Crop producers deal with a significant amount of uncertainty daily. Consequently, farmers are compelled to take decisions based on incomplete knowledge. Azubugwu and Osuafor [1] noted that crop farming is known for its unpredictable and risky nature. The term “Risk” is employed to describe a combination of probability of an event and its consequences [2]. The study of Olila et al. [3] noted that risk refers to imperfect knowledge where the probabilities of the possible outcomes are known. Risk is ever-present in agriculture and involve vagaries of weather, the unpredictable nature of biological processes, the seasonality of production cycles, the geographical separation of producers and end users of agricultural products [4]. In the same vein, Risk-Smart (RS) refers to an approach for re-orienting agricultural systems and farmers’ aptitude to support food security under the uncertain realities of agricultural production [5]. Building farmers’ risk-smart capacity seem to be the only way out of devastation [6]. Studies have identified some risk-smart options that are better suited to crop production and crop diversification. However, the adoption of these options have not been effectively explored, particularly in Enugu State. Therefore, this study examined the constraints to the use of risk-smart options among crop producers in Enugu State, Nigeria.

Objectives of the Study

The aim of this study was to examine the constraints to the use of risk-smart options among crop producers in Enugu State, Nigeria. The specific objectives were to:

• Describe the socio-economic characteristics of crop farmers in the study area

• Assess the sources of risks faced by crop farmers

• Describe crop farmers' agricultural enterprise type

• Determine constraints affecting crop farmers’ level of use of risk-smart options

The study was carried out in Enugu State, Nigeria. Enugu State is in Southeast Nigeria and located between latitudes 6°30N and 7°10N of Equator and Longitudes 6°35E and 7°30E of Greenwich Meridian [7]. Enugu State has seventeen Local Government Areas. The state has an estimated population of about 4,167,163 million people (NPC). The state has a land area of 16,727 square km². Enugu State is known to be characterized of wet climatic zone with a rainfall of about 1800mm to 2500mm per annum, temperature range of 29°C to 35°C and relative humidity of 68%. The state is agrarian [8]. 

Two-stage random sampling technique was used in selection of respondents. Firstly, a list of crop farmers was collected from the state’s Agricultural Development Programmes (ADPs), crop farmers were then differentiated, classified and randomly selected in the second stage using Taro Yamane formulae of sample size determination with 10% error of margin and 95% confidence level giving a representative sample of two hundred and ninety-three (293) crop farming households. The main tool for data collection was a set of semi-structured questionnaire and it was supplemented with verbal interview in places where the respondents could neither read nor write. All data elicited were first cleaned through careful inspection of individual response to correct any outliers. The data were classified according to the respondents, coded and entered using a uniform logical frame. The questionnaire sought for information on socio-economic characteristics of the farmers, the sources of agricultural risks faced by farmers, crop farmers' agricultural enterprise type and the barriers of farmers to adoption of risk-smart options in the study area.

Descriptive statistics such as frequency, percentages, mean and Principal Component Analysis (PCA) were used to achieve the objectives. PCA, a dimensionality-reduction method was used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set. The model is specified below.

The principal component factor analysis model is stated thus:

Y1 = a11X1 + a12X2 + * * *+ a1nXn

Y2 = a21X1 + a22X2 + * * * + a2nXn

Y3 = a31X1 + a32X2 + * * * + a3nXn

* = *

* = *

* = *

Yn = an1X1 + an2X2 + * * + annXn

Where:

• Y1, Y2 … Yn: Observed variables/constraints affecting crop farmers use level of risk smart options in the study area

• a₁ – an:  Factor loadings or correlation coefficients

• X₁, X₂… Xn:    Unobserved underlying factors affecting crop farmers in the study area

Twenty (20) factors was extracted based on the response of the respondents. Only variables with factor loading of 0.40 and above at 10% overlapping variance [9] was used in naming the factors. 

