DOi: 10.2478/V10051-012-0016-2
System Dynamics Model for Policy Scenarios of Organic Farming Development
Črtomir Rozman1, Karmen Pažek1, Jernej Prišenk1, Andrej Škraba2, Miroljub Kljajič2
University of Maribor, Faculty of Agriculture and Life Sciences, Pivola 10, 2311 Hoče, Slovenia, crt.rozman@uni-mb.si (corresponding author), karmen.pazek@uni-mb.si, jernej.prisenk@uni-mb.si university of Maribor, faculty of organizational sciences, Kidričeva cesta 55a, 4000 Kranj, slovenia, andrej.skraba@fov.uni-mb.si, miroljub.kljajic@fov.uni-mb.si
This paper presents the system dynamics model of organic farming development in order to support decision making. The model seeks answers to strategic questions related to the level of organically utilized area, levels of production and crop selection in a long-term dynamic context. the model will be used for simulation of different policy scenarios for organic farming and their impact on economic and environmental parameters of organic production at an aggregate level. using the model, several policy scenarios were performed.
Key words: organic farming; system dynamics; simulation; model
1 Introduction
Organic agriculture system dynamics (SD) methodology (Forrester, 1958) can be used as an alternative to the econometric and mathematical programming approaches (Bockermann et al., 2005; Elshorbagy et al., 2005; Saysel et al., 2002; Škraba et al., 2003) for policy modeling. Recently, there have been many important SD applications in the field of agriculture and environment: Nalil (1992) describes the conceptual development of FOSSIL2, an integrated model of U.S. energy supply and demand, which is used to prepare projections for energy policy analysis in the U.S. Department of Energy's Office of Policy, Planning, and Analysis. Munitič and Trosic, (1997) used system dynamics for the modeling of the ecological subsystem of "Kastela Bay". Guo et al. (2001) presented an environmental system dynamics model named ErhaiSD and developed for supporting an environmental planning task. The ErhaiSD consists of dynamic simulation models that explicitly consider the information feedback that governs interactions within the ecosystem. Such models are capable of synthesizing component-level knowledge into a system behavior simulation at an integrated level. Fischer et al. (2003) utilized the power of three-dimensional visualization to present simulation results from a system dynamics model of global protein consumption. A similar approach has been presented by Weber et al. (1996). Shen et al. (2009) presented
a system dynamicsmodel for sustainable land use and urban development in Hong Kong. The model is used to test the outcomes of different development policy scenarios and to make forecasts. It consists of five sub-systems including population, economy, housing, transport and urban/developed land. Yin and Struik (2009) reviewed recent findings on modeling genotypes and environmental interactions at a crop level, moving from system dynamics to system biology. However, the most important works 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 by Kljajic et al. (2000, 2002, 2003), who developed an integrated system dynamics model for development in the Canary Islands, where interactions between agriculture, population, industry and ecology were taken into consideration. The preliminary investigations into SD simulation of organic farming development have been conducted by Rozman et al. (2007) and by Škraba et al. (2008). In this model, the overall demand and production has been considered, which is important on the national level and represents certain limitations for expansive development of organic farming.
This paper describes a further improvement of the previous model and presents a system dynamics model for the development of organic agriculture in Slovenia in order to identify key variables that determine conversion dynamics and
Received: 11'h January 2012; revised 4th March 2012; accepted 3rd July 2012
to propose development policy in order to achieve strategic goals as set in the Action plan ANEK. First, we present the main flows and feedback loops within the systems and the development of the system dynamics model. The results section presents scenarios (different policies in organic farming) and their evaluation through application of the developed SD model. The main findings and suggestions for further study conclude the article.
2 Methods
The simulation model should consider the key variables that influence the development of organic farming, such as:
■	the number of conventional farms,
■	the number of organic farms,
■	conversion process,
■	subsidies,
■	the promotion of organic farming (marketing, market development, education),
■	the organization of a general organic farming support environment,
■	a system of self-awareness, and
■	the delay constants of process change.
A key variable in the model is the number of organic farms. These are the farms that are under the control system of one of the control organizations. The growth in the number of organic farms was initially (in year 1998) almost linear; however, in the years from 2003-2005, the growth moderated to approximately 4%, despite an increase in subsidies of 20-30%.
