Agricultural production still suffers from a wide range of distortions, risks, uncertainties, and influences that limit its contribution to food sustainability and food security. Hence, there is a high need for a coherent action at various levels of farming activities of the rural households in order to stabilize her apparently very volatile income. The agricultural production process by its nature is a very risky business as it comprises a high level of risk compared to the risk mitigation strategy adopted, thus, making the income of farmers uncertain. Diversification of assets, incomes, and activities is encouraged for a variety of reasons. The first set of motives consists of what are known as push factors: diversification, which extracts a household from poverty and the pull factors: opportunities that attract households to engage in additional sources of income in order to raise their living standards. However, income insecurity, on the other hand, has posed a significant issue for rural farming households and has had a negative impact on agricultural productivity.

According to⁽¹⁾, Drought reduces household farm production by up to 90% compared to a normal year. Drought has a negative impact on economic growth, and its spatial coverage and frequency have increased in recent decades, resulting in significant economic losses and negative social consequences. To reduce risk, rural households are encouraged to follow good farming practices. Financial institutions are not very established to aid rural farmers in the sense of offering diverse services like insurance, hedging and so on that will cover 75 percent of crop failure ⁽¹⁾.

Most of the researchers on risk mitigation like ⁽¹⁾ paid more attention to the degree of effort on good farming practices to mitigate risk. According to⁽²⁾, Contract farming and crop insurance are two types of external assistance that farmers in North America, Central America, and the Caribbean frequently use to reduce their vulnerability to extreme weather and climate events. When faced with a climate shock like a drought, flood, or heat wave,⁽³⁾, In Sub-Saharan Africa, farmers used strategies such as pulling children out of school to work on farms or tend to livestock, reducing nutrient intake, and over-exploiting natural resources. 

Also, ⁽⁴⁾ indicated that rural households in Bangladesh, South Asia, use diversification to mitigate market and price risks and smooth their consumption.

Despite the numerous studies on area risk mitigation conducted around the world, there has been little or no research on the use of off-farm income diversification as a risk mitigation strategy among crop farmers in the study region. As a result, this study is set out to fill the void in the literature.

The broad objectives of this study is to analyze the off farm income diversification as a risk mitigation strategy among crop farmers in Igbo-Eze North LGA of Enugu state, Nigeria. Specific objectives are to:

1. examines the socio-economic characteristics of crop farmers;

2. ascertain various risk faced by crop farmers;

3. examine the Perception of the farmers on the various types of risks;

4. identify different risk mitigation strategies used by farmers;

5. identify the socio-economic factors that influence the use choice of off-farm diversification as a risk mitigation strategy; and

6. examines the various off-farm income activities used by farmers.

We therefore hypothesize that the socioeconomic characteristics do not significantly influence the choice of off-farm income diversification as a risk mitigation strategy.

The research is considered useful for rural households in many developing countries, rural households, particularly Enugu state, Nigeria with the majority of its population engages in agriculture to earn a living. Diversifying their sources of income will allow them to reduce income-related risks and smooth their consumption in agricultural-based economies, where they face a variety of risks that keep them trapped in a vicious cycle of poverty. It will be employed as a key component of rural farming households' livelihood strategies, and it will add to the body of knowledge by highlighting a variety of off-farm income diversification decisions as a risk mitigation technique.

Study area

The study was conducted in Enugu's Igbo-Eze North Local Government Area. Igbo-Eze North LGA is located in the tropical rain forest zone ecologically. The local government area is a geographical unit that lies between latitude 6^o 59’ 0^o N and 7^o 27’ 0⁰ N longitude. It has a tropical environment with daily temperatures ranging from 200 to 300 degrees Celsius, ⁽⁵⁾. Igbo-Eze North is an area of about 293 km with a population estimate of about 259,431 people. It is bounded by Ette on the North, Ofante on the East, on the west by Akpanya and on the South by Ibagwa. It dominated mainly with petty trading. It is reputed as the largest single community in black Africa. Igbo-Eze North has certain things in common with Benin, Igala, Idoma (Kogi State) and Enugwu Ukwu (Anambra State). Benue state borders the local government on the north, Ovoko (Igbo-Eze South), Amalla and Obollo (Udenu) on the south, and Kogi state on the west. ⁽⁵⁾. The major crops grown in the area include: cassava, yam, maize, oil palm and vegetables. The animals that are reared are goat, sheep, poultry and pigs.

