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Organic products form a growing segment of the food market. Recent estimates speak about market shares between 1 to 4.25 per cent. In Belgium the share is only 1 per cent, but the market is growing exponentially. Organic farmers in Belgium often have to sell their products as conventional products because of non‐efficient marketing systems. Marketing problems are also mentioned as one of the main reasons by conventional farmers for not converting. Most conventional farmers are not only rather sceptical about the long‐term perspectives of getting a price premium for organic products, but consider the organisation of the supply chain as one of the main sources of uncertainty and therefore as a constraint for conversion. Therefore more efforts should be made to organise the supply chain for organic products. The problem seems to be that of the egg and the chicken: for a cost‐effective supply chain a condition is to have enough producers, while for a lot of producers a cost‐effective swupply chain seems to be a necessary condition to convert, mainly because of high transaction costs linked to non‐efficient marketing.

It is difficult to develop an overall picture of the practice of management accounting (MA) in farms and farm enterprises (FEs) because little research has been published on the topic, and these studies are mostly discrete and unconnected to the others. The purpose of this paper is to provide an overview of the available research, develop an explanatory framework for MA practices in farming entities and identify some major avenues for future research on the topic.

This paper uses systematic literature review methods. After an extensive database search and an examination of references/citations, 41 empirical journal articles published between 1964 and 2016 are identified, described and analyzed in this research paper.

The findings reveal that the practice of MA in farms is subject to information problems and that the empirical research on this topic largely lacks a theoretical explanation. Therefore, the explanatory framework of MA practices in farming entities reveals that these practices are subject to influencing factors such as familism, government farm policies, market competition, technological changes, the seasons and the weather/climate.

The overall limited findings on the practice of MA in FEs indicate that caution should be taken when generalizing the current knowledge on the use of MA practices in other organizational forms to farming entities. Moreover, future research should draw on explicit theories to explain empirical results.

This paper is the first comprehensive literature review of studies on MA practices in farms and FEs.

The purpose of this paper is to provide a multidimensional framework for the identification, description and comparative analysis of alternative farm structures and their properties for economic development.

Integrating previous typologies and considering a large set of examples, the authors identify six attributes that are necessary to characterize and compare farm structures: size; strategy; organizational form; legal form; who the owners are; and degree of separation of ownership and control. They also discuss potential complementarities between those organizational attributes and specific features of the institutions of developing and emerging countries, such as contract enforcement and property rights protection regime, and developed capital markets and corporate law.

Conceptually and empirically, effective farm structures can deviate from the templates traditionally considered – “small family-owned farm” or “large factory-like corporate farm,” combining structural attributes in diverse ways. The dimensionalization of farm structures also helps in revealing complementary institutional traits at the regional or larger system level that may foster development processes.

The paper is limited to theory building and case-based evidence. Nevertheless, it provides dimensions that can be measured on a larger scale and by quantitative studies.

This paper sheds light on organizational diversity in agriculture and on a wider set of feasible development paths.

Agriculture in Ethiopia relies heavily on traditional farm power sources and is designated by the lowest farm machinery access, in contrast to other Sub-Sahara African (SSA) countries. The purpose of this research is to analyze the heterogeneity of mechanization service transactions and factors determining farmers' cooperation in mechanization clusters and willingness to accept land consolidation.

The authors conducted a cross-sectional survey of producer households in major crop production areas in the Oromia, SNNPR, Amhara and Tigray regions. The sampling design involved three stages: districts were selected using a stratified sampling approach accompanied by simple random samples of kebele units and producer households in the second and final stages, respectively.

This study’s results show that mechanization service costs, service relationships, clustering and land consolidation exhibit significant heterogeneity across the study areas. Cluster farming was found to be advantageous against diseconomies, rationalized by upgrading the mechanization scale. The probit model parameterization of the probability distributions reveals that household, land, crop, mechanization service, remoteness and location-related factors determine participation in mechanization clusters and willingness to accept land consolidation.

Fostering cooperation by focusing on constraints and demand of users is suggested to reduce transaction costs and expand hired mechanization services to unaddressed areas. The findings are relevant to most SSA countries where mechanization development is hampered by land fragmentation.

Limited information is available on agricultural mechanization development for smallholder farmers, particularly in Ethiopia, and this study adds empirical evidence about the synergy between cluster farming and mechanization, horizontal coordination and alternative supply models.

The purpose of this paper is to provide an analysis of the risk factors influencing non-compliance in UK organic farming.

The paper uses a formal econometric model of risk analysis to provide empirical evidence on the determinants of non-compliance in organic farming. A panel of data from the archives of the largest control body in the UK for 2007-2009 is used, and specific analyses are performed for two types of non-compliances. A zero inflated count data model is used for the estimation, taking into account the fact that the occurrences of non-compliance are very sparse.

