CITATION: Onuwa, G., Mailumo, S., Chizea, C., Alamanjo, C., (2022). SOCIOECONOMIC DETERMINANTS OF LIVELIHOOD DIVERSIFICATION AMONG ARABLE CROP FARMERS IN SHENDAM, PLATEAU STATE,
SOCIOECONOMIC DETERMINANTS OF LIVELIHOOD DIVERSIFICATION AMONG ARABLE CROP
FARMERS IN SHENDAM, PLATEAU STATE, NIGERIA
Godfrey Onuwa
1*, Sambo Mailumo
2, Chizoba Chizea
3, Cosmas Alamanjo
41Department of Agricultural Extension and Management, Federal College of Forestry, Jos, Nigeria
2Research Coordinating Unit, Forestry Research Institute of Nigeria, Ibadan
3 Research Unit, Northern Guinea Savannah Research Station, Nasarawa
4Department of Agricultural Technology, Federal College of Forestry, Jos, Nigeria
*corresponding author: [email protected]
Abstract Livelihood diversification is an integral component of household income. Diversification determinants were estimated. Multistage sampling was adopted, and primary data were analyzed using descriptive statistics and Logit regression. Socioeconomic factors affected livelihoods. Several diversification activities and derivable benefits are indicated. Variations in diversification decisions were attributable to the regression variables. Policy modifications and strategies that improve livelihoods are recommended.
Keywords:Agrarian community, determinants, diversification, livelihoods, smallholders, Nigeria
http://dx.doi.org/10.21776/ub.agrise.2022.022.4.7 Received 24 April 2022 Accepted 22 October 2022 Available online 31 October 2022
INTRODUCTION
Livelihood diversities is the process of switching from low-income crop produce to higher value crops, livestock, and non-farm activities; that yield higher economic return per unit of labor (Adger, 2006). Household motives for diversification differ significantly across agrarian communities; it serves as a source of income growth (Chavuim et al., 2012; Joshi et al, 2004). Livelihood diversification is undertaken to manage risks, cope with the shock, or find an alternative to agricultural production. The accompanying increase in poverty level has led residents of these communities to devise alternative economic activities. Livelihood diversification strategies include on-farm (crop, livestock, fisheries) and off-farm activities or market and non-market activities to mitigate risks inherent in unpredictable agro-climatic, political, and economic circumstances (Bryceson, 2002; Ellis, 2000;
Hussien, 1998).
Consequently, the rural economy is not based only on agricultural production but rather on a diverse array of economic activities; farming remains important, but the rural population still seeks diverse opportunities to increase and stabilize their incomes (Aye, 2013). Livelihood diversification is a survival strategy for rural households in developing countries (Ellis, 2000). Rural households are engaged in a wide range of income-generating activities for their livelihood. Livelihood diversification can be broadly categorized into a farm and non-farm activities (Barett et al., 2001a). Non-farm income has become germane for rural farm households.
Non-farm employment includes self or wage employment in manufacturing, craft, artisan work, commerce, and services. These strategies have become an integral component of income- generating activities among rural farm households (Barett et al., 2001a). In addition to providing strategic employment alternatives to agriculture, the non-farm sector stimulates inter-sectoral linkages and exchanges, reduces rural-urban
irrigation, promotes equitable income distribution, broadens economic participation, and serves as a buffer during off-seasons in agricultural production (Bezu and Barrett, 2012; Oluwatayo, 2017). Livelihood diversification is also regarded as the means of making a living; the various activities and resources jointly determine the living gained by an individual or a household. While livelihood strategies are the range and combination of activities and choices individuals make to achieve their livelihood goals (Sati et al., 2015; Oni-Fashogbon, 2013). A household in a particular context and economy is usually constrained to choose between three main clusters of livelihood options: agricultural intensification and extensification, livelihood diversification, and migration (Sati et al., 2015).
These strategies change in response to shifts in a rural household’s access to resources and many other external factors (Oni-Fashogbon, 2013).
It should be noted that the main goal of livelihood strategies is to ensure economic and social security (Koczberski et al., 2001). The prevalence of non- agricultural activities in rural households dates back in time. Studies over several decades highlight the increasing importance of non-agricultural income to rural households. The focus on livelihood diversification comprises the process and scope targeted at broadening income and livelihood strategies away from a mono-economy of agricultural (crop and livestock) production to include both farm and non-farm activities. The function is to generate additional income via producing other agricultural and non-agricultural goods and services and selling waged labor or self-employment in small enterprises, as posited by Bezu et al. (2012).
