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Northern Vietnam is a mountainous area where most farmers' livelihoods depend on maize production. Beside training in farming techniques, since 2013 extension courses on market access have been gradually delivered to maize farmers to help them save production costs and increase earnings.

By applying robust regression and Tobit models to estimate technical efficiency (TE), the difference-in-difference (DID) technique is used in this study to determine the change in the TE of 1,035 farmers in the period 2013–2015 in Northern Vietnam.

The DID estimator indicated the initial success of extension courses by showing a faster increase in the TE of the receivers compared to that of non-receivers. However, co-operation in production and marketing by maize farmers and access to micro-credit resulted in an insignificant impact on the TE of maize farmers in 2015. These low impact results highlighted the inefficiency of the extension and micro-credit programmes and that raised the concern of policymakers. The distance to local market signifies the determination of farmers on their crops affected by how close their location to their input/output marketplaces.

The Land Policy of the government should take into careful consideration measures to exploit all potential production and to mitigate poverty as well as foster the development of the region.

The expansion of extension courses on access to market has been recognised as an effective method to increase farmer's efficiency.

The purpose of this paper is to explore the effect of agricultural extension services provided by public sector on the individual technology adoption behaviour of rice farmers in Sri Lanka.

The study used data from a cross sectional survey conducted in seven rice procuring regions in Sri Lanka. Eight rice technologies were selected to explore the effect of extension service on adoption behaviour of rice farmers using probit models. The extension service variable was identified as potentially endogeneous and instrumented using average extension for each region.

The results revealed that the extension service variable was positive and indicative of a high level of significance in all the rice technologies promoting the adoption. Hence the public agricultural extension service programmes were considered as significant explanators of technology adoption. The farmers who received agricultural extension service were more likely to adopt a technology.

At present, the position of agricultural extension service is questioned and the future is unknown. Therefore, this study advises policy makers to prioritise agricultural policies to strengthen public spending on agricultural extension for effective adoption of technological innovations.

The paper adds solid empirical evidence to the literature on technology adoption behaviour from a peasant agricultural context in a developing country scenario that uses farm level data. Moreover, the study contributes to the literature by reiterating the significance of public provision of extension and training programmes as a direct motive in the technology adoption behaviour of farmers.

The purpose of this study is to propose a performance measurement (PM) model for agricultural extension agents. Based on an interdisciplinary approach, management accounting-agricultural extension, the study has three main research objectives: highlight the main concepts to be embedded in a PM model for agricultural extension agents in an agricultural extension organization (RO1); identify main PM components of the proposed PM model for agricultural extension agents (RO2); and investigate empirically the causal relationships in the proposed PM model (RO3).

An interdisciplinary literature review and a proposed PM model for agricultural extension agents are presented (RO1 and RO2). An empirical survey is incorporated, carried out in early 2011 (RO3), to examine three groups, totaling around 274 respondents. Data were collected through personal interviews using structured questionnaire forms. Path analysis technique was applied.

The authors propose a PM model consisting of five components. The five components are: agricultural extension agents’ characteristics, agents’ work attitudes, services provided, use of agricultural extension services and farmers’ satisfaction with agricultural extension services. The overall findings of the empirical surveys were found to validate the suggested causal relations among the components of the model. Findings indicate that 85 per cent of changes in farmers’ satisfaction with services are explained by changes in the preceding variables in the model.

It is, however, important to view this study with a few limitations in mind; for instance, using a survey method (e.g. sampling and the use of questionnaires in data collection); and the constraints associated with the model. That is to say that the components of the model could be further increased to incorporate other aspects of stakeholders, e.g. the economic impact of governmental financial policies on tax and the customs duties on agricultural products.

A Food and Agriculture Organization of the United Nations agricultural extension reference manual recommends certain purposes for a PM in agricultural extension organizations; interestingly, all these are already embedded in the proposed PM model, which makes it unequivocally a useful PM model for agriculture extension agents in agricultural extension organizations worldwide. Furthermore, the proposed model contributes significantly to agricultural extension practitioners and academics alike. It focuses the attention of agricultural extension organizations on the causal relationships among the model’s components. These components are linked to the agricultural extension organization strategies.

