Template-Type: ReDIF-Article 1.0 Author-Name: Yu Wang Author-Workplace-Name: College of Economics & Management, Northwest A&F University, Yangling, P.R. China Author-Name: Boqian Wang Author-Workplace-Name: College of Economics & Management, Northwest A&F University, Yangling, P.R. China Author-Name: Xiuguang Bai Author-Workplace-Name: College of Economics & Management, Northwest A&F University, Yangling, P.R. China Title: Effects of different tillage practices on cropland carbon efficiency in wheat-maize rotation cropping Abstract: Enhancing cropland carbon efficiency (CCE) is vital for carbon-neutral and sustainable agricultural development. In this paper, we use the minimum distance to strong efficient frontier with undesirable outputs model and survey data from wheat-maize farmers in the Yellow River Basin of China in 2020 to investigate the effect and mechanism of different tillage practices on CCE. The results show that the average CCE in the study area is 0.592 6, which has large potential for improvement. Among the tillage practices, reduced tillage with straw returning (RTS) achieves the highest mean CCE, followed by no-tillage with straw returning (NTS), and conventional tillage with straw returning performs the worst. Both NTS and RTS are positively associated with higher CCE, with a stronger effect for NTS. Mechanism test results indicate that these practices enhance CCE primarily by increasing yield and net carbon sequestration. NTS improves CCE by reducing energy use and emissions, and RTS tends to increase fertiliser and energy inputs, partially offsetting its efficiency gains. Heterogeneity analysis results further indicate that the positive effects are stronger among small-to-medium-scale, less-educated and elderly farmers. These findings provide micro-level evidence that conservation tillage can promote low-carbon and efficient agricultural production in China. Keywords: conservation tillage, greenhouse gas mitigation in agriculture, influence mechanism, MinDS-U model, sustainable agriculture Journal: Agricultural Economics Pages: 407-416 Volume: 72 Issue: 7 Year: 2026 DOI: 10.17221/96/2025-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/96/2025-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202607-0001.txt Handle: RePEc:caa:jnlage:v:72:y:2026:i:7:id:96-2025-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: László Vancsura Author-Workplace-Name: Department of Marketing and Supply Chain Analysis, Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, Kaposvár, Hungary Author-Name: Arnold Csonka Author-Workplace-Name: Department of Marketing and Supply Chain Analysis, Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, Kaposvár, Hungary Title: Corn price forecasts in the shadow of the Russian-Ukrainian conflict Abstract: The conflict between Russia and Ukraine has caused serious disruption to agricultural markets, affecting both food prices and food security. In our study, we examine how global economic problems, such as COVID-19 or wartime conditions, affect corn prices and their predictability. We used deep neural network models [Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU)], experimenting with both simple and hybrid versions, as well as univariate and multivariate types. The results show that the GRU model outperformed the other models in predicting corn prices. It was also found that multivariate models tended to yield more accurate results than their univariate counterparts, suggesting that forecast quality can be significantly improved by including additional variables. In the robustness analysis, it was shown that the COVID-19 crisis in 2020 and the Russia-Ukraine war in 2022 led to a deterioration in model performance. The research contributes to the development of forecasting methods for the corn market and provides a basis for decision-making strategies for market players. To ensure methodological rigour, the forecasting results were statistically validated using the Diebold-Mariano test. Keywords: deep learning, forecasting, market shocks, predictive modelling, time series analysis Journal: Agricultural Economics Pages: 461-472 Volume: 72 Issue: 7 Year: 2026 DOI: 10.17221/102/2025-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/102/2025-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202607-0002.txt Handle: RePEc:caa:jnlage:v:72:y:2026:i:7:id:102-2025-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Valentin Marian Antohi Author-Workplace-Name: Department of Business Administration, Faculty of Economics and Business Administration, 'Dunarea de Jos' University of Galati, Galati, Romania Author-Name: Jean Vasile Andrei Author-Workplace-Name: Department of Business Administration, Economical Sciences Faculty, Petroleum and Gas University of Ploiești, Prahova, Romania Author-Workplace-Name: Center for Renewable Energies and Energy Efficiency, National Institute for Economic Research 'Costin