In developing economies, rural income inequality remains a persistent and pressing challenge, this study identifies grain processing clusters as a pivotal factor for mitigating disparities. Based on the National Fixed Point Survey (NFP) from 2004 to 2017, we constructed an unbalanced panel dataset covering 2300 counties. Using the National Economic Industry Classification Code, we identified information on rough and deep grain processing enterprises in the China Academy for Rural Development-Qiyan China Agri-research Database (CCAD), thereby generating a unique dataset for grain processing clusters. Employing a two-way fixed effects model, we empirically analyze how grain processing clusters affect rural income inequality in China and explore the underlying mechanisms. The baseline regression results indicate heterogeneity in the impact of grain processing clusters on income inequality. Deep processing clusters significantly reduce both farm and non-farm income inequality, whereas rough processing clusters have a significant effect only on non-farm income inequality. Mechanism analysis reveals that grain processing clusters primarily enhance the income of low-income households through three channels: providing inclusive employment opportunities, accelerating the deepening of capital, and increasing grain revenues, thereby reducing the extent of inequality. Heterogeneity analysis further demonstrates that this effect is more pronounced in China’s major grain-producing regions and central areas. The research results of this paper comprehensively analyze the effective path to reduce income inequality in rural China while ensuring food security, providing valuable insights for policymakers.
Abstract The COVID-19 lockdown caused severe supply-chain bottlenecks across India, disrupting the transport of agricultural commodities from farm to market, especially for perishable commodities such as vegetables. India exhibits widespread variation in vegetable production. West Bengal and Uttar Pradesh are leading states in vegetable production. However, agricultural commodities like vegetables vary in their levels of perishability; this study measures it using the cross-sectional variation in maximum storage days for 10 vegetables. This paper investigates how supply chain bottlenecks caused by lockdown stringency during the COVID-19 pandemic affected product prices in vegetable-producing states, using secondary panel data on daily wholesale price for all the wholesale markets in the two states, West Bengal and Uttar Pradesh. Using an interactive fixed-effects panel methodology, the paper finds that an increase in stringency leads to a significant increase in the price of vegetables. An increase in price due to stringency implemented during the lockdown is prominent for vegetables that have fewer storage days (high perishability) compared to those that can be stored for more days (low perishability). Doubling of stringency leads to 3% increase in prices for highly perishable commodities but reduces the prices by 1% for high storage vegetables. Further, the market’s location at the epicentre of the production region reduces the impact of stringency on prices. The availability of cold storage facility at the location, and market reforms, and better logistic facilities in the state attenuate the impact of stringency on prices in the wholesale market. The study indicates that the market reforms, infrastructure development, like the creation of green corridors and multipurpose cold storage facilities, will be highly beneficial.
Abstract This paper assesses the economic consequences of the 2025 U.S. trade policy on global agricultural markets, with a focus on the “reciprocal tariffs” initiated by the United States. By using a multi-region, multi-sector computable general equilibrium model, we evaluate the macroeconomic and sectoral effects of alternative tariff scenarios, with a particular focus on the U.S. agricultural sector. Our results indicate that the imposition of uniform tariffs on U.S. imports from the rest of the world generates substantial welfare losses for the U.S. (0.63%) and China (1.28%), with corresponding GDP declines of 0.82 and 0.39%, respectively. Tariffs result in significant contractions in global agricultural trade and higher consumer prices for key agri-food products in the US, including vegetables (6.05%), crops (7.48%), and cattle (4.13%). China’s oilseed imports from the U.S. decrease by 38.32%, while imports from Canada and Brazil increase by 17.48 and 3.92%, respectively, if U.S.–China tariffs are enacted. U.S. imports of high-value vegetables and fruits from Canada and Mexico decline sharply, partially offset by increased imports from Australia and Argentina if new tariffs are enacted in North America. These findings show the complex sectoral adjustments and market realignments driven by escalating trade tensions.
Although previous research on sustainable consumption has investigated consumers’ trade-offs of product sustainability against other valued product features, the differences in consumers’ perceptions of social versus environmental dimensions of sustainability tend to be overlooked. This study examines consumers’ choices that involve trade-offs between social and environmental sustainability. From a metacognitive perspective, this study investigates the roles that metacognitions (attitude magnitude and attitude uncertainty) play in consumers’ trade-offs. Based on 715 valid questionnaires from Chinese consumers, this study tests the distinct effects of social and environmental sustainability attributes of agricultural products on consumers’ choices using a discrete choice experiment. The results reveal that social sustainability attributes (traceability, safety certification, and poverty alleviation) have stronger effects on consumer choices than environmental sustainability attributes (packaging, location, and temperature). Consumers respond to social sustainability attributes more positively when their environmental attitudes have higher magnitude or lower uncertainty; interestingly, consumers are less responsive to environmental sustainability attributes when their environmental attitudes have a higher magnitude or lower uncertainty. These findings provide important implications for interventions on ethical consumption.
