This study analysed price of tomatoes and ginger in Giwa market
Maize supply is limited by the structural and institutional constraints that have persisted despite market reforms in the State. The resultant effect is that smallholder subsistence farmers remain mostly outside the mainstream exchange economy, unable to take advantage of the exchange economy. Using data collected from 600 randomly selected maize farmers, descriptive statistics and the multiple regression model, the paper examined the correlation of maize supply and market participation in Kaduna State. Results of the descriptive statistics show that the average age, level of education, number of household members participating in maize production of the farmers were 44 years, 7 years and 9, respectively. The outcome of the multiple regression analysis established that maize supply is directly and significantly responsive to quantity of maize output sold (proxy for market participation) (0.956) and technical efficiency of farmers (739.29) at (p<0.01) of probability. Other significant variables were age (61.653), level of formal education (114.074) and years of farming experience, which were significant at different levels (p<0.05, p<0.01 and p<0.01, respectively). In conclusion, maize supply is directly and significantly responsive to market participation and technical efficiency of farmers. Maize farmers can increase commercialization through contract farming and produce buying centres. There is also the need for continuous government support to the agricultural sector.
The degree of responsiveness of agricultural supply to input either in the short or long-term production decision is crucial in understanding the role of price and non-price factors in increasing supply. This study analysed output supply and input demand of maize production using a farm survey data of 600 randomly selected maize farmers from all agricultural zones in Kaduna State of Nigeria. Data were analysed using a modified Nerlovian model and set of input demand equations. The results showed that in all estimates (yield and hectarage) long run estimates are greater than the short run values and both were inelastic. The elasticity for lagged own price of maize was 0.23% in the short run and 0.17% in the long run were positive, marginal and inelastic. The hectarage elasticity of supply response for maize is 1.04 in the short run and 0.78 in the long run. The result of the input demand equations showed that the coefficients cost of agrochemical and farm size statistically affect seed, fertilizer and labour demand. The study portrayed that the most critical issues in maize supply are the lack of improved production technology, poor capital investment, land unavailability or poor land tenure system and poor policy incentives. The study recommends that, there is a need for State policy on agricultural research and extension, and adequate input price policies. The government is advised to dissolve the agricultural extension service system to local governments. This will allow agricultural extension system to be more location specific.
The study examined the comparative analysis of technical efficiency of cluster and non-cluster rice farming in Borno state, Nigeria. Primary data were collected through structured questionnaire administered to 232 farmers comprising of 93 clustering and 139 non-clustering rice farmers in Borno State, Nigeria. Data were subjected to analytical techniques that included descriptive statistics, gross margin, t-test and stochastic frontier production function (SFPF). Cluster rice farming enterprise per hectare was more profitable by producing a gross margin (GM) of 196,020.62/ha thus returning N1.72 on every N1.00 invested as compared to non-cluster farming which produced a GM of 99,619.32/ha and thus had a return of N0.96 on every N1.00 invested. The SFPF revealed an average technical efficiency (TE) of 0.76 for cluster farming was higher than 0.58 for non-cluster farmers. Hence, cluster rice farming was more technically efficient compared to non-cluster rice farming. The determining factors of TE in cluster farming include seed (-0.49), fertilizer (0.242), agro-chemicals (0.341) and labour (0.747) compared to non-cluster which included fertilizer (0.207), agro-chemicals (-0.291) and labour (0.668). Inefficiency variables were insignificant in cluster farming while household members active in farming (0.811), years of farming experience (-0.226), and amount of credit utilized (0.5e-4) were statistically significant in non-cluster farming. Insecurity, pest infestation and shortage of water were critical production constraints faced by cluster farmers compared to non-cluster farmers faced with constraints such as shortage of water, insecurity and flooding. Non-clustering farmers should adopt production cluster farming to boost their profit, increase their efficiency and take advantage of the enormous services attributed to working in groups.
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