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Go Back       Himalayan Journal of Agriculture | Volume :3 Issue:2 | March 20, 2022
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DOI : 10.47310/Hja.2022.v03i02.002       Download PDF       HTML       XML



Income Diversification Strategies of Agricultural Households in Okigwe Local Government Area of Imo State, Nigeria


F.O. Nwosu, Ebisike, P.A and Chinenye, P.C


Department of Agricultural Economics Federal University of Technology Owerri, Nigeria

*Corresponding Author

F.O. Nwosu, Ebisike, P.A


Article History

Received: 28.02.2022

Accepted: 06. 03.2022

Published: 20.03.2022


Abstract: The study was carried out to examine the income diversification strategies of agricultural households in Okigwe Local Government Area of Imo state, Nigeria. Its specific objectives were to; describe the socio-economic characteristics of Agricultural households in the study area, identify the various sources of on-farm and off-farm income of the rural households, estimate the income derived from Agricultural and non-Agricultural sources, identify the determinants of income diversification among the households in the study area and examine the constraints in sampling income from the various sources. Data were collected using structure questionnaire. Data were analyzed using descriptive statistics and econometric tools. The findings showed that 52.2% of the respondents were female with the mean age of 44 years. 65.6% of the respondents were married with the mean house size was 6 persons and 63.3% of the respondents attained secondary education. The results also revealed that the major income diversification sources in the study area was crop farming with Agricultural sources accounted for 56% of the total annual income of the farmers. The results of the regression analysis showed that household size (p˂0.01) positively influenced farmers income diversification strategies with 0.01152 coefficient while lack of collateral to secure loan was the major constraints facing the farmers income diversification. The study recommended that Financial institution should set cooperative membership as a prerequisite for providing agricultural loans.


Keywords: Income, Diversification, Agricultural household

INTRODUCTION

Nigeria’s agricultural sector has a high potential for increased growth, but this potential is not being fully realised. The agricultural sector contributed over 60% to the country’s Gross Domestic Product (Ogwumike and Akinnibosun, 2013). Agriculture still suffers from a wide range of distortions and influences that limit its contribution to food sustainability. According to Olugbire, Falusi, Adeoti, Oyekale& Adeniran (2011), diversification is being advocated in many parts of rural Nigeria today to ensure food security since farming as a livelihood activity is associated with immense risks and by extension income variability (Adebayo, Akogwu&Yisa, 2012). This is because high levels of income inequality are likely to create a hostile atmosphere for economic growth and development (Adepoju&Oyewole, 2014).


Enete and Achike (2008) asserted that unstable income of farm households could be accounted for by unfavourable weather changes, outbreak of plague, pollution in coastal waters, eruption of negative externalities, and other uncertainties which pose threats to farming activities and yields, thereby causing income to fluctuate erratically. Diversification, therefore refers to the patterns involved in individual’s voluntary exchange of assets and their allocation of assets across various activities (on- and off-farm) so as to achieve an optimal balance between expected returns and risk exposure conditional on the constraints they face (Zhao & Barry, 2013). Income diversification is often used to describe expansion in the importance of non-crop or non-farm income. Diversification therefore supports farm households to accumulate income for farm expansion engagement in non-farm businesses (Dimova And Sen, 2010; Lay and Schuler, 2008) and to solve immediate household needs (food, shelter, health care, payment of school fees etc.). The degree to which farm households diversify their sources of income and the associated incomes generated have increasingly distinguished poor from rich households (Canagarajah et al., 2001).


According to Ping et al., (2016) agricultural farming is a complex system and the economic well-being is not only exaggerated by income, but also its fluctuations. For addressing the fluctuations in income lead from various risks, farmers can develop informal and formal system to deal with the income risk. Historically, the farming households’ perceptions were, to just only to rely on agriculture, and off-farm activities were uncommon. Therefore, the policymakers


had just been focusing on the farming sector. Since quite some time summative indications have indicated that marginal farming households are not reliant on agriculture only.