This paper explores coverage selection strategies for the Annual Forage insurance program using robust portfolio optimization approaches, such as shrinkage and ensemble learning methods. The objective of the proposed models is to obtain an expected return with the lowest risk possible. Compared to previous efforts, a wider range of coverage selection parameters (i.e., coverage level, productivity factor, and index intervals) is considered. The proposed methods are used to protect cool-season forage production in Texas, using historical market, production, and actuarial data. This work provides empirical evidence on the effectiveness of the Annual Forage program in managing forage production risks.
This case study explores AirSmat Inc., a Nigerian digital agriculture startup addressing low agricultural productivity. Established in 2019, AirSmat leveraged digital technologies such as artificial intelligence, data analytics, and remote monitoring systems to enhance farm productivity and sustainability. Its flagship digital farm management platform, AnyFarm, integrated precision tools to improve yields, reduce environmental impacts, and promote adoption of climate-smart agricultural practices. Despite its innovative approach, AirSmat faced challenges in balancing financial sustainability with its commitment to Environmental, Social, and Governance (ESG) principles. Operating in a resource-constrained setting characterized by limited ESG regulatory incentives, infrastructural deficits, and low purchasing power, the company exemplifies a pragmatic approach to fostering ESG performance through digital technologies, innovative pricing models and strategic partnerships. This case underscores the often-overlooked contributions of small and medium enterprises (SMEs) to global sustainability goals and highlights the critical role of digital technologies in advancing ESG objectives in emerging markets. It emphasizes AirSmat’s unique approach to ESG in an environment with limited formal regulations, showcasing the importance of partnerships and innovation in achieving dual goals of social impact and profitability. The case invites readers to critically analyze the intersection of technology, sustainability, and business strategy in a rapidly evolving agricultural sector.
This article shows how standard econometric methods, such as Ordinary Least Squares (OLS), provide a valuable basis for understanding state-of-the-art Machine Learning. We introduce nonlinearity within a polynomial regression framework to illustrate the bias–variance trade-off and overfitting and then extend the discussion to regularization methods such as LASSO. The model-selection procedure, i.e., cross-validation, is broken down into manageable steps, each illustrated with visual aids, to clarify how Supervised Machine Learning predicts outcomes. Subsequent sections explain the limitations of these prediction methods and how they can be adapted for causal inference. We also highlight the potential and limitations of Machine Learning in variable selection for regression models to increase the replicability and credibility of empirical results and thus contribute to the p-value debate.
In this article a new view of the theoretical frameworks of Teece’s dynamic capabilities and Mintzberg’s emergent strategy is offered. Merging both of these concepts into one framework provides insight into their reciprocal relationship. Situations are highlighted in which these two concepts can support an organization and aid during periods of internal and external change and disruption, currently presenting to the agricultural sector by demanding advances in digitalization and sustainability, or by external events. This article is relevant to management scholars and management practitioners alike. Providing an extension of the theoretical frameworks, the article presents organizational and individual factors aiding both concepts, and areas of further investigation to fully understand the mechanics behind emergent strategies and dynamic capabilities. With the understanding of impactful factors, organizational leaders will learn how they can support their organization and foster innovation to sustain a competitive advantage within their market environment.
A genetically edited high-oleic soybean seed has been recently introduced. There has also been increasing demand for healthy substitutes for low-oleic oils and even a regulatory ban on trans-fats in processed foods. The likely market and welfare impact of the new variety on the U.S. soybean and oil markets, with and without the transfat ban, are quantified by using an equilibrium displacement model. The results indicate that the introduction of the variety alone would have little market impact, given the estimated yield drag relative to current varieties. The primary beneficiaries of the variety introduction combined with trans-fat bans we find to be consumers of low-oleic oil because of a price decrease and land owners because of a price increase. The biggest losers would be corn consumers because of a price increase and soybean processors because of a price decrease. The food and agribusiness sector might increase benefits further through marketing campaigns potentially driving higher demand for high-oleic products.
