Global Conference on Agriculture

There is still a big difference in European agriculture concerning technological development and organization. Consequently, in a vast majority of European countries and regions, especially those of southern and eastern Europe, a well-developed sphere of formation and transfer of knowledge is required. In that context the FARM project, funded within the framework of the EU Erasmus+ program, is focused on innovative approaches of knowledge exchange that will be taking advantage of new digital technologies. Information and communication technologies should play a vital role in the development of agriculture and empowerment of rural communities. Within the project a special focus lies on spatial decision support systems (SDSS). Spatial information is indispensable in the agricultural sector, and regional planning in general. Therefore, spatial data are vital for decision making in such areas. As a first step, a systematic review of literature and Internet sources was carried out in order to collect information on existing DSS tools. In order to visualize the findings, a dashboard was created, thus including statistics of the results, and foremost a digital map that provides a comprehensive overview of locations where DSS are under development or in usage throughout Europe. The dashboard provides furthermore a valuable database for future research and builds the basis for the next tasks to be carried out within the FARM project in which partners from Spain, North-Macedonia, Lithuania, Cyprus and Germany cooperate closely on the mentioned subjects.

In the first quarter of the twenty-first century, the most substantial global concern is environmental contamination, and it has gained the prioritization of both the national and international community. Keeping in mind this crucial fact, this study conducted different statistical and econometrical methods to identify whether the gross national income of the country has a significant impact on electricity production from nonrenewable sources and different air pollutants like carbon dioxide, nitrous oxide, and methane emissions. Besides, the primary objective of this research was to analyze whether the environmental Kuznets curve hypothesis holds for the examined variables. After analyzing different statistical properties of the variables, this study came to the conclusion that the environmental Kuznets curve hypothesis holds for gross national income and carbon dioxide emission in Bangladesh in the short run as well as the long run. This study comes to this conclusion based on the findings of ordinary least square estimations; ARDL bound tests, short run causality analysis, the Error Correction Model, and other pre-diagnostic and post-diagnostic tests that have been employed in the structural model. Moreover, this study wants to demonstrate that the outline of gross national income and carbon dioxide emissions is in its initial stage of development and will increase up to the optimal peak. The compositional effect will then force the emission to decrease, and the environmental quality will be restored in the long run.

Thailand's government approached the smart farming project in 2006, and it was introduced to farmers in 2013. The purpose of this paper was to study the effect of Thailand's smart farming project on agricultural productivity. Data from the FAOSTAT database of the United Nations Food and Agriculture Organization were obtained from 2006 to 2019. (FAO). The study's variables were agricultural total factor productivity, which is a measure of agricultural productivity. The outcome will be estimated using a specified equation. The results reveal that if total output grows faster than total inputs, agricultural total factor productivity increases. Smart farming innovations such as precision agriculture technology, as well as better farmer knowledge and best practices, are driving agricultural productivity development in Thailand. Thus, smart farming is strongly linked to agricultural output in Thailand. This meant that the Thai government should implement policies that integrate modern information and communication technology (ICT) into agricultural management, maintain resource management, and invest more in R&D, innovation, and technology.

A strategic business analysis can be optimized, depending on the resources and technologies employed in the process. Based on that, a comparison between two algorithms is implemented in this paper to verify the accuracy of each method on the sales predictions of a large company in the food industry. The experiments compared the analyzes performed by the SVM (Support Vector Machines) structure with the results obtained in a previous research (which optimized the business management and resulted in almost 200 thousand Brazilian Reais of savings), that used the MLP (Multilayer Perceptron) on a database with information collected over 4 years (23,093 records) in an Enterprise Resource Planning (ERP) system. Several experiments were implemented as part of the methodology of the analysis. The experiments were designed according to the following specifications: the SVM reference kernels for a non-linear separation (Hyperbolic tangent, Radial Basis and Polynomial), the k value for a resampling based on k-fold cross validation k={3,5,7}, and a violation cost C between the values C={3,5,7}. The same sales data of the company that was used in the first survey were evaluated, which ranges from November of 2015 to December of 2018. In addition, the AUC (Area Under the Curve) values of the first research were used as reference. The results obtained show that the MLP outperformed the SVM structure by 7 to 10%. However, the SVM presents a much smaller variation than the MLP, and does not present the worst results and outliers, showing a structure with more stable results, that can figure as comparative reference in scenarios of uncertainties, such as large business during the risks that occurred during the COVID-19 pandemic.