Igbo-Eze North local government consist of 36 villages and they include Ogurute, Umuida, Umuopu, Aji, Uda, Amuife, Inyi, Amaja, Umuogbo Inyi, Umuogboagu, Igbrle, Isugwu, Ekposhi,  Ikpuiga, Ufodo, Ezillo, Okata, Igogoro, Ikpamaodo, Onitsha enugu ezike, Nkpamaute, Owerre eze, Amachi, Amube, Ogbodu, and  Aguibeje. The people are predominantly traders, famers and cash crop producers.

Sampling Techniques 

The respondents were chosen using a multi-stage sampling technique. In the first stage, five agricultural communities were purposely selected based on the concentration of farming activities in the areas. In stage two, two villages were randomly selected from the five communities selected. Finally in stage three, ten famers were randomly selected from each of the two villages selected, given a total of 100 respondents for the study.

Data Collection

Data for the research was collected from primary source using structured interview schedules that was administered by the researcher to the respondents. The interview schedule contained a relevant questions based on each of the specific objectives of the study. The questionnaire was divided into five sub section, with each section tailored towards eliciting information to a particular objective.

Data Analysis

Objectives i, ii, iii, iv and vi was realized using descriptive statistics such as means, percentages, and frequency distributions as well as Likert type scales such as extremely severe, very severe, severe, and not severe. Objective v was realized using probit regressions. 

Model Specification

This model was used to analyze the socioeconomic elements that influence the decision to diversify off-farm as a risk management approach. The model's functional form is explicitly specified as follows: Y = βo + β₁X_I + β₂X₂ + β₃X₃ + β₄X₄ + β₅X₅ + β₆X₆ + β₇X₇ + β₈X₈ + Ɛ

Where, Y = off-farm diversification.  Y = (1 if > 0 otherwise 0) 

X₁= Age (years)

X₂= Income (₦)

X₃= Marital status (married 1, otherwise 0)

X₄= Education (years)

X₅= Farm size (ha)

X₆= Farming experience (years)

X₇= Access to credit (Access 1, otherwise 0)

Ɛ = Error term

Likert scale rating

A four point Likert type rating was used to identify the various perceptions of farmers on different types of risk. The questions and their corresponding point were as follows; extreme severe (ES) = 4, very severe (VS) = 3, severe (S) = 2 and not severe (S) = 1. 

Arithmetic mean (X) was considered appropriate and used for the analysis of the research questions.  Calculated as ∑fx = 4+3+2+1÷ 4

This gave a limit value of 2.5 showing that any risk type with a scale less than 2.5 (˂2.5) were not be perceived as a serious threat to the farmers while risk type with score bigger than or equal to 2.5 (≥2.5) will be perceived as a serious threat to farmer.

Socio-economic characteristics of respondents

The socio-economic characteristics examined in the study include; sex, age, marital status, farming experience, primary occupation, secondary occupation, education level, farm size, household size, monthly income and membership to cooperative.

The findings in Table 1 showed that greater proportions (63%) of the respondents were female while 37% were male. This could be because most males in the area usually migrate to urban centers after secondary education in search of greener pasture. Table 1 show that 56.0% of the respondents were between the age brackets of 21- 40 years. The mean age of the respondents was 43.0 years. The result implied that majority of the respondents were in their youth age and can adequately be regarded as active, agile and energetic farmers with positive attitude towards mitigating risk. This finding is in line with ⁽⁶⁾ who indicated that young people's engagement is a driving force for innovation adoption since they are more willing than older people to try new methods and accept new technologies. Table 1 also show that majority of the respondents (90.0%) were married, while 10% were single. This result indicates that farming in Igbo-Eze North L.G.A is dominated by the married population.  The findings could be attributed to the role of agriculture in sustenance of family food security. Farming income also enables households to cater for the fundamental necessities of their families while also lowering labor costs through the utilization of family labor. The Table also showed that greater proportion of the respondents (64%) had 1-20 years’ experience in farming while 25% had 21- 40 years of experience.