Objective 1: Socio-Economic Characteristics of the Crop Farmers

The result of the crop farmers’ distribution based on socio-economic characteristics is shown in Table 1. It revealed that majority (58.7%) of the crop farmers fell within the age bracket of 41 and 50 years. The mean age was 49.5 years. The implication of the finding is that there is a huge hope in reduction and effective management of agricultural risk as these younger farmers are more likely to adopt various improved risk-smart options faster than the older ones in the area. The finding is in line with the studies of Kwesi and de-Grafts Acquah [10] and Onubuogu and Esiobu [11] who reported that majority of farmers within the age range of 41 to 50 years are still in their active age, more receptive to innovation, more technically efficient, effective and could withstand the stress and strain involved in risk management. The result on sex showed that majority (64.5%) of the crop farmers were males. This result indicates that both men and women are involved in crop production in the area but males were more involved than females. The finding is a positive hope for effective and efficient risk management in the area as both sexes are key in achieving eco-friendly environment for agricultural production to strive. Onubuogu et al. [12] maintained that where males have greater proportion, technical efficiency and productivity is expected to be higher because males have the tendency to be more labour efficient. In the same vein, the result could also be attributed to the socio-cultural factor which gives males more access to production variables such as farmland than females in the study area. Building both genders’ capacity to risk management is necessary. On educational level, the result showed that majority (45.4%) of the crop farmers had secondary education. The mean educational level was 13.4 years. The result implies that approximately 87.63% of the crop farmers had trainings in formal educational institutions which no doubt increases their literacy levels. It is expected that the higher level of education of the crop farmers will contribute significantly to understanding the concept of agricultural risk. Extension agents in the area may have less work to do in educating the crop farmers due to the findings, as crop farmers will have the ability to receive, decipher and comprehend information relevant to effective and efficient risk management in the study area. The result supports the finding of Okoli et al. [13] who reported that exposure to high level of education is an added advantage in terms of achieving understanding, huge income and running efficient and sustainable agribusiness enterprise. The result of the crop farmers’ distribution based on marital status revealed that majority (57.7%) were married. This shows that crop production in the area is an enterprise mainly undertaken by married farmers, who are seen to be responsible according to societal standards [12]. The implication of the finding is that married crop farmers would be more involved in effective and efficient in risk management than their single counterpart. Since they would have easy access to production variables such as land and large family size which are traditionally owned and provided by household heads (husbands) to compliment family labour and to enhance     production, reduce   the   cost   of   hired   labour. 

Table 1: Socio-Economic Characteristics of Crop Farmers

Source: Field Survey Data (2020)

The finding of the crop farmers’ distribution based on crop farming experience indicated that majority (50.5%) had 10-19 years of farming experience. The mean farming experience was 34.3 years. Experience in agribusiness enhances output performance. The finding supports Onubuogu and Esiobu [11] who reported that crop farmers with higher years of farming experience would be more efficient, have better knowledge of climatic conditions, better knowledge of efficient allocation of resources and market situation and are thus, expected to run a more efficient and risk-averse agricultural enterprise. As years of farming experience increases, farmers tend to build stronger capacity, develop better technical skills to handle various kind of risk they encounter. The implication of the findings is that crop farmers would set realistic time and cost targets, allocate, combine and utilize better risk-smart options to thwart the negative impact of agricultural risk and enhance crop production.

The result on household size showed that majority (70.3%) had household size of 6-10 persons. The mean household size was 6 persons. This implies that crop farmers in the study area have large household size. Large household size ensures availability of labour and expansion of farm size. This finding supports the result of Teweldemedhin and Ykapimbi [14] who reported that large household size compliment labour to enhance production and reduce the cost of hired labour. For the purpose of this study, a household comprises all persons who generally live under the same roof and eat from the same pot. It is expected that famers who have a bigger household size would adopt several risk-smart options to effectively and efficiently manage agricultural risk than their counterpart with small household size. The result of the farmers’ distribution based on membership of cooperative society revealed that majority (67.2%) of the crop farmers are members of cooperative society. The implication of this result is that majority of the farmers have access to credit facilities through cooperative society to which they belong, to enhance risk-smart option. Membership of cooperative society affords farmers the opportunity of sharing information on modern production techniques, purchasing inputs in bulk as well as exchanging labour [13]. The result supports the findings of Onubuogu et al. (2014) and Azubugwu and Osuafor [1] who reported that membership of cooperative society help agribusiness entrepreneurs obtain information and project a collective demand. The result of the crop farmer’s distribution based on extension contact depicted that majority (61.8%) of the crop farmers receives 1-2 of extension visits per month. This implies that the farmers in the area are poorly visited by extension agents to ascertain their crop farming problem and know where they need assistance in risk management. This could bring about low productivity and threaten farmers’ various agricultural enterprises due to lack of innovative information in the area.