During the development of the CLD diagram (Figure 1) as the first step toward the development of the SD model, the following key variables were identified:
1.	the number of potential candidates (farms) for conversion to organic farming,
2.	the number of farms already converted to organic farming, and
3.	the flow between (1) and (2): conversion rate (transition).
Loop B1 represents a negative loop, with a goal value of 0 (depleting the number of "Conventional Farms"). The number of "Conventional Farms" divided by the "Total Number of Farms" yields the "Concentration of Conventional Farms", which is initially high, meaning that there should be a high initial preference for "Conversion". "Concentration of Conventional Farms" positively influences the "Communication". This variable represents the general communication between the conventional approach members and the organic approach
Concentration of Conventional Farms


O
+ + Communication
Communication Success
Success Factor
O
Information Spread +

Information Spread Factor
Conventional Farms
Conversion
Organic Farms
Total Number of Farms
Production Capacity of Farms
Price
Desired Production
Subsidy
Self Organization Resources
Application of Resources
Promotion and
Market Development
Figure 1: Causal Loop Diagram (CLD) of conversion process to organic farming
+
+
members. "Conversion" positively influences the number of "Organic Farms". If the number of "Organic Farms" increases, the "Information Spread" increases above the level that it would otherwise have been. "Information Spread" by "Organic Farms" members is positively influenced by the "Information Spread Factor" which could be, for example, increased by marketing campaigns. "Information Spread" positively influences "Communication". The number of "Conversion" farms is determined by the "Success Factor", which determines the "Communication Success", yielding the number of convinced conventional members that decide to make a "Conversion". Loop R1 is a reinforcing feedback loop compensated for by the initial balancing feedback loop marked with B1. If the number of "Organic Farms" increases, the "Promotion and Market Development", supported by the "Policy Support Factor", increases as well. Higher "Promotion and Market Development" positively influences the "Self Organization Resources", which contribute positively to the "Support Resources" on which the "Conversion" is dependent.
There is a delay mark between the "Promotion and Market Development" and "Self Organization Resources". Longer delays should be expected here since a significant amount of time is needed in order to promote both the organic farming idea and the marketing channels that will support organic farming.
The "Support Resources" are significantly dependent on the government "Subsidy". Furthermore, the higher the "Organic Farming Goal" is set, the more "Support Resources" are available, meaning that a larger number of organic farms can be supported. If the "Organic Farming Goal" increases, the "Conversion" increases above the level that it would otherwise have been.
The interconnections marked with "R2" have the characteristic of reinforcing feedback loop. According to government policy, the growth in the number of "Organic Farms" should be properly supported in order to promote an increase in self-organization of, for example, organic food marketing and pro-
Organic_farms
Concentration_of_potentional_farms Communication Information_spread Spread_factor
Cum number of farms
Transition_F_based_on_production(/v

Coeff of food demand Food Demand Ratio FD WP
Delay Initial DLY value
Figure 2: System dynamics model oof organic farming d^^velopment
Promotion and development factor
motion. Thus, the reinforcing feedback loop R2 should serve as a growth generator in the system.
Loop B2 represents a balancing loop. If the number of "Organic Farms" increases, the "Application of Resources" increases above the level that it would otherwise have been. The "Application of Resources" is also dependent on the resources needed per farm, i.e. "Support Demand per Farm". Higher "Application of Resources" can cause the depletion of the "Support Resources". The "Organic Farming Goal" is dependent on the "Support Demand per Farm". If more resources are needed per farm, fewer organic farms can be supported, and therefore lower numbers of "Conversion" should be expected. In considering a real case, the negative loops R1 and R2 are dominant, leaving the system in an undesirable state of equilibrium. This would mean that the number of organic farms is constant and well below that desired. In order to move the system away from the equilibrium, one should consider the policies that would raise the impact of the reinforcing feedback loops B1 and B2, which should move the system state, i.e. the number of "Organic Farms", to the higher equilibrium values. "Price", "Desired Production" and "Production Efficiency" are also important factors which impact the intensity of the transition.
A system dynamics model structure is shown in Figure 2. The model consists of 36 variables and 60 links.
