EFFECT OF SELECTED INSECTICIDE ON WHITEFLY (Bemisia tabaci) INFESTING BRINJAL PLANTS 145 Factors influencing rural migrant households’ participation in livelihood activities and income diversification strategies in Southwestern Nigeria Omolehin, Ajayi Raphael Adeniyia Ajiboye, Akinyele Johnb aDepartment of Agricultural Economics and Farm Management, Federal University Oye- Ekiti, Nigeria. bDepartment of Vocational Education, University of Ilesa, Ilesa. Osun State, Nigeria.   akinyele_ajiboye@unilesa.edu.ng (Corresponding author) Article History ABSTRACT Received: 28 May 2024 Revised: 14 April 2025 Accepted: 29 April 2025 Published: 12 June 2025 Keywords Livelihood activities Livelihood diversification Rural migrants Household expenditure Farm income Non-farm income Off-farm income South west. This study focuses on factors influencing rural migrant households’ participation in livelihood activities and income diversification strategies in southwest Nigeria. A multistage sampling technique was used for the study. This involved purposive selection of Ekiti, Osun, and Oyo states and twenty percent of the rural Local Government Areas (LGAs) in the respective states. Ten percent of wards in each LGA and 40% of migrant households were randomly selected to arrive at 413 respondents. A structured questionnaire was used to collect relevant data for the study. The collected data were analysed using descriptive statistics, estimation of SID, and multinomial logit regression. The findings showed that 72.75% of household heads were males. 76.75% of the respondents were first-generation migrants, with a mean duration of stay of 29.51 years. The SID revealed that income- generating activities among migrants are moderately (0.53) diversified. The primary factor influencing migrant households’ preference for non-farm and off-farm activities over farm activities was the size of their land holdings. The study recommends that government and other stakeholders should adopt strategy to improve access to non-farm and off-farm employment opportunities, particularly in rural areas, by encouraging corporate and private investors to invest in rural areas to solve the problem of unemployment during off-seasons. Contribution/Originality: The study contributes to existing body of knowledge of livelihood diversification. The study assisted in identifying the role of asset endowment in livelihood diversification strategies and income generation among migrant households in the region. Specifically, it offered a glimpse of what rural migrants consider the most attractive livelihood option. DOI: 10.55493/5005.v15i2.5401 ISSN(P): 2304-1455/ ISSN(E): 2224-4433 How to cite: Adeniyi, O. A. R., & John, A. A. (2025). Factors influencing rural migrant households’ participation in livelihood activities and income diversification strategies in Southwestern Nigeria. Asian Journal of Agriculture and Rural Development, 15(2), 145–155. © 2024 Asian Economic and Social Society. All rights reserved. Asian Journal of Agriculture and Rural Development Volume 15, Issue 2 (2025): 145-155 mailto:akinyele_ajiboye@unilesa.edu.ng https://doi.org/10.55493/5005.v15i2.5401 Asian Journal of Agriculture and Rural Development, 15(2)2025: 145-155 146 1.INTRODUCTION Agriculture is an important sector for rural dwellers in Nigeria and other developing countries of the world because it is their main source of livelihood. Although Nigeria’s rural economy is largely agrarian, only a minority derive their sources of income exclusively from farming. This view is corroborated by Djido and Shiferaw (2018), who pointed out that about eighty percent of rural households in Nigeria diversify their livelihood activities. Despite constraints that appear to limit their social and economic opportunities, these rural people find ways to mitigate the impact of declining income from farm activities (Eneyew & Bekele, 2012; Samuel & Sylvia, 2019). Migration has been documented as one of the many strategies that rural households and individuals employ to diversify their livelihoods (Awumbila, 2017; World Bank, 2017). The existence of inequalities and livelihood opportunities between rural areas in terms of access to economic resources and development is known to be a major cause of rural-rural or urban-rural migration (Chand, 2012; Ogunniyi, Mavrotas, Olagunju, Fadare, & Rufai, 2018). Migrants are people who have left their homes or usual place of residence to a new location temporarily or permanently to reap social and economic benefits (Olagunju, Ogunniyi, Oguntegbe, Rahji, & Ogundari, 2019; Young, 2013). Despite conditions that tend to limit their social and economic opportunities, migrants in rural areas find ways to ensure their survival. One of the commonest ways by which they meet their needs is by combining various activities that will ensure their survival within the society or localities they find themselves (Gberu, Amare, Mavrotas, & Ogunniyi, 2018). Intra-rural migration is typically undertaken by poorer