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The aim was to analyze the output market outlets accessible to rice farmers and determinants of farmers’ choice among alternative rice production in Kano State, Nigeria. Primary data were collected from 164 rice farmers with the aid of structured questionnaire. This study was conducted in Kura, Garun Malam and Bunkure Local Government Areas of Kano State during 2015 cropping season. A multistage sampling techniques were used for data collection through the use of structured questionnaire. The ordered probit  model was used to estimate the parameters of the determinants of farmers’ choices among alternative rice output market outlets by rice farmers in Kano state. The generalized likelihood ratio statistics was -113.401.  This ratio exceeds the critical chi-square values at p<0.01 level of significance. The log likelihood ratio value represents the value that maximizes the joint densities in the estimated model. This shows that at least one of the predictors' regression coefficient is not equal to zero in the model. The Prob > chi2 was (50.03) and statistically significant at p<0.01 level of probability. The probability of obtaining this chi-square statistic shows the effect of the predictor variables on specified alpha level. This implies that at least one of the regression coefficients in the model is not equal to zero. farmers’ choices among alternative rice output market outlets was significantly determined by educational status of the farmers, access to credit, cooperative membership, distance to market, quantity of output produced by the farmers and  market price of rice (P<0.10). Based on the findings of this study, it could be concluded that the most commonly used output markets by rice farmers was rural assembler (82.3%). Despite increasingly competitive markets, pricing issues for rice remains a concern for farmers.

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The level of yield among sweet potato farmers is on a decline; low output and yield differences was observed, indicating the existence of inefficiency in production systems and variations in input utilization. Efficiency in resource use must be sustained in order to improve productivity and maximize farm output. This study therefore analyzed the technical efficiency of sweet potato production. Multi-stage sampling techniques were adopted in selecting 94 respondents for this study. Data collected was analyzed using descriptive statistics and stochastic frontier production function. The socioeconomic variables of the respondents affected their farm efficiency and level of farm output. The estimated ratio of the L/R test was 0.579; indicating a goodness of fit of the frontier model and thus a rejection of the null hypothesis. The coefficients of sweet potato seeds (vines) (0.362) and labour (0.439) were positive and statistically significant at 5% level of probability, while the coefficients of farm size (-1.333), fertilizer (-0.452) and herbicides (-0.766) were negative but statistically significant at 5% level of probability. The inefficiency model revealed that the coefficient of farm capital (-0.172), education (-2.281), access to credit (-0.472), farming experience (-0.639), extension contact (-0.733) and membership of cooperatives (-0.396) were negative and statistically significant at 5% level of probability. The mean technical efficiency was 0.62 (62%) implying that the sweet potato farmers in the study area were not producing at optimal capacity. The constraints identified significantly affected sweet potato production in the study area. Subsidizing input costs; sensitizing farmers on appropriate farming practices, cooperative formation and efficiency in resource utilization; improving access to agricultural inputs, technology, farm capital, credit and extension services, market linkages, farm labour supply and the development of indigenous technologies in sweet potato production are strongly recommended.

This study analyzed the economics of sweet potato production in Bokkos, Plateau State, Nigeria. Multi-stage sampling technique was adopted. Primary data collected was analyzed using descriptive statistics, gross margin and regression analysis. The results of the study revealed that the socioeconomic factors significantly affected sweet potato production. The estimated gross margin/ha was N154, 150. The estimated value of Sigma square (δ 2 ) was 0.699, indicating that the model was well fitted for the data analysis. The coefficients of farm size (1.333), labour (0.439), fertilizer (0.452) and age (0.172) were positive and statistically significant at p<0.05 level; seed (0.362) was also positive and significant at 10% level; education (-0.639), household size (-0.472) and farm experience (-0.733) were negative but significant at p<0.05 level. Estimated mean technical efficiency index was 0.62, suggesting that farm yield can be increased by an index of 0.38, through improved management practices. The constraints identified significantly affected sweet potato production. Input subsidies, improved credit access, extension services, technology and market linkages are strongly recommended.