Hitherto, farmers have been trying to sustain their income activities in which off-farm played a significant role (Ping et al., 2016). However, Haggblade, Hazell, and Reardon (2010) indicated that half of the income of rural households is derived through different sources in developing countries. However, for shaping farm and rural development policies, off-farm work is a significant addition to farm income (Corsi and Salvioni, 2012). Likewise, income diversification is considered as a household’s strategy to cope with diminishing marginal returns to labour problem (Nin-Pratt and McBride, 2014). Especially, in rural areas where seasonal unemployment is common.


Furthermore, income diversification could be employed to reduce risk or to fulfill the increasing basic needs of household, which depends on the research circumstances (Estruch et al., 2013).Most rural households in developing countries are undergoing the process of diversifying their income sources (Zhao & Barry, 2013). Rural households in many different countries have been found to diversify their income sources allowing them to spread risk (Ellis, 1998, in Ibrahim, Rahman, Envulus&Oyewole, 2011). The food crisis experienced in 2006 which soared in 2007 (Stakeholders Forum, 2009) seemed to have driven rural farmers to delve into diversification. Several researchers maintained that these adjustments in agricultural activities are found to have an important impact on income, income distribution and welfare across rural households (Block & Webb, 2001; Ibrahim &Onuk, 2012).


Income diversification has significant influence on farmers. It could enhance the viability of small farm agriculture, particularly in the context of the on-going process of globalization.Multiple motives prompt households and individuals to diversify assets, income and activities. The first set of motives comprise what are traditionally termed “push factors” such as risk reduction, response to diminishing factors returns in any given reaction to crises or liquidity constraints, high transaction costs that induce household to self-provision in several goods and services. The second set of motives comprise “pull factors” realization of strategic complementaries between activities such as crop-livestock integration or milling and hog production specialization according to comparative advantage accorded by superior technologies, skills or endowments (Davis and Pearse, 2001).


For this research, diversification is defined as the act of venturing into non-farming activities mitigate farming risk or practice of producing and/or processing a variety of farm products so that a failure in an environmental slump affecting one of them will not be devastating on the farm household.

By keeping the capability to operate a heterogeneous set of activities, diversifying households are likely to enjoy higher “flexibility” and “resilience” capacity than agricultural dependents rural households. Thus, it is not surprising that in the lights of the reiterated environmental, economic and political shocks affecting rural areas in developing countries, diversification has been, during the last 30 years, increasingly attractive for many rural households (Warren 2002). This makes non-farm income determinants imperative to evolve strategies adaptable to local rural farm household.


The key factors of farmers’ participation in off-farm work are more benefits with lesser risk of investing in other sectors (Kotu, 2014). Thus, non-farm employment positively influences on agricultural production, as the income gained from non-farm could be utilized on farm if needed and benefits the farmer to practice timely (Ping et al., 2016)


Diversification at the individual or household level (livelihoods diversification) simply means adding new economic activities. These could include agricultural or non-agricultural work, work for one’s self or for an employer, home based work or work at other places (Adebayo, 2012). but despite the fact that income diversification among households in Nigeria has been meant to improve the wellbeing and reduce the level of abject poverty bedeviling them and their family in both rural and urban areas but study conducted by World Bank (2009), showed that 52% of Nigerians live on less than a dollar per day.

Hence, the need to investigate into the on-farm and off-farm income diversification decisions among farm households. Perhaps, farm households that have more assets should be less risk averse and more willing to participate in market production, while farm households with fewer assets are more likely to settle for subsistence production in a desire to avoid high transaction costs in selling crops and buying food (Olale&Nazli, 2010). One of the researcher gaps here will be whether the decisions they take is in the best pursuit of improving the general economy and rural economy in particular.


Despite the growing importance of farm and off-farm activities, very little is known about the role they play in the income generation strategies of rural households in developing economies like Nigeria (Ibekwe et al., 2010). The tendency for rural households to engage in multiple occupations is often noticeable, but it is pertinent to link income diversification in a systematic way to rural farming households. Also, less emphasis has been given to household level choices and especially to the explanation of differences of strategies among households in terms of income-source diversification. This creates a gap in literature in respect of income diversification as none of the previous studies were found to analyse how four different dimensions of income diversification (namely: hawking, artisan, handcrafting and transportation service) affect the level of wellbeing among agricultural households in Okigwe Local Government Area of Imo state, Nigeria.


