Current issue No 1/2026, March | Agris on-line Papers in Economics and Informatics

Domestic Production Linkages and Sectoral Shifts in Central Europe: An Input–Output Perspective

Ľudmila Bartóková DOI: 10.7160/aol.2026.180101
No 1/2026, March
pp. 3 - 13

ABSTRACT

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.

KEYWORDS

Input-output analysis, sectoral transformation, domestic inter-industry linkages, primary sector, agriculture, V4 countries, Austria.

VIEW MORE PDF (.pdf, 7.58 MB)

A Heuristic Approach to Ensemble-Based Deep Learning Models for Plant Disease Classification and Farming Decision Support

Menaga Dhanasekarana Sudha M. DOI: 10.7160/aol.2026.180102
No 1/2026, March
pp. 15 - 27

ABSTRACT

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%.

KEYWORDS

Stacked ensemble learning, conventional learning, base learners, meta learners, inception, classification and accuracy.

VIEW MORE PDF (.pdf, 11.26 MB)

Does Fermentation of Cocoa Beans Increase Farmers' Income? A Case Study Using a Nationwide Survey in Indonesia

Yuli Hariyati Vina Yunita Ria Rena Yunita Rahman Indah Ibanah Setiyono Muhammad Ghufron Rosyady DOI: 10.7160/aol.2026.180103
No 1/2026, March
pp. 29 - 42

ABSTRACT

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.

KEYWORDS

Cocoa bean fermentation, Propensity Score Matching (PSM), non-fermented cocoa, fermented cocoa, input costs, economic outcomes

VIEW MORE PDF (.pdf, 954.89 KB)

Assessing the Impact of Covid-19 on Farm Profitability in Czechia and Slovakia: A Comparative Dupont Analysis Using Firm-Level Data

Jana Hinke Dirk Beyer DOI: 10.7160/aol.2026.180104
No 1/2026, March
pp. 43 - 52

ABSTRACT

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.

KEYWORDS

Profitability, agriculture, COVID-19, DuPont, RoE, labour productivity, financial leverage.

VIEW MORE PDF (.pdf, 3.08 MB)

Food Price Inflation, GNPIP Policy, and Economics Growth in Indonesia

Danang Indrajaya Hermanto Siregar Tony Irawan Ferry Syarifuddin DOI: 10.7160/aol.2026.180105
No 1/2026, March
pp. 53 - 65

ABSTRACT

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.

KEYWORDS

GNPIP, GDP per capita, food inflation, announcement effect

VIEW MORE PDF (.pdf, 452.18 KB)

Enhancing Innovative Potential in Ukraine's Agro-Industrial Complex: Leveraging Information Technologies for Sustainable Growth and Efficiency

Iryna Petrova Nataliia Sysolina Lesia Kononenko Iryna Sysolina Hryhorii Savelenko DOI: 10.7160/aol.2026.180106
No 1/2026, March
pp. 67 - 82

ABSTRACT

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.

KEYWORDS

Innovative potential, agro-industrial complex, information technologies, resource management, sustainable agriculture, economic efficiency.

VIEW MORE PDF (.pdf, 5.06 MB)

Fintech Integration into Business Processes of Financial and Agricultural Companies: A Strategic Paradigm for Stable Development in the Capital Market

Larysa Satyr Dmytro Starostenko Yurii Zadoia Serhii Salin Oleksandr Mykhalchuk DOI: 10.7160/aol.2026.180107
No 1/2026, March
pp. 83 - 93

ABSTRACT

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.

KEYWORDS

Fintech sector, Blockchain, technologies, strategy, digital services.

VIEW MORE PDF (.pdf, 17.33 MB)

Assessing Climate Vulnerability and Its Linkages to Adaptation Strategies and Farm Resilience in Rice Farming Systems

Merna Ayu Sulastri Yulia Sari Jerry Antonio Trissa Silvian DOI: 10.7160/aol.2026.180108
No 1/2026, March
pp. 95 - 109

ABSTRACT

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 stratified random sampling, the research evaluates vulnerability components, identifies context-specific 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.

KEYWORDS

Adaptation strategies, climate change, resilience, farmers, rice farming, Value Chain Analysis (VCA).

VIEW MORE PDF (.pdf, 10.82 MB)

Standardized Data Infrastructures for Plant Phenomics: A Review of MIAPPE and BrAPI Integration within High-Performance

Vladimír Voral Michael Anderle Michal Stočes Pavel Ambruz Pavel Šimek Jan Jarolímek Jiří Vaněk DOI: 10.7160/aol.2026.180109
No 1/2026, March
pp. 111 - 131

ABSTRACT

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.

KEYWORDS

MIAPPE, BrAPI, plant phenomics, high-performance computing (HPC), FAIR principles, data management, ontology, data governance, interoperability, open science.

VIEW MORE PDF (.pdf, 27.63 MB)

Assessing and Forecasting the Competitiveness of Indonesian Downstream Coffee Industry

Mochammad Yusuf Arif Imam Suroso Ujang Sumarwan Suhendi Adi Haryono DOI: 10.7160/aol.2026.180110
No 1/2026, March
pp. 133 - 148

ABSTRACT

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 Advantage (RSCA) and the Autoregressive Distributed Lag (ARDL) model, based on the Porter Diamond 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.

KEYWORDS

Business analytics, coffee industry, competitiveness, downstream, Porter Diamond Model

VIEW MORE PDF (.pdf, 17.71 MB)