Template-Type: ReDIF-Article 1.0 Author-Name: Shangjia Guo Author-Workplace-Name: College of Economics and Management, Northwest Agricultural and Forestry University, Yangling, P. R. China Author-Name: Rong Niu Author-Workplace-Name: College of Economics and Management, Northwest Agricultural and Forestry University, Yangling, P. R. China Author-Name: Yanbo Zhao Author-Workplace-Name: College of Economics and Management, Northwest Agricultural and Forestry University, Yangling, P. R. China Title: Credit evaluation and rating system for farmers' loans in the context of agricultural supply chain financing based on AHP-ELECTRE III Abstract: Farmers, often vulnerable within the agricultural supply chain, frequently encounter difficulties accessing and affording loans. This study introduces an innovative credit risk evaluation framework for farmers tailored to the agricultural supply chain. It includes three key aspects: farmers' credit characteristics, the operational status of the agricultural supply chain, and overall credit conditions. Initially, the analytic hierarchy process (AHP) was used to assign weight coefficients to indicators. Then, the Elimination et Choix Traduisant la Réalité III (ELECTRE III) model was employed to determine farmers' credit ratings. To demonstrate the impact of the agricultural supply chain on microfinance, the model's effectiveness was then tested with 398 microfinance survey responses from Fuping County (World Dairy Goat Industry Development Demonstration Zone), Shaanxi Province, China, and its accuracy was further verified using BP neural network analysis. The results demonstrated the model's proficiency in assessing farmers' credit levels within the agricultural supply chain, which can aid in the resolution of various credit assessment and rating challenges. Furthermore, this study offers valuable insights into the integration of multi-criteria decision-making and machine-learning methods. Keywords: credit evaluation model, credit rating, credit risks, Back Propagation neural network, rural finance Journal: Agricultural Economics Pages: 541-555 Volume: 70 Issue: 11 Year: 2024 DOI: 10.17221/434/2023-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/434/2023-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202411-0001.txt Handle: RePEc:caa:jnlage:v:70:y:2024:i:11:id:434-2023-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Huseyin Tayyar Guldal Author-Workplace-Name: Department of Agricultural Economics, Ankara University, Ankara, Türkiye Author-Name: Ozdal Koksal Author-Workplace-Name: Department of Agricultural Economics, Ankara University, Ankara, Türkiye Author-Name: Osman Orkan Ozer Author-Workplace-Name: Department of Agricultural Economics, Adnan Menderes University, Aydin, Türkiye Author-Name: Onur Terzi Author-Workplace-Name: The Economy Bank of Türkiye, Istanbul, Türkiye Author-Name: Erdogan Gunes Author-Workplace-Name: Department of Agricultural Economics, Ankara University, Ankara, Türkiye Author-Name: Aysegul Selisik Author-Workplace-Name: Food and Agriculture Organization of the United Nations, Ankara, Türkiye Title: Unravelling risk factors in Turkish wheat in a changing global landscape Abstract: This study comprehensively examines multifaceted risk factors influencing wheat production among Turkish farmers, aiming to deepen understanding of how these factors shape farmers' perceptions and decision-making processes. Utilising Structural Equation Modeling (SEM), we analysed the interplay of climate-related issues (F1), market dynamics (F2), and external events (F3), like COVID-19 and wars, alongside socio-demographic factors such as education, income, and land ownership. Findings revealed that higher education and increased agricultural income reduced price-related risks while expanding wheat cultivation areas heightened risk perceptions. Farmers in irrigated regions prioritised cyclical risks, whereas those in dry areas perceived climatic risks as more severe. Capital-intensive practices and storage facilities mitigate climate change and market variability risks, with committed wheat producers showing lower climate change risk perceptions. External factors like the Russian-Ukrainian war and the COVID-19 pandemic disproportionately impact irrigated area farmers. This study contributes to the existing literature by using empirical evidence from Turkish wheat farming to explore diverse risk perceptions, employing SEM to unravel complex risk factors and decision-making processes, thereby offering new insights for future agricultural risk management research. Keywords: climate change, COVID-19, food security, Russian-Ukrainian war, Structural Equation Modeling Journal: Agricultural Economics Pages: 527-540 Volume: 70 Issue: 11 Year: 2024 DOI: 