METHODOLGY

The study area was Okigwe Local Government Area (LGA) of Imo State. The LGA is one of the 27 Local Government Areas found in Imo State. It is found between latitudes 50 56’24”N to 50 42’19”N and longitudes 7012’58”E to 7024’02”E. Okigwe Local Government is bounded to the North by Orumba South LGA of Anambra State and Umu-Nneochi LGA of Abia State, to the East by Isuikwato LGA of Abia State and to the South by Umuahia North LGA of Abia State, Ihitte/Uboma and Ehime Mbano LGAs both of Imo State while Onuimo and Ideato North L.G.As both of Imo State forms the border to the west. The study area is sub - divided by the Port Harcourt – Enugu – Maiduguri rail line and the Port Harcourt – Enugu Expressway. Okigwe L.G.A has numerous tourist and historical attraction sites. It also has a number of secondary schools to include Federal Government College, Agbobu community secondary school, Community secondary school among others (Wikipedia, 2020).


Multi-stage sampling technique was used for the study. In the first stage, five (5) communities were randomly selected from the LGA. A total of 5 communities were selected for the study. In the second stage 2 villages were randomly selected from each of the selected communities, thus making the sample frame to bethe 2 selected villages. In the third stage; nine(9) Farming households were selected at random from each of the 2villages. Thus, making the total sample size for the study to be 90 farming households selected for the study. A structured questionnaire was used to obtain information on Farmers socio-economic characteristics and other relevant information. Data was analyzed using descriptive statistics and ordinary least square model. The ordinary least square model is specified below:

Y = B0 + B1 Ar + B2 Ge + B3 Educ + B4 Oa + B5 Hs + B6 Al + B7 Ms + B8 Fe + Ui … (3.0)

Where:

Y = Income diversification

Ar = Age of respondent

Ge = Gender (1= Male, 0 = Female),

Educ = Educational level of household head

Oa = Ownership of assets

Hs = Household size

Al = Access to loan

Ms = Marital status (1 = Married, 0 = Single),

Fe = Farming experience

Ui = Error term

RESULTS AND DISCUSSION

Socio-Economic characteristics of the respondent households


The result of the analysis of socioeconomic characteristics of the respondent households is presented in Table 1

Table 1: socioeconomic characteristics of the respondent households

Socio-economics frequency.

Percentage

Characteristics

Gender

Male. 43

Female. 47

Total. 90


Age

10 - 30. 18

31 - 51. 44

52 -72. 28

Total. 90

Mean. 44years


Marital status

Single. 11

Married. 59

Widowed. 20

Total. 90


Household size

1 - 3. 10

4 - 6. 54

7 - 9. 26

Total. 90

Mean 6persons


Educational level.

Primary Education. 23

Secondary Education. 57

Tertiary Education. 10

Total. 90


Faming Experience

1 - 3. 49

4 - 8. 31

9 - 12. 10

Total. 90

Mean. 5years




47.8

52.2

100



20

48.9

31.1

100




12.2

65.6

22.2

100



11.15

60

28.9

100




25.6

63.3

11.1

100



54.5

34.5

11

100

Source: Field data, 2021


Table 1 showed that majority (52.2%) of the agricultural households within the study area were women while the remaining 47.8% were men. The implication of this finding is that more women are now being involved in agricultural activities which is consistent with the findings of Wanyama et al., (2010) who reported that men are much more likely to engage in any occupation other than farm labour unlike the women. Furthermore, the high rate of women’s involvement in agricultural activities in the study area is indicative of women’s efforts towards mitigating risks factors facing their household food security by the diversification of their sources of livelihood, as Yang (2012) opined that women are consistently more risk-averse than men, an opinion similarly held by Nelson (2014).


It also revealed that majority (48.9%) of the farmers were within the age bracket of31 to 51 years, this was closely followed by 31.1% of them aged within 52 to 72 years, while only 20.0% of them were aged within 10to30 years. The mean age of 44.1 years implies that the aged are more interested in agriculture. This may be due to the fact that the aged farmers are aware of a myriad of risks facing agricultural production in a developing country such as Nigeria.This finding is in line Sanusi, (2013) that middle aged farmers are more productive.