In addition to the practical implications above, the proposed PM model demonstrates the need for placing equal importance on all five components included and setting performance indicator (PI) targets.

The importance of this study emerges from the fact that it is helpful to examine the development and implementation of PM models across various disciplines to enhance understanding. The PM model overcomes the shortcomings in previous PM models of agricultural extension agents’ criteria/models in the agricultural extension literature. It is not merely a theoretically proposed model because the proposed causal relations amongst its variables are empirically investigated. Following management accounting and strategy theories, the authors propose that the relative importance of the attributes of PI in the proposed model differs according to each agricultural extension organization’s strategy, size and organizational structure.

The Million Farmers School (MFS) program—also known as Kisan Pathshala was launched to impart training to the farmers by the government of the state of Uttar Pradesh (India) in December 2017. This study estimates the impact of training on agricultural knowledge of the farmers.

The study is based on household survey conducted in Uttar Pradesh (UP), India, during March–May 2019. The authors employed matching methods, the two-stage least square (2SLS)-residual and endogenous switching regression approaches to control for selection bias and endogeneity.

The results suggest that knowledge outcomes are significantly better among participants vis-à-vis non-participants. The results are robust to different model specifications. Further, the benefits are observed across different regions and social groups.

The MFS program can go a long way in enhancing agricultural know-how and the farmers' economic well-being, bringing a transformative change in the agricultural landscape of UP.

This study is based on a field survey data and analyzes various aspects of the program's impact, design and implementation, and offers implementation advice for greater efficacy in future.

This study aims to address two research questions. First, do the agricultural extension services have an impact on the potential outcomes considered in the primary studies, and to what extent? Second, how sensitive is the reported impact to the study-specific characteristics in the primary studies?

The paper synthesizes 45 studies that assessed the causal impact of agricultural extension services published in 2004–2021, using meta-regression analysis. It considers three measures of effect sizes – Cohen’s, Hedges and principal correlation coefficient (PCC) – to standardize the reported impact of agricultural extension services in the primary studies.

The empirical results show that, on average, agricultural extension services have statistically significant and positive impacts on the potential outcomes identified in the primary studies. However, the magnitude of the impact is considered medium-sized. Other results show that the effect size estimates of agricultural extension services' impact significantly vary with the data type (cross-sectional data vs. panel data), research design (non-experimental vs. experimental design) and econometric methods employed in the primary studies.

One can argue that the medium-sized impact we estimated indicates evidence of a moderate, weak relationship between agricultural extension services and the potential outcomes considered in the primary studies. This means that agricultural extension services need to be restructured in the current form to stimulate change in the agricultural sector globally. In addition, the sensitivity of effect sizes to study attributes (i.e. data types, research design and econometric methods) shows that researchers and academicians need to pay attention to these attributes to provide more reliable estimates for policy purposes.

This is the first study that attempts to shed light on the overall performance of agricultural extension services using a meta-regression analysis approach.

Farmers' risk attitudes and risk perceptions play an essential role in shaping risk management strategies to address risks and uncertainties. Contract farming is considered as one of the feasible approaches to tackle farmers' concerns. However, risk perspectives under various categories have not been included in studies on farmers' preferences for contract farming in the literature, especially in Vietnam. This study aims to determine factors affecting farmers' choices of different contract farming practices.

The explanatory factor analysis (EFA) and multinomial logit model (MNL) were applied to explore the impacts of risk perspectives on farmers' preferences for contract farming. Data have been collected from 211 rice farmers in An Giang Province, “the rice bowl” of the Mekong Delta, Vietnam.

The study found that farm size, cooperatives, extension, market access and trust have significantly impacted on contract participation while a delay payment was a barrier for farmers' motivation to opt for the contract. Farmers' contract choices were also influenced by their risk attitudes and perceptions under different risk dimensions. The financial, policy and human risk-averse behavior predisposed farmers to single out the full contract while the policy and human risk-loving and production, market and finance risk-averse respondents were in favor of the marketing contract. Moreover, the findings indicated that the more farmers concerned about risk of weather and market, the more choices for the full contract, whereas the risk perceptions of weather and policy encouraged farmers to use the limited contract. By contrast, farmers who perceived the impacts of risk of diseases/pests and human were likely to adopt the marketing contract.