The findings of the farmers’ distribution based on quarterly average farm income, revealed that majority (33.4%) of the crop farmers had an average farm income of between ₦81,000 and above. The mean farm income was ₦72,453.00. The insinuation of the findings is that crop farmers with the higher farm income will adopt various risk-smart options to enhance risk management as well as achieve huge yield/output than their counterparts who have poor average farm income in the study area. The finding supports the studies of Onubuogu and Esiobu [11] who opined that incomes (whether on-farm or off-farm income) have a positive relationship with the adoption of agricultural technologies since the latter requires sufficient financial wellbeing to be undertaken. Table 1 also reveals that majority (52.9%) had a farm size of less than 1.0 hectares. The mean farm size was 1.42 hectares. This implies that farmers in the area are mainly small holder crop farmers operating on less than or equal to 1.0 hectares of farmland. This could be as a result of land tenure system predominant in the area or due to the increasing population. Onubuogu et al. [12] reported that large farm size increases agricultural productivity and improves crop farmers’ technical, allocative and resource use efficiency. This implication of the findings is that crop farmers might have several risk-smart options to practice but limited farm size would compel them to intensively farm on a small plot of land. This is no doubt that a small farm size poses a serious threat to effective and efficient risk management in the area.

Objective 2: Sources of Risks Faced by Crop Farmers 

The result on sources of farm risks faced by crop farmers is presented on Table 2. Result revealed that the major source of production risk was disease outbreak as identified by 113.9% of the crop farmers in the area. The result reflects the heavy epidemic disease that has inundated the crop farming activities in sub-Saharan Africa especially Nigeria during the last decade (FAO). About 91.67% of the farmers reported adverse weather condition such as drought, excessive rainfall as the major source of production/technical risks in the study area. It is becoming apparent that with the current global climate changes, changes in weather condition are creating a great concern for many farmers. This is consistent with the recent studies of Onubuogu and Esiobu [11] who opined a significant increasing trend for changes in weather condition of crop production while predicting future increase if the trend continues in the area. Similar findings were also reported by Enete et al. [15]. About 91.5 and 51.2% of the crop farmers identified technological failure and poor technical know-how respectively as their technical-related risk in the area. Good farm technologies and adequate technical know-how will energise and mobilise farmers to produce optimally as well as cope with any impending risk positively. This is in consonant with the findings of Ekeleme et al. [16] who maintained that farmers in Nigeria still rely on traditional technology, outdated knowledge and low yielding variety of inputs. 

Approximately, 85.00, 56.67 and 15.00% of the farmers identified pest infestation, flooding and drought respectively as the other sources of crop production risk in the area. Pest infestation is expected as it has been the bane of low crop performance as well as poor crop farmers’ income in Nigeria. However, with elaborate outreach programme by agrochemical dealers, any reported pest attacks may have been due to negligence or financial constraints by farmers. These findings are comparable with those of Salimonu and Falusi [17] who identified that between year 2015 to 2019, 64.5% sample households in Nigeria were affected by pest infestation. The result also shared view with the finding of Nto et al. [18]. Meanwhile, the result on drought and flood seems justifiable in view of the flood disaster which occurred in most parts of Nigeria (Enugu State in particular) in year 2020. A relatively small proportion report on drought is also expected as there has not been any case of serve drought in Enugu State, Nigeria. Result in Table 2 also showed the distribution of price/marketing risk of the farmers in the area. Approximately 106.5, 62.1 and 56.7% of the crop farmers identified change in price of inputs (seeds, pesticides, farm tools etc.), Fluctuation in output price/Loss of market access due to wholesale buyer or processor relocation (low price of food) and Poor market conditions/Products failing to meet market standard respectively. In situations where produce prices are liberalized as it is in Nigeria, seasonal and regional fluctuations are expected [18]. The finding is consistent with those of Satit et al. [19] and Kwesi and de-Graft Acquah [10] who argued that marketing risks associated with the variability of product and input prices were the most important sources of risk considered by the farmers in their respective study areas.

About 129.0 and 84.6% of the crop farmers reported non-accessibility of loan and high interest rate as their financial risk in the area. Poor access to credit and high interest rate left farmers unable to cope with farm risk. This could be attributed to high collateral demand and grantor expected of farmers before receiving loan from any credit institution. The finding tallies with the studies of Salimonu and Falusi [17] and Nto et al. [18] who asserted that financial risk associated with poor access to credit and high interest and collateral demand were the bane of poor coping strategies by farmers Osun and South east Nigeria of which Enugu State is included respectively.