It shows that 65.6% of the farmers were married; this is consistent withan earlier study by Osuji et. al, (2012)that farmers are usually married. This also implies active family support for farm work via the supply of family labour which is an alternative cheap source of labour for agricultural activities.


It shows that most (60%) of the farmers had between 4 to6 persons in their household, 28.9% of them had 7to9 persons in their household and approximately 11% of the farmers had about 1 to 3 persons. The mean household size was approximately 6 persons. Household according to Balogun et., al, (2012) is a significant source of human power utilized in farming operation.


It shows that most (63.3%) of the farmers had tertiary education, about 25.6% of the farmers had primary education and only 11.1%had secondary education. It is important to note that none of the farmers were uneducated. This could be beneficial to agricultural activities,this is probably because school education increases the human capital levels and provides the necessary skills which enable the entry into more remunerative labour markets especially for non-farm activities such as non-farm wage labour or self-employment. This result is consistent with the results from other studies on diversification behaviour (e.gWinters et al., 2009; Idowu et al., 2011) where education was found to be a key determinant of the diversification of income generating activities.


In Table 1 above,about 54.5% of the farmers had between 1 to 3 years of farming experience, about 34.5% of them had between 4 to 8 years of farming experience and only 11% of the farmers had 9 and above years of farming experience. The mean year of experience was 5.27 years. The results agree with the inference of Nandi et al., (2011) that most farmers in Nigeria have been farming for years. Farming experience is very essential to the performance offarming activities.


Sources of On-Farm and Off-Farm Income


Table 2: Shows the various sources of on-farm and off-farm income available to farmers in the study area.

On-Farm Sources of Income

Frequency

Percentage of farmers

Crop Farming

47

22.82

Livestock Farming

43

20.87

Processing of farm product

1

0.49

Selling of Poultry droppings

3

1.46

Farm Labour

7

3.40

Off-Farm Sources of Income



Petty trading

43

20.87

Tailoring

17

8.25

Barbing

5

2.43

Construction

5

2.43

Bricklaying

3

1.46

Carpentry

6

2.91

Telecom Services

11

5.34

State grants

2

0.97

Private employment

1

0.49

Jobs in education

7

3.40

Handicrafts

2

0.97

Shoe cobbling

3

0.46

Total

206

100

Source: Field data, 2021.

* Multiple responses were allowed, hence total frequency exceeded sample size.


It was revealed in table 2, that the major income diversification sources in the study area are crop farming (22.82%), livestock farming and petty trading (a non-farming activity)were 20.87% respectively.This result is similar to that of Bowman and Zilberman’s (2013) findings who noted that most employed diversified farming systems tend to concentrate more on crop production together with complementary livestock production for its flexibility and for fertilizer production.


Other diversification sources include tailoring (8.25%), telecom services (5.34%) and jobs in education (3.40%). While shoe cobbling was the least income diversification sources in the study area with only 0.46%. These income diversification sources are quite revealing and informative. Respondents are mostly farmers who engage in farming during the rainy season and are of the opinion that they engage in these income diversification sources mostly in the dry season. It is noteworthy that if respondent have access to loan and other credit services, their income diversification patterns are sufficiently dependable to generate more income and subsequently alleviate poverty.


Estimated Income Derived from Agricultural and Non-Agricultural Sources

Table 3: Show the distribution of farmers based on their different income sources in the study area. It shows the average income in naira and the income share in total income percentage.


Table 3: Estimated incomes from agricultural and non-agricultural sources

Income from Agricultural Sources

Average income(N)

Income share in total income (%)

Arable cropping

222,173.91


16.05


Tree cropping

105,000.00


7.59


Livestock sales

282,500.00


20.41


Processing of farm products

163,000.00


11.78


Total on-farm income

772,673.91

55.83

Income from Non-Agricultural Sources



Non-farm wage labour


263,500.00

19.04

Self-employment

166,860.46


12.06


Rents

181,000


13.08


Total off-farm income

611,360.47

44.17

Grand Total

1,384,034.37

100

Source: Field Survey, 2021.