households with low levels of education and other assets, as it requires lower investment. It is one of the commonest coping strategies adopted by poor rural households to stabilize their livelihoods and to adapt to climatic, environmental, social, and economic changes. Farmers often combine this with other livelihood activities like agricultural intensification and local non-farm and off-farm activities (Gberu et al., 2018). In recent years, the body of literature on development process has emphasized what is generally referred to as rural livelihood and livelihood diversification. A key concept of livelihood is the strong link between asset endowments, activities, and income generation, as well as socio-economic factors affecting the use and return of household assets. According to Ellis (2000), livelihood comprises the assets (human, social, financial, physical, and natural), activities (which include crop production, livestock production, self-employment, farm labour, non-farm labour, off-farm labour) and access to all these, facilitated by social institutions that determine the welfare of an individual or a household. These assets can be stored, transferred, or exchanged during income-generating activities (Walelign, 2016). There are several ways to define livelihood diversification. A definition of livelihood diversification is the attempt by households or individuals to find new ways to raise income. It is also the process by which individuals and households construct a range of activities and social supports for survival in order to improve their well-being. Thus, a household or an individual with three sources of income is considered more diversified than the one with two income outlets (Eneyew & Bekele, 2012; Samuel & Sylvia, 2019). Most often, the term “rural livelihood diversification” refers to expanding into non-farm and off-farms income, as solely relying on farm activities has not ensured a sustainable livelihood for rural households. Diversification as a rural livelihood strategy involves maintaining over a long time a diversified portfolio of activities and a regular adjustment to contingencies for the purpose of increasing profit, spreading risks, reducing income shocks, or achieving other household goals (Awotide, Awoyemi, Diagne, Kinkingnihoun, & Ojehomone, 2012; Murata & Miyazaki, 2014; Nguyen, Nguyen, Do, Nguyen, & Grote, 2022). With respect to factors influencing livelihood diversification, the empirical literature offers mixed results. Furthermore, the conceptualization of livelihood diversification as having multiple income streams without considering their respective weights and transfer payments, such as remittances, is overly restrictive. Some studies in Nigeria, which include Babatunde and Matin (2009), Fabusoro, Omotayo, Apantaku, and Okuneye (2010), and Abiodun, Olutumise, and Ojo (2019), have used Simpson’s Index of Diversity to report the degree of livelihood diversification without any recourse to other factors that affect choice of livelihood activities before conclusions can be drawn on drivers of livelihood diversification. Some reports, including that of FAO (2018), have revealed that migrants in rural areas are sometimes restricted due to a range of factors, including climate change, resource limitations, material uncertainties, and land tenure systems prevalent in their various locations. Therefore, it is necessary to examine the factors that influence migrant households’ choice of livelihood activities and the income shares from those activities. The emphasis here is on the role of households’ asset endowments, namely, human, social, natural, physical, and financial capital. The total household income is the aggregate measure of all the outcomes of all activities (farm, non-farm, off-farm) the household is engaged in. There is also the need to ascertain the key drivers of livelihood diversification. 2. METHODOLOGY 2.1. Scope of the Study The study was carried out in Nigeria's southwestern geopolitical zone, which spreads between latitudes 6°21’ and 8°37’ N and longitudes 2031’E and 2031’. It has a land area of 114,271 square kilometers, representing 12% of the country’s land mass. The 2006 national population census estimated the zone’s population at 21,974,678. This zone is made up of four different sub-ecologies, namely, derived savannah, moist and dry lowland, mangrove forests, and swamps. It comprises 6 states, namely; Ekiti, Lagos, Ogun, Ondo, Osun, and Oyo. Large portion of Osun, Oyo, and Ekiti are covered with derived savannah, while Lagos, Ondo, and Ogun states have a sizeable portion of their lands covered with tropical rainforests with swamps along the coastline zone. The region is characterized by a tropical climate, usually with distinct rainy and dry seasons and a main growing season lasting about 9 months. The average Asian Journal of Agriculture and Rural Development, 15(2)2025: 145-155 147 annual rainfall with peaks in July and September is 1480 mm. During the dry season, the