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efficiently reflect the situation in agricultural systems and can also be used for forecasting
policy consequences (Akinwumi et al., 2000, Turk (1998)). 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. Mathematical
programming is frequently applied in farm planning. It allows determination of an optimal
allocation of land, labour and capital, given a set of goals (e.g. maximisation of income and
leisure and minimisation of risk) and constraints (e.g. labour and land). Boutkes and Van
Keuler (2003) argue that the study of agricultural systems requires the use of non-linear
dynamic models that allow simulation of the system in a qualitative way, based on a
description of the underlying processes. Their approach is illustrated with a regional model
that has been developed to simulate agricultural development in the Koutiala region in
the south-western part of Mali. There are many factors, such as farm type and soil quality,
that might influence farmers' decisions. However, attempting to consider the complex
interactions of all factors in a single model is not a productive approach. Hence, the authors
(Kaufmann et al., 2009) adopted the approach of isolating parts of a system and examining it
assuming that all other things are equal. The diffusion of organic farming practices is
modeled by a generic agent model (Borchev and Filippov, 2004) based on Theory of planned
behavior for understanding and modeling the farmers decision making process.
System dynamics (SD) methodology (Forrester, 1961) can be and has been used as an
alternative to econometric and mathematical programming approaches (Bockerman et al
(2005)); Elshorbagy et al (2005)). SD model in its essence, is a continuous model because it is
presented as a system of non-linear differential equations (Munitič and Trosić, 1997). There
have been many important SD applications in the field of agriculture
recentlyShen et al. (2009)
present system dynamics model for the sustainable land use and urban development in
Hong Kong. The model is used to test the outcomes of development policy scenarios and
make forecasts. It consists of five sub-systems including population, economy, housing,
transport and urban/developed land. Similar approach is presented by Weber
et al. (1996).
However, the most important work in the field of simulation of development policy
scenarios are presented by Shi and Gill (2005) who developed a system dynamics based
simulation model for ecological agriculture development for Jinshan County (China) and
Kljajić et al. (2000, 2001, 2002, 2003) with an integrated system dynamics model for
development of Canary Islands where main interactions between agriculture, population,
industry and ecology were taken into consideration. The preliminary results of SD
simulation of organic farming development is conducted by Rozman et al. (2007) and Škraba
et al. (2008). The model incorporates key variables affecting the organic farming systems and
is used in identification in of main reasons that the strategic (15% or organic farms) has not
been achieved. Yet this research did not incorporate the full aspects of food market and
consumer factor (Rozman et al., 2007). However, consumer concerns are inherently dynamic
because they respond to difficult and complex societal and technological situations and
developments. For example, because of the rising concern with global warming, carbon
dioxide absorption of crops is now attracting public attention, which means that new
requirements are being proposed for the environmentally friendly production of crops
(Korthals, 2008). In this light Rozman et al. (2008) and Rozman et al. (2010) upgraded the
model with the inclusion of organic market development factor.
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