While much of the literature defines 'diversification' in terms of productive activities or income, the introduction of the concept of 'livelihoods' has broadened the debate to include the process by which rural households construct a diverse portfolio of activities and social support capacities to improve their standard of living (Sati et al., 2015). How a household copes with and withstands economic shocks depends on the options available in terms of capabilities, assets (including both material and social resources), and activities. It implies that households will behave differently concerning income generation and livelihood standards. Particularly, households belonging to different socioeconomic groups have different strategies to earn their living which, in turn, may ensure different levels of resilience to food insecurity and welfare status. As a result, households belonging to different socioeconomic groups (e.g., farm households and public servant households)
require different interventions. Policymakers and stakeholders should tailor entrepreneurship and livelihood development strategies to consider the different needs of the population. Increasing poverty levels, especially in developing countries, and the need for its alleviation through improved living standards have resulted in the diversification of livelihoods (Bosc et al., 2006). The poverty index in sub-Saharan countries has increased within the last decade, attributable to the economic and socio- political instability experienced in these countries.
The situation is further aggravated by the declining and irregular income, low rate of capital accumulation, and declining agricultural output due to the rapidly changing climatic conditions.
Moreover, it has been established that in many agrarian communities, agricultural production alone is grossly insufficient; hence diversification into non- farm activities is seen as a form of financial security (Oluwatayo, 2009). Rural farmers in developing countries derive income streams from non- agricultural activities, accounting for a substantial share of total household income (Oluwatayo, 2009).
These farmers combine two or more economic activities (i.e. multiple job holding) to enhance household consumption expenditures and acquire other basic needs. Increasing the profitability and range of such activities would improve their livelihood security and living conditions (Mwabu and Thorbecke, 2001; Awoyemi, 2004). The accompanying increase in poverty levels has led farm households to devise several livelihood strategies as financial buffers. Hence, the assumption amongst rural farmers that agricultural production alone can adequately provide all household needs is no longer tenable (Chavuim et al., 2012). It is widely agreed that diversification is beneficial for rural households at or below the poverty line. Alternative income streams can make the difference between minimally viable livelihoods and destitution. The burgeoning literature on livelihood diversification across the developing world has pointed to the increasing role of non-farm incomes in poverty reduction (Oluwatayo, 2009). The existing gaps in poverty levels in sub-Saharan African rural sectors have therefore necessitated livelihood diversification studies (Saha, 2010). These studies on livelihood diversification would be useful to researchers, policymakers, rural farmers and other stakeholders in understanding the factors that affect diversification strategies. Given the above background, this study will attempt to answer the following research questions;
i. What are the socioeconomic characteristics of the respondents?
ii. What are their livelihood diversification activities?
iii. What are the benefits of livelihood diversification?
iv. What are the factors influencing livelihood diversification among respondents?
v. What are the constraints of livelihood diversification in the study area?
Null Hypothesis (Ho): There is no significant relationship between socioeconomic variables and livelihood diversification decisions.
METHODOLOGY Study Area
This study was carried out at Shendam Local Government Area (LGA), Plateau State, Nigeria. Its headquarters are in the town of Shendam. The LGA has a geographical location between latitude 8º53'00"N and longitude 9º32'00''E; with an estimated area of 2,477 Km² (247,700 Hectares). The LGA comprises four (4) districts namely; Shendam, Dorok, Derteng and Dokan-Tofa (NBS, 2012).
Sampling Procedure
A multistage sampling technique was used in this study. The first stage involved a purposive selection of the two (2) districts (Shendam and Derteng); due to the prevalence of arable crop farmers in the area.
In addition, three (3) villages were purposively selected from each district to a total of six (6) villages. In the final stage, through balloting methods; at constant proportionality of 9% (0.09), and with the assistance of local enumerators. 107 respondents were randomly selected using a compiled list of 1,200 arable farmers from the LGA secretariat.
Method of Data Collection
Primary data for this study was collected with the aid of well-structured questionnaires.
Analytical Techniques
The analytical tools used include: descriptive statistics (frequency counts, percentages and means) and binary logit regression model.
Logit Regression Model
Logit regression analysis was adopted in the estimation of the determinants of adoption of
livelihood diversification strategy. It specifies the relationship between the probability of choosing a set of diversification strategies or otherwise and the explanatory variables influencing this choice (Joshi et al, 2004). The implicit model is expressed as follows in equation (1):Yi= β 0 + βiXij + Ui ………… (1) Where;
Yi=a dichotomous response variable such that; Y=1, if farmers adopt a livelihood diversification strategy;
and Y = 0, if farmers do not; β0 = intercept; βi=
coefficient of the estimated parameters; Xij= Set of independent variables; and Ui = error term which is normally indicated as zero mean and variance.