This study just focuses on collecting data from farmers’ perspective. Future studies involving stakeholders such as enterprises and policy makers are strongly recommended so as to design suitable contracts and enforce contract schemes effectively in Vietnam.

The findings also contribute to the literature on different types of contracts and the multidimensional aspect of risk for rice production in Vietnam.

The purpose of this paper is to examine the impact of agricultural credit on technical efficiency of Ghanaian maize farmers using a unique dataset drawn from the database of Sub-Saharan Africa’s intensification of food crops agriculture (Afrint II) in 2008 period.

In this study, a two-stage estimation procedure is employed to determine impact of agricultural credit on technical efficiency of Ghanaian maize farmers. The first stage utilized probit model while the second stage utilized stochastic frontier approach to estimate impact of credit on technical efficiency of Ghanaian maize farmers.

The study found that farmers are producing below the frontier with average technical efficiency of 47 percent. Policy variables such as credit access; education, extension access and farm size played a stronger role in technical efficiency. Agricultural credit in particular increased technical efficiency by 3.8 percent.

The results should not be extended to the impact of agricultural credit on economic efficiency since the allocative efficiency component is not considered in this study. Also, caution should be taken in the interpretation of these results because the data could not permit the incorporation of all variables that might affect technical efficiency.

The originality of the paper and its contribution to existing literature largely lies from the use of a unique dataset to find evidence of the impact of credit on efficiency in Ghana.

Poor post-harvest handling practices by seaweed farmers are a key issue in seaweed value chains, contributing to low-quality seaweed being supplied to processors. To address this, a range of advanced drying technologies and methods have been developed, yet uptake by farmers remains low. This study examines factors affecting drying technology uptake by seaweed farmers to identify opportunities to incentivise improved drying practices.

This study draws on a quantitative survey of 273 seaweed farmers in two villages in South Sulawesi, 16 months of ethnographic fieldwork and 166 semi-structured interviews.

Farmers engage in limited adoption of improved drying technologies and practices as they don't receive higher prices for higher quality products, instead aiming to meet only the minimum acceptable standards to avoid a price discount or rejection of their product. Technologies and techniques that have been adopted are often used in ways that differ from their original purpose, such as to reduce drying times and labour input, rather than to produce products of low moisture and dirt contents. Similarly, local traders mix high- and low-quality seaweed in order to supply warehouses with seaweed which on average meets minimum quality standards.

This study reveals that improved drying practices are unlikely to be adopted unless incentivised by more targeted price-grade differentials.

The purpose of this paper is twofold: first, to take stock of the current state of knowledge about inclusive value-chain development (VCD) in the context of international agricultural research; and second, to draw out the implications for future research and action.

This paper is based on a review of recent research papers authored by professionals affiliated with international agricultural research centers and their partners in Africa, Asia, and Latin America.

The studies reviewed in the paper identify the opportunities emerging from new and expanding markets for agricultural products and challenges to smallholder participation in these markets. It identifies key attributes of successful value-chain interventions, emphasizing the importance of combining value-chain approaches with other approaches, including those emerging from innovation systems and rural livelihoods frameworks. Methods are offered for evaluating complex value-chain interventions.

The paper summarizes the state of knowledge as of early 2016 in a dynamic field. Important contributions to knowledge may have been made since then.

The paper summarizes the state of knowledge in the field, and identifies emerging issues and policy implications, knowledge gaps, and priorities for future applied research.

The purpose of this paper is to measure the profit impacts of adopting input and output risk management tools at the farm level.

The analysis examines three years of farm‐level data from the Agricultural Resource Management Survey (ARMS) that include detailed questions on the adoption of risk management tools. Propensity score matching is used to control for the endogeneity of adoption decisions, and the profit impacts are estimated based on the matched dataset.

The adoption of input price risk management tools improves farm‐level profits by 13‐17 percent.

This is the first paper which examines the profit impacts of risk management tools while controlling for the endogeneity of adoption.