Template-Type: ReDIF-Article 1.0 Author-Name: Gabriela KOLACKOVA Author-Name: Igor KREJCI Author-Workplace-Name: Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic Author-Name: Ivana TICHA Author-Workplace-Name: Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic Title: Dynamics of the small farmers' behaviour - scenario simulations Abstract: The paper deals with the dynamic simulation of the possible development scenarios of small farmers. The model is based on the official data sources but also on the qualitative research of small farmers. The modelling structure reflects the specifics of the examined field and personal and social specifics of small farmers. For the purposes of the analysis, the business model describes the value creation in the small and individual farms, thereafter the model is extended into the dynamic simulation model and the selected scenarios of development are simulated. The analysis shows the impact of the current setup in the field. Despite the fact that the paper contains the optimistic scenarios, a simple change of parameters leads to an unsustainable situation. The pessimistic scenarios grow from the realistic conditions when the parameters reflect the recent period settings. This clearly depicts the influence of the weak market position of the farmers and advocates the diversification tendencies. Keywords: farmer, system dynamics, computer simulation, business models, scenarios Journal: Agricultural Economics Pages: 103-120 Volume: 63 Issue: 3 Year: 2017 DOI: 10.17221/278/2015-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/278/2015-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-201703-0001.txt Handle: RePEc:caa:jnlage:v:63:y:2017:i:3:id:278-2015-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Jazmin ENRIQUEZ-SANCHEZ Author-Workplace-Name: Universidad Autónoma Chapingo/Posgrado en Ciencia y Tecnología Agroalimentaria, Chapingo, Mexico Author-Name: Manrrubio MUNOZ-RODRIGUEZ Author-Workplace-Name: Universidad Autónoma Chapingo/Posgrado en Ciencia y Tecnología Agroalimentaria, Chapingo, Mexico Author-Name: J. Reyes ALTAMIRANO-CARDENAS Author-Name: Abraham VILLEGAS-DE GANTE Author-Workplace-Name: Universidad Autónoma Chapingo/Posgrado en Ciencia y Tecnología Agroalimentaria, Chapingo, Mexico Title: Activation process analysis of the Localized Agri-food System using social networks Abstract: The objective of the work was to analyse the prevailing activation process of the Localized Agri-food System (LAS) by using social networks as a tool to value the pre-existing social capital. There were 27 producers of "Chiapas Cream Cheese" and the members of the formal cheese maker organization from the state of Chiapas, Mexico that were interviewed. By the means of cluster analysis and the graphic design of friendship, the kinship, the "compadrazgo" knowledge, the collaboration and cooperation networks, we concluded that the structural activation must transcend the formal creation of an organization. It is best to value and then mobilize the pre-existing social capital in a territory with a specific traditional know-how as a foundation to the structure and activation process of the LAS. Four actors were identified for their active participation in all analysed networks; these were the information diffusers and network structures. Weak links in the cheese maker organization favour the innovation adoption; whereas the strong links maintain the know-how. Keywords: collective action, social capital, genuine cheese, "know-how" Journal: Agricultural Economics Pages: 121-135 Volume: 63 Issue: 3 Year: 2017 DOI: 10.17221/254/2015-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/254/2015-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-201703-0002.txt Handle: RePEc:caa:jnlage:v:63:y:2017:i:3:id:254-2015-AGRICECON Template-Type: ReDIF-Article 1.0 Author-Name: Tao XIONG Author-Workplace-Name: College of Economics and Management, Huazhong Agricultural University, Wuhan, China Author-Name: LI Chongguang Author-Workplace-Name: College of Economics and Management, Huazhong Agricultural University, Wuhan, China Author-Name: Yukun BAO Author-Workplace-Name: School of Management, Huazhong University of Science and Technology, Wuhan, China Title: An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China Abstract: Short-term forecasting of hog price, which forms the basis for the decision making, is challenging and of great interest for hog producers and market participants. This study develops improved ensemble empirical mode decomposition (EEMD)-based hybrid approach for the short-term hog price forecasting. Specifically, the EEMD is first used to decompose the original hog price series into several intrinsic-mode functions (IMF) and one residue. The fine-to-coarse reconstruction algorithm is then applied to compose the obtained IMFs and residue into the high-frequency fluctuation, the low-frequency fluctuation, and the trend terms which can highlight new features of the hog price fluctuations. Afterwards, the extreme learning machine (ELM) is employed to model the low-frequency fluctuation, while the autoregressive integrated moving average (ARIMA) and the polynomial function are used to fit the high-frequency fluctuation and trend term, respectively, in a multistep-ahead fashion. The commonly used iterated prediction strategy is adopted for the implementation of the multistep-ahead forecasting. The monthly hog price series from January 2000 to May 2015 in China is employed to evaluate the forecasting performance of the proposed approach with the selected counterparts. The numerical results indicate that the improved EEMD-based hybrid approach is a promising alternative for the short-term hog price forecasting. Keywords: ensemble empirical mode decomposition (EEMD), extreme learning machine (ELM), hog price forecasting, hybrid approach, iterated prediction strategy Journal: Agricultural Economics Pages: 136-148 Volume: 63 Issue: 3 Year: 2017 DOI: 10.17221/268/2015-AGRICECON File-URL: http://agricecon.agriculturejournals.cz/doi/10.17221/268/2015-AGRICECON.html File-Format: text/html X-File-Ref: http://agriculturejournals.cz/RePEc/caa/references/age-201703-0003.txt Handle: RePEc:caa:jnlage:v:63:y:2017:i:3:id:268-2015-AGRICECON