However, the binary logit regression model can be specified explicitly as follows in equation (2):
Yi= β0 + β1X1+ β2X2+ β3X3+ β4X4+ β5X5+ β6X6+ β7X7+ Ui ………… (2)
Where;
Yi=a dichotomous response variable such that; Y=1, if farmers adopt a livelihood diversification strategy;
and Y = 0, if farmers do not; B0 = intercept;
βi (β1 – β7) = vector of the estimated parameters or unknown coefficients; Xi = vector of the predictors (independent variables).The explanatory variables are as follows;
X1 = Age (years); X2 = Marital status (married =1, single= 0); X3 = Level of education (years); X4 = Household size (numbers); X5 = Farm size (hectares);
X6= Access to credit (yes=1, no=0); X7 = Assets owned (yes=1, no=0); Ui = error term which is normally indicated as zero mean and variance.
The livelihood diversification activities prevalent in the study area include the following;
i. Local brewery ii. Trading iii. Food vending v.
Transportation vi. Agro-processing; vii. Hair saloon;
viii. Tailoring; ix. Civil Service; x. Artisans; xi.
Leasing/lending; and xii. Fishing.
RESULTS AND DISCUSSION
Socioeconomic Characteristics of the Respondents Gender
Table 1. Distribution based on the Gender of Respondents
Gender Frequency %
Female 27 25.2
Male 80 74.7
Total 107 100
Source: Field Survey, 2020
Table 1 revealed that most (75%) of the respondents were male, while 25% were females, suggesting a male dominated population and engagements in livelihood activities. It can be attributable to the fact that men have more access to productive assets and therefore engage in more economic activities than their female counterparts. It corroborates with the Onuwa et al. (2021) who reported similar results in their study on the determinants of off-farm investments.
Age
Table 2. Distribution based on the Age of Respondents
Age Frequency %
≥20 5 4.6
21 – 40 73 68.2
41 – 60 25 23.3
≤ 61 4 3.7
Mean=30.4years
Total 107 100
Source: Field Survey, 2020
Table 2 above revealed that most (68%) of the respondents are between the age bracket of 21-40.
Also the estimated mean age of the respondents was 30 years. It implies that most of the farmers are still at their productive age brackets and have the energy and ability to engage in multiple income generating activities. Age also significantly influences economic and technical efficiency (Martin and Lorenzen, 2016).
Marital Status
Table 3. Distribution Based on the Marital Status of Respondents
Marital Status Frequency %
Single 46 42.9
Married 61 57.0
Total 107 100
Source: Field Survey 2020
Table 3 reveals that most (57%) of the respondents are married. It implies that married farmers have higher demand for income due to family needs. The married farmer's need money to cater for the welfare needs of their families; therefore multiple income streams are required, hence the engage in multiple livelihood activities. It corroborates with the Onuwa et al. (2021) who reported similar results in their study on the determinants of off-farm investments.
Level of Education
Table 4. Distribution based on the Level of Education of Respondents
Education Level Frequency % Non-formal
education
11 10.2
Primary 10 9.3
Secondary 55 51.4
Tertiary 31 28.9
Total 107 100
Source: Field Survey 2020
Table 4 revealed that most (51%) of the respondents attained secondary education; an indication of high literacy level among the farmers in the study area.
Education is important in the diffusion of knowledge on modern innovations and livelihood activities. The level of education tends to determine the livelihood strategies and opportunities available to farmers (Alobo-Loison, 2015; Joshi et al., 2004).
Household Size
Table 5. Distribution based on the Household Size of Respondents
Household Size Frequency %
≤9 64 59.8
10–14 38 35.5
≥15 04 03.7
Mean = 7.4
Source: Field Survey 2020
Table 5 reveals that most (60%) of the respondents have household populations of ≤9 people; the estimated mean household population is 7 people. It denotes that the respondents have relatively populated households. Family size may enhance the chances of livelihood diversification within households (Babatunde and Martin, 2009).