monthly temperature ranges from 18°C to 24°C and 30°C to 35°C during the rainy season. The main source of income for the people living in this zone is rain-fed agriculture, which is characterized by arable cropping with cassava, maize, and yam as the main crops. Commonly grown are tree crops such as cocoa, cashew, oil palm, and kolanut. The zone is also well-known for business and trade in addition to farming, which is the main economic activity in rural villages. There are many micro, small, and medium-sized businesses in the area that are involved in production, manufacture, and agro-allied enterprises. Culturally, the South West zone is home to a diverse array of ethnic nationalities, with the Yorubas being the majority and having the most dominant language. The study placed emphasis on migrant households in the selected rural areas. For this study, respondents must have been residing in their new location for at least one year and have migrated from one local government area to the other. Another layer of migrants that were considered are foreigners who move in from different parts of Africa. 2.2. Sample Size and Sampling Procedure The study used a multistage sampling technique to select the respondents for the study. Ekiti, Osun, and Oyo states were purposively selected from the six states of the southwest of Nigeria due to their low level of urbanization. Twenty percent of the rural LGAs were randomly selected in the respective states. Ten percent of wards in each LGA were randomly selected. Prior to the survey, leaders of selected descendant associations in chosen wards facilitated the availability of member lists, which formed the sampling frame for migrant households, from which 40% of households were randomly selected to arrive at 413 respondents. 2.3. Data Collection Structured questionnaire was used to collect data on respondents’ generational status, duration of stay, household assets, livelihood activities, and other important variables relevant to the study. 2.4. Method of Data Analysis Data were analysed using descriptive statistics (frequency counts, percentages, mean and standard deviation), estimation of Simpson’s Index of Diversity (SID), and multinomial logit regression model. 2.5. Measurement of Overall Income Diversity Simpsons Index of Diversity encompasses both the count of income sources and the significance or uniformity of these sources, as articulated by Minot, Epprecht, and Trung (2006). It is calculated using the following formula: d = 1 − ∑ Pi2 (1) Where d = Simpson’s index of diversification. n = number of livelihood activities. Pi = proportion of income generated from each activity. i = number of income sources indexed by i. This metric of income diversification (d) quantifies the variety of income sources numerically and ranges between zero and one. A value closer to one indicates greater diversity in household income, with a diversification index of one signifying extreme diversification. The index is formulated by evaluating each income source, weighted by its proportionate contribution to the total income. The empirical model for (d) is articulated as follows: d =1- (2) Where: Fincom = Farm income. Nfincom = Non-farm income. Offincom = Off- farm income. Thi = Total household income. This approach, aimed at assessing income diversification, is more inclusive as it considers transfer payments to individual households. The index has been employed in studies by Ahmed, Bhandari, Gordoncillo, and Quicoy (2018), Djido and Shiferaw (2018), and Abiodun et al. (2019). 2.6. Econometric Specifications 2.6.1. The Multinomial Logit Model The classification of households based on their income mix strategies served as the foundation for formulating and estimating the multinomial logit model, which assesses the probability of belonging to one of the activity income groups. Farm, non-farm, and off-farm were the designated categories for farm activities. The objective is to understand how each explanatory variable influences the likelihood of a household head engaging in non-farm activities compared to farm activities, which serves as the base case. The model assigns each individual to one of the categories with a specific probability. Households in the study area can choose from three different income activity options. Researchers have used a similar methodology to examine the characteristics of households associated with various activities. Examples include studies by Adepoju and Oyewole (2014), Maja and Oluwatayo (2018), and Abiodun et al. (2019). Following Kennedy (1986), the multinomial logit model is expressed as: n i Asian Journal of Agriculture and Rural Development, 15(2)2025: 145-155 148 Where Pij = The probability linked to the income activity choices of a household "i" is denoted by "j," where j=0 if the household exclusively engages in farm activities, j=1 if the household participates in non-farm activities, and j=2 if the household partakes in off-farm activities. Xi = represents the explanatory variables that remain constant across these alternatives. 