In table 3, income sources of the household heads per year are categorized in two sources. Most of the farmers have an average annual on-farm income of N282,500.00 from livestock farming.


It was found that 20.41 percent derived their income from livestock farming, 19.04 percent from non-farm labour wages and 16.05 percent from arable crop farming. Others where; 13.08% from rents, 12.06% from self-employment and 11.78% from processing of farm produce. Only 7.59 percent obtained income from tree cropping. Annual income from Agricultural sourcesaccounted for approximately 56% of the total annual income of the farmers. These results showed that agriculture remains the major source of rural income for the farmers and is consistent with that of Babatunde and Qaim (2009) on the patterns of income diversification in rural Nigeria which found agricultural production to be the most important single source of income providing about 55% of total income. They also found that more than half of their respondents derived income from livestock enterprises.


Determinants of Income Diversification Among Farming Households

The determining factors influence income diversification among the households in the study area, some socio-economic variables such as;sex, age, marital status, years spent school, household size and farming experience were regressed against the income diversification of farmers in the study area. The result for the regression analysis is presented in Table 4.


Table 4: Regression analysis of the determinants of income diversification among farming households in the study area

Variables

Linear+

Exponential

Semi-log

Double-log

Constant

2.384263

2.027314

2.235556

1.294134


(1.216566)

(1.98937)

(1.41907)

(2.271193)

Age

0.035251

0.002318

3.730045

0.230599


(1.03293)

(0.43360)

(1.17491)

(0.48062)

Years in School

-0.01783

-0.00193

-1.90692

-0.26719


(-0.31612)**

(-0.21798)

(-1.12535)

(-1.04334)

Marital Status

-0.50581

0.001427

-0.984308

-0.08292


(-1.05565)

(0.019017)

(-2.04254)**

(-1.13855)

Household Size

0.307726

0.042321

4.789821

0.70362


(3.683264)***

(3.23374)***

(3.62165)***

(3.52032)***

Sex

0.161731

-0.00739

-0.00739

0.064429


(0.290443)

(-0.08469)

(-0.08469)

(1.37583)

Farming Experience

0.011522

0.001473

0.54282

0.100143


(0.326454)**

(0.266358)

(0.72804)

(0.88874)

R-squared

0.472674

0.417364

0.433306

0.417214

Adj. R-squared

0.344406

0.275641

0.314002

0.294522

F-statistic

3.685042

2.944938

3.631949

3.400499

Prob(F-statistic)

0.002246

0.009706

0.003152

0.004857

Source: Field Survey, 2021

+ = Lead equation

*** = sign. @ 1%, ** = sign @ 5% and * = sign @ 10%

Figures in parentheses are t-statistics


Four functional forms of the Ordinary Least Squares multiple regression technique were run and were evaluated in terms of the statistical significance of the coefficient of multiple determinations (R2) as indicated by F-value, the significance of the coefficients and the magnitude of t-values and follow apriori expectation and economical rationale. Among the four functional forms: the one with the highest R2 value, highest F-value, which test the goodness of fit of the overall model, highest number of significant explanatory variables and consistency of the signs with apriori expectations. Hence, the linear functional form was selected as the lead equation. The result shows that the estimated coefficient of multiple determinations (R2) of 0.472674indicates that about 47.26% in the variation of the farmer’s income diversification is explained by the factors included in the regression model while the remaining of the variation in income diversification was due to error term (omitted variables). The F-statistics of 3.685042is greater than F- tab of 3.20 which means it was significant (i.e F-cal> F-tab at 1%), this indicated the significance of R2 which is the measure of goodness of fits of the linear regression model in explaining the determinants of income diversification.


Of the six (6) variables included in the model, linear functional form has the highest number of significant variables (3 variables are statistically significant out of 6 variables used in the model) and the signs on the variables. The result shows that the coefficients of years spent in school, household size and farming experience were the determinants of-income diversification among farmers in the study area. The coefficients of years spent in school and farming experience were statistically significant at 5 percent confidence level while household size was significant at 1 percent.

