![]() ![]() ![]() maximisation of income and leisure and minimisation of risk) and constraints (e.g. It allows determination of an optimal allocation of land, labour and capital, given a set of goals (e.g. Mathematical programming is frequently applied in farm planning. Although econometric models have a great methodological value and forecasting capabilities the modeler must ensure relatively long and consistent data series that are rarely available. At the national and regional level we often encounter econometric models that can efficiently reflect the situation in agricultural systems and can also be used for forecasting policy consequences ( Akinwumi et al., 2000, Turk (1998)). Boorsma (1990) distinguishes three approaches in modelling the behaviour of the farmer: econometric modelling (based on linear regression equations of a data set) mathematical programming and modelling decision processes based on decision rules. Also, technologic economic simulation at farm level and multicriteria decision analysis are often used for decision support at farm level ( Rozman et al, 2005 Pažek et al, 2006). Organic agriculture represents a complex system at national level ( Shi and Gill, 2005) and different modeling approaches have been described in the literature (farm level, regional level and national level). In this light the conceptual methodological approach for evaluation of development policies for organic farming must be developed. ![]() The consequences of policies are long term and irreversible. This alternative agricultural paradigm may provide the link between objectives of sustainable resource use and sustainable regional development. With respect to terms of multifunctionality, organic agriculture is the highest environmentally valuable agricultural system ( Rozman et al., 2007a, 2007), and has strategic importance at national level that goes beyond the interests of agricultural sector. Contemplated as a whole, any sound agricultural reform would entail not only necessary positive shifts in economic efficiency levels concerning the production and processing of food, but should specifically address some key socio-economic issues that are at the core of preserving and maintaining the ecological balances in the Slovene countryside with biodiversity becoming an increasingly important agricultural policy concern ( Ivančič et al., 2003). One way of emulating the prevailing EU policy reform trends is also to support and encourage organic farming, which is gaining in importance in Slovene agricultural production. AnyLogic is being used in industries such as supply chain, transportation, healthcare & pharma, marketing, competition, manufacturing & production, pedestrian flows- airports, stations, malls etc.Agricultural activity, beyond its primary function, can also shape the landscape, provide environmental benefits such as land conservation, sustainable management of renewable natural resources and preservation of biodiversity, and contribute to the socioeconomic viability of many rural areas ( Majkovič et al., 2005). AnyLogic models can be exported as fully functional standalone Java applications that can be run anywhere by any user, with or without AnyLogic software installed. ![]() Its features include visual model development environment, flexibility, and 3D animation, drag-n-drop capabilities with multiple application-specific libraries, Java environment for extensibility of model functionality and the possibility to create agent-based, discrete event, system dynamics or hybrid models in one software. AnyLogic models can be based on any of the main simulation modeling paradigms: discrete event or process-centric (DE), systems dynamics (SD), and agent-based (AB). Is used to create digital twins for various business sectors. AnyLogic is a provider of dynamic simulation tools, technologies and consulting services for business applications. ![]()
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