There are two levels to the elements applied in the upper part of the model: The variable "Conventional_farms" represents the number of conventional farms. By the flow "Conversion", the "Conventional_farms" become "Organic_ farms".
This structure is commonly known as the market absorption model. "Conversion" is dependent on the "Organic_farm-ing_goal". The goal is set by the "Support_resources" available, modeled as a level element. The desired conversion can be achieved only if there are enough "Support_resources" present in order to make a "Conversion". The "Support_resoures" are not only the financial means. Here, the support of the society is also considered; for example, education should create positive attitudes in relation to organic farming. In this category, the market development, as well as the demand, should also be considered. However, at present, the "Support_resources" are mainly dependent on subsidies from the government. The important variable "Self_organization_resources" is driven by the impact of the policy and the level of societal support, which will intensify with increasing numbers of "Organic_farms". This represents the application of a reinforcing feedback loop
Table 1: Input parameters for each scenario
Scenario	Subsidies	Coefficient of food demand	Delay	Promotion factor	Population
1	2000	1,2	24	0,8	2M
2	3000	1,2	24	0,8	2M
3	4000	1,2	24	0,8	2M
4	4000	1,2	24	2	2M
5	4000	1,2	48	2	2M
6	4000	1,2	48	2	2.3M
7	4000	1,1	48	2	2.3M
80,000-r
Figure 3: Number of organic farms
Figure 4: Conversion dynamics
5,000-


-t-
-t-
0
20 40 60 Time
1 6
-\-1
80 100
which should be augmented. The "Development_limit" represents the function which considers the variable consumption of the resources. If the resources are scarce, the usage is lower than in the case of abundance. Resources are consumed by the "Organic_farms". The prosperity of the "Organic farms" therefore depends on the "Support_resources", which are not only financial means. Here, the social impact of organic farming represents the supportive environment which should sustain such an activity, which in the world of consumption is counterintuitive. The "Conversion" is also dependent on the total food production and "Food demand".
3 Results and discussion
The model is used in order to simulate different scenarios that enable the assessment of policy scenarios with respect to the development of organic farming. Table 1 shows input parameters for 7 scenarios simulated. The main policy parameter being changed is the "subsidies" category.
Scenarios 1, 2 and 3 (Figures 3 and 4) represent the increase of the subsidies and the impact on the transition rate. Scenario 4 shows the impact of the increased promotion factor, which would yield the higher limit conversion to the organic farming. The impact of the increased delay in providing self-support resources is shown by Scenario 5. Here, one assumes that this delay is increased from two to four years on average. Scenario 6 represents the increase in the population which would lead to the status quo in the number of Organic and Conventional farms. It is supposed that the transition in this case would not occur due to the increased food demand. In this case, the negative conversion could also be considered; however, this is the limitation of the proposed model. Scenario 7 shows the transition to organic farming if the coefficient of
food demand decreased, which would be the case if, for example, the imports of food increased.
However, the system dynamics model does not provide numerical forecasts. It is rather a policy tool that examines the behavior of key variables (number of organic farms) over time. Historical data and performance goals provide baselines for determining whether a particular policy generates the behavior of key variables that is better or worse when compared to the baseline or other policies. Furthermore, models provide an explanation for why specific outcomes are achieved. Simulation allows us to compress time so that many different policies can be tested, the outcomes explained, and the causes that generate a specific outcome can be examined by knowledgeable people working in the system before policies are actually implemented.
4 Conclusion
After performing several simulation scenarios, the following findings could be abstracted:
■	Conversion to organic farming relies on subsidies which provide the main source of conversion from conventional farming to organic farming.
■	Subsidies are not the only driving force in the system; even more important are other activities that promote organic farming.
■	Subsidies could not be provided in sufficient amounts in order to complete conversion from conventional to organic farming.
■	A feasible strategy to achieve complete conversion should consider reinforcing the feedback loop between resources, number of organic farms and supportive actions which are bounded to the number of organic farms.
0
■	The current output parameter, i.e. number of organic farms, is caught in an unwanted equilibrium value due to the domination of balancing feedback loops in the system.
