This systematic literature review synthesizes current knowledge on the European Innovation Partnership for Agricultural Productivity and Sustainability (EIP-AGRI) by analyzing 31 peer-reviewed studies published between 2013 and 2025. The review identifies three interconnected research directions: implementation mechanisms and governance structures, multi-actor collaboration and knowledge co-creation processes, and impact assessment. Findings reveal substantial heterogeneity in national and regional governance approaches, with critical structural barriers including horizontal and vertical fragmentation, inadequate funding, and compartmentalized implementation. The research highlights the importance of boundary-spanning actors, trust-building mechanisms, and structured facilitation in enabling effective multi-actor collaboration. Evidence suggests that EIP-AGRI contributes to sustainable agricultural innovation through enhanced knowledge exchange and network formation; however, impact assessment remains challenging due to methodological limitations and temporal constraints. The review establishes a future research agenda that emphasizes longitudinal evaluation, cross-country comparative analysis, and the potential for systemic transformation.
Existing studies on the role of Information and Communication Technology (ICT) in agriculture often reduce farmer welfare to economic outcomes, overlooking its social, psychological, and environmental dimensions. This narrow perspective limits a comprehensive understanding of how ICT contributes to rural development. To address this gap, this study systematically reviews peer-reviewed articles published between 2014 and 2024 using the PRISMA protocol. The results map the types of ICT interventions, welfare indicators, and pathways through which ICT influences farmer welfare. The findings show that ICT adoption through mobile communication, digital platforms, and internet-based services enhances not only income and productivity but also social capital, livelihood assets, and subjective well-being. These positive outcomes are more pronounced when ICT adoption is accompanied by extension services, credit access, and capacity-building programs. However, the analysis reveals that infrastructural limitations, digital illiteracy, and financial barriers hinder ICT’s full potential, especially among marginalized farmers. The evidence also shows regional imbalances, with research concentrated in a few countries, limiting generalization. By developing a conceptual framework, this review advances a multidimensional understanding of ICT’s role in improving farmer welfare. The results provide actionable insights for policymakers and development practitioners to design inclusive and context-sensitive ICT interventions for sustainable rural transformation.
This study analyzes geoeconomic patterns in Colombian imports of agricultural inputs by applying the k-means algorithm to the CIF value and gross weight complemented by an analysis of trade agreements and tariffs. The results show high dependence on a few suppliers such as Russia and the US for fertilizers and China for technology, even without preferential agreements; On the other hand, the limited effectiveness of FTAs was analysed, where tariff reduction did not generate diversification of critical suppliers; opportunities for diversification with medium-sized suppliers such as Brazil in animal feed; and the relevance of the European Union in veterinary medicines, agricultural technology, fertilizers, and seeds. The methodology integrates data from DIAN (2005-2024) and five-year analyses, showing that competitiveness in prices and logistics outweighs tariff advantages, China dominates 65% of the CIF value in technology and Russia and the United States consistently accounted for over 60% of the CIF value and gross weight of fertilizers. Regulatory, trade, and innovation policies are proposed to reduce the risk of input shortages in agri-food value chains.
This study investigates how digital innovation (DI) enhances environmental-performance improvement (EPI) in the Jordanian industrial sector and whether this relationship is channelled through sustainability (SUS). A structured questionnaire was administered to managers in large and medium-sized manufacturing firms, and the data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results show that DI exerts a significant positive effect on both SUS and EPI, while SUS itself has a direct positive impact on EPI; moreover, sustainability partially mediates the DI → EPI pathway, confirming that technological upgrades yield meaningful ecological gains only when embedded in explicit sustainability programmes. The model explains 44 % of the variance in SUS and 46.7 % in EPI, indicating substantial explanatory power. These findings underline the strategic necessity of integrating ESG principles into digital-transformation roadmaps: real-time data capture and analytics equip firms to anticipate environmental risks, while sustainability frameworks ensure that digital tools are harnessed toward long-term economic, social and environmental objectives. By coupling digital innovation with institution-wide sustainability initiatives, industrial organisations can achieve resource efficiency, bolster regulatory compliance and strengthen competitive advantage in increasingly eco-conscious markets.
