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The paper explores the extent to which degree‐based postgraduate training programmes contribute to the development of individual skills and careers in agricultural science as well as towards institutional capacity for national agricultural research in Africa. In the case of the International Livestock Research Institute’s Graduate Fellowship Programme it was concluded that advanced agricultural science training in Africa provides a vital process of human and institutional development. The results of this study indicate the success of degree‐based training in terms of achieving improved gender equity, rapid employment, and a high rate of return to the country of origin. These results stand out against the “brain drain” commonly associated with capacity building in national agricultural research systems (NARS). The paper recommends strategies to be adopted by ILRI and its partners for future degree training and capacity building in sub‐Saharan Africa.

Despite favourable agro-ecological conditions and being the largest international mango producer, India still struggles to build competence in sustainable mango production and post-harvest. The purpose of this paper is to contribute to the literature on innovation capacity development, and to explore aspects of innovation systems ideas in the analysis of mango production and marketing by small-scale farmers in the South Indian state of Andhra Pradesh.

This paper uses case study research methods to an analysis of the sector ' s recent history combined with an empirical account of systems thinking on integrating technology supply chains and commodity supply chains.

Findings suggest that the case of mango production and post-harvest in the Krishna district is a dismal one and the remedial actions to strengthen mango innovation systems in the district relate to aspects of capacity development to promote upward spiral of learning and innovation, and involve multistakeholder processes to integrate the supply chains of technologyand commodity.

This paper, with its aim to contribute to the literature on innovation capacity development, brings together conventionally distinct bodies of literature on strengthening innovation systems and developing stakeholder capacity. The value of this paper lies on how it addresses technology supply and commodity supply issues in the analysis of competence challenges to strengthening mango innovation systems performance.

Reflections on negotiation processes between farmers and scientists in research projects provide insights into issues of participation, power and equity. The purpose of this paper is to illustrate how actors chose places to meet, negotiate and represent technologies.

The research involved semi‐structured interviews and participant observation with farmers, scientists, government agricultural advisors (extensionists), policymakers, and staff of development organisations involved in a research for development project in Western Shewa, Ethiopia from 2009 to 2011. It combines theories from social studies of science as well as development studies.

Using blueprint approaches in research projects will not yield sustainable results. Participation must go beyond consultation or trying to educate farmers. Social relations are at the core of cooperation between farmers and scientists and require much more attention. Powerful choices on modes of representation and communication technologies as well as unilateral decisions on places have important implications for the way decisions are eventually made and by whom.

This approach to studying research for development projects from social studies of science perspectives adds new insights into debates on participation and power in technology transfer and multicultural cooperation in rural development.

Digitalization is becoming the subject of considerable interest in the literature. This is in view of its relevance in addressing social problems and contributing to the development of communities and societies. In the agri-food-industry, digitalization is also expected to contribute significantly to solve several challenges the sector is facing at this moment, such as the increasing food demand and resource use. However, the effects of advanced technologies are less a function of the technologies themselves than of how they are used by people. The study analyses the dominant challenges faced by firms in the agri-food industry in the usage and adoption of digital technology. Also, they show how these challenges impact on the sustainable development of digital technology for firms in the industry and provide avenues for future research.

The authors propose a structured literature review aiming to investigate the following research question: what are the main challenges faced by firms within the agri-food industry in the adoption of smart technologies?

Results illustrate the dominant challenges faced by firms in the agri-food industry in the usage and adoption of digital technology. Also, they show how these challenges impact on the sustainable development of digital technology for firms in the industry and provide avenues for future research.

So far, in the context of digitalization in the agri-food industry, various researchers have analysed different kinds of challenges to the adoption of smart technologies. This work reviews these contributions to create a clear reference framework of the challenges faced by agri-food firms while providing future avenues of research and implications at a policymaking, economic-managerial and socio-environmental level.

This paper aims to investigate how actors in the farmer’s network influence the adoption of smart farming technology (SFT) and to understand how social media affects this adoption process, in particular focusing on the influence of social media on trust in knowledge dissemination within the network.

The methodology used a two-stage process, with semi-structured interviews of farmers, augmented by a netnographic approach appropriate to the social media context.

The analysis illustrates the key role of the farmer network in the dissemination of SFT knowledge, bringing insight into an important B2B context. While social media emerges as a valuable way to connect farmers and promote discussion, it remains underused in knowledge dissemination on SFT. Also, farmers exhibit more trust in the content from peers online rather than from SFT vendors.

Novel insights are gained into the influence of the farming network on the accelerated adoption of SFT, including the potential role of social media in mitigating the homophilous nature of peer-to-peer interactions among farmers through exposure to more diverse actors and information. The use of a social network theory lens has provided new insights into the role of trust in shaping social media influence on the farmer, with variances in farmer trust of information from technology vendors and from peers.

