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In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The knowledge of artefact design in design science research can have an important application in the improvement of decision support systems (DSS) development research. Recent DSS literature has identified a significant need to develop user-centric DSS method for greater relevance with respect to context of use. The purpose of this paper is to develop a collective DSS design artefact as method in a practical industry context.

Under the influence of goal-directed interaction design principles the study outlines the innovative DSS artefact based on design science methodology to deliver a cutting-edge decision support solution, which provides user-centric provisions through the use of design environment and ontology techniques.

The DSS artefact as collective information technology applications through the application of design science knowledge can effectively be designed to meet decision makers’ contextual needs in an agricultural industry context.

The study has limitations in that it was developed in a case study context and remains to be fully tested in a real business context. It is also assumed that the domain decisions can be parameterised and represented using a constraint programming language.

The paper concludes that the DSS artefact design and this development successfully overcomes some of the limitations of traditional DSS such as low-user uptake, system obsolescence, low returns on investment and a requirement for continual re-engineering effort.

The design artefact has the potential of increasing user uptake in an industry that has had relevancy problems with past DSS implementation and has experienced associated poor uptake.

The design science paradigm provides structural guidance throughout the defined process, helping ensure fidelity both to best industry knowledge and to changing user contexts.

Effective land use management in lesser developed countries is problematic due to a variety of factors including inexperience and turnover of decision makers, lack of communication among experts in functional areas, and scattered or missing data needed by managers to make informed decisions. This paper describes a “first step” approach toward the solution of these problems that was implemented in the Dominican Republic. The paper introduces a framework used to organize and facilitate the sharing of data needed for land use decision across multiple disciplines. The framework provided the basis for the development of a prototype agricultural geographic decision support system for use in the Dominican Republic. This system is unique in that it combines concepts from semantic data modeling and database design, geographic information systems, and knowledge‐based systems.

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.

This study aims to explore the need for improved data sources and models for COVID-19 and climate-related risk scenario analysis in primary production. The COVID-19 pandemic is impacting global markets for agricultural produce, making short-term forecasting highly uncertain. Meanwhile climate-related risk continues for agricultural businesses. Farmers and their accountants need to plan and make estimates about the potential effect of COVID-19 and ongoing climate risks to their natural and financial capital and so they need accounting-integrated biophysical and socio-economic data streams.

This research note reviews the current state of scenario-based planning for COVID-19 and other risks for Australian businesses generally, in addition to planning for farming businesses more specifically. Discussion of the authors’ current research in integrating accounting and farming data for water-related risk caused by climate and other challenges is presented as an analogous case.

Review and analogous case comparison demonstrate the need for farm data to be integrated more efficiently and effectively with accounting data for accurate scenario planning for COVID-19 and other risks, including those posed by climate.

While not strangers to the need for scenario analysis, given exposure to ever-increasing natural resource and climate variability, this research note highlights how primary producers and their accountants require increased accounting-integrated farm data and systems to make judgements, assumptions and estimates about the potential effect of COVID-19 and ongoing climate risks to their business.

The sustainability of the agricultural sector is of great relevance to all of us and so the development of tools and resources that can assist food producers in times of ongoing climate pressures and new crises, such as COVID-19, is important. Better understanding of such risks can help farm businesses develop effective strategies which minimise the potential loss of agricultural value resulting in improved flows of greater capital value for society.

Through application to the analogous case of water-related risk and decision-making, the research note demonstrates that linking of biophysical and accounting data streams will be essential for evidence-backed numbers included in scenario plans with enough legitimacy to be interrogated inside and outside of the business. The “best estimate of the directors” is no longer enough in challenging socio-economic and biophysical times ahead for primary producers.

Driven by scientific and technological intensification, Brazilian agribusinesses are among the main food producers and exporters worldwide. Knowledge management contributes to the development of agribusinesses. The objective of this study is to analyze knowledge management practices in the supply chain of broilers at the level of farmers and compare the differences between farmers who are members of cooperatives, and those who are suppliers of firms.

Using data from a sample of broiler farmers (n = 240, where 80 respondents are associated with cooperatives) from Brazil, descriptive statistics were used to measure the four knowledge management dimensions (leadership, people, process and technology). The Mann–Whitney test was performed to verify the difference in the adoption of knowledge management practices between farmers who are members of cooperatives, and those who are suppliers of firms.

The results showed significant differences between the analyzed groups of broiler farmers that are members of an agricultural cooperative and suppliers of firms. Farmers who supply firms adopt knowledge management practices with greater intensity. They are also more technified and have a larger scale of production.

The study was conducted in a single agribusiness chain. Future studies may look at another method of knowledge management assessment to see if the results will be similar.

The results can contribute to the development of programs and policies, incorporating knowledge management into agribusiness as a competitive strategy.

While knowledge management is a widely studied topic, there has been little focus on this subject with regard to agribusiness. Empirical findings of an important agribusiness supply chain are provided and show the differences in knowledge management perceptions between farmers who are members of cooperatives and those who are suppliers of firms.

Social marketing theories have habituated to a theoretical and methodological focus that is criticised for being myopic and stigmatising. Following recommendations to redirect focus theoretically, the purpose of this paper is to apply an observational methodology to understanding how project stakeholders interact to examine whether consideration of stakeholders can identify factors facilitating or impeding farming practice change.

