A Logit Analysis Of The Participation In The Nigerian Agricultural Insurance Scheme By Maize Growing Farmers In Kaduna Stat
Insurance remains a logical option for coping with knowledge imperfection that is no more than risk, Agriculture in Nigeria, like elsewhere, had been subject to the vagaries of nature. The Nigerian agricultural insurance scheme (NAIS) became operational in 1987. The scheme was designed to project a wide array of arable crops, cash crops and livestock against such perils as fire, flood, pests and disease. The decision to participate in the NAIS by farmers and the influencing factors have not received much attention. This study was based on the expectation that such decisions will be affected by certain factors in the farmers’ social, cultural and economic environments. This study aims at modeling and explaining the decision to participate or not in NAIS by farmers in maize-based cropping systems in Kaduna State of Nigeria. The use of qualitative response models in explaining discrete decision making is well documented. The logit model is in this category and has been extensively applied, mainly to technology adoption decisions. A form of the logit model investigated in this study. A total of 51 non-participating and 49 participating farmers in the NAIS were drawn purposively based on maize growing during the 1996 survey period, whether under sole or mixed cropping systems. About 94% of the farmers studied were correctly classified by the model. The statistically significant adoption factors are the amount of loan, year of education and contact with extension agents. These significant at the 1% level. Job status was significant at the 5% level and the size of loss was significant at the 10% level. The signs of all the statistically significant parameter estimates except the size of loss suffered and the number of contact with extension agents, were consistent with a priori expectations. This study has shown the need to take full advantage of the indicated directions of influence of the factors influencing the decision to participate in the NAIS, particularly the statistically significant ones, towards the strengthening of the NAIS or similar ones elsewhere. Further studies will however be required on the relationship between the decision to insure or not in the study area and factors such as farm size, membership of farmers’ associations and farming experience. These factors, whose signs were consistent, did not statistically explain agricultural insurance decisions of the farmers in this study.