10.17221/173/2024-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/173/2024-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202411-0002.txt Handle: RePEc:caa:jnlage:v:70:y:2024:i:11:id:173-2024-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Kevin Nowag Author-Workplace-Name: Department of Statistics and Operation Analysis, Faculty of Business and Economics, Mendel University in Brno, Brno, the Czech Republic Author-Name: Jitka Janová Author-Workplace-Name: Department of Statistics and Operation Analysis, Faculty of Business and Economics, Mendel University in Brno, Brno, the Czech Republic Title: Micro-data efficiency evaluation of agricultural companies: The case of Germany and neighbouring countries Abstract: This study uses micro-financial data to examine the efficiency of agricultural enterprises in Germany and its neighbouring countries. The aim of the study is to introduce a model for the agricultural sector and conduct an efficiency analysis using these data, interpreting the results with specific knowledge in the management of an agriculture company. Both technical and allocative efficiencies were determined, and the companies were ranked. Possible correlations between company size, measured by turnover, and the determined efficiency were analysed. At present, there is a lack of studies in the agricultural sector with high aggregated financial data, which are the basis and necessity for well-founded decision support to increase efficiency. The data envelopment analysis method was used, as a non-parametric procedure from operations research and economics field. Both the constant returns to scale (CCR) and variable returns to scale (BCC) models were used to calculate the efficiency values. The results showed that large and very large companies achieved the highest levels of efficiency. Interestingly, very large companies lost efficiency compared to large companies, suggesting that the optimal efficiency level lies with the latter. Furthermore, the Netherlands was the absolute efficiency leader, while the other countries achieved similar lower efficiencies. This study contributes to the literature by providing a comprehensive efficiency analysis in the agricultural sector based on financial data, thus offering a basis for future studies and political decisions. Keywords: bioeconomy, company size, data envelopment analysis, efficiency analysis, non-perennial crop Journal: Agricultural Economics Pages: 565-576 Volume: 70 Issue: 11 Year: 2024 DOI: 10.17221/190/2024-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/190/2024-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202411-0003.txt Handle: RePEc:caa:jnlage:v:70:y:2024:i:11:id:190-2024-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Mariusz Hamulczuk Author-Workplace-Name: Department of International Economics and Agribusiness, Institute of Economics and Finance, Warsaw University of Life Sciences, Warsaw, Poland Author-Name: Karolina Pawlak Author-Workplace-Name: Department of Economics and Economic Policy in Agribusiness, Faculty of Economics, Poznan University of Life Sciences, Poznan, Poland Author-Name: Daniel Sumner Author-Workplace-Name: Department of Agricultural and Resource Economics, University of California at Davis, Davis, United States of America Author-Name: Grzegorz Szafrański Author-Workplace-Name: Economics and Sociology Department, Institute of Econometrics, University of Lodz, Lodz, Poland Title: Did the COVID-19 pandemic disturb intra-EU trade in agrifood products? Evidence from a counterfactual forecasting approach Abstract: In this study, we attempt to infer the effect of the COVID-19 pandemic on the intra-European Union (EU) agrifood trade from out-of-sample forecasts. We compare the actual level of trade during the COVID-19 period with counterfactual values derived from univariate forecasting models [regARIMA (Linear regression with autoregressive integrated moving average errors) and Holt-Winters methods]. We analyse agrifood imports and exports of specific EU countries and the EU-27 aggregate on the basis of monthly data for the period from January 2010 to February 2022. The findings reveal a significant decrease in trade activity in the first year of the pandemic that was negatively correlated to COVID-19 restrictions applied by EU countries. Surprisingly, COVID-19 restrictions do not significantly explain the diversified agrifood trade response among EU countries during the pandemic. Keywords: agricultural products, COVID restrictions, EU countries, food products, foreign trade Journal: Agricultural Economics Pages: 556-564 Volume: 70 Issue: 11 Year: 2024 DOI: 10.17221/253/2024-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/253/2024-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202411-0004.txt Handle: RePEc:caa:jnlage:v:70:y:2024:i:11:id:253-2024-AGRICECON