The purpose of this paper is to focus on the implications of claimed detrimental impacts for the agricultural activity of the Great Barrier Reef (GBR) ecosystem health in Queensland, Australia. The authors discuss the complex interaction of factors that have contributed to the decline in reef ecosystems and the challenges presented by multiple industries operating within the GBR catchment area. The authors then discuss measures employed to address agricultural run-off, claimed to be a significant factor in declining reef water quality.

Surveys of land managers were undertaken in partnership with two of the six natural resource management (NRM) organizations operating in areas adjacent to the GBR identified as having very high risk of natural and anthropogenic runoff. The sample population was obtained from a membership database within the two regions. Participants include land managers from the both regions who engaged in sugar cane production (Region 1 and Region 2, included in this paper) and cattle production (Region 2, to be reported later). Quantitative and qualitative data were analyzed including open-ended responses.

A large-scale study of land managers reveals several reasons for the lack of success at reducing agricultural run-off. The authors discuss the rationale for a move to a theory-grounded social marketing approach to encouraging land manager behavior change, highlighting barriers, and potential enablers of sustained behavior change.

This study is first of its kind that discusses the behavior of land managers in the GBR catchment area and highlights facilitators and impediments of land managers’ behavior change toward GBR protection actions.

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.

This study aims to explore, in the context of Machinery and Equipment sector of Malaysia, the association between average wages and share of employment in automatable jobs, specifically whether the association between average wages and share of employment automatable jobs is asymmetric in nature.

The responses obtained from the structured interview of 265 firms are used to build up the empirical models (conditional mean regression and quantile regression).

The conditional mean regression findings show that employment levels in some low-waged, middle-skilled jobs are negatively associated with average wages. Furthermore, the quantile regression results add that firms that possess higher levels of share of employment in automation jobs are found to have a stronger association to average wages than those possessing a lower share of employment in automation jobs.

From the theoretical perspective, the findings of this study add to the body of knowledge of the theory of minimum wages and the concept of job polarization. From a policy perspective, the findings of this study can serve as a critical input to standard setters and regulators in devising industrial and as education policies.

Based on the assumption of a constant average policy effect on automatable jobs, conditional mean regression models have been commonly used in prior studies. This study makes the first attempt to employ the quantile regression method to provide a deeper understanding of the relationship between wages and employment in automatable jobs.

This study examines the adoption drivers of scale-appropriate mechanization in Nepal's maize-based farming systems. The authors also assess the contribution of scale-appropriate mechanization to the United Nations Sustainable Development Goals (SDGs) of zero hunger (SDG2) and no poverty (SDG1).

Propensity score matching and doubly robust inverse probability-weighted regression adjusted methods were applied to estimate the effects of mini-tiller adoption. These methods control the biases that arise from observed heterogeneities between mini-tillers users and nonusers.

The study findings show that farm size, labor shortages, draft animal scarcity, market proximity, household assets and household heads' educational level influence the adoption of mechanization in Nepal. Mechanized farms exhibited enhanced maize productivity, profits and household food self-sufficiency. Reduced depth and severity of poverty were also observed. Nevertheless, these effects were not uniform; very small farms (≤0.41 ha) facing acute labor shortages benefited the most.

The study results suggest that policymakers in developing nations like Nepal may wish to expand their emphasis on scale-appropriate mechanization to improve farm productivity and household food security, reduce poverty and contribute to the SDGs.

This first-of-its-kind study establishes the causal effects between scale-appropriate farm mechanization and SDG1 (no poverty) and SDG2 (zero hunger) in a developing nation.

The purpose of this paper is to propose a progressive inverse identification algorithm to characterize flow stress of tubular materials from the material response, independent of choosing an a priori hardening constitutive model.

In contrast to the conventional forward flow stress identification methods, the flow stress is characterized by a multi-linear curve rather than a limited number of hardening model parameters. The proposed algorithm optimizes the slopes and lengths of the curve increments simultaneously. The objective of the optimization is that the finite element (FE) simulation response of the test estimates the material response within a predefined accuracy.

The authors employ the algorithm to identify flow stress of a 304 stainless steel tube in a tube bulge test as an example to illustrate application of the algorithm. Comparing response of the FE simulation using the obtained flow stress with the material response shows that the method can accurately determine the flow stress of the tube.

The obtained flow stress can be employed for more accurate FE simulation of the metal forming processes as the material behaviour can be characterized in a similar state of stress as the target metal forming process. Moreover, since there is no need for a priori choosing the hardening model, there is no risk for choosing an improper hardening model, which in turn facilitates solving the inverse problem.

The proposed algorithm is more efficient than the conventional inverse flow stress identification methods. In the latter, each attempt to select a more accurate hardening model, if it is available, result in constructing an entirely new inverse problem. However, this problem is avoided in the proposed algorithm.