Table 1: Socio-economic Characteristics of The Respondents

Source: Field Survey, 2021

Respondents had an average of 16 years of farming experience. The results suggest that the farmers have agricultural experience, which will assist them make risk management decisions and increase farm profitability. Result further showed that majority 43% of the respondents had trading as their primary occupation, while 38% had farming as their primary occupation. Another 6% and 4% had artisan and teaching as their primary occupations respectively. This indicates that majority of the respondents in the study area had trading as their full-time occupation. Aside farming, the respondents engaged in other secondary occupations as a way of diversifying their income and to mitigate risk. This implies that only farm income alone was not able to sustain the farm household and thus, farmers need to diversify their income source.  They do this as a form of social and economic insurance against crop failures that may occur as a result of climate change and other circumstances, ⁽⁷⁾. As regards the educational attainment of the respondents, the result showed that majority (95%) had formal education with mean age spent in school as seven years. This implies that the respondents are literates and enlightened and thus will be able to make better farming decisions.  The result agrees with the discoveries of ⁽⁷⁾ who posited that education facilitates adoption and makes farmers more objective in evaluating innovation which influences the farmers’ productivity.Table 1 also shows that the mean farm size is 5 hectares. This implied that the respondents are mainly small-scale farmers and can grow different cropping and farming types so as to mitigate risk. Result in Table 1 also showed that majority of respondents (58 %) had household size of 5 to 8 persons; while the mean household size was 6 persons. The relatively high household size will be of immense benefit as it will reduce the amount of income spent of hired labour. Entries in Table 1 show that greater proportion (49%) of the respondents had income of ₦1,000 to ₦10,000 while about 24% had ₦30,000 to 50,000 estimated monthly incomes. The mean monthly income was ₦16, 760.00. This indicates that on the average, crop farmers are poor due to low monthly farm income occasioned by the numerous risks and uncertainties associated with agriculture. Finally, the result in Table 1 showed that majority of the respondents (97%) did not belong to any cooperative society while only 3% respondents were members of cooperative in the study area. This may be due to lack of awareness of the benefits of cooperative in the area. 

Different risk faced by crop farmers 

Results in Table 2 showed that greater proportion of respondent experienced production risk (81%) and biological risks (64%) respectively. This implies that production and biological risks such as pest and disease attack, drought, flooding, broken or unavailability of equipment or spare, crop failure, poor soil lack of spraying equipment, among others were very pronounced in the study area. Result in Table 2 also revealed that institutional risks (23%) such as government policies and program, market and price risk (10%), such as price fluctuations, uncertainty about quantities of inputs and outputs, high-loading variables on high price of inputs, inputs shortage and financial risk (8%) such as inability to pay back borrowed funds with interest, enough finance to continue farming were not predominant in Igbo-Eze North LGA of Enugu state. 

Table 2: Different Risk Faced by Crop Farmers

Multiple Responses

Source: Field Survey, 2021

Perception of farmers on different types of risk

A four-point Likert type scale was used to indicate the severity of different types of risk as perceived by crop farmers in the study area 

Table 3: Perception of Farmers on Different Types of Risk Faced in Farming

Source: Field Survey, 2021

The result in Table 3 showed that production risk was the most severe (3.00); and was indicated and perceived by the respondents. This was followed by biological risks with mean value of 2.51. This implies that production risk is perceived as a serious threat by crop farmers. This is due to the overwhelming impacts on the farming activities of the farmers in the study area. The unrelenting climate change and activities of killer herdsmen have worsened the farmers’ situations and exposed them to even greater risk. However, findings showed that the perception of production risk is the most important factor that encourages the adoption of off-farm income diversification as a risk mitigation strategy (Ullah et al, 2016). The farmers in the study area did not perceive market and price risks (1.46), institutional risks (1.32) and financial risks (1.13) as serious threat.

