Research Article
Determinants of Poultry Farmers Participation in Livestock Insurance in Southwest Nigeria
Department of Agricultural Economics, University of Ibadan, Ibadan
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ORCID: 0000-0002-5392-4122
Poultry enterprises are usually faced with a lot of risks and uncertainties, some of which are natural hazard such as floods, drought, fire outbreak, diseases, pest attacks and theft. Since, the poultry farmer cannot predict the probability of occurrence of any of these and cannot bear these risks and uncertainties alone, he is faced with the option of transferring or sharing the risks involved in the production. Therefore, poultry farmers underwrite livestock insurance in order to mitigate the ill effects of risks. Agricultural insurance is an economic component of farm management designed to reduce the adverse effect of natural disaster on farmers incomes through the payment of indemnity (Ajieh, 2010).
In Nigeria, the Government introduced agricultural insurance programme with the tripartite aim of broadening farmers access to farm resources, positively changing farmers attitude to risk in their choice of resource use and to achieve increased food supplies in the market (Olubiyo et al., 2009). The previous studies have shown that agricultural insurance protects farmers against financial disaster after suffering any of the insured risks and the farmer is not only able to continue in business but also empowers the farmers to obtain farm credit (Hardaker et al., 1997; Olubiyo and Hill, 2003; Epetimehin, 2010).
The National Agricultural Extension and Research Liaison Services identified the following as the benefits of agricultural insurance to farmers: (a) It protects farmers against financial disaster after suffering any of the insured risks for which indemnity (compensation) is paid. The farmer is not only able to continue in business but also the stability of his income is enhanced, (b) Agricultural insurance empowers the farmers to obtain farm credit. Since, insurance guarantees protection against crop and/or livestock failure, the insured farmer has greater confidence in obtaining loans, (c) It facilitates better planning and project implementation since there is a high level assurance for continuity in business, (d) It serves as an assurance to banks and other financial institutions who grant loan for agricultural purposes that loans given will be repaid and (e) It build farmers confidence in using new technologies and making greater investments in agriculture (NAERLS., 1991).
Nigerian farmers are not very excited about taking an insurance policy. This can be traced to the less than satisfactory image of the insurance industry regarding loss compensations and this problem has created mixed feelings towards Agricultural insurance by prospective farmers and hence, the farmers become reluctant in their willingness to take an insurance cover and also considering the very low incomes, the small sizes of holdings aimed at subsistence production, large scale ignorance and poverty and the adverse view of other peoples experiences with activities of insurance companies in other sectors, peasant farmers are generally reluctant to patronize the insurance market, let alone willingly forgo a small payment in the form of premiums in exchange for their farm risks (Olubiyo et al., 2009).
Despite the existence of insurance services rendered by Nigerian Agricultural Insurance Corporation and other private firms in Nigeria, there has been a low level of participation of farmers buying insurance premium. It is in the light of the above that this study was conceived to examine determinants of participation of poultry farmers in livestock insurance in southwest Nigeria. The specific objectives are to:
• | Determine the level of awareness of poultry farmers livestock insurance |
• | Profile the level of participation in livestock insurance policy by poultry farmers |
• | Examine the factors influencing the poultry farmers inparticipation in livestock insurance policy |
Study area: The study was carried out in Osun and Oyo states, Southwest, Nigeria. Osun State has 30 local government areas with an estimated population of 3.4 million (NPC., 2006) and land area of 14,875 km2 on latitude 5°N and 8°N, between longitude 4°E and 5°E. The climate is humid tropical type with a mean annual temperature of about 28°C and a mean annual rainfall of over 1600 mm. Oyo State has 33 Local Government Areas with an estimated population of 5.6 million (NPC., 2006). The land area is 35,743 km2 located within latitude 3°N and 5°N, between longitude 7°E and 9.3°E. The average temperatures are between 24°C and 25°C. Rainfall figures over the state vary from an average of 1200 mm at the onset of heavy rains to 1800 mm at its peak in the southern part of the state to an average 800 and 1500 mm at the northern part of the state.