The Taiwanese egg industry employs both conventional caged and ethically-oriented non-caged production systems, yet potential inefficiencies can arise from using the same production parameters for both. This study analyzed data from a major Taiwanese egg producer, developing nonlinear bioeconomic models for egg production and hen mortality, revealing distinct differences between the two systems. While conventional cages optimize early production, non-caged systems offer more stable long-term egg production and lower mortality. Treating flock replacement as an asset replacement problem, simulations identified optimal replacement cycles to maximize profitability using average daily return (ADR). For caged systems, the current 456-day cycle is optimal. However, extending the cycle to 499 days in non-caged systems can boost egg production by 8.51% per flock and can increase ADR by 8.72% per hen over the same duration. Overall, these findings highlight the need for tailored management strategies and demonstrate the economic viability of the more ethical non-caged system.
In niche markets such as the European legume market, transparency is crucial to promote competitive conditions and enable informed decisions. The LeguDash dashboard aims to increase the transparency of the legume market through a publicly accessible and interactive information platform thereby facilitating legume cultivation and increasing sustainability in cropping systems. Guideline-based qualitative interviews with selected experts were conducted to discuss financing and sponsorship models. The interviews were subjected to qualitative content analysis, both manually and using a Large Language Model (LLM)-based methodology. ChatGPT was used for the LLM-supported analysis. As a result of the interviews, various financing and sponsorship models were proposed, including public funding, subscription-based paid content with a paywall and a model where companies provide data in exchange for free access. Flexible, customizable solutions that can be integrated into existing systems are required. Methodologically, the LLM-supported content analysis of the qualitative data material provided valuable, partly inconsistent and partly corrective, insights. These insights can be used to fine-tune manual analysis characterized by specialist context knowledge beyond the immediate interview content. LLM-enriched manual analysis can potentially enable a deeper and more contextually appropriate analysis of the qualitative data. Therefore, LLM-assisted analyses of qualitative interviews seem promising to complement manual qualitative research for agribusinesses.
Armenia’s wine sector, steeped in a 6000-year-old vinicultural heritage, is undergoing a profound shift as it contends with modern-day pressures of sustainability and digitalization. This article examines the sector through the lens of “digitainability,” exploring how emerging technologies from IoT sensors to Artificial Intelligence to blockchain-based traceability can address systemic issues, including fragmented supply chains, financial constraints, and inconsistent quality standards. The research incorporates survey responses from 35 wineries and 20 in-depth interviews with industry stakeholders, ensuring a robust assessment of digital adoption trends and barriers. The quantitative analysis employs descriptive statistics, correlation analysis, and multiple regression modeling, while qualitative data are examined using thematic analysis. Findings reveal significant disparities in digital literacy and resource availability between large and small producers. While larger wineries exhibit higher adoption rates of digital tools, smaller producers face considerable financial and infrastructural barriers. The study also highlights opportunities for wineries to leverage digital solutions for enhanced resource efficiency, market differentiation, and sustainable growth. However, challenges remain, including limited policy support, inadequate technical knowledge, and concerns over the return on investment in digital infrastructure. This research contributes to both academic and practical discussions. From a theoretical perspective, it enriches the understanding of digital transformation in transitioning economies, drawing on concepts from the Resource-Based View and Dynamic Capabilities frameworks. For industry practitioners and policymakers, it provides actionable recommendations such as investing in digital literacy programs, fostering collaborative knowledge-sharing platforms, and integrating digital solutions with global sustainability initiatives like the European Green Deal. By illustrating how Armenian wineries navigate the intersection of tradition, economic transition, and technological innovation, we shed light on broader lessons in the global agri-food domain.
In times of crisis, a stable and secure food supply is essential. Despite Germany’s status as a developed economy with decades of food security, recent crises have highlighted the vulnerability of the national food supply chain. This explorative study aims to provide an overview of the current state of research on food emergency preparedness in Germany along the value chain. It also uses the example of livestock production in Germany to illustrate the challenges at the level of primary agricultural production during a blackout. A semi-systematic literature review was conducted, which identified ten relevant scientific articles and ten national security research projects. While the majority of these studies focus on the middle and downstream parts of the supply chain, primary production and upstream businesses are rarely examined. In addition, eleven expert interviews were conducted with livestock experts who identified ventilation, water and feed supply as time-critical and vulnerable areas at both the farm and supply chain levels. The results of this paper bring together scientific work and practical expert knowledge from different areas of crisis management and agricultural science, highlighting the need for future research on crisis resilience of primary production and the early stages of the value chain.
