The traditional approach to modeling productive efficiency assumes that technology is constant across the sample. However, farms in different regions may face different production opportunities, and the technologies they employ may differ due to environmental factors. Therefore, rather than using a traditional stochastic frontier model in such cases, a stochastic meta-frontier (SMF) analysis is recommended to account for environmental factors between regions. It follows that differences in environmental factors between the upland and lowland regions in Anambra State, Nigeria, may result in farmers producing rice under different production and environmental conditions. Using the SMF model, this study, for the first time, determines technical efficiency (TE) and technological gap ratios (TGRs) of rice production from the upland and lowland regions in the Awka North Local Government Area of Anambra State, Nigeria. Our data are from a cross-section sample of randomly selected rice farmers. Results reveal that lowland regional rice producers are on average, significantly more technically efficient (91.7%) than their upland counterparts (84.2%). Additionally, mean TGRs associated with lowland rice farmers are higher (92.1%) than their corresponding upland producers (84.7%). While the upland rice producers are less technically efficient and further away from their full potential, results indicate that both sets of farmers do not use advanced technologies to match the industry’s potential. We suggest that agricultural policy should focus on providing regionally specific technologies, such as improved rice varieties that fit the working environment of the lagging area, to help rice farmers improve their resource efficiency and minimize technological gaps.

The study on the adoption of improved cassava production technologies among small-scale cassava famers in Anambra State, Nigeria, specifically; described the socioeconomic characteristics of small-scale cassava farmers, determine the adoption levels of improved cassava production technologies, examine the effects of socioeconomic variables on the adoption level and identified constraints to adoption of improved cassava production technologies in the area. A multi stage sampling technique was employed to randomly sample a cross section of 120 small-scale cassava farmers. Data were collected using a well-structured questionnaire. The data were analyze with a combination of tool like descriptive statistics, multiple regression and principal factor analysis. The study revealed that the average age of the cassava farmers was 44.08 out of which 51.67% are male. The average educational level (9.17), farming experience (14.89), household size (5.86), annual income (861.103 USD), farm size (0.15 ha) and extension contacts (3.59) were also revealed. The results further showed that out of the eleven (11) improved cassava production variables considered, the respondents have not fully adopted any of the technologies. Findings on influence of socioeconomic characteristics on adoption shows that F-statistic value of 4.05*** was significant at 1% level of significance, which implies that the predictor variables influenced adoption. Major constraints of the cassava farmers were rotated into three component factors which are institutional (25.4%), economic (17.7%) and managerial factors (12.6%) using principal factor analysis. A KMO of 0.580 and cumulative Eigen-value of 55.7% explained the variance of factors. We therefore encourage extension agents to sit up in disseminating recent innovation to farmers.

Apple is considered as an important fruit crop in temperate regions of the world including India. It is one of the major fruit crops, with a considerable area under cultivation throughout the world and a large associated population. Despite this, the productivity of this important fruit is not up to the expected standard. To gain a practical understanding of the low productivity of apple fruit and its probable causes, a study was undertaken to analyze productivity differentials and their determinants to enable sustainable cultivation. A multistage sampling procedure was adopted to select districts, horticultural zones, and villages, and data were collected from randomly selected apple growers (300). The collected data were empirically analyzed with simple descriptive statistics, logistic regression, polynomial plots, and inferential statistics such as t-tests. The results indicated that apple yields followed a sigmoidal pattern, with the average yield per hectare for the current season as 9.43 t/ha, which depends on experience, education, annual income, and the adoption rate of apple growers. This yield average was significantly lower than the yield of the previous season at a probability level of 1%. To determine the root cause of low productivity, different constraints were studied, creating yield disparities in different quarters; hence, their percentage and value contributions (socioeconomic 11.1%, credit 4.2%, pests and diseases 0.05%, technology 0.9%, extension 2.0%, and market 3.5%) were also established in the study. The study will be of great interest to the relevant authorities in the study area, and the areas globally having similar congenial agro-climatic conditions, who are seeking to address the issues raised in this study through sustainable policy decisions. The different constraints that were the fundamental reasons for low productivity and that prevented the apple growers from adopting innovative techniques/improved practices to increase their yields need to be addressed as a matter of urgency.

The study is aimed at determining the technical efficiency of rice farmers in Anambra State value chain development programme. A well-structured questionnaire was administered to elicit information from 372 rice farmers from the five participating Local Government Areas (Ayamelum, Awka North, Anambra West, Anambra East, and Orumba North) after which only 337 respondents were fit for the study. Cobb Douglas stochastic frontier model was used to ascertain the frontier line of the farmer’s production potentials. The determinant of technical inefficiency was sex and farming experience. The findings revealed that the gamma value (0.0315) captures the variation in technical efficiency by farmers, therefore, about 3.15% variation is observed and frontier output is due to rice farmer’s technical inefficiency effect. The study equally showed that the mean technical efficiency as predicted in the study was 84.76% implying that the farmers are operating 15.24% below their optimum production capacity. These, therefore, justify the need for policymakers to constantly organize training and sensitization workshops for the rice farmers in Anambra State and Nigeria at large paying particular attention to women farmers and the general farmer’s farming experience which will help to tailor down training to specific needs. Key words: Stochastic frontier, technical efficiency and inefficiency, return to scale, influence, utilization, sensitization.

This paper aims to examine the technology gap efficiency of small-scale rice processors in Anambra State, Nigeria. The research was conducted through a survey of 100 small-scale rice processors in Anambra State. Data were collected with structured questionnaires and analyzed using descriptive, stochastic frontier analysis, stochastic meta-frontier model, and inferential statistical techniques. The study revealed that important variables to rice processing present in the state are paddy, firewood, water, and huller. Concerning the economic-specific factors, the cost of grading and other processing assets contributes to inefficiency. The results equally showed that the technology gap efficiency of small-scale rice processors in the industry is tied to or tangential to the frontier output, meaning that the processors in Anambra State are making use of the best technology available. The average efficiency index for the processors in the industry was 0.506, implying that their output is below potential by 49.4%. The results also revealed the technology gap efficiency for the participating and non-participating processors as 0.924 and 0.983, respectively, meaning that the participants need to close an 8.0% gap, and the non-participants need to close a 2.0% gap. These gaps are caused by the high cost of processing equipment, high cost of input, and inadequate infrastructure, among others contributing factors. The paper concluded that the average meta-technical efficiency (0.498) of small-scale rice processors in Anambra State is low and needs to be improved through the provision of adequate technology, training, and infrastructure to bring the current industrial production capacity to 100.0%.