There are two distinct ecological zones in both states; the rainforest and derived savannah zones. Major crops found in these states are yam, cassava, maize, rice, vegetables and cash crops like cocoa, rubber, kolanut and citrus. Rural households in the states rear sheep, goats, local chickens and pigs. Also, intensive rearing of exotic breeds of cockerels, layers and broiler birds have become popular in the study areas.
Source and type of data: The primary data were obtained with the aid of well-structured questionnaire that captured socio-economic/demographic characteristics of poultry farmers and farm characteristics. These include age of the poultry egg farmer, gender, level of education, poultry farming experience, household size and sources of credit. It also includes information on awareness about the livestock insurance policy and participation in the policy by the poultry farmers.
Data collection and sampling technique: A multistage sampling technique was employed in selecting the poultry farmers in the study areas. The first stage was the purposive selection of Osun and Oyo States from the six states that made up the Southwest, Nigeria, based on the highest exotic-poultry population distribution in Southwest, Nigeria (FDLPCS., 2007). The second stage involved purposive selection of six (6) Local Government Areas (LGAs) from Osun State and eight (8) local governments from Oyo State. The size of the local governments chosen from each state was based on available records of number of registered members of the Poultry Association of Nigeria (PAN) in which Oyo State has the highest number of poultry farmers than Osun State. The purposive selection of the local governments in each state was based on those with the highest number of registered members of the Poultry Association of Nigeria (PAN). They are Iwo, Ejigbo, Irewole, Ayedire, Irepodun and Ilesa West in Osun State and Afijio, Egbeda, Lagelu, Akinyele, Atiba, Oyo East, OnaAra and Oyo West in Oyo State.
The third stage was the random selection of two hundred and forty (240) and one hundred and eighty (180) poultry farmers selected from Oyo and Osun States respectively proportionate to the size of registered members of the Poultry Association of Nigeria (PAN) in each state. Also, the number of poultry farmers selected in each selected Local Governments Area is proportionate to the size of registered members of the Poultry Association of Nigeria (PAN) in each LGAs. In all, total of four hundred and twenty (420) poultry farmers. However, due to incomplete responses, only four hundred and three (403) questionnaires were used for the analysis.
Analytical techniques: Data collected were analyzed with descriptive statistics and logit regression model. The descriptive statistics was used to examine the level of poultry farmers awareness and participation in livestock insurance policy while the logit regression model to determine the factors influencing poultry farmers willingness to participate in livestock insurance livestock policy.
Logit regression model: The logit regression model is a unit or multivariate technique which allows for estimating the probability that an event occurs or not by predicting a binary dependent outcome from a set of independent variables. There are two reasons for choosing Logit model for this study instead of linear probability and probit models according to Rahman and Alamu, (2003). Logit model ensures production of probability of choice within (0, 1) range. This is an advantage over linear probability model and it is easier and more convenient to compute than probit model. The logit model is based on cumulative logistic probability function and it is computationally tractable. According to Gujarati and Porter (2009), it is expressed as:
(1) |
For ease of estimation, Eq. 1 is further expressed as:
(2) |
Pi | = | Probability of an event occurring |
(3) |
The empirical model of the logistic regression for this study assumed that the probability of the poultry farmers participation in livestock insurance policy is expressed as:
(4) |
Pi range between zero and one and it is non-linearly related to Zi. Zi is the stimulus index which range from minus infinity to plus infinity and it is expressed as:
(5) |
To obtain the value of Zi, the likelihood of observing the sample was formed by introducing a dichotomous response variable. The explicit logit model is expressed as:
(6) |
Y | = | Dichotomous response variable (1 for poultry farmers who participated in livestock insurance policy, 0 otherwise) |
X1 | = | Age of farmers (years) |
X2 | = | Years of formal education (years) |
X3 | = | Gender (dummy = 1 if female, 0 otherwise) |
X4 | = | Household size (number of persons) |
X5 | = | Hired labour (man-days) |
X6 | = | Poultry rearing experience (years) |
X7 | Access to Extension services (dummy = 1 if yes, 0 otherwise) | |
X8 | = | Access to Credit (dummy = 1 if yes, 0 otherwise) |
Poultry farms characteristics:
X9 | = | Rearing system (dummy = 1 if battery cage, 0 otherwise) |
X10 | = | Stock size (number of layers stocked) |
b1-b10 | = | Coefficients of stimulus variables |
b0 | = | Constant term |
u | = | Error term |
Socio-economic characteristics of poultry egg farmers and farm characteristics: Table 1 presents socio-economic characteristics of poultry egg farmers. Majority (70.5%) of the poultry farmers were below 50 years of age with an average age of 45.5±9.1 years. Poultry farmers were mostly male (81.4%). Most of the poultry farmers were married (85.6%) with average household size of 5.4±1.68 persons. Majority had secondary education (45.2%) followed by those with tertiary education (36.7%). More than half (56.3%) of the poultry farmers had between 5-10 years of poultry farming experience with the mean years of experience being 10.0±5.1 years. Majority (70.0%) of the poultry farmers had an access to credit while the remaining (30.0%) were discovered not to have access to any source of credit. Also, majority (73.9%) of the poultry farmers had access to livestock extension services.