3. RESULTS AND DISCUSSION 3.1. Socio-Economic Characteristics of Rural Migrant Households The socio-economic characteristics of the respondents (Table 1) show that the majority (72.25%) are male, whereas 27.25% were female. This indicates a higher occurrence of male-headed households in rural settings. Consequently, this gender imbalance is likely to affect the respondents' choices regarding diversifying their livelihoods. Men often perceive themselves as more active and motivated to improve the wellbeing of their household members, which may lead them to pursue additional income-generating activities. The average age of household heads, which is 49.97 years, indicates that the age range of migrant household heads falls within the productive years. This aligns with previous research conducted by Fabusoro et al. (2010) and Oyesola and Ademola (2011), who reported that rural labor forces in the southwestern region of Nigeria generally range from 20 to 55 years old. Consequently, the household heads in this study are more likely to seek additional avenues for income generation. The majority (85.25%) of the respondents are married, while about 84.5% had access to formal education, with the average year of formal education estimated at 7.81 years. The literature widely acknowledges a positive relationship between access to education and involvement in income-generating activities. Babatunde and Matin (2009) emphasized that education significantly and positively influences the diversification into non-farm income. The level of literacy within rural farm households is vital for improving productivity, enhancing production efficiency, and ultimately increasing income, leading to a more diverse range of income sources for improved well-being. As household heads are typically responsible for decision-making within the household, their educational background greatly influences their choice of livelihood strategies. The result further indicates that majority of respondents (73.50%) had a household size of 4 to 8 individuals, with an average household size of 8. This suggests a relatively large family size within migrant households. These results align with the research conducted by Fabusoro et al. (2010), which also found that relatively large household sizes are prevalent in rural areas. Consequently, in the study area, a larger household size implies that there is a higher likelihood of diversified income sources if all members contribute to the overall welfare of the household. The results also showed that majority (91.25%) of household heads were physically fit, 76.75% of them were first- generation migrants, and 85% of the respondents have spent at least 15 years as migrants in their respective locations, with the mean duration of stay estimated to be 29.51 years. According to Hendriks, Martinj, Julie, and Espinova (2018), length of stay in a location is an important determinant of migrants’ welfare. They are of the opinion that migration is often considered a long-term investment into one’s future and that standard of living is expected to gradually improve over time after the initial challenges, such as cultural differences and environmental-related issues, are overcome. Demireva (2019) also found out that most migrants experience more positive and significant impact of migration over time than negative ones. Some results have also emerged from literature that length of stay of migrants is positively related to acquisition of assets and social integration (Borodak & Tichit, 2013; Mara & Landesmann, 2013). The results further reveal that about 95.5% had access to electricity from the national grid for less than 36 hours in a week, and about 78.75% had at least one livestock unit. Power is a crucial element of rural infrastructure that plays a significant role in expanding non-farm opportunities in rural areas. It contributes to the establishment of rural enterprises, reduces operational costs, minimizes labor requirements for time-consuming tasks, and enables individuals to allocate more hours towards selected rural non-farm activities. Access to electricity and its reliability directly impact the income-generating activities of rural migrant households and encourage them to diversify their livelihood strategies. Livestock provide income and employment for producers (Herrero et al., 2013). The respondents had various categories of livestock, such as goats, sheep, cows, rabbits, and chickens, that are at various stages of development. The Tropical Livestock Unit (TLU) conversion index was used, depending on the type of livestock. About 73% of the respondents were members of one or more social organizations. The level of participation in social organizations and Asian Journal of Agriculture and Rural Development, 15(2)2025: 145-155 149 decision-making of such organisations largely affects access to networks, credit information of cooperatives, and other forms of social assets that enhance the