Different risk mitigation strategies

Table 4 showed the various ways farmers tend to mitigate and cope with many risks and uncertainties in their farming enterprise in the study area. Result in Table 4 showed that majority of respondents (82%) adopted diversification of income as a risk mitigation strategy. This implies that most crop farmers in study area engaged in other businesses outside the farming enterprise to generate extra income (off farm income) as a means of mitigating risks in their farm business. This is not surprising considering the risky nature of agriculture and uncertain environment of the farm business coupled by economic hardship currently being experienced in Nigeria. 

Table 4: Different Risk Mitigation Strategies

Multiple Responses

Source: Field Survey, 2021

Thirty-six per cent of the respondents used agronomic practices such as mixed cropping, mulching, shifting cultivation among others as a way of coping with agricultural risks while another 47% and 29 % mixed farming (integration of crop and animal production) and crop diversification respectively to mitigate risks in the study area.  A cursory look at Table 4 showed that only 1% of the respondents each used agricultural credit and share tenancy as means of managing risks. Surprisingly, none of the respondents used crop insurance in the study area to mitigate risk. The result could be attributed to low level of awareness of crop insurance and also the difficulty in accessing agricultural credit in Nigeria. The low level of share tenancy could be attributed to the fact that most farmers in the area own their own farm land.  The result also showed that spreading of sales was not used as a mitigation strategy; this may be due to lack of storage facilities and the perishable nature of most crops, which push the farmers to sell immediately after harvest even at a very low price.   

Socio-economic factors influencing the choice of off-farm diversification as a risk mitigation strategy 

The probit regression was used to ascertain the factors that influence the choice of off-farm income diversification. The factors considered were age, marital status, farming experience, education, farm size, monthly income and access to credit. The likelihood ratio chi-square of 19.81 with a p-value of 0.0060  indicates that the model as a whole is statistically significant at p < 0.01. The result in Table 5 revealed that monthly income and access to credit where significant at 5% level and thus influenced the choice of off-farm  income as a risk mitigation strategy in the study area.  The coefficient of monthly income positively influenced the choice of off-farm income as a risk mitigation strategy; thus, a unit change in monthly income will increase the choice of using off-farm income as a risk mitigation strategy by 0.0001629 units.  Access to credit was found to be negative and statistically significant at 5% level and thus, influenced the choice of off-farm income as a risk mitigation strategy in the study area. Therefore, a unit increase in access to credit will decrease the choice of off -farm income as risk mitigation strategy by -4.759957. The result is attributed to the fact that access to credit is one of the ways of mitigating risk and farmers who had access to credit, will use less of off-farm income diversification to mitigate agricultural risk. 

Table 5: Socio-economic Determinants of Crop Farmers Choice of Off-farm Income Diversification

* p < 0.05

Source: Field Survey, 2021

Off-farm Income Activities

The result of off-farm income activities used by the respondents as income diversification strategy is presented in Table 6. 

Findings in Table 6 revealed that the majority of respondents (60%) engaged in petty trading as a means of diversifying their income sources and coping with the negative impact of agricultural risks and uncertainties. As their off-farm income-generating activities, another 13% and 12% worked in agricultural storage and processing of agricultural produce, respectively. According to the findings, the majority of farmers address production risk proactively by adopting diversity, such as trade, as a farm-level risk reduction technique. The result implied that majority of farmers address production risk proactively by using diversification such as trading as a risk mitigation tool at the farm level. The respondents also engaged in the following off-farm income-generating activities: teaching (6%), artisanry (6%), tailoring (4%), among others as indicated in the Table 6. This implies that most of the crop farmers in the study area do not pass through tertiary institution to enable them to be employed as a teacher, as off-farm income activity.

