Risk management strategies among smallholder arable crop farmers in Ibiono Ibom Local Governemet Area, Akwaibom State, Nigeria

The study investigated risk management strategies among smallholder arable crop farmers in Ibiono Ibom Local Government of Akwa Ibom State, Nigeria. Purposive and random sampling techniques were used in selecting 80 respondents used in the study. Primary data collected with the use of well-structured questionnaire were analyzed with both descriptive and inferential statistical tools. The result of the study revealed that majority of the farmers (66%) were males, young and active with mean age of 44 years. About 62% were married, 98.25% of them were literate and on the average had spent 23 years in farming. It was observed that the level of adoption of risk management strategy was low (x̄ =2.8). The significant variables influencing production were sex (p < 0.10), age of the farmer (p < 0.10), education (p < 0.01), farming experience (p < 0.10), income (p < 0.01), capital (p < 0.10), and the index of risk situations (p = 0.10). The result revealed that enterprise diversification (x̄ =3.3) was widely adopted as the most important management strategy. The result also shows that farmers in the study area placed higher preference on risk- taking behavior as affirmed by 50% of the farmers. The results therefore call for policies aimed at encouraging financial institutions to collaborate with insurance companies to insure agricultural credit facilities in order to indirectly insure crop farms due to inevitable risk involve in food crop farming business. There is need to encourage farmers to insure their farms and products against uncertain events. Banks and financial NGOs as well as government’s Poverty Alleviation Fund programme are encouraged to strengthen the provision of credit assistance to food crop farmers to enable them adopt the most efficient practices to increase produce beyond subsistent level. Farmers are encouraged to form cooperatives to help manage marketing related problemsKeywords: Risk, Smallholder, Management strategies and Risk behaviou

Access to microfinance facilities among agriprenuers in Abia State, Nigeria

The study evaluated the level of access and use of microfinance by farmers in Abia State, Nigeria. A purposive sampling technique was adopted using a list of 2018 microfinance beneficiaries generated from Bank of Industry’s database and other agricultural enterprise lending institutions (LAPO, First Bank) Bank of Agriculture). A total of 150 respondents were chosen from a sampling frame of beneficiaries of microfinance programme, and administered with a structured questionnaire. Descriptive statistics and ordered logit regression analysis were used to analyze the data collected. Results showed that majority of the farmers had moderate access to microfinance facilities and that consumer microfinance loan was more readily available to the respondents with a mean of 1.94. The coefficients of the multiple choice questions on ordered logit regression analysis estimated significant factors influencing credit access as marital status and distance to microfinance source is long (+ve at 5% each), educational level (-ve at 5%), loan repayment period and inconsistent policy (-ve at 1% each). Others are; livevestock production, medium scale, small scale, local source of finance and commercial source of finance (=ve at 1% each) and long bureaucratic process (-ve at 10%). Further analysis on constraints to access to microfinance revealed that distance to microfinance source, no internet facilities, lack of co-operate affair commission registration and inconsistent policy were the most importatnt constraints militating against access to microfinance by agriprenuers in the study area with mean scores ≥ 2.5. The results therefore call for policy direction to ensure speedy procedures/ requirements to encourage farmers to access loans and more liaison offices for microfinance institutions should be established at locations closer to the farmers.Keywords: Microfinance, Access, , Ordered logistics regression model, and Constraint