economic strengths of rural dwellers for livelihood diversification. Regarding the size of land holdings of respondents, 86.5% had at least 2ha with a mean of 11.29 ha. About 92.25% of respondents covered at least 15 km from their locations to urban center, with a mean distance of 18.37 km. The household disposable asset of the majority (51%) was in the range of N 1,000,000 to N 2,000,000, while only 43% had access to credit. Disposable assets are those that can be used to pay off debt in a hurry and reduce the impact of income shocks. Having disposable assets also gives households freedom to get quick access to cash when there is a need to dabble into new business ventures. Having disposable assets is crucial for meeting current needs and creating long- term plan. By implication, the more the household’s disposable assets, the more likely they are to be able to diversify their income sources. Access to credit by households or individuals residing in rural areas is important because it helps to break the vicious circle of low capital, low productivity, and low levels of savings that usually characterize rural economies. Access to credit plays a crucial role in rural households as it aids in mitigating the negative effects of income fluctuations and enables the diversification of livelihood activities (Abraham, 2018). The rural economy can grow only if it has an adequate flow of finance. Provision of credit facilities has been discovered to enable rural dwellers to buy farm inputs and expand their businesses. Access to credit helps alleviate financial constraints and enhances the ability of households to initiate off-farm and non-farm enterprises (Oyinbo & Olaleye, 2016). Table 1. Socio-economic characteristics of rural migrant households. Variables Frequency Percentage Female 109 27.25 Male 291 72.75 <30 years 6 1.50 30-40 years 80 20.00 41-50 years 134 33.50 51-60 years 102 25.50 Above 60 years 78 19.50 Mean 49.97 Standard deviation 10.25 Married 341 85.25 Widow/Widower 37 9.25 Divorced/Separated 22 5.50 0 year 62 15.50 1-6 years 157 39.25 7-12 years 129 32.25 Above 12 years 52 13.00 Mean 7.81 Standard deviation 4.86 <4 persons 29 7.25 4-8 persons 294 73.50 9-12 persons 59 14.75 Above 12 persons 18 4.50 Mean 8 Standard deviation 2.69 Healthy 365 91.25 Permanently ill 35 8.75 First generation 307 76.75 Second generation 93 23.25 <15 years 60 15.00 15-25 years 140 35.00 26-35 years 67 16.75 Above 35 years 133 33.25 Mean 29.51 Standard deviation 14.71 0 111 27.75 <15 hours 23 5.75 15-25 hours 132 33.00 26-35 hours 116 29.00 Above 35 hours 18 4.50 < 1 85 21.25 1 – 2.5 156 39.00 2.6 – 3.5 43 10.75 Above 3.5 116 29.00 Mean 12.95 Standard deviation 27.77 0 106 26.50 1 130 32.50 2 111 27.75 Asian Journal of Agriculture and Rural Development, 15(2)2025: 145-155 150 Variables Frequency Percentage 3 52 13.00 4 1 0.25 <2ha 54 13.50 2ha- 4 ha 164 41.00 4.4ha -6 ha 102 25.50 Above 6 ha 80 20.00 Mean 11.29 Standard deviation 6.38 6.38 < 5km 31 7.75 5-15km 129 32.25 16-25km 153 38.25 Above 25km 87 21.75 Mean 18.37 Standard deviation 8.73 <1,000,000 Naira 58 14.50 1,000,000-2,000,000 Naira 204 51.00 2,000,001-3,000,000 Naira 83 20.75 Above 3,000,000 Naira 55 13.75 Mean 2.186,513 Standard deviation 2,122.428 No 220 55.00 Yes 180 45.00 Source: Field survey, 2023. 3.2. Major Sources of Income of Household Heads in the Study Area Results in Table 2 show that more than half (53%) of the respondents engaged in farming as their major occupation, 27.75% chose non-farm as their major source of livelihood, and 19.25% of the migrant households were involved in off- farm activities as their primary means of livelihood. Non-farm activities provide employment for the landless. Off-farm activities are agriculturally related but occur beyond the farm. Examples of off-farm services include extension services, processing, packaging, storage, and transportation. Diversification of income sources into farm and non-farm enhances food security, increases agricultural production by alleviating capital constraints, and also helps to better cope with environmental shocks. Table 2. Distribution of respondents based on major source of income of household heads. Major source of income Frequency. Percent Farm 212 53.00 Non-farm 111 27.75 Off-farm 77 19.25 Total 400 100 Source: Field survey, 2023. 3.3. Number of Income Outlets of the Respondents Rural areas have unique socio-economic dynamics, meaning that most rural dwellers make their living from their relationship with land-based industry. This section presents livelihood diversity in the study area based on the number of income sources. Table 3 shows that 21.25% of the respondents had only one source of income, while 24% relied on two income sources. Also, 20.75% of the respondents had three sources of income, 15.75% had four sources, 8.75% had five income outlets, and 6.75% had 6 income outlets, while 2.50% of them had 7 sources of income. On cumulative basis, 46.25% of the migrants had 1 to 2 income sources, while 66.00% had between 1 and 3 income outlets. Table 3. Distribution of households according to number of income outlets. Income source outlets Freq. Percent 1 85 21.25 2 96 24.00 3 83 20.75 4 63 15.75 5 35 8.75 6 27 6.75 7 11 2.75 Total 400 100 Source: Field survey, 2023. 