Results show the existence of strong co-dependence of non-compliant behaviours (i.e. the occurrence of major and critical non-compliance increases the probability of occurrence of the minor one; similarly the probability of occurrence of major non-compliance increases when minor non-compliance occur). Besides, livestock production and farm size are relevant risk factors.

Albeit highly representative, the findings are based on Soil Association data only and not on all UK organic farms.

The paper provides practical indications for control bodies, concerning aspects that could be strengthened for more efficient risk-based inspections. The paper advocates the use of financial information like turnover or capital stock, and of data concerning the characteristics of the farmers, that could substantially improve the probability of detecting the most severe non-compliances.

Certification is essential for organic farming, and an improvement of inspection procedures through a risk-based approach could add efficiency and effectiveness to the whole organic food system, with obvious advantages for consumers and the society as a whole.

This paper provides for the first time empirical evidence concerning the implementation of the organic certification system in the UK.

As a response to global population growth and increasing demand for food, farmers have been complementing traditional agriculture practices with vertical farming (VF) and indoor hydroponic systems. To facilitate the growth of the VF industry, this paper aims to identify business model elements and their configurations that lead to high firm performance.

The research goals were met by conducting literature reviews coupled with a fuzzy-set qualitative comparative analysis (fsQCA) on five business model elements, “superior” OR “strong” performance as two possible outcomes, and the top-ranked global VF growers listed in the Crunchbase Database.

From the fsQCA results, it was observed that several business model configurations lead to strong firm performance. Vertical farms growing in urban settings and having strong customer engagement platforms, coupled with a presence of business-to-business (B2B) sales channels, are more consistently associated with superior performance. These results imply that the decision configuration of location, along with customer engagement activity and sales activity are differentiating factors between good firm performance and superior firm performance in the case of vertical farms.

This paper contributes to expanding the knowledge of business model theory, business model configurations and VF management, providing specific guidelines for vertical farm owners and investors related to decision-making for higher firm performance, as well as positive environmental, social and economic impact.

The purpose of this paper is to identify the potential relationship between farm income variability and off-farm employment decisions in the short and medium term for the case of Irish farm operators.

Panel probit models of off-farm labour supply are estimated using Teagasc National Farm Survey data for Irish farms. The framework is based largely on standard expected utility but includes a constraint for recent employment history.

The analyses identifies some evidence of a positive association between farm income variability and off-farm employment in the medium term but no significant relationship in the short term. This suggests that off-farm employment is part of a wider portfolio decision but is not a strong solution to short-term farm income shocks.

European farmers increasingly face high income variability but financial risk management tools are not sufficiently developed or widely accessible to assist farmers in managing the associated risk. This deficiency can have negative implications for household economic welfare and future farm investments and hence the future farm income. Off-farm employment can form part of a wider medium-term portfolio strategy but more effective tools are also required for risk management particularly in dealing with short-term volatility and where off-farm employment is not a realistic endeavour given time constraints and/or demographics.

The estimation of farm income variability includes a detrending method thus reducing the likelihood of overestimating farm income variability for farms in deliberate expansion or decline. While previous research has typically focused on the short-term response of farmers to historical farm income variability, this research has distinguished between the short and medium term.

The main intention of this paper is to analyze various factors hindering the growth of the agricultural supply chain and several industry 4.0 technologies to eliminate the same. In addition to a detailed assessment on the implementation of these technologies in agriculture, this manuscript also presents a priority list providing a rank to them based on the relative efficiency of these advancements in addressing these obstacles.

This research proceeds with a two-step process. The particular barriers in the agriculture supply chain and industry 4.0 technologies are determined in the first step. Next, the proposed framework, a combination of data envelopment analysis (DEA) and analytic hierarchy process (AHP), i.e. DEA-AHP, is used to determine a hierarchical structure for the factors and the relative productive efficiencies of the alternatives. The DEA methodology gives a performance analysis of various decision-making units. At the same time, AHP helps in evaluating alternatives weights based on numerous criteria, allowing us to categorize their importance further.

This study reveals how the involvement of technological advancements in agriculture can help manage the supply chain more efficiently. It also justifies how the large quantities of data generated can handle these increasing challenges in the agricultural supply chain.

The results of this study provide a priority list of alternatives based on their final weights. This ranking system can help farmers and the government select the best-suited technology for bringing automation into the agricultural supply chain.

This research is unique as it analyes the general factors hindering the development of the agriculture supply chain while simultaneously providing a list of alternatives based on their relative efficiencies. The study enriches existing literature by providing an analytic approach to determine the weightage of various critical success factors that can help improvise and entrust the real and undeniable requirements of consumers, suppliers and producers.