Agrifood value chains (AVCs) play crucial roles in food security in fragile and conflict-affected economies where there are widespread challenges and disruptions to business operations, food access, and incomes. Yet, given these challenges, safe data collection is challenging in conflict-affected settings and, as a result, the evidence on the disruptions AVC businesses face is thin. In this paper, we rely on novel panel data from AVC businesses in Myanmar, one of 7 countries in the world with extreme conflict. This short paper documents the disruptions experienced by businesses at several levels of the food supply chain, including farmers, input retailers, crop traders, rice millers, and food vendors. We also provide evidence on the implications for prices by analyzing price changes over this period: farm input and sales prices using farm survey data, food retail using data from food vendors, and dietary cost estimates combining consumption and price data. Our results highlight vulnerabilities in food supply chains in fragile and conflict-affected settings. Potential opportunities to strengthen food supply chains in such settings include ensuring access to banking and financial services; minimizing transportation disruptions to mitigate widening gaps between producer and consumer prices; and maintaining access to fuel and electricity as well as cellphone internet networks. Efforts to support these areas could stabilize food availability and reduce food prices, while also increasing farm-gate shares of food prices thereby supporting rural incomes.
The European Union’s new paradigm of “growing sustainably” underscores the urgent need to complete its digital and green transformation. This study investigates how the integration of digital transformation and sustainability — termed “digitainability” — can be effectively implemented in small and medium-sized enterprises (SMEs) within Eastern Europe. By exploring multi-stakeholder perspectives, this research identifies the systemic digital innovations that can enhance the implementation of Agriculture 4.0. The paper is structured into three stages: Stage 1 conducts a thorough literature review to identify successful factors and barriers related to the digital transition of SMEs in Europe; Stage 2 proposes a conceptual framework that evaluates the synergistic potential of these factors and outlines a step-by-step business model tailored for agri-food SMEs; and Stage 3 validates this model through a case study based on qualitative assessments from semi-structured interviews with company owners and managers. Findings reveal that while innovative agri-food companies in Eastern Europe are increasingly adopting smart technologies to mitigate climate change impacts, there remains a significant gap in their understanding of Industry 4.0 concepts. This research contributes to the discourse on digitainability by offering practical insights for SMEs aiming to leverage digital tools for sustainable growth in an evolving market landscape.
This paper examines the rapid adoption of digital technologies by Ukrainian agroholdings, highlighting the transformative potential of agrifood digitainability, where digital innovation and sustainability intersect within agricultural practices through diverse stakeholder collaborations. We introduce a conceptual framework that categorizes firm-stakeholder interactions across two dimensions — primary versus secondary stakeholders and value creation versus legitimacy — capturing the complexity of these engagements. This framework redefines the concept of societal value creation, particularly emphasizing stakeholders traditionally classified as “secondary” within stakeholder theory. Our empirical analysis reveals that collaborations with both primary and secondary stakeholders generate significant social and environmental benefits, repositioning secondary stakeholders as active contributors to firm value creation. We argue that by treating secondary stakeholders as co-creators of societal value, agrifood firms can harness digital technologies to achieve both competitive advantage and enhanced legitimacy within their stakeholder environment.
This paper reports on the finding of a single-case study, employed to design a new product development (NPD) process model of foods for special medical purposes (FSMPs) for infants and young children within a business-to-business (B2B) company. The research involves modelling a framework from NPD models existing in the literature, tailoring the framework to the specific case and describing the NPD process while highlighting aspects characteristic to B2B food processing industry and food industry specialized in FSMPs. Direct customer involvement emerged as a crucial factor in successful product development in B2B organizations, ensuring customer satisfaction and accelerating the NPD process. Overall, the NPD process for FSMPs was discovered to align with common practices in the food processing industry, incorporating sensory evaluation and storage tests to maintain product quality. However, considering nutritional requirements and legal regulations was found to be especially important in the development process of FSMP, in particular. The described NPD process and highlighted aspects can serve as a valuable reference for B2B companies establishing or enhancing their product development processes.
Given that counties encompass approximately 90% of the agricultural population, enhancing agricultural income levels and bridging the urban-rural income (URI) gap constitute fundamental prerequisites for achieving common prosperity. Utilizing panel data from 817 Chinese counties (2014–2019), this study empirically investigates how digital financial inclusion (DFI) influences the URI gap at the county level. The results demonstrate that DFI not only contributes to reducing URI gap through agricultural incomes growth, but also generates significant spatial spillover effect. on neighboring regions. Mechanism analysis reveals that county-level entrepreneurial activity serves as a critical mediating mechanism through which DFI alleviates income disparities. Furthermore, rural development policies — particularly the E-commerce Pilot Program in Agricultural Areas — significantly amplify DFI’s equalizing effects. These findings provide actionable insights for optimizing DFI deployment to advance equitable development and common prosperity within China’s county-level socioeconomic framework.