3.4. Primary Occupation of Household Heads Table 4 presents the primary occupations of household heads. The table shows that 44.25% of the respondents chose farming as their primary occupation. Those who chose teaching, commercial transportation, commercial motorcycle riding, and retailing formed 13.00%, 11.00%, 6.00%, and 6.00% of the rural migrants, respectively. Others include blacksmithing, carpentry, hair plaiting, palm wine tapping, paid employment, tailoring, and vulcanizing. Asian Journal of Agriculture and Rural Development, 15(2)2025: 145-155 151 Table 4. Distribution of household heads according to primary occupation. Primary occupation of households Freq. Percent Blacksmithing 16 4.00 Carpentry 9 2.25 Farming 177 44.25 Hair plaiting 18 4.50 Motorcycle operator 24 6.00 Palm wine tapping 14 3.50 Paid employment 4 1.00 Retailing 24 6.00 Tailoring 16 4.00 Teaching 52 13.00 Transporter 44 11.00 Vulcanizing 2 0.50 Total 400 100.00 Source: Field survey, 2023. 3.5. Livelihood Diversification among the Rural Migrant Households The results presented in Table 5 show that among household heads who engaged in farming as their major source of income, 47.64% had SID values that ranged from 0.41-0.60, and 30.19% had SIDs that were above 0.60. Those with SIDs of 0.20-0.40 accounted for 21.23%, while the remaining 0.94% had SID estimates that were less than 0.20. For household heads that chose non-farm as their main livelihood activity, 45.95% had SID estimates that ranged from 0.41-0.60, 40.54% had SID estimates well above 0.60, while those who had SIDs of 0.20-0.40 formed 12.61%, and the remaining 0.90% had less than 0.20. With respect to household heads that took off-farm as their main source of livelihood, 44.16% and 44.16% had SID values that range from 0.41-0.60 and above 0.60, respectively. The remaining 11.69% had SID estimates between 0.20-0.40. It will be observed that the household heads with SID estimates that range from 0.41-0.60 were prevalent across the three income sources. Furthermore, the results revealed that the off- farm activities have the highest SID. This is similar to the findings of Anang and Yeboah (2019), who asserted that the income from off-farm work complements income from farm activities and helps to expand other economic activities. Overall, the results revealed that less than one percent (0.75%) of the rural migrant household heads had SID of less than 0.20. While seventeen percent (17%) of the migrant households had SID ranging between 0.20 and 0.40, 47.5% (0.41-0.60) and 35.75% had above 0.60. The average livelihood diversification index was 0.53; this is an indication that livelihood activities of the respondents are moderately diversified. The findings are similar to those of Oni and Fashogbon (2013), who asserted that households in rural areas, including those of migrants, are risk-neutral with SID of 0.53. The moderate level of livelihood diversification in this study could be attributed to major challenges that are peculiar to rural migrants. These may include constraints such as low levels of assets, inadequate rural infrastructural facilities, erratic power supply, and inadequate credit facilities, among others, which militate against a suitable environment for venturing into many livelihood activities. Table 5. Distribution of the migrants based on Simpson's income diversity index. Simpsons income diversity index Farm Non-farm Off-farm Total Mean <0.20 2 (0.94) 1 (0.90) 0 (0.00) 3 (0.75) 0.53 0.20-0.40 45 (21.23) 14 (12.61) 9 (11.69) 68 (17.00) 0.41-0.60 101 (47.64) 51 (45.95) 34 (44.16) 186 (46.50) Above 0.60 64 (30.19) 45 (40.54) 34 (44.16) 143 (35.75) Total 212 (100.00) 111 (100.00) 77 (100.00) 400 (100.00) Note: Figures in brackets are in percentages (%). 3.6. Determinants of Household Choice of Livelihood Activities Table 6 presents the results of the multinomial logit model, which examines the determinants of migrant households’ choice of livelihood activities. The model includes three outcome variables: farm activities, non-farm activities, and off-farm activities, with farm activities serving as the reference group. The total assessed a total of 10 explanatory variables, identifying four statistically significant in the non-farm activity equation and two as significant in the off-farm activity equation. The overall model was determined to be statistically significant, with a Wald chi- square ratio of 42.27 and a p-value of 0.0025. The model has a log likelihood of -378.11236. 