Farm Size
Table 6. Distribution based on the Farm Size of Respondents
Farm Size (ha) Frequency %
≤4.9ha 78 72.9
5- 9.9 18 16.8
≥ 10 11 10.2
Total 107 100
Mean = 2.3ha
Source: Field Survey, 2020
Table 6 revealed that most (73%) of the respondents have farm size of ≤4.9ha. Also the estimated mean farm size was 2.3ha. It implies the prevalence of smallholders in the study area. This farm size is an indication of subsistent agricultural production which may be a push factors for the farmers to diversify into other livelihood activities. It corroborates with the Onuwa et al. (2021) who reported similar results in their study on the determinants of off-farm investments.
Cooperative membership
Table 7. Distribution based on the Cooperative membership of Respondents
Membership Frequency %
Yes 35 32.7
No 72 67.2
Total 107 100
Source: Field Survey, 2020
The result presented in Table 7 revealed that most (67%) do not belong to a cooperative while those who were members of cooperative society constituted 33%. It suggests that most farmers may need a structured medium or platform for information sharing and exchanges on enterprise opportunities and livelihood activities. This result corroborates with Barrett et al. (2001b) who reported that cooperatives ensure that members derive benefits from the group that they could not have achieved individually. Barrett et al., (2000) also added that membership of cooperative affords farmers opportunities to share information on modern production practices and livelihood strategies.
Arable Crop Production
Table 8. Distribution based on Arable Crop produced by Respondents
Crop Frequency %
Yam 35 32.7
Rice 72 67.2
Total 107 100
Source: Field Survey, 2020
Table 8 revealed that most (67%) of the respondents are rice farmers, while 33% cultivated yam;
suggesting a predominance of rice farmers in the study area. It is so because farmers there take advantage of the region's climatic and soil condition supporting rice cultivation. This marshy and fadama soil type is more conducive and suitable for rice growth (Dcrcon, 2002).
Farm Output
Table 9. Distribution based on Farm Output Farm output
(kg/ha)
Frequency %
≤ 4999 96 89. 7
5000 – 9999 10 09.3
≥ 10,000 1 00.9
Total 107 100
Mean = 2,978.7
Source: Field Survey, 2020
Table 9 revealed that most (90%) of the respondents had farm output of ≤ 4999kg/ha of rice; Also the estimated mean farm output was 2,978.7kgha-1. It is an indication of low farm output among smallholders and poor remunerative income derivable thereof. This push factor facilitates livelihood diversification among smallholders; hence multiple income streams provide additional income for farmers to cater for domestic obligations. It corroborates with Onuwa et
al. 2021 who also reported similar results of firm efficiency among smallholders.
Access to Microcredit
Table 10. Distribution based on the Respondents Access to microcredit
Access Frequency %
Yes 35 32.7
No 72 67.2
Total 107 100
Source: Field Survey, 2020
The result presented in Table 10 revealed that most (67%) of the farmers in the study area do not have access to microcredit. It indicates that most farmers were excluded from credit support from formal financial institutions in the study area. Djurfeldt and Jirstrom (2013) posited that credit was a strong factor needed to acquire develop, or engage in livelihood enterprise. Its availability could determine the extent of production capacity. Djurfeldt et al. (2011) posited that access to credit allows the farmers to expand and improve their agricultural and economic activities.
Productive Assets of Respondents
Table 11. Distribution based on the Productive Assets of Respondents
Ownership of Asset
Frequency %
Yes 58 54.2
No 49 45.7
Total 107 100
Source: Field Survey 2020
Table 11 revealed that (54%) of the respondents own productive assets, while 46% do not possess productive. Productive assets are major determinants of diversification. Martin and Lorenzen (2016) posited that asset ownership is important for agricultural and non-agricultural diversification;
productive assets are germane for livelihood diversification strategies.
Livelihood Diversification Activities
Table 12. Distribution based on the Livelihood Diversification Activities
Livelihood Activity Frequency* %
Trading 87 81.5
Food vending 80 74.7
Fishing 17 15.8
Artisan 40 37.3
Local brewery 100 93.4
Agro- processing 70 65.4
Transportation 75 70.0
Hair saloon 68 63.5
Civil Service 47 43.9
Tailoring 60 56.0
Lease/Financial 30 28.0
services
Source: Field Survey 2020; *Multiple response Table 12 sows the various livelihood activities engaged in, by the farmers in the study area. It is revealed that most (93%) engage in local breweries;
attributable to the sociocultural norms and lifestyle in the study area. The farmers in the study area also engage in trading (81%); attributable to the economic interactions among the farmers. About 74% of the farmers also engage in food vending. Other prevalent livelihood activities in the study area also include transportation (70%), agro-processing (65.4%), hair saloon (63.5%), tailoring (56%), civil service (43.9%), artisans (37.3%), lease/financial services (28%) and fishing (15.85). This finding aligns with Onuwa et al. 2021 who also reported similar livelihood diversification activities among smallholders.