Table 6: Off-farm Income Activities

Multiple Responses

Source: Field Survey, 2021

Off-farm income diversification is an important part of a risk-mitigation strategy in agriculture. Diversification of farm revenue is thought to have a positive impact on agricultural output and farmer income. Off-farm income diversification can be viewed as a strategy to reducing poverty and increasing livelihood income. Despite of its significance, little or no work has been done in the study area.

The study analyzed off-farm income diversification as a risk mitigation strategy in Igbo Eze North LGA of Enugu state, Nigeria. Specifically, it examined the socio-economic characteristics of the crop farmers; ascertained the various risks faced by crop farmers, examined the perception of the farmers on the various types of risks, identified different risk mitigation strategies used by the farmers, identified the socio-economic determinants of crop farmers’ choice of off-farm diversification as a risk mitigation strategy and examined the various off-farm income activities used by farmers. A multi-stage sampling technique was used to select 100 respondents for the study. Data for this study were collected from primary sources by the means of a well-structured questionnaire and analyzed using descriptive statistics such as frequency distribution, percentages, Tables, and probit regression model. 

The result showed that 63% of the respondents were female while 37% were male; 56.0% of the respondents fell within the ages of 21-40 years; with the mean age of 43 years indicating that they are still in their active age. Ninety per cent of the respondents were married with the mean farming experience and education of 16 and seven years respectively. The study also showed the mean ₦16,760.00 monthly income of the respondents in the study area while 97% of the respondents did not belong to any cooperative society

The result on the type of risk faced by the respondents showed that production risk (81%) and biological risks (64%) were the most experienced while financial risk (8%) was the least. The result on the perception of risk faced by the respondents showed that production risk was the most severe (3.00); as was indicated and perceived by the respondents. This was followed by biological risks with mean value of 2.51. Market and price risks (1.46), institutional risks (1.32) and financial risks (1.13) were not perceived as serious threat by the farmers in the study area.

Results on the risk mitigation strategies used by the crop farmers showed that majority of respondents (82%) adopted diversification of income as a risk mitigation strategy. Thirty-six per cent of the respondents used agronomic practices such as mixed cropping, mulching, shifting cultivation among others as a way of coping with agricultural risks while another 47% and 29 % mixed farming (integration of crop and animal production) and crop diversification respectively. However, only 1% of the respondents each used agricultural credit and share tenancy as means of managing risks. 

The result of the probit analysis showed the likelihood ratio chi-square of 19.81 with a p < 0.01. The result indicating that monthly income and access to credit where significant at 5% level and thus, influence the choice of off-farm income diversification as a risk mitigation strategy in the study area. Finally, the result on the off-farm income activities showed that majority (60%) of the respondents were engaged in petty trading as a way of diversifying their income sources and coping from the adverse effect of agricultural risks and uncertainties. Another 13% and 12% engaged in agricultural storage and processing of agricultural produce as their off-farm income generating activities.

The study explored the various off-farm income activities adopted by crop farmers in Igbo-Eze North LGA of Enugu state. The majority of crop farmers used petty trading as an off-farm income diversification activity, according to the survey. In the research area, off-farm income diversification was a significant adaptation strategy for dealing with diverse agricultural risks, particularly production risks and biological risk. The socio-economic characteristics of the crop farmers as indicated by the monthly income and access to credit influenced their choice of off-farm income activities. Thus, the null hypothesis that socioeconomic characteristics of the crop farmers do not influence the choice of off -farm income diversification as risk mitigation strategy was rejected and the alternative was accepted. 

Based on the findings of this study, the following recommendations were made:

1. Government should create enabling business environment that will enable various off -farm activities in the study area to thrive. 

2. Government should provide skill acquisition centers to enable farmers diversify to various non-farm income activities. 

3. Government through ministry of Agriculture should create more awareness on crop insurance as alternative means of mitigating risks in the study area. 

4. It is suggested that government and non-governmental organization (NGO) partners support large-scale production or that development partners provide grants and loans to smallholder farmers to help them expand their farm size and efficiency. 

Conflict of Interest: No

Funding: No funding sources

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