Agricultural productivity in the presence of undesirable output: The case of African agriculture

The motivation for this study stems from two major concerns that are interlinked. First, the on-going food security crisis of African countries. Second, the negative impact greenhouse gas (GHGs) emissions from agriculture have on future food production which worsens the food insecurity problem. The conundrum SSA faces is the need to increase food output through productivity growth while minimizing GHG emissions. To measure changes in productivity growth and GHG emissions, this study evaluates agricultural performance of 18 African countries by utilizing the Malmquist-Luenberger index to incorporate good and bad outputs for the years 1980 to 2012. The empirical evidence demonstrates that productivity is overestimated when not considering bad outputs in the production model. The analysis will also provide a better understanding of the effectiveness of previous mitigation methods which would then allow for appropriate course of action to achieve the twin objectives of increasing agriculture productivity while reducing GHG emissions

Productivity and efficiency of the agricultural sector : Africa with a special focus on rice farming and processing in Kenya

Food security remains a serious concern in Africa because of famine, drought and low yields. To address this concern, the thesis quantifies sources of productivity and efficiency; and provides policy recommendations needed to raise African agricultural productivity. The results indicate that there is room for improvement in lifting African agricultural productivity through appropriate policy implementation. These include R&D spending, schooling, and lowering of HIV prevalence rates. The thesis also evaluates the efficiency of Kenyan rice farming and processing as a special focus. The results suggest differences in rice farming efficiency levels across regions largely attributed to age, gender and technology. For rice processing, farmers can improve their efficiency with better knowledge of servicing and maintenance of rice processing machines

Does hiring a manager improve efficiency - owner vs. non-owner management control of rice mills

Purpose In this study, the impact of owner-operator and non-owner operator rice mills on productive efficiency is investigated. Design/methodology/approach Primary data collected from a survey of 111 rice mills in the Mwea region of Kenya are used. A metafrontier approach is employed to measure overall technical efficiency which is decomposed into managerial and organisational efficiency. Findings The results reveal no significant difference in overall technical and managerial efficiency between owner and non-owner operated mills. However, a significant difference exists in organisational efficiency of mills: non-owner operated mills were found to be performing significantly better than owner-operated. Practical implications The authors provide supporting evidence to the study and discuss some of the significant policy implications stemming from the study. Originality/value It is recognised that for owners to take the risk of divesting control to a hired manager rather than manage the firm themselves can have major strategic, financial and often emotional consequences. However, there is little empirical evidence on how production efficiency will develop as a result of hiring a manager with the underlying economic theory providing ambiguous guidance. Standard economic theory assumes that firms behave as profit maximisers, which can be achieved by operating efficiently. However, this may not always be the case and as the literature indicates, this may especially be so for small businesses in low- and middle-income countries.open access</p

Multi-lateral multi-output measurement of productivity: the case of African agriculture

Total factor productivity (TFP) of agriculture for eighteen African countries is measured using panel data from Food and Agriculture Organization of the United Nations database for the period 1980 to 2007. Using the Färe-Primont productivity index, TFP was decomposed into measures of technical and efficiency change. The efficiency change was further decomposed into measures of technical, mix and scale efficiency changes. The results reveal TFP and technical change growth rates of 0.85% and 1% respectively. In the same period there is a decline in total technical productivity efficiency, mix efficiency, residual scale efficiency and scale mix efficiency change of 0.15%, 0.23%, 0.02% and 0.25% respectively while technical efficiency improved by 0.1%. From the results it is evident that the main driving force of TFP growth is technological progress while negative efficiency levels are contributing to reduced average productivity growth. Promotion of irrigation facilities, improving governance, improving mechanization and reducing land fragmentation are identified as necessary measures to improve TFP growth

Impact of agricultural credit access on agricultural productivity among maize and rice smallholder farmers in Rwanda