Benefits of Livelihood Diversification
Table 13. Distribution of Respondents based on the Benefits of Livelihood Diversification
Benefits Frequency* %
Income diversification Overcoming cash constraints Poverty mitigation strategy
Augment declining farm income
Improve household welfare
Creates a portfolio of economic opportunities Buffer against
production and market risks
101 98 90 85 77 72 63
94.4 91.6 84.1 79.4 72.0 67.3 58.9
Source: Field Survey, 2020; *Multiple response The benefits of livelihood diversification among the respondents are presented in Table 13. The result revealed that farmers engaged in livelihood diversification activities for various reasons. The critical factors include income diversification (94.4%), overcoming cash constraints (91.6%), poverty mitigation strategy (84.1%), augment declining farm income (79.4%), improve household welfare (72%), creates a portfolio of economic opportunities (67.3%) and buffer against production and market risks (58.9%). The implication of the preceding finding is that the intensity of livelihood diversification activities among farmers in the study area was mainly to diversify household incomes and overcome cash constraints. This finding agrees with Martin and Lorenzen, 2016; Onuwa et al. 2021 who
also reported similar benefits of livelihood diversification activities among farmers.
Determinants of Livelihood Diversification Table 14. Factors Influencing Livelihood
Diversification among Respondents Variables Coefficient Standard
error
T-Value
Constant 4.986 1.91 2.61**
Age -0.843 0.337 -2.534**
Marital status 0.829 0.547 1.516n.s Educational
status
0.548 0.214 2.561**
Household size
0.672 0.25 2.688**
Farm size -0.764 0.281 -2.719**
Credit access 0.786 0.279 2.817**
Productive assets
0.574 0.233 2.506**
-2 Log likelihood
74.258**
Cox & Snell R square
0.623 Nagelkerke
R square
0.681 Source: Field Survey, 2020; ** significant at 5%
level; n.s = not significant
The result of the regression diagnostics and determinants of diversification are presented in Table 14. There was a significant change in -2 log- likelihood (74.258), suggesting a significant cause- effect relationship between livelihood diversification and the specified explanatory variables in the model.
The estimate of Cox & Snell R square (coefficient of determination) (R2) is 0.623. It indicates that 62.3%
variation in livelihood diversification decisions was accounted for by variations in the explanatory variables in the regression model, suggesting that the model has explanatory power on livelihood diversification decisions among the farmers. The Nagelkerke R square (adjusted R2) also supported the claim with a value of 0.681 or 68.1%, implying that the specified explanatory variables are significant determinants of the outcome of the dependent (livelihood diversification investment decisions) variable at 68% level of confidence.
Also, the result of the determinants of livelihood diversification among the respondents revealed that the coefficient of age (-0.843) was negative but statistically significant at 5% (p<0.05) probability level, implying that the likelihood of livelihood diversification among the respondents decreases with increase in age. It suggests that older farmers are more likely to receive lower incomes and economic opportunities. It may be attributable to the fact that
the mental and physical energy required for increased productivity decline with age. Older farmers are less likely to engage in livelihood diversification work, which may reflect differences in attitudes regarding work that correlate with age. This finding agrees with previous studies (Onuwa et al. 2021; Shi et al. 2004).
The result further revealed that the coefficient of educational status (0.829) was positive and statistically significant at 5% (p<0.05) probability level. The probability of livelihood diversification increases relative to the educational status of the respondents. Farm households with more literate population tend to engage in more livelihood diversification activities. Education and training produce a mobilized labor force, more skilled, prone to risk taking and adaptable to the needs of a changing economy. Hence an educated population may tend to be reluctant to work in the farm sector or on a part time basis, as they have better economic prospects and potentials. This finding agrees with previous studies (Babatunde and Martin, 2009;
Oluwatayo, 2009).
Also, the result revealed that the coefficient of household size (0.548) was positive and statistically significant at 5% (p<0.05) probability level. The household size of the respondents significantly affects livelihood diversification decisions. Also, the household size comprised a significant population of dependent household members, with increased consumption demand and expenditures and an increased need to engage in multiple income generating activities to supplement budgetary constraints. It is in agreement with Oluwatayo (2009) who reported that households with a larger population have a relatively higher marginal utility of income and need to participate in multiple livelihood activities.