The purpose of the study is to argue modern aspects of digital marketing management and e-commerce in the agricultural sector, as well as to identify effective practices and emerging trends that contribute to increasing the competitiveness of agricultural enterprises in the conditions of digitalization of the economy. The work uses methods of comparative and statistical analysis, case studies, as well as synthesis of secondary data from open sources on the development and functioning of digital marketing. An analysis of practical cases of the implementation of digital marketing tools and online trading platforms in small and medium-sized agricultural enterprises in various regions of the world was carried out. Attention to the strategy of using social networks, marketplaces, CRM systems and mobile applications in agriculture is argued. The study showed that the use of digital promotion and sales channels allows farmers and agricultural companies to expand the market, minimize the costs of intermediaries and build direct interaction with the end consumer. Structured positive examples of sales growth after the integration of digital solutions, such as SEO promotion, contextual advertising, e-mail and messenger marketing. In addition, the main barriers to digitization in the agricultural sector are identified and argued: lack of IT skills, weak infrastructure and limited access to investments. The scientific novelty consists in the systematization of disparate data on the use of digital marketing in agriculture and the formalization of a model of successful digital transformation of agribusiness. The work offers a classification of digital promotion strategies depending on the type of production, business scale and target audience. Research results can be used by agrarian entrepreneurs, consultants and government bodies when developing programs to support the digital transformation of the agricultural sector. The proposed recommendations make it possible to adapt best practices to local conditions and increase the effectiveness of marketing campaigns in agriculture.
This research presents a smart irrigation system that integrates Internet of Things (IoT) and machine learning (ML) to optimize water usage in agriculture. The system consists of a wireless sensor network that continuously monitors real-time environmental parameters such as soil moisture, temperature, humidity, wind speed, and rainfall. A Node-MCU microcontroller processes sensor data and transmits it to the Thing-Speak cloud for predictive analysis. The system follows a structured irrigation scheduling method, dynamically adjusting water distribution based on sensor feedback and environmental conditions. The proposed irrigation framework integrates an inverted U-shaped structure with a T-shaped hybrid irrigation system, enabling efficient water management through solenoid valves and sub-pipelines. This system, previously developed for sprinkler irrigation, was evaluated using machine learning models to assess its performance based on soil moisture and temperature parameters. In the present study, several machine learning algorithms, including Decision Tree, XG-Boost, Gradient Boosting, and Random Forest, were employed to predict irrigation requirements. The models consider multiple factors, such as soil moisture, rainfall, wind speed, and water availability, to forecast future irrigation demands, thereby facilitating optimal water utilization. Gradient Boosting achieved the highest accuracy (98.38%) and the lowest RMSE (0.1272), while Decision Tree and XG-Boost also performed strongly, with accuracy of 98.24% each. For controlling and monitoring the developed system, an android-based mobile application developed, allowing farmers to monitor and control irrigation remotely. The results demonstrate significant improvements in water conservation, reduced manual intervention, and enhanced crop yield. Future work will focus on refining predictive models, integrating additional environmental factors, and expanding system capabilities for broader adoption in precision agriculture.
Some European countries have no sea and are close to other countries' mainlands. Trading agricultural products from different places may be difficult because of this circumstance. This study assesses EU Member States’ agricultural trade competitiveness and the impact of landlocked conditions on that competitiveness. This study analysed 27 EU countries between 2000 and 2022 using the Revealed Comparative Advantage, the Error Correction Model, and Propensity Score Matching. Landlocked conditions reduced the EU Member States' agricultural competitiveness. These findings support Diamond Porter's theory, which holds that any country must have factor conditions to generate advantages. Similarly, the New Trade theory promotes economic scale for all countries, even landlocked ones. Other factors in this study have varying impacts on the agricultural competitiveness of EU Member States.
The aim of this paper is to examine how monetary conditions are associated with firm performance in the Czech agricultural sector. Using a balanced panel of 167 firms observed over the effective estimation period 2016-2024, the paper estimates static firm fixed-effects models for three complementary outcomes: return on equity (ROE), year-on-year log sales growth and cash flow to assets. The objective is to assess whether tighter monetary conditions were linked to weaker profitability, slower expansion and lower internal financing capacity in the broad agricultural economy. The results indicate that higher interest rates are associated with lower ROE, weaker sales growth and lower cash flow to assets, while higher real rates are negatively associated with ROE and internal liquidity. Exchange-rate appreciation is positively associated with sales growth and cash-flow capacity, which suggests that, in this sector, the imported-input cost channel may dominate the conventional export-price competitiveness channel.