More than 48 events involving as many as 150 people including project stakeholder meetings, one-on-one consultations and annual events were observed over more than 100 h by between one and five researchers. Field notes were gathered, and thematic coding focussed on understanding how stakeholders facilitated or impeded practice change.

Observations identified limited provision of information about the project by on ground project stakeholders to targeted individuals (farmers). On the rare occasions where information sharing was observed, communication was delayed making it difficult for individuals to connect actions with outcomes observed. Participating stakeholders did not freely support delivery of activities needed for individual practice change.

This study indicates the value of wider process and outcome assessment encompassing stakeholders to identify factors impeding and facilitating farming practice change.

Approaches that centre attention on individuals fail to acknowledge the inputs, activities and outputs delivered by project stakeholders within a system of change. By redirecting evaluation focus, shared responsibility is gained and stigmatisation of one stakeholder group can be avoided.

This study demonstrates how observations can be used to redirect focus to consider actions and interactions occurring between on ground project stakeholders. A stakeholder evaluation approach extends monitoring and evaluation focus beyond individuals targeted for behaviour change. Implications, limitations and future research directions are outlined.

This paper reviews the academic contributions that have emerged to date on the broad definition of farm-level management information systems (MISs). The purpose is twofold: (1) to identify the theories used in the literature to study the adoption of digital technologies and (2) to identify the drivers of and barriers to the adoption of such technologies.

The literature review was based on a comprehensive review of contributions published in the 1998–2019 period. The search was both automated and manual, browsing through references of works previously found via high-quality digital libraries.

Diffusion of innovations (DOIs) is the most frequently used theoretical framework in the literature reviewed, though it is often combined with other innovation adoption theories. In addition, farms’ and farmers’ traits, together with technological features, play a key role in explaining the adoption of these technologies.

So far, research has positioned the determinants of digital technology adoption mainly within the boundaries of the farm.

On the practical level, the extensive determinants’ review has potential to serve the aim of policymakers and technology industries, to clearly and thoroughly understand adoption dynamics and elaborate specific strategies to deal with them.

This study’s contribution to the existing body of knowledge on the farm-level adoption of digital technologies is twofold: (1) it combines smart farming and existing technologies within the same category of farm-level MIS and (2) it extends the analysis to studies which not only focus directly on adoption but also on software architecture design and development.

The aim of the study is to present the implications for the financing and sustainability of enterprises based on a ranking methodology for categorical financial data.

Taking advantage of the optimal scaling properties of correspondence analysis (CA), a ranking‐clustering procedure is proposed. The proposed method was applied to categorical financial variables (i.e family farm income, gross profit, gross income, labour income and profitability) collected from a stratified random sampling of 80 Greek pig farms using a structured questionnaire.

The cluster analysis revealed three distinct groups of pig farms. Several recommendations for managerial practices and financial development resulted from this study. For the farms belonging to cluster C₁, that present low rankings on both criteria, a development planning process must be applied that will focus on organizational and management issues. For the farms belonging to cluster C₂, that present low rankings on the “composite income” criterion, policy measures have to be undertaken, aiming at exploiting their own production coefficients, reducing fixed costs and increasing productivity. Finally, for the farms in cluster C₃, that present high scores on both ranking criteria, it is recommended to take actions that will improve their competitiveness.

The findings are limited to five selected financial variables. Therefore, future studies in the same or other business fields would benefit from incorporating a greater number of variables.

The proposed methodological scheme could be useful to practitioners and academics, due to the fact that limited studies have dealt with this ranking problem, particularly in relation to the Greek agricultural business environment.

The purpose of this paper is to verify if, in the dairy business ecosystem (dairy farmers, dairy cooperatives, government, research agencies, consultants and financial institutions) perspective, the dairy production barriers presented by Bonamigo et al. (2016b) can be mitigated through the dairy production key factors indicated in the literature.

To achieve the proposed goal of this study, the methodology used for the study comprises three stages: exploratory search in the literature to identify barriers and drivers of dairy production, data collection and statistical model. For the first stage, the authors conducted exploratory research in the literature, to better understand the dairy production ecosystem and to find arguments that characterize the barriers and drivers of this activity. In the second stage, a structured questionnaire with 13 closed-ended questions in a single block, with ordinal responses, following the suggestion of Likert (1932), was developed and applied to the dairy production ecosystem players of Santa Catarina, Brazil. A total of 305 responses suitable for analysis based, that were analyzed through an exploratory factorial analysis and modeling of structural equations via partial least squares, resulting in the Statistical model.

Based on the analyses results, the authors verified that there is an inverse association between the characteristics of the barriers and the dairy production key factors. In this sense, the authors can confirm that the increase of the loads in the dairy production key factors, reduce loads of the milk production barriers. Based on the results of this test, new theoretical and practical insights can be identified to develop new studies with the aim of boosting the dairy sector development as well as providing support in the decision making of the dairy system players.

This study may not have enabled a complete coverage of all existing peer-reviewed articles in the field of dairy production. It seems reasonable to assume that the review process covered a significant proportion of studies available.

It is the first study that compares identifies barriers and key factors of dairy production in Santa Catarina using as an analytical lens the business ecosystem.