The coefficient of household size (0.30773) is positive and significant at 1 percent level which implies that farmers income diversification increases with an increasing household size. It means that consumers with higher household size tend to diversify their sources of livelihood more than farmers with fewer household size.This finding is similar with the earlier findings of Kilic et al., (2009)


The coefficient of years spent in school (-0.01783) is negative and significant at 5 percent level. This implies that as the number of years spent in schoolby the farmersincreases, their income diversificationdecreases. This is because the older the farmers the less their willingness to take risks as opined byFabyan, (1999).


The coefficient of farming experience (0.01152) is positive and significant at 5 percent level which implies that farmers with higher farming experience will tend to diversify their sources of income more when compared to farmers with relatively fewer farming experience. Farmers with comparatively high farming experience tends to diversify their income source as a way of securing their household food security, Nandi et al., (2011) concurred.

Constraints to Diversification of Income Sources


Table 5: Factors hindering the diversification of income sources

Constraints

Frequency

Percentages

High cost of transportation

48

16.72

Lack of capital to set up a farm business

36

12.54

Bad road network

36

12.54

Lack of technical know-how

13

4.53

Government policy

6

2.09

Lack of collateral to secure loan

57

19.86

High cost of labour

28

9.76

Distance to farm

11

3.83

Distance to market

15

5.23

Lack of extension services

4

1.39

Low returns from farming

33

11.50

Source: Field data, 2021

* Multiple responses were allowed, hence total frequency exceeded sample size.


The result in table 5 shows that the major constraints facing the diversification of farmer’s household income source waslack of collateral to secure loan (19.86%), this was closely followed by high cost of transportation with 16.72 percent, while lack of capital and bad road network were 12.54 percent respectively. Others significant constraints included; low returns from farming (11.50%) and high cost of labour (9.76%). Lack of extension services was found to be the least constraining factor hindering the farming household income diversification in the study area. This is in line with the findings of Bowman and Zilberman (2013) who noted that financial and credit constraints are one of the constraints that play into farmers’ decisions. High cost of transportation and bad road network is indicative of the poor rural infrastructure existing in developing country such as Nigeria. This finding is somewhat in agreement with that of Wanyama et al., (2010) who stated that poor infrastructure will continue to be a disincentive to farmers diversifying in other farming activities.


CONCLUSION

Food production does not only serve as an integral vehicle for food security, but also as a source of income diversification and employer of labour in the producing areas in Nigeria and particularly in Okigwe Local Government of ImoState. Lack of finance that is being collaborated by the lack of collateral to secure loan, bad road network and high cost of transportationare identified as the primary constraints to income diversification in the country. Income diversification among farming households in Nigeria has been meant to improve the wellbeing and reduce the level of abject poverty bedeviling them and their family especially in rural areas (Adebayo, 2012).


The major conclusions drawn were that the social economic characteristics of the farmers such as; household size, farming experience and number of years spent in school significantly affected their income diversification in the study area. Since the hypothesis testing showed a significant relationship with farmers income diversification. The null hypothesis that socio-economic characteristics of the farmers has no significant effect on the income diversification strategies in the study area is thus rejected and alternative hypothesis was accepted.


Thus, this study could be concluded by saying that the socio-economic characteristics of the farmers had a significant effect on their income diversification strategies in OkigweLocal Government Area of Imo state.


RECOMMENDATIONS

On the forgoing the study recommends that Financial houses, Governmental, Non-GovernmentalOrganisations (NGOs) and other development workers in Okigwe Local Government of Imo State should;

  • Financial institution such as banks and micro finance banks should provide loans to the farmers with flexible collateral packages.

  • Rural farmers should be encouraged to join cooperative as this could avail them the opportunity to access financial packages; this can be equally achieved by setting cooperative membership as a prerequisite for agricultural loans in the rural areas.

  • Government should initiate a periodic agricultural sensitization program geared towards agricultural risk mitigation strategies for farmers with adequate incentives to stimulate rural farmers to be in attendance.


If all these are done, it would go a long way in reducing the constraints inhibiting income diversification among farmers in Okigwe Local Government Area.


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