The result also revealed that the coefficient of farm size (-0.764) was negative but statistically significant at 5% (p<0.05) probability level, implying that the probability of livelihood diversification decreases with increase in farm size. Increase in farm size and the attendant increase in farm activities will reduce livelihood diversification activities. It is in agreement with Babatunde and Martin (2009) who also reported that farm size was negatively correlated with income diversification investment. Oluwatayo (2009) also reported that the rural household farming model provides an income source. The higher the farm income the lower the need for livelihood diversification income to satisfy the budget constraints. Also, different farming systems influence the decisions to work off the farm. The reason for such specifications is that farming systems that are labour intensive will be less likely to have operators involved in livelihood diversification employment.
Furthermore, the coefficient of access to credit (0.786) was positive and statistically significant at 5% (p<0.05) probability level. The probability of livelihood diversification increases with improved access to credit, suggesting that improved credit access provides respondents with capital required for investments in farm assets, livelihood diversification activities, and critical domestic expenditures. This result corroborates with Babatunde and Martin, (2009) who reported similar determinants of income diversification among farmers.
The result also revealed that the coefficient of ownership of productive assets (0.574) was positive and statistically significant at 5% (p<0.05) probability level. Productive assets are significant and positively correlated to livelihood diversification activities. The probability of livelihood diversification increases with the ownership of productive assets in the study area, such productive assets include; grinding machines, tricycles, sewing machine, saloon equipment, etc. Also, this result corroborates with the findings of Babatunde and Martin (2009) who reported similar determinants of income diversification among farmers.
Constraints of Livelihood Diversification
Table 15. Distribution based on the Constraints of Livelihood Diversification
Constraints Frequency* %
Inadequate capital Microcredit constraints
98 90
91.6 84.1
Enterprise risks 80 74.8
High investment cost 75 70.1
Government and economic policies
72 67.3
Lack of vocational skills 67 62.6 Inadequate
infrastructural facilities
61 57
Insecurity 54 56.1
Source: Field Survey 2020; * = Multiple Response The result presented in Table 15 revealed that most (84%) respondents indicated that inadequate capital to diversify their livelihood activities as a significant constraint. Inadequate capital was attributable to the meager farm incomes from their subsistent level of agricultural production and was a deterrent factor in livelihood diversification in the study area. Without microcredit support from the institutional agencies, poor farming households cannot start their non-farm or business enterprise. Other significant constraints include microcredit constraints (84.1%), enterprise risks (74.8%), high investment cost (70.1%), Government and economic policies (67.3%), lack of vocational skills (62.6%), inadequate infrastructural facilities (57%) and insecurity (56.1%). This result corroborates with Onuwa et al. (2021) who reported
similar constraints of income diversification and off- farm investments among farmers in their respective studies.
CONCLUSION AND RECOMMENDATIONS
This study analyzed the factors affecting livelihood diversification among Arable crop farmers in Shendam LGA, Plateau State, Nigeria. The study's result revealed that the respondents' socioeconomic variables influenced their livelihood diversification decisions. Also, several livelihood diversification activities were prevalent among the respondents in the study area. In addition diversifying household incomes and overcoming cash constraints were the critical benefits of livelihood diversification among respondents. Furthermore, the result of the study revealed that the likelihood of livelihood diversification among the respondents was significantly influenced by age, educational level, household size, farm size, access to credit and ownership of productive assets.
Moreover, the constraints identified affected livelihood diversification decisions among farmers in the study area. The outcome of this study would be of immense benefit to farmers on appropriate patterns and the determinants of livelihood diversification and investment decisions that maximizes income. It will be useful to policy makers, stakeholders, government and other researchers. It will facilitate policy formulation that supports and boosts livelihood diversification activities among farmers as alternative sources of farm capital and household income. In view of the above background, the following recommendations are suggested to mitigate the constraints of livelihood diversification decisions among arable crop farmers to improve diversification activities in the study area:
(i) Policy modifications to improve factors that influence investments in non-farm assets.
(ii) Policy modifications to enhance livelihood diversification activities among rural farmers.
(iii) Formulation of policies to improve access to capital and microcredit for livelihood diversification activities
(iv) Formulation of policies that subsidize cost of business operations.
(v) Increasing farmers access to training in non- agricultural skillsets will improve their capacity to invest in livelihood diversification activities.
(vi) Formulating policies that facilitate and support livelihood diversification activities in the study area.
(vii) Provision of infrastructural facilities that support livelihood diversification activities, e.g. electricity, road networks, etc.
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