Poultry farm characteristics: Table 2 shows that half (50.4%) of the poultry egg farmers preferred to raise Harco breed of hen while a few (2%) raised Rhode Island Red. Majority (87.3%) of the farmers stocked pullets. Farm size was classified following Adene and Oguntade (2006), Obi et al. (2008) and Arowolo et al. (2012).
Table 1: | Socio-economic characteristics of poultry farmers |
Field survey data, 2013 |
Table 2: | Poultry Farm’s characteristics and system of management practice by the farmers |
Field survey data, 2013 |
Farms having between 500 and 2000 birds were considered as small scale commercial farms, those farms having more than 2000 birds and flock size which is less than 10000 birds were regarded as a medium commercial farm while those having 10000 birds and above is classified as large commercial poultry farms. The result shows that the medium scale poultry egg farmers constituted more than half (54.3%) of the farmers. Also, the least number (13.6%) of the farmers are large scale operators while the small scale constituted 32.0% of the poultry egg farmers. The average flock size was 4924.2±3838.9 layers. Majority (75.9%) of the poultry egg farmers operated battery cage system while lesser number (24.1%) of the farmers reared their birds on deep litter system. The average mortality was 7.7±4.9%; 27.3% of the farmers had less than 5% of mortality, 53.6% of the poultry egg farmers had 5-10% of mortality rate, 17.4% of the farmers had 11-20% of mortality rate while 1.7% of the farmers had more than 20% of mortality rate.
Awareness and participation of poultry farmers in livestock insurance policy: Table 3 shows that more than half (59.6%) of the poultry farmers were aware of livestock insurance policy. Table 4 reveals that only 11.9% of the poultry farmers insured their poultry farms.
Determinants of poultry farmers participation in livestock insurance: The factors determining the poultry farmers participation in livestock insurance are presented in Table 4. The diagnostics statistics revealed that the chi square distribution which was used to test the overall model adequacy was significant at 1% (χ2 = 106.11, p<0.0000). The result of the logit regression in Table 5 shows that stock size, rearing system, access to extension services and poultry rearing experience were significant variables that influenced the participation of the poultry farmers in livestock insurance policy. Both access to extension services and poultry rearing experience were significant at 10% significance level while rearing system and stock size were significant at 5 and 1%, respectively. However, gender, age of the poultry farmers, household size, years of education, access to credit and labour were not significant.
Table 3: | Distribution of poultry farmers by awareness of livestock insurance |
Source: Field Survey Data (2013) |
Table 4: | Distribution of poultry farmers by participation in livestock insurance |
Source: Field Survey Data (2013) |
Table 5: | Determinants of poultry farmers’ participation in livestock insurance |
Field Survey Data (2013), *Significant at 10% level, **Significant at 5% level, ***Significant at 1% |
Most of the poultry farmers were in their active and productive years who can easily adopt new innovations that could enhance poultry production. The result implied that modern poultry farming is still predominantly a male occupation likely because of the high level of risk involved, labour intensive and other husbandry processes which are not attractive to most women. Consistent with this finding are the studies of Lawal et al. (2009), Adisa and Akinkunmi (2012) and Uzokwe and Bakare (2013). Most of the poultry farmers were married (85.6%) with average household size of 5.4±1.68 persons. More than half of the poultry farmers had between 5-10 years of poultry farming experience. This is expected to manifest in high level poultry management as the longer the years of poultry farming experience, the more exposed the farmer becomes and the more efficient the farmer is expected to be in poultry management.