The outbreak of war in Ukraine in 2022 significantly reshaped agricultural trade dynamics between Ukraine and the European Union (EU). The main goal of this study is to examine the factors associated with increased exports of Ukrainian agricultural products to EU countries in light of the complex situation that includes the outbreak of war, trade liberalization, and provisional trade bans. The study employs a gravity model to analyze Ukrainian imports of selected agricultural products to EU countries, using monthly data from 2020 to 2023. The Poisson Pseudo-Maximum Likelihood model with high-dimensional fixed effects is utilized. EU countries that are more geographically distant significantly increased their imports of Ukrainian agricultural products, driven by a higher market absorption capacity and robust infrastructure, challenging the traditional assumptions of gravity models. Meanwhile, Ukraine's neighboring countries played a crucial role in absorbing Ukrainian exports due to logistical advantages, regulatory support, and the suspension of tariffs. However, the main effect of trade intensification for these countries was primarily observed in the first year of the war. This study makes a novel contribution by examining the cumulative effects of distance, war, and liberalization on trade volumes, marking the first such analysis in the context of EU-Ukraine relations. The use of monthly data enables us to accurately capture short-term changes in trade, both before and after the onset of the war, offering new insights into how crises reshape trade patterns.
This study examines the relationship between monetary policy and food inflation in the Visegrad Group, using monthly data and applying both OLS and quantile regression methods. Because the model is estimated in first differences and includes a three-month lag of the policy rate, all results reflect short-run month-to-month dynamics of food inflation. The analysis reveals that the monetary policy rate is significantly associated with food inflation across several quantiles, with stronger effects observed during periods of higher inflation. The study also examines the roles of exchange rates, industrial and transport inflation, with a robustness check replacing transport inflation with energy prices. This adjustment confirmed the relevance of energy prices in food inflation dynamics. The results indicate that while monetary policy does affect food prices, its effectiveness depends on the level of inflation and underlying supply-side factors. Quantile regression proves to be a valuable tool in capturing these heterogeneities. These findings can support policymakers in designing more responsive and effective strategies to manage food inflation under varying economic conditions.
This study evaluates the impact of cocoa bean fermentation on costs and revenues among cocoa farmers using Propensity Score Matching (PSM). The study used nationally-representative data of Indonesian cocoa farmers from the Indonesian Plantation Farm Household Survey 2014 comprised 23,189 farmers. The result shows that non-fermented cocoa bean farmers achieve higher production (1.04 kg/year) compared to fermented bean farmers (0.83 kg/year), a 26.27% increase. They also have higher revenue, earning $100.67 per year versus $84.42 for fermented bean farmers, a 19.25% increase. Additionally, non-fermented farmers exhibit higher farm value per hectare ($1,772.50 compared to $1,350.00). However, non-fermented farmers incur higher costs: seed costs ($7.19 vs. $5.58), labor costs ($329.05 vs. $295.25), and fertilizer costs ($39.95 vs. $36.46). Conversely, they have lower pesticide costs ($21.95 vs. $26.12). The findings indicate that while non-fermented cocoa beans result in higher production and revenue, they also come with higher input costs. Fermented cocoa farmers benefit from lower costs but achieve lower production and revenue, highlighting the trade-offs between fermentation practices and economic outcomes.
This study examines the impact of food inflation and the role of the National Food Inflation Control Movement (GNPIP) on regional economic growth measured through Gross Domestic Product (GDP) per capita, using the 2018-2023-time panel data with cross-section of 34 provinces in Indonesia. Using cross-regional panel data analysis, the results show that food inflation in general has a significant negative impact on GRDP per capita, with a delay in one period, especially through a decrease in household purchasing power, especially in low -income groups. Conversely, rice inflation shows a significant and delayed positive effect on economic growth, driven by revenue redistribution to rural producers and multiplier effects in the agricultural economy. However, corn and soybean inflation does not show a significant impact, which is caused by the limited role of these commodities in direct consumption, weak economic linkages, import dependence, and low supply elasticity. The GNPIP policy has proven to have a positive and significant influence on GRDP per capita, confirms its multiple roles in maintaining price stability while encouraging regional economic growth through increasing consumption and investment activities. Nevertheless, GNPIP is unable to moderate the relationship between rice inflation and economic growth, indicating its limited capacity in reducing the shocks of certain commodity prices. One of the important mechanisms of GNPIP is announcement effect, which helps prevent panic buying by giving positive signals to the public about food availability and price stability. This study confirms that GNPIP has a strategic role in maintaining economic stability by averting panic buying and bolstering the advancement of the domestic economy.