This paper assesses the impact of access to agricultural credit on the agricultural productivity of 422 smallholder farmers that cultivate maize or rice in the Western and Eastern province of Rwanda. Stratified, simple random and convenience sampling techniques were used to sample districts, sectors, cells and households. Data were collected using structured interviews and analyzed using propensity score matching techniques. Results indicated that productivity was higher by 44% among the farmers who accessed credit implying that they harvested on average an extra 440 kilograms of maize or rice. According to a crop-specific analysis, agricultural credit access had a more significant impact on maize productivity, with a difference in proportion of 68% (p = 0.000) but had no impact on rice productivity (p = 0.149). The study concludes that agricultural credit was important for Rwanda’s agricultural productivity. Thus policy measures should aim at improving smallholder farmers’ access to agricultural credit and promoting the use of modern agricultural inputs, particularly among rice farmers in Rwanda

A network data envelopment analysis (NDEA) model of post-harvest handling: the case of Kenya's rice processing industry

Food security is a global challenge. With rising world population and demand for food being compounded by resource and arable land constraints, raising the efficiency of food production and use has become increasingly important. While much of the research on food security is focused on farm efficiency and productivity, most neglect post-harvest (PH) handling which is critical in determining the availability of food. In this study, we employ the network Data Envelopment Analysis (DEA) model to evaluate the PH efficiency of milling, using data from Kenya’s rice processing industry. The results show lower efficiency scores when using a network DEA model, which reflects its greater discriminatory power when compared to the standard DEA approach. The study also quantified sources of productive efficiency using a fractional regression model and identified storage space and distance to market as having an impact on drying efficiency; while experience, age of mill, servicing and energy type influenced milling efficiency. The results suggest that policy makers should focus on investing in drying technologies and storage facilities to improve drying efficiency. To improve milling efficiency, policy recommendations include enhancing millers’ access to better technologies, investing in reliable sources of energy and providing PH handling workshops to reduce PH losses

Determinants of adoption of climate smart agricultural technologies among potato farmers in Kenya: Does entrepreneurial orientation play a role?

Climate-smart agriculture (CSA) is an important strategy for supporting farmers against climate change challenges. However, CSA adoption among smallholder farmers particularly in Sub-Saharan Africa (SSA) remains low. This article investigates the factors that influence CSA adoption among smallholder potato farmers in Nyandarua County, Kenya. We specifically focus on the role of the farmers’ entrepreneurial orientation (reflected in the farmers’ innovativeness, proactiveness, and risk-taking), a contribution that has received limited research attention. Data were collected through a cross-sectional survey of 350 potato farming households and analyzed using descriptive statistics, principal component analysis (PCA) and a multivariate probit regression model. Based on PCA analysis, the study considered six categories of CSA practices; soil nutrient management, crop management, crop protection, seed management, water harvesting, and crop quality improvement. The multivariate probit results show that farmers' entrepreneurial orientation had mixed influence on CSA adoption. While farmers’ innovativeness had a positive influence on crop management and improvement practices uptake, its influence on water harvesting technologies was negative. Similarly, proactive farmers were more likely to adopt seed management practices, whereas risk-takers were more likely to adopt protection and water harvesting technologies. Potato producers' willingness to engage in seed multiplication was linked to use of crop protection, seed management, and water harvesting technologies, indicating a path that could help potato farmers’ access clean seed. Other important factors influencing CSA adoption included access to financing through mobile-based applications, gender, land size, trust in extension officers, household income, and farm characteristics. The study discusses the implications of these findings

Characterization and Determinants of Baobab Processing in Kenya

Baobab is an iconic tree that is utilized as a source of food and income generation. While extant literature on baobab has focused on its morphological attributes and nutrient composition, there is a gap in literature in understanding the characteristics of processors and the factors that determine baobab processing. Using cross section data of 304 baobab processors in Kenya, we employ Principal Component Analysis and Cluster Analysis to characterize baobab processors and identify determinants of baobab processing. Results of processed volumes show that baobab processors are grouped in three clusters of high, average and low categories. Clusters of processors are shaped by number of years in processing, access to training, quantity processed, processing cost, income from other sources, access to land, and profit levels. The study suggests the need to train processors on baobab processing to increase their efficiency and returns. Through training, processors will be able to make informed decisions on input use, packaging and presentation of their products to the customers. Also, investment in baobab conservation, harvesting more trees and reducing thematurity rate of the baobab trees will increase baobab inputs thus lowering processing cos