecting of livelihood diversification strate - Institution‑related factors gies in Borana pastoralist communities of Ethiopia and Table 7 shows that distance to district center was found revealed that the level of credit access and use was sig- to be negatively but significantly influencing the number nificantly but negatively impacted livelihood diversifica - of livelihood options adopted by the landless rural house- tion of households. hold at 1% significance level. This means as the distance between the residence area of the landless rural house- Major livelihood challenges and opportunities of landless holds and the district center increases by one more kilo- rural households meter, the number of livelihood sources would expect to The result landless rural households were asked to list decrease by factor of 0.982, holding other factors con- out and rank the major challenges and opportunities of stant. This result seems to some extent similar with the their livelihood options and the results are summarized empirical finding of Ref. [40] who reported that the scope in Table 8. In the same fashion, focus group discussants for livelihood diversification gets boosted when there is were requested to list out the major challenges that land- better proximity to urban or district centers. Further- less rural households are faced in their localities. Even more, similar finding has been documented by Ref. [19] if there were few district and Tabia specific challenges, who affirmed that diversifying livelihood beyond the the following are the common challenges specified by agricultural practice was likely to reduce as the distance the focus group discussants and surveyed households: Negash et al. Agriculture & Food Security (2023) 12:6 Page 15 of 16 shortage of arable land, youth unemployment, lack of road and distance to nearest market, were also found to access to infrastructure (like water, rural electrification, significantly affect the livelihood of choice the landless and road), poor land administration, and absence of rural households. collateral to take credit for the landless household were The result of the Negative Binomial Regression also identified to be the top five major challenges faced with indicates that the number of livelihood options of the the landless rural households in the study area. Moreover, landless rural households depends on sex and age square the surveyed landless rural households were also asked of household head, membership in Edir, and distance to if there are any unexploited opportunities in the rural district center. This paper also concludes that the cur - area which they could rely on as livelihood sources and rent way of government intervention to address the their responses are summarized in Table 8. This study problem of landlessness in the study area needs to take identifies that the major opportunities in the study area into account the interest of the landless rural house- include stone or sand resources, dairy farming, poultry holds. Furthermore, all stakeholders effort to address production, hillside distribution, animal fattening, and the problem of landlessness has to be geared in such way bee keeping. Similarly, the participants of the focus group that can boost the access of landless rural households to discussion also highlighted that if the local government the different livelihood capitals, such as human, social, administration makes significant supports and follow- financial, and physical of the rural landless households. ups, the aforementioned activities can be helpful to rely Moreover, concerned bodies has to focus on rural town- on as sources of livelihood by the landless rural house- ship to enhance job opportunities and access to nearest holds in the study districts. market for the landless rural households. More specifically, the focus group discussant in Kilte- Awlaelo stressed that stone resources and bee keeping Abbreviations are the two most underutilized livelihood options for the CSA Central statistical agency landless rural households. The focus group discussant in FDRE Federal Democratic Republic of Ethiopia FGD Focus group discussion Degua-Tembien also identified that bee keeping, dairy GDP Gross Domestic Product farming, shoat fattening, and stone selling could be the KM Kilometer potential livelihood options for the landless rural house- MoFED Ministry of finance and economic development MVP Multivariate probit holds. Similarly, the focus group discussant in Hintalo- NBR Negative binomial regression Wajerat identified that diary farming and stone selling PSNP Productive safety net program could be the potential livelihood sources for the landless TLU Tropical Livestock Unit WRI World Resources Institute rural households. Acknowledgements Concluding remarks The authors would like to thank Mekelle University for funding this research under the small-scale research grant scheme. They would also like to appreci- This study was conducted in randomly selected districts ate data enumerators and the staffs of agriculture and rural development of Tigrai region to explore the livelihood strategies, the office of the study districts. Moreover, they appreciate the dedication of the number of livelihood options adopted, and identifying surveyed households and FGD participants for providing reliable information. the major challenges and opportunities of the landless Author contributions rural households. This study concludes that the major TN participated in supervision, data collection, data analysis, description, draft- livelihood source of the landless rural households is farm ing, and revising the manuscript. HE, MA, GK, and ZM were also involved in data analysis, data collection, and commenting the draft and revised versions (90%) followed by non-farm (72%) and off-farm (41%) of the manuscript. All authors read and approved the final manuscript. economic activities. This study finds that household head-related characteristics, like age, sex, and marital Funding Mekelle University under the competitive the small-scale research grant status of the household head, and human capital-related scheme. factors, such as educational status of household head and labor force, were significantly determining the choice Availability of data and materials Not applicable. of livelihood sources of landless rural households. This study also reveals that social capital-related variables, Declarations like traditional labor sharing and membership in Edir, and financial capital-related factors, such as remittance, Ethics approval and consent to participate credit access, and membership in Equb, were also signifi - Prior to starting the work, the study design was explained to officials of agriculture and Administrative of the study districts for their permission and cantly influencing the choice of livelihood sources by the support. The nature of this study was fully explained to respondents to obtain landless rural households. Moreover, physical capital- consent. No false promise, such as remuneration or per diem, was given. related factors, like livestock holding of households, and Information was collected after securing consent from study participant. Data institution-related factors, such as distance to all-weather Negash et al. 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Agriculture & Food Security – Springer Journals
Published: Apr 5, 2023
Keywords: Landless rural households; Livelihood strategies; Multivariate probit model; Negative binomial regression; Tigrai region
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