3.7. Non-Farm Activities Relative to Farm Activities The age of household heads was found to have a positive impact at a significance level of 5%. This suggests that a one-unit increase in age increases the likelihood of choosing non-farm activities over farming activities by 0.0357 units, assuming all other variables remain constant. This finding aligns with the research by Nmeregini and Udoka (2021) Asian Journal of Agriculture and Rural Development, 15(2)2025: 145-155 152 indicating that older household heads are more likely to participate in non-farm activities, potentially due to health challenges associated with old age. Household size negatively influenced the choice of livelihood activity at a significance level of 10%. This implies that a one-unit increase in household size decreases the likelihood of choosing non-farm activities as a livelihood option relative to farming activities by 0.1271 units, assuming other variables remain constant. This result is consistent with the findings of Babatunde and Matin (2009) and Adepoju Abimbola and Obayelu Oluwakemi (2013) but contradicts the findings of Ahmed and Melesse (2018). The large household size often leads to a focus on farm-related activities as a way to increase income and meet the family's food needs. Furthermore, the multinomial regression estimates demonstrate that farm size is highly significant at a significance level of 1% and has a negative influence. When all other variables remain constant, a one-unit increase in farm size reduces the likelihood of choosing non-farm activities as a livelihood choice by 0.0585 units. This finding aligns with the research by Abiodun et al. (2019), suggesting that households with larger farm sizes tend to specialize in farming as their primary income source. Vocational training has a positive and significant influence on livelihood activity choice at a significance level of 1%. This means that the log odds of migrants who have undergone vocational training relative to those who have not undergone training are 0.7447 units higher in choosing non-farm activities over farming activities, assuming all other variables remain constant. This implies that migrants with specialized skills have more time dedicated to their vocations and are less likely to engage in farming activities. 3.8. Off-farm Activity Relative to Farm Activity Participation in off-farm activities refers to engaging in paying jobs related to farming outside of one's own farm. Researchers have found that these activities significantly contribute to livelihood security. Off-farm income, derived from activities such as crop processing, hunting, gathering, and palm tapping, supplements on-farm income and promotes economic expansion among rural communities. The estimates from multinomial regression (Table 6) demonstrate that farm size has a significant impact on the choice of off-farm activity as a livelihood. This significance is observed at a 1% level and is negative. This suggests that the availability of land for farming purposes influences the decision to engage in off-farm activities. Specifically, a one-unit increase in farm size leads to a 0.0682 reduction in the log odds of choosing off-farm activity as a source of livelihood, holding other variables constant. These findings align with previous studies by Ahmed and Melesse (2018) and Abiodun et al. (2019). Furthermore, the coefficient for the value of disposable assets significantly influences the choice of activity at a 1% significance level and is negative. This suggests that a one-unit increase in the value of assets leads to a 3.852e-07 unit decrease in the log odds of choosing off-farm activity, while maintaining the same level of other variables. The results indicate that migrant household heads with higher levels of disposable income are less inclined to engage in off-farm activities. This finding contradicts the expectations set forth by Babatunde and Matin (2009), which anticipated a positive relationship. The discrepancy may be due to the poor compensation associated with self-employment in the non-farm activities in rural areas. Table 6. Multinomial logit regression estimates of determinants of household choice of livelihood activities. Variables Non-farm Off-farm Coefficient Z P> |z| Coefficient Z P> |z| Sex -0.3346 (0.2658) -1.26 0.208 -0.0793 (0.3235) -0.25 0.806 Age 0.0357** (0.0163) 2.19 0.028 0.0094 (0.0177) 0.53 0.596 Household size -0.1271* (0.0675) -1.88 0.060 -0.0273 (0.0681) -0.40 0.688 Education years -0.0017 (0.0260) -0.07 0.947 -0.0211 (0.0301) -0.70 0.483 Farm size -0.0585*** (0.0217) -2.69 0.007 -0.0682*** (0.0235) -2.90 0.004 Value of assets -0.000000109 (0.0000000807) -1.36 0.175 -0.000000385*** (0.000000137) -2.81 0.005 Dependency ratio 0.1869 (0.2567) 0.73 0.466 0.0877 (0.2822) 0.31 0.756 Migrant generation -0.3229 (0.2903) -1.11 0.266 -0.2021 (0.3276) -0.62 0.537 Vocational training 0.7447*** (0.2775) 2.68 0.007 0.0802 (0.2968) 0.27 0.787 Credit access 0.1139 (0.2469) 0.46 0.644 0.2131 (0.2779) 0.77 0.443 Constant -0.9524 (0.8335) -1.14 0.253 0.3455 (1.0425) 0.33 0.740 Observation = 400 Wald chi2 (20) = 42.27 Prob> chi2 = 0.0025 Log likelihood = -378.11236 Standard error in parenthesis Note: ***, ** and * represent level of significance at 