The article presents the contribution of economic linkage models, especially agricultural value chain models to the sustainable agricultural development of Vietnam. Through the statistical method of descriptive and document analysis, the author presents the theory of economic linkages and the theory of sustainable agricultural development. Since then, it has been applied to study the role of agricultural value chain in sustainable agricultural development in Vietnam. The results of the study point to the positive contributions of agricultural value chain to sustainable agricultural development in Viet Nam such as increasing economic efficiency, promoting the production of large commodities, promoting the specialization of production, enhancing the application of science and technology, protecting environmental resources and so on. The study also pointed out the limitations of agricultural value chain in Vietnam include: some value chains are small and unsustainable, businesses invest in chains with few contracts, the role of the participants in the chain has not been effective, and it is difficult to access capital for the chains. Therefore, author proposed solutions to overcome the main limitations like strengthening realtionship of participants, reinforcing for recommendation and popularization effectively value chain models to society, focusing on education high quality human resource for value chain models, etc. In addition, article also recommended effective agricultural value chain models that need to be replicated in Vietnam.

Italy has always represented a crossroads of different civilizations that have left their mark in the variegated livestock genetic landscape. In Basilicata (Southern Italy), the introduction of Spanish genetic material may have played a role in the formation of a local goat population, the Nera acorne (NA), locally defined as "Murciunara", echoing the term "Murciano" present in the Spanish Murciano-Granadina goat breed name. In the frame of a local project (ValBiOviCap, PSR Basilicata Region), by using Single Nucleotide Polimorphism (SNP) markers, we investigated genetic relationships among NA and goat breeds raised in Puglia (Garganica and Jonica), Basilicata (Nera dalle Corna a Sciabola, Grigia di Potenza, Garganica Lucana and Facciuta Lucana) and Campania (Valfortorina), goat breeds from the south of the Iberian Peninsula (Malaguena and Murciano-Granadina), and the ubiquitous Saanen breed. We performed NeighborNet, Netview and Bayesian clustering analyses. Overall, the results suggest a significant level of genetic proximity between Garganica Lucana, Nera dalle Corna a Sciabola, NA and Grigia di Potenza, while the introgression of material from other breeds (in Jonica and Facciuta Lucana), together with phenomena of inbreeding (in Facciuta Lucana), could explain their greater differentiation. The results provide evidence of introgression of Spanish genetic material into goat populations from Basilicata, thus supporting the hypothesis about the origin of the name "Murciunara"; however, the fact that the genetic influence was mediated, in our studio, by Malaguegna and not by Murciano-Granadina and was more marked in Facciuta Lucana that in Nera Acorne, impose further insights.

In this work, by setting a financial framework, we investigate certain characteristics of the effect of the COVID-19 crisis in the major cryptocurrencies related to agriculture. More precisely, we examine the spillover effects among these cryptocurrencies before, and during the pandemic, partially provoked by COVID-19. In this context, by performing econometrical modeling, we capture the spillover effects of these cryptocurrencies and test their statistical significance. For the period of the pandemic, we utilize the confirmed COVID-19 cases and the lockdown stringency index for the G7 countries in average format, declaring them as exogenous variables. The endogenous variables of the model, for both periods, are the major cryptocurrencies related to agriculture. Based on our findings, the variables examined are non-stationary, which means that their characteristics change over time, are not co-integrated during the pre-COVID period, while they are co-integrated during the pandemic. More importantly, there are not spillover effects before the pandemic, while during the COVID-19 era, for most comparison couples, there are statistically significant spillover effects. The present work provides evidence for the importance of the relationship among the major cryptocurrencies related to agriculture, especially during crises, such as COVID-19, showing a change in their relationship and characteristics, as proved by the spillover analysis. The present paper highlights that more thorough research is required for this important field of study.