Template-Type: ReDIF-Article 1.0 Author-Name: Alessandro Scuderi Author-Name: Mariarita Cammarata Author-Workplace-Name: Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy Author-Name: Ferdinando Branca Author-Workplace-Name: Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy Author-Name: Giuseppe Timpanaro Author-Workplace-Name: Department of Agriculture, Food and Environment (Di3A), University of Catania, Catania, Italy Title: Agricultural production trends towards carbon neutrality in response to the EU 2030 Green Deal: Economic and environmental analysis in horticulture Abstract: The European Green Deal to tackle climate change sets emission reduction targets for 2050. Particular attention has been paid to the agricultural sector, where there is a strong need to reduce carbon emissions and re-establish the natural carbon cycles. The concept of carbon neutrality is emerging in a scenario where it is necessary to reduce carbon dioxide (CO2) emissions from cultivation to near zero. The quantification of carbon emissions was carried out by the carbon footprint (CF) of conventional, organic and zero residue potato cultivation in Sicily. In order to provide farmers and consumers with answers regarding the most sustainable cultivation regime, the results showed that the organic and zero residue methods have the best results in terms of emissions; the latter instead revealed the positive results in economic terms. It becomes a new model to follow in the pursuit of sustainability as it is based on the reduction of synthesis inputs and is free from the constraints imposed by organic production standards. Keywords: carbon footprint, economics, sustainability, zero residue Journal: Agricultural Economics Pages: 435-444 Volume: 67 Issue: 11 Year: 2021 DOI: 10.17221/145/2021-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/145/2021-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202111-0001.txt Handle: RePEc:caa:jnlage:v:67:y:2021:i:11:id:145-2021-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Łukasz Kryszak Author-Workplace-Name: Department of Macroeconomics and Agricultural Economics, Institute of Economics, Poznań University of Economics and Business, Poznań, Poland Author-Name: Thomas Herzfeld Author-Workplace-Name: Department Agricultural Policy, Leibniz Institute of Agricultural Development in Transition Economies, Halle (Saale), Germany Author-Workplace-Name: Institute of Agricultural and Food Sciences, Faculty of Natural Sciences III, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany Title: One or many European models of agriculture? How heterogeneity influences income creation among farms in the European Union Abstract: Agricultural structures are quite heterogeneous across the European Union (EU), and it is likely that the underlying technology also differs across regions. In this article, we claim that the heterogeneity of agriculture across the EU affects the process of income creation (i.e. the relative importance of the factors of farm income differ for different agricultural models). A panel of farms representative for 125 regions reporting to the EU Farm Accountancy Data Network (FADN) during the period from 2007 to 2018 is used. In this article, those regions are grouped into three clusters. A system generalised method of moments (GMM) panel estimator is applied to each cluster. The results showed that total factor productivity (TFP), relative prices and agricultural subsidies make different contributions to farm net value added (FNVA). In particular, the income growth of farms in regions dominated by large farms seems to react more to marginal changes of the explanatory variables. Keywords: cluster analysis, dynamic panel models, Färe-Primont index, farm income, total factor productivity Journal: Agricultural Economics Pages: 445-456 Volume: 67 Issue: 11 Year: 2021 DOI: 10.17221/154/2021-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/154/2021-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202111-0002.txt Handle: RePEc:caa:jnlage:v:67:y:2021:i:11:id:154-2021-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Yan Guo Author-Workplace-Name: College of Information and Engineering, Sichuan Agricultural University, Yaan, China Author-Workplace-Name: The Lab of Agricultural Information Engineering, Sichuan Key Laboratory, Yaan, China Author-Name: Xiaonan Hu Author-Workplace-Name: College of Information and Engineering, Sichuan Agricultural University, Yaan, China Author-Workplace-Name: The Lab of Agricultural Information Engineering, Sichuan Key Laboratory, Yaan, China Author-Name: Zepeng Wang Author-Workplace-Name: College of Information and Engineering, Sichuan Agricultural University, Yaan, China Author-Name: Wei Tang Author-Workplace-Name: College of Information and Engineering, Sichuan Agricultural University, Yaan, China Author-Name: Deyu Liu Author-Workplace-Name: College of Information and Engineering, Sichuan Agricultural University, Yaan, China Author-Name: Yunzhong Luo Author-Workplace-Name: College of Information and Engineering, Sichuan Agricultural University, Yaan, China Author-Name: Hongxiang Xu Author-Workplace-Name: College of Information and Engineering, Sichuan Agricultural University, Yaan, China Title: The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining Abstract: With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation. Keywords: agricultural economics, data mining, industrial chain, machine learning, price forecast Journal: Agricultural Economics Pages: 457-467 Volume: 67 Issue: 11 Year: 2021 DOI: 10.17221/128/2021-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/128/2021-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202111-0003.txt Handle: RePEc:caa:jnlage:v:67:y:2021:i:11:id:128-2021-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Agus Dwi Nugroho Author-Workplace-Name: Department of Agricultural Socio-Economics, Faculty of Agriculture, Gadjah Mada University, Yogyakarta, Indonesia Author-Workplace-Name: Doctoral School of Economic and Regional Sciences, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary Title: Agricultural market information in developing countries: A literature review Abstract: In most developing countries, the agricultural industry has the potential to alleviate poverty at a faster rate compared to other sectors. As such, many governments have initiated policies and programme to improve agricultural performance. However, some of these projects have not achieved success because the local agricultural markets are often controlled by a small group of stakeholders who tend to hide information in the market despite having huge market power. This paper aimed to determine the issues and impacts of the lack of market information in developing countries and to provide strategies for solving such problems. Results show that the presence of an oligopsonic market system, current information and communication technology (ICTs), the lack of market infrastructure and gender limitations are the main issues related to a lack of information. In turn, lack of information leads to an inefficient agricultural market, causes negative impacts on market participants, and ultimately leads to harmful socio-economic effects. Thus, to solve these problems, it is necessary to improve capacity building, increase access to ICTs and improve market infrastructure. Keywords: asymmetric information, capacity building, information and communication technologies, market infrastructure, price volatility Journal: Agricultural Economics Pages: 468-477 Volume: 67 Issue: 11 Year: 2021 DOI: 10.17221/129/2021-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/129/2021-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-202111-0004.txt Handle: RePEc:caa:jnlage:v:67:y:2021:i:11:id:129-2021-AGRICECON