Table 2: Sources of Risks Faced by Crop Farmers

*Multiple responses were recorded. Source: Field Survey Data (2020)

Figure 1: Distribution of Farmers’ Enterprise Types; *Multiple Reponses were Recorded. Field Survey Data, 2020

Some crop farmers (39.6, 58.0 and 47.1%) identified environmental degradation, illness and herdsmen cow raid on crops respectively as their major legal and environmental, human resource and social risks respectively in the area. Sickness could be attributed to the unpredictable nature of human body system, environmental degradation as a result of unpredictable nature of the climate and the herdsmen cow attack may be as result of communal misunderstanding. Under the institutional/social risk, theft of production equipment (18.4%) maybe attributed to the high level of poverty that have engulfed the study area as reported by (NBS). The result also shared view with the findings of Satit et al. [19] and Salimonu and Falusi [17] who reported theft and sickness as the risk associated with farming activities. The activities of herdsmen have had a negative significant effect on crop production in recent times in the study area.

Objective 3: Crop Farmers’ Agricultural Enterprise-Types

The result of the crop farmer’s distribution based on agricultural enterprise type is presented in Figure 1. It showed that the crop farmers are involved in one form of agricultural enterprise or the other. The crop production enterprises were cassava (97.5%), vegetable (92.5%), millet (86.7%), plantain (77.9%), maize (67.2%) and rice (43.9%). 

Table 3: Rotated Component Matrix for Constraints Affecting Crop Farmers Level of Use of Risk-Smart Options

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization

This implies that crop farmers are conscious of the risk situation of agriculture and have started becoming risk-smart through the adoption of several Risk-Smart options to thwart the negative impacts of any peril. Farmers in the area have several enterprise-types to hold-on to in case of any negative occurrence. The study is in line with the findings of Nto et al. [18] who observed that diversification is the major option farmers used in the means of uncertainty of agricultural production. Also, a sizeable number of researchers [6,11] has noted that diversification served the best by spreading risk across a number of enterprises. Diversification acts as a strategy to stabilize firms’ incomes.

Objective 4: Constraints Affecting Crop Farmers’ Usage Level of Risk Smart Options in the Study Area.

As presented in Table 3, Principal Component Analysis (PCA) was used as a data reduction technique in order to group 20 statements into meaningful components and/or to select the most suitable components. The first component was named as ‘institutional factors’ viz farming experience (-0.716), poor farm income (0.462), number of times visited by loan supervisors (-0.725), poor road network (0.638), poor technical know-how (0.612). The second component was called ‘credit need factors’ and considered items viz poor farm income (0.492), diversion of credit (0.665), lack of assistance from the government (0.494), negative government interference (0.051) and natural disasters (0.835). The third component is named ‘social factors’ which are number of times visited by loan supervisors (-0.725), poor road network (0.638), Natural disasters (0.835) and poor technical know-how (0.612). All three components extracted the items in which the loadings were greater than 0.4. The coefficients of farmers’ income (0.462 and 0.492) was positively significant in both components. The positive sign of the variable is in consonance with a priori expectations. This indicates that, the higher the farmers’ income, the higher his use level of risk smart options. Increased income would probably place the farmer on a good pedestal for higher levels of business that would warrant the farmer to seek for risk smart options. Moreover, the farmers’ business ingenuity and the simultaneous drive for innovations would warrant the need for additional investment funds.

The paper examined the constraints to the use of risk-smart options among crop producers in Enugu State of Nigeria. The study confirmed the evidence of various agricultural risks in the area. Thus, crop farmers have started adopting several risk-smart options to thwart the negative impacts of the risk in the area. The major risk-smart options crop farmers used were diversification of livelihood (92.8%). Sadly, crop farmers risk-smart options are not yet sufficient to prevent them from devastation. However, if crop farmers’ risk-smart capacity is strongly built, agricultural production in the area may be more favourable with time. Increasing education, farm income and extension contact of the farmers will significantly propel an increase in effective risk management among crop farmers in the area. Crop farmers should be encouraged to form agricultural production and marketing cooperative to enhance their risk-smart capacity in the study area. In the same vein, effective agricultural policies and programmes should focus on granting farmers improved access to farm credit at low single-digit interest rate. Building farmer’s capacity to risk management is also necessary given the trends of farm risk in recent times. Government at all levels should identify genuine farmers and insure their farms against risks as well as check seriously activities of these herdsmen in the area.

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