This study evaluates the impact of COVID-19 on agricultural profitability in Czechia and Slovakia, distinguishing between crop, livestock, and mixed farms. Using firm-level financial data from the Orbis database, an extended DuPont model incorporating labour efficiency is employed to compare profitability drivers pre- (2018-2019) and during (2020-2021) the pandemic. The findings reveal persistent national differences and highlight labour efficiency as a stabilising factor, underscoring agricultural resilience and the importance of structural efficiency in mitigating shocks.
This study aims to identify the factors influencing the competitiveness of the Indonesian downstream coffee industry and provide a forecast through 2030. This study uses Revealed Symmetric Comparative model, to identify the position and determinants of competitiveness in Indonesia's downstream coffee industry. In developing the forecasting model, this study employs three approaches: ARIMA, HP-Filter, and ARDL forecasting, utilising data from 1990 to 2023. The study indicates that Indonesia's downstream coffee industry has comparative advantages, as reflected in the continuous increase of RSCA values over the past two decades. The Porter Diamond model shows that GDP, manufacturing value-added, and foreign direct investment are key drivers of competitiveness. Coffee prices negatively affect both the short and long term, while domestic consumption negatively affects competitiveness only in the short term. Land area, however, does not show a significant effect. The forecasting results show that the competitiveness of the downstream coffee industry in Indonesia is projected to experience continued growth from 2024 to 2030.
The article examines the integration of the fintech sector into the business processes of financial institutions and agricultural companies as a strategic paradigm for stability and security in the capital market. The directions for implementing the data complementarity methodology within an integrated fintech model that unites the fintech sector and the banking ecosystem within the digitalised global space are outlined. This model proposes methods and mechanisms for delivering fast, secure fintech services to business clients on a three-level collaborative platform and digitising financial assets in the capital market. The adaptive market hypothesis is outlined, according to which the assessment of data complementarity in big data analytics in fintech may expand the scope of financial analysis, support risk assessment, and improve analytical accuracy in an unstable investment environment. The results suggest that the most effective pragmatic strategies for stability in the capital market tend to prevail, while investors' financial behaviour is adaptive during crises. The findings also indicate that the capital market reflects economic trends and risks when stock prices exhibit non-random behaviour and may be analysed using big data analytics in fintech. The scale of transactions, investment activity, and market capitalisation of fintech companies is assessed. The paper also presents the market capitalisation of the top 10 global stock exchanges, the dynamics of the PFTS and Ukrainian Exchange indices, initial public offering (IPO) results of public agricultural companies, and changes in the WIG-Ukraine Index.
The increasing complexity and volume of plant phenotypic data have driven the emergence of new computational and standardization frameworks to enable data integration, reproducibility, and reuse. This systematic literature review examines the current state of software tools, data models, and interoperability standards in plant phenomics, focusing on the implementation of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Using a structured PRISMA-based methodology, we analyze two major community driven initiatives MIAPPE and BrAPI as representative solutions for standardized data description and exchange. Furthermore, the study evaluates the role of High-Performance Computing (HPC) and deep learning in addressing computational challenges associated with large-scale datasets, including multi-sensor and 3D capture technologies. Special consideration is given to data governance, encompassing secure access, ethical use, and GDPR compliance within expanding phenomics ecosystems. The synthesis identifies persistent gaps in data harmonization and semantic alignment, proposing future research directions toward more integrated, secure, and scalable infrastructures. This review emphasizes that the success of plant phenomics depends on bridging the gap between standard definitions and their practical implementation within high-performance workflows.