C. Kiriţescu', Romanian Academy, Bucharest, Romania Author-Name: Costinela Fortea Author-Workplace-Name: Department of Business Administration, Faculty of Economics and Business Administration, 'Dunarea de Jos' University of Galati, Galati, Romania Author-Workplace-Name: Departament of Finance, Accounting and Economic Theory, Faculty of Economic Sciences and Business Administration, Transilvania University of Brasov, Brasov, Romania Author-Name: Dumitru Nancu Author-Workplace-Name: Finance and Accounting Department, Faculty of Economic Sciences,Ovidius University of Constanta, Constanta, Romania Title: Sustainable agricultural performance in the European Union in the context of ecological transition, economic fairness, and support policies Abstract: This paper investigates the structural determinants of sustainable agricultural performance across the 27 European Union member states over the period 2012-2023, focusing on the interplay between economic efficiency, environmental sustainability, and social equity. Employing a panel quantile regression approach, the study captures the heterogeneous effects of organic farming practices, public support for agricultural research and development, income inequality, and environmental emissions on multiple dimensions of agricultural performance.  The analysis underscores the importance of empirical evidence in designing integrated agricultural policies that enhance sustainability, competitiveness, and social cohesion, in alignment with key European strategies, including the Common Agricultural Policy 2023-2027, the European Green Deal, and the Farm to Fork strategy. The findings reveal that the influence of structural determinants varies significantly across the performance distribution, with stronger effects observed in lower-performing contexts. These results support increased public investment in agricultural research and innovation, the development of customised incentives for organic farming tailored to performance levels and farm structures, and the implementation of redistributive mechanisms to mitigate regional disparities in agricultural income. Higher agricultural income is consistently associated with greater value-added efficiency and lower ecological intensity, suggesting structural decoupling between economic growth and environmental pressure. Overall, the study highlights the need for targeted, data-driven policy interventions to foster innovation, resilience, and inclusive rural development across the European Union. Keywords: ecological efficiency, economic development, European agriculture, income distribution, public policy, quantile panel analysis Journal: Agricultural Economics Pages: 435-460 Volume: 72 Issue: 7 Year: 2026 DOI: 10.17221/329/2025-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/329/2025-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202607-0003.txt Handle: RePEc:caa:jnlage:v:72:y:2026:i:7:id:329-2025-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Zhao Ding Author-Workplace-Name: College of Economics, Sichuan Agricultural University, Chengdu, P.R. China Author-Name: Qianyu Zhang Author-Workplace-Name: College of Economics, Sichuan Agricultural University, Chengdu, P.R. China Title: Empowering farmers through agricultural supply chains: An ESR and IVQR analysis of income effects in China Abstract: Integrating farmers into the modern agricultural industrial system is key to sustaining rural household income growth. Based on field survey data from rural areas in three western Chinese provinces, this study uses an endogenous switching regression (ESR) model to analyse the impact of farmers' participation in agricultural supply chains on their income, and explores the heterogeneous effects of credit constraints and agricultural loans on the incomes of participating and non-participating farmers. The results show that supply chain participation significantly increases farmers' total and operational income, while driving the modernisation of agricultural production methods. Specifically, supply chain participation enables farmers to use agricultural loans efficiently to drive income growth and alleviate income constraints caused by credit restrictions. Furthermore, the positive effect of supply chain participation on operational income is more prominent, especially for low- and middle-income farmers. In contrast, the income-increasing benefits of agricultural loans are more pronounced among middle- and high-income farmers, reflecting differences in loan utilisation capacity across income groups. Keywords: credit constraints, farmers' participation, income growth, supply chain Journal: Agricultural Economics Pages: 417-434 Volume: 72 Issue: 7 Year: 2026 DOI: 10.17221/366/2025-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/366/2025-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202607-0004.txt Handle: RePEc:caa:jnlage:v:72:y:2026:i:7:id:366-2025-AGRICECON