Majority of the poultry farmers had access to livestock extension services. This implies, that majority of these poultry farmers had access to advisory services and adequate information on improved poultry management techniques. Medium scale poultry egg farmers constituted more than half of the farmers. Also, the least number (13.6%) of the farmers are large scale operators while the small scale constituted 32.0% of the poultry farmers.
More than half of the poultry farmers were aware of livestock insurance, however, few of the farmers insured their poultry farms. This indicates a preponderance of low participation in livestock insurance by the poultry farmers in the study area. This result confirms the findings of Ajieh (2010) who revealed the low participation of poultry farmers in agricultural insurance in Kwara state of Nigeria. This is probably due to constraints to farmers participation in agricultural insurance which include fears that claims may not be paid; inadequate knowledge on the benefits of livestock insurance, delay in payment of compensation and high premium rate. However, the implication of the result is that the layers chicken farmers in the study area have to mitigate against production risks that might occur through other means.
The coefficient of years of poultry rearing experience of the farmers was found to be positive and significant at 10% implies that the longer the years of poultry rearing experience, the more exposed the farmer becomes and the higher their participation in livestock insurance policy. The coefficient of accessibility to extension services by the poultry farmers was found to be positive and significant at 10% implying that the higher the access to extension services by the poultry farmers, the higher their participation in livestock insurance. This result is inconsistent with findings of Farayola et al. (2013), who confirmed contacts with extension agents to be insignificant in influencing the farmers participation in agricultural insurance scheme. The coefficient of poultry rearing system of the farmers which was found to be positive and significant at 5% implies that the poultry farmers that operate battery cage system tend to insure their poultry farms.
The coefficient of stock size of was found to be positive and significant at 1% implies that poultry farmers with larger stock tend to insure their poultry farms than small scale poultry farmers. This result on stock size obtained from this study is similar to the previous study of Oyinbo et al. (2012), who reported farm size as a significant variable that influenced the probability of participation of the farmers in agricultural insurance scheme. This result is expected as the risk is higher in the large scale farms than small farms. Also, most of the large scale poultry farmers have access to credit and they have to insure their farms as one of the conditions to obtain the loans.
This study concludes that stock size and rearing system in that order were the most important factors determining the poultry farmers participation in livestock insurance policy. Also, gender, age of the poultry farmers, household size, years of education, access to credit and labour were found not to be the determinants of poultry farmers participation in livestock insurance policy. The conclusion of this study disagrees with the similar studies conducted by Mishra and Godwin (2006), Oyinbo et al. (2012) and Farayola et al. (2013), who reported that the coefficient of age of the farmers which was found be negative and significant which implies that the older the farmers, the lower their participation in agricultural insurance scheme.
The study recommends that the government should formulate a policy that will make livestock insurance more affordable to poultry farmers by increasing the present level of subsidy granted for livestock insurance cover. Nigerian Agricultural Insurance Company (NAIC) should endeavour to keep to contractual arrangements of indemnifying the farmers in the advent of disease outbreak so as to allay the fears of other farmers that claims may not be paid. Also, adequate dissemination of knowledge on the benefits of livestock insurance by extension agents is crucial to increase the level of participation of poultry farmers in the use of livestock insurance policy to mitigate against risk in poultry enterprise.
The authors are grateful to Mr Phillip Oni of office of FADAMA, Oyo State, Nigeria state headquarters, MrSalawu Ismail of Department of Agriculture and Food Security, Oyo state Local Government Civil Service Commission and MrAdelakun of ADP, Osun state for their assistance in field survey and technical assistance.