Leaf based plant diseases detection is one of the significant factors affecting crop yield and productivity. Most of the current techniques used for disease prediction are trained using observational records featuring numerous plant image parameters, with a higher frequency of diseased images compared to blight-free images. Hence, discriminating against the crucial insights from irrelevant and redundant images has been a crucial and challenging study. This research inspects the suitability of machine learning models in disease prediction focusing both specific and wide range of plant leaf images. Also, most classical methods are pretentious by various issues such as the format of image statistics, computation, and representation. To address this crucial setback in the present prediction methods, the proposed system develops a hybrid model utilizing stacked ensemble learning, which enhances the detection of plant disease attacks beyond what conventional learning methods. The proposed stacked ensemble-based disease prediction framework is designed to identify both misclassified and correctly classified images. This approach features a two-tier classification mechanism that involves a base learner (Level 0) and a meta learner (Level 1). It considers both image datasets and image features as inputs to facilitate the two-tier classification process. It also focuses on extracting internal features from the damaged leaves. The proposed model was trained with over 30,000 images at various levels. The experimental results revealed that the stacked ensemble learning technique outperformed with a prediction accuracy of 99.93%..
Rice farming systems in swamp lowland ecosystems are highly vulnerable to climate change due to their dependence on hydrological conditions and limited adaptive resources. This study aims to examine the interaction between vulnerability, adaptation strategies, and farmer resilience to strengthen the sustainability of swamp-based rice farming. Using a mixed-methods approach that integrates Vulnerability and Capacity Assessment, SWOT analysis, and Structural Equation Modeling with data from 80 farmers selected through adaptive strategies, and tests causal relationships among key variables. The findings show that farmers face high vulnerability driven by strong exposure and sensitivity, while adaptive capacity remains limited. Although farmers possess experiential knowledge and social capital, technological gaps, low climate literacy, and financial constraints reduce adaptive readiness. The SEM results indicate that farmer characteristics significantly shape adaptation strategies, and both factors play a critical role in determining resilience. Overall, the study demonstrates that resilience emerges from the interaction between biophysical pressures and socioeconomic constraints, highlighting the importance of strengthening knowledge, technology access, and institutional support to enhance adaptive capacity and ensure the long-term sustainability of rice farming in vulnerable swamp ecosystems.
The article analyzes key trends in enhancing the innovation potential of enterprises in Ukraine's agro-industrial complex and proposes strategies for their improvement. A model has been developed for the effective use of innovative capabilities of the regional agro-industrial pool, which adapts to various conditions. The research emphasizes significant progress in innovative processes that optimize resource management and increase productivity. It includes calculations and presents the obtained results. The study also examines the practical application of these achievements, focusing on agro-industrial companies that have successfully implemented information technologies to enhance operational efficiency, reduce costs, and promote sustainable agricultural practices. To facilitate growth and efficiency in Ukraine's agro-industrial sector, the research emphasizes the need to create a robust innovation ecosystem that combines theoretical concepts with real-world applications. Additionally, it proposes using the MS Excel FORECAST tool for analyzing future economic dynamics models. The implementation of a structured approach to strengthening innovation potential at all levels of the agro-industrial complex is expected to lead to increased investment, competitive advantages, and overall economic effectiveness for enterprises.
This paper examines changes in productive structures and domestic inter-industry linkages in five Central European countries- the V4 group and Austria- over 2000-2023. Despite previous studies, evidence remains limited on how domestic inter-industry linkages and sectoral transformations have evolved across these economies, particularly in the primary sector and agriculture. Using national input-output tables from the Asian Development Bank, demand multipliers (output, import, and value added) were calculated at the industry level and aggregated by sector and subsector to reflect sectoral trends and relationships. The analysis focuses on structural transformation, with emphasis on the primary sector and agriculture. Findings confirm a more stable sectoral structure in Austria, while structural shifts persisted in the V4 countries even after 2010. Transformation was most pronounced in the early period, with the primary sector declining in favour of secondary and tertiary sectors, dominated by manufacturing and services. Agriculture's value added remained relatively stable, despite weakening domestic linkages and rising import dependence. At the same time, integration into global value chains increased reliance on imported inputs across sectors. The results suggest that V4 countries should strengthen agricultural resilience by focusing on innovation to improve domestic value added creation and reduce vulnerability to external shocks.