■	The important factor is self-organization of the organic farming environment, which includes market development and general public awareness.
Further strategic actions should consider the dynamic response of the system and the feasibility of the stated system target values. Consideration should be paid to the interaction between the four main feedback loops indicated in the system which determine the system performance and provide the means for proper definition of control strategy. The main advantage of the SD model is its capability to assess policy changes and the response of target variables over time. Such models should be useful tools for policymakers to use in planning strategies for the sustainable development of organic farming. Furthermore, it could be extended to other fields closely related to supplemental activities on organic farms, such as farm tourism.
5 Acknowledgement
This work was supported partly by Slovenian Research Agency ARRS Program Grant »Sistemi odločanja v elektronskem poslovanju« No. UNI-MB-0586-P5-0018 and partly by the project CRP V7-1118 Economics of organic farming in Slovenia.
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Črtomir Rozman achieved his Ph.D. at University of Maribor, Faculty of Agriculture. He is active as Associated Professor for farm management at the department for agriculture Economics and Rural development (faculty of agriculture and Life sciences, university of Maribor). His research includes development of decision support systems for farm management (simulation modeling, multi criteria decision analysis. machine learning) and economics of agricultural production. he is also involved in teaching activities as Head of department and as thesis supervisor at post graduate study programs and multiple national and international research projects. he is author or coauthor of 45 scientific papers including 18 papers in journals with impact factor.
Karmen Pažek achieved her Ph.D. at University of Maribor, Faculty of Agriculture in 2006. She is active as Associated Professor for farm management at the department for agriculture Economics and Rural development on faculty of agriculture and Life sciences, university of Maribor. Her research includes development of decision support tools and systems for farm management (simulation modeling, multicriteria decision analysis, option models) and economics of agricultural production. she is involved in teaching activities as thesis supervisor at postgraduate study programs and involved in national and international research projects. she is author or coauthor of 32 scientific papers including 15 papers in journals with impact factor.
Jernej Prišenk achieved his M.sc. at university of Maribor, faculty of agriculture in 2012. he is active as technical assistant for rural development and agricultural economics in the department for agriculture economics and rural development on faculty of agriculture and life sciences, university of Maribor. his research includes development of multi-criteria models (using multi criteria analysis) for assessment local food products in slovenia, development of non-parametric models using linear programming and weighted goal programming for optimization feed rations for different animal species. he is involved in researching development of mountain rural regions in slovenia too
(different types of food supply chains). he is coauthor of 2 scientific papers.
Andrej Škraba obtained his ph.d. in the field of organizational sciences from the university of Maribor. he works as a researcher and assistant professor at the faculty of organizational sciences in the cybernetics and Dss laboratory. his research interests cover modeling and simulation, systems theory and decision processes. he is a member of system dynamics society and slosiM.
Miroljub Kljajic completed his ph.d. at the faculty of electronics in ljubljana in 1974. he is a professor emeritus at the faculty of organizational sciences in Kranj in the area of systems Theory, cybernetics and computer simulation. his main fields of research include decision support systems in global e-commerce and methods of modeling and simulation of organizational systems. he has been the principal investigator of many national and international modeling and simulation projects. he was the head of cybernetics and decision support systems at university of Maribor, faculty of organizational sciences. as author, he has published more than 30 scientific articles recognized in sci with more than 300 citations.
Dinamični model razvoja ekološkega kmetijstva
v prispevku je predstavljen model za simulacije razvoja ekološkega kmetijstva na osnovi metodologije sistemske dinamike. Metodologija sistemske dinamike je celoviti pristop za podporo dinamičnemu reševanju kompleksnih problemov, ki omogoča vpogled v posege s trajnostnimi posledicami. Model pri kreiranju razvojne politike išče odgovore na strateška vprašanja povezana z razvojem ekološkega kmetijstva in trga ekoloških pridelkov. Model se uporablja za simulacije različnih scenarijev razvojnih politik za ekološko kmetijstvo ter njihov vpliv na gospodarske in okoljske parametre ekološke pridelave na agregatni ravni. z modelom smo analizirali več možnih strategij razvoja ekološkega kmetijstva.
Ključne besede: ekološko kmetovanje; sistemska dinamika, simulacija, model