production economics; farm management; agricultural policy; agricultural environmental issues; tourism; regional planning; rural development; methodology; marketing of agricultural and food products; international trade; development; sport management


Selection of agricultural land for multifunctional agriculture

The modern concept of rural development implies the use of agricultural resources, primarily agricultural land, for other (non-agricultural) activities besides its agricultural purpose. The integral aim of this concept of rural development is the maximization of economic results, besides the sustainable development of rural areas, environmental protection and the production of strategic (staple) agricultural products. The objective of this paper is to define the general, theoretical, quantitative model for the determination of the size and quality of agricultural land which, considering the above-mentioned demands (criteria), is optimal for the utilization in agricultural production in certain regions. The remaining agricultural land would be available for the non-agricultural purposes. The economic optimal model for the selection of agricultural land in the traditional agriculture is the model of linear programming. The criteria of the land selection for traditional agriculture are the economic effectiveness (measured by net income or by gross national product) and the economic efficiency (measured by the production economy). The maximum economic effectiveness is determined by the standard method of linear programming and the maximum economy by the method of broken linear programming. The solution of compromise can be determined by multi-criteria programming, based on the minimum differences. The limitation groups in the mentioned variations of the model are: limitations of production quotas of agricultural products, minimum quantities of staple agricultural products, limitations of processing plants in a region (minimum and maximum), limitation of crop rotation, limitations of the needs in animal husbandry for bulky for age and limitations of agricultural land according to various types of utilization. By quantitative defining of the structure and size of agricultural land for traditional agriculture, “the surplus” and structure of agricultural land available for non-agricultural purposes is automatically determined.

Analysis of the influential factors on gross value added in the Hungarian sheep sector

The competitiveness of the Hungarian sheep sector has been in steady decline for some time now. Crucial has been the problem that the value added in the sector is not generated in Hungary, as most of the produced lambs in Hungary leave the country with an average weight of 21 kilograms, with slaughtering happening abroad.A model has been constructed for our investigations, which introduces the connections between the product cycle phases for mutton in Hungary. This model allows us to calculate the volume of gross value added generated within specific product cycle phases. We used Monte Carlo simulation for our examination, for which the Crystall ball software package was utilized, namely the OptQuest module, for optimization. First, we conducted an optimization of an experiment number of 500,000 for “Gross value added” in the case of the slaughterhouse. During the optimization, Easter, Christmas and August lamb ratio and ewe number, as well as progeny, were set as decision variables and examined as values of gross value added, the decision variables of which contribute to obtaining the best results. The gained decision variables were set in the model and a Monte Carlo simulation was run with an experiment number of 500,000, where only the values of the conditions were changed along the pre-set dispersion; the values of the decision variables were fixed. The most significant aim of our investigation was to identify the volume of gross value added generated during processing in various phases of the product chain and the change of which inputs affected this volume the most. The findings proved that, in the case of capital uniformity, the output of processing was most influenced by sheep progeny on the bottom level of the mutton product chain. This factor is followed by that of weight gain in the source material producing and fattening sub-modules, as well as the gross wage in starter lamb feed and meadow hay in the source material producing sub-modules. Contour plots helped to describe the connections between these factors. By using contour plots, the volume of gross value added might be forecast for various combinations of factors.