1%, 5% and 10% respectively. Asian Journal of Agriculture and Rural Development, 15(2)2025: 145-155 153 3.9. Marginal Effect of Determinants of Household Choice of Livelihood Activities Table 7 presents the Marginal Effect (ME) of the explanatory variables. With an increase in age, there is a decrease in the probability of choosing farm activity as the main livelihood activity by 0.0055 unit (P= 0.068). However, the same variable increases the probability of choosing a non-farm activity by 0.0062 unit (P= 0.026), assuming all other variables remain constant. An increase in household size by one unit decreases the probability of choosing a non-farm activity by 0.0224 unit (P=0.060), holding other variables constant. The marginal effect of farm size on the choice of livelihood activity shows that an increase in the farm size by one unit increases the probability of choosing farm activity by 0.0140 unit (P= 0.000). In contrast, increasing the same variable by one unit will reduce the probability of choosing non-farm activity (P= 0.064) and off-farm activity (P= 0.038) by 0.0071 and 0.0068 units, respectively, assuming other variables remain constant. Increasing the value of assets by one unit increases the probability of choosing farm activity (P=0.004) by 5.02e- 08 units, while reducing the probability of choosing off-farm activity (P=0.010) by -5.12e-08 unit, holding other variables constant. Acquiring vocational training lowers the probability of choosing farm activity (P= 0.037) by 0.1060 units, while having vocational training increases the probability of choosing non-farm activity (P= 0.005) by 0.1360 units, assuming other variables remain constant. Table 7. Marginal effect of determinants of household choice of livelihood activities. Variables dy/dx Std. error Z P>|z| Sex Farm 0.0516 0.0527 0.98 0.327 Non-farm -0.0587 0.0472 -1.24 0.214 Off-farm 0.0070 0.0455 0.15 0.877 Age Farm -0.0055* 0.0030 -1.83 0.068 Non-farm 0.0062** 0.0027 2.23 0.026 Off-farm -0.0006 0.0024 -0.25 0.801 Household size Farm 0.0193 0.0120 1.60 0.109 Non-farm -0.0224* 0.0119 -1.88 0.06 Off-farm 0.0030 0.0096 0.32 0.749 Education years Farm 0.0021 0.0050 0.43 0.667 Non-farm 0.0008 0.0046 0.19 0.853 Off-farm 0.0030 0.0042 -0.72 0.472 Farm size Farm 0.0140*** 0.0038 3.66 0.000 Non-farm -0.0071* 0.0039 -1.85 0.064 Off-farm -0.0068** 0.0033 -2.07 0.038 Value of disposable assets Farm 0.0000000502*** 0.0000000173 2.9 0.004 Non-farm 0.00000000103 0.0000000156 0.07 0.947 Off-farm -0.0000000512** 0.0000000199 -2.57 0.010 Dependency ratio Farm -0.0328 0.0487 -0.68 0.500 Non-farm 0.0303 0.0459 0.66 0.509 Off-farm 0.0025 0.0399 0.06 0.949 Migrant generation Farm 0.0615 0.0560 1.10 0.272 Non-farm 0.0495 0.0511 -0.97 0.332 Off-farm 0.0119 0.0457 -0.26 0.795 Vocational training Farm -0.1060** 0.0509 -2.08 0.037 Non-farm 0.1360*** 0.0483 2.82 0.005 Off-farm -0.0299 0.0412 -0.73 0.467 Credit access Farm -0.0348 0.0477 -0.73 0.466 Non-farm 0.0095 0.0439 0.22 0.828 Off-farm 0.0253 0.0389 0.65 0.516 Note: ***, ** and * represent level of significance at 1%, 5% and 10% respectively. 4. CONCLUSION AND RECOMMENDATIONS The study area is characterized by predominantly active, first-generation male migrants with limited education. While migrant households exhibit high levels of capital availability, the quality of various capital sources is notably Asian Journal of Agriculture and Rural Development, 15(2)2025: 145-155 154 poor. Respondents participated in three primary income-generating activities, namely: farming, non-farm, and off-farm activities. Although farming is the primary occupation for most households, the majority have reasonably diversified income sources, recognizing that sole reliance on farming may not sustain their desired livelihoods. The livelihood diversification index suggests a moderate level of rural livelihood diversification. There is a high degree of heterogeneity in terms of asset ownership and income-generating activities at household level among the respondents. The level of diversification estimated using SID revealed that income generating activities among migrants are moderately diversified. The major factors that determine migrant household choice of non-farm activities and off-farm over farm activity was size of landholdings. Based on the findings of the study, government and other stakeholders should adopt a comprehensive strategy to improve access to non-farm and off-farm employment opportunities, particularly in rural areas. To address the issue of unemployment during off-seasons, we can encourage corporate bodies and private investors to invest in rural areas. Additionally, government and other policymakers should focus on creating a conducive environment by establishing microfinancial institutions to tackle the problem of access to various livelihood assets, offering training to rural inhabitants, and developing rural infrastructure, including improved roads, electricity, and market facilities. 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