A system dynamics approach to decision-making tools in farm tourism development

Žibert, Maja; Rozman, Črtomir; Škraba, Andrej; Prevolšek, Boris Article A system dynamics approach to decision-making tools in farm tourism development Business Systems Research (BSR) Provided in Cooperation with: IRENET - Society for Advancing Innovation and Research in Economy, Zagreb Suggested Citation: Žibert, Maja; Rozman, Črtomir; Škraba, Andrej; Prevolšek, Boris (2020) : A system dynamics approach to decision-making tools in farm tourism development, Business Systems Research (BSR), ISSN 1847-9375, Sciendo, Warsaw, Vol. 11, Iss. 2, pp. 132-148, https://doi.org/10.2478/bsrj-2020-0020 This Version is available at: https://hdl.handle.net/10419/318732 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 132 Business Systems Research | Vol. 11 No. 2 |2020 A System Dynamics Approach to Decisionmaking Tools in Farm Tourism Development Maja Žibert Faculty of Agriculture and Life Sciences, University of Maribor, Slovenia Črtomir Rozman Faculty of Agriculture and Life Sciences, University of Maribor, Slovenia Andrej Škraba Faculty of Organizational Sciences, University of Maribor, Slovenia Boris Prevolšek Faculty of Tourism, University of Maribor, Slovenia Abstract Background: Besides visiting the main tourist attractions in Slovenia, many tourists want to spend their free time in the countryside as well, but the number of farming establishments in Slovenia diminished distinctly in the last years. Objectives: This paper aims to develop a system dynamics model, with the goal to analyse dynamics of the diversification of agricultural holdings into farm tourism activities in Slovenia. Methods/Approach: A system dynamics methodology was chosen to model the diversification in farm tourism. First, we present a basic concept of a system dynamics model with a causal loop diagram. Further, a system dynamics model with different scenarios is presented. Results: The main feedback loops were identified, and the simulation model was used to analyse different simulation scenarios of the transition of farming establishments into farm tourism facilities. Conclusions: The model provides the answers to the strategic questions about the dynamics of transfer into tourist farms, using several simulation scenarios. The transition mainly relies on subsidies, promotion of diversification and the growth of rural tourism, which provides a relevant direction for the development of future incentives. Keywords: farm tourism, rural tourism, modelling, system dynamics, causal loop diagram, simulation JEL classification: Q13 Paper type: Case Study Received: Jan 31, 2020 Accepted: Jul 6. 2020 Citation: Žibert, M., Rozman, Č., Škraba, A., Prevolšek, B. (2020), “A System Dynamics Approach to Decision-making Tools in Farm Tourism Development”, Business Systems Research, Vol. 11, No. 2, pp. 132-148. DOI: 10.2478/bsrj-2020-0020 Introduction Rural tourism has grown in many parts of the world in the last few decades, including Slovenia. In adition, rural tourism allows the development of the countryside, which is 133 Business Systems Research | Vol. 11 No. 2 |2020 increalisngly important in the era of growing urbanization. Countries are urgently aiming to find the ways on how to incite economic activitiy in the rural areas, and rural tourism is gaining popularity as one of the most important tools in that endaveour. However, various factors impacts the transitioning of rural areas to the tourism attractive destinations. Bontkes & van Keulen (2003) defined different factors that affect mainly the socio-economic conditions of rural areas. Through his research, Sharpley (2002) emphasized that low income, relatively low demand, and a lack of skills to develop activities affect the development of rural tourism. Studies showed that tourism as a non-agricultural activity on the farm does not represent only economic benefits. Non-economic benefits are also important (Tew & Barbieri, 2012). Ollenburg and Buckley, (2007), Barbieri, (2010), Jaafar et al. (2015), Cuncha et al. (2018) and Park et al. (2015) address the importance of marketing opportunities, family connections, and personal pursuits as the most common non-economic benefits of farm tourism. Many scientists have solved problems related to the development of agriculture and tourism by using different models of system dynamics. Sedarati (2015) proves with his study that the use of the method of system dynamics in tourism is very widespread. Through the research, he found as much as 369 articles that are connected with applying the system dynamics in tourism indirectly or even directly. Several examples will be described. According to Johnson et al. (2008), a model for understanding the ecological, agronomic economic and social dimensions of rural regions has been developed. In their article, Lazanski & Kljajić (2006) described important contributions in the development of models of system dynamics that are bound with the field of tourism. Jakulin (2016) discusses the usage of system dynamics in the area of tourism. The goal of our work is to boost the development of rural areas into rural tourism destinations by using system dynamics modelling, with the focus to the diversification, where agricultural holding extend its basic activities. In addition to the studies mentioned earlier, numerous of researchers (e. g. Bastan et al., 2018; Blumberga et al., 2018; Rozman et al., 2013) already used such types of dynamics, and in our work we extend their previou research. The subject of the research are the main factors that influence the diversification of the agricultural holdings into tourism as the non-agricultural activity. This paper aims to analyze, utilizing systems system dynamics, the main variables and their causal relationships in the system structure, presenting the diversification of the agricultural holding into farm tourism. The case study of Slovenia has been used in order to explore different scenarios for farm tourism development. Background Farm tourism is not a new phenomenon (Busby & Rendle, 2000). It is a form of countryside tourism which dates back a century in some destinations (Dernoi, 1983). The developmental trends show that more supplementary activities are registered within farming establishments every year. Their common denominator is tourism. It is the consequence of the increasing number of tourists, i.e. lodgings in the country. In 2018, Slovenia recorded 5.93 millions of tourists’ arrivals and 15.96 millions of lodgings of the tourists. A part of them resides in tourist farms too. A rich history of the development of tourism in the countryside is recorded in Germany (Oppermann, 1996) and Austria. State policies are positively oriented towards the development of tourist facilities in the countryside with subsidies and programs of development also in Italy (Giaccio et al., 2018) and France (Bel et al., 2015). 134 Business Systems Research | Vol. 11 No. 2 |2020 The number of farms in Slovenia is decreasing. Figure 1 presents the number of Farms in Slovenia from 2003 to 2016 (SORS, 2020). The important factor that influences this trend is farm income, which can be improved by diversification. That is why nowadays more and more agricultural holdings decide on developing marketoriented multi-function farming. Figure 1 Number of Farms in Slovenia from 2003 to 2016 Source: SORS (2020) All manners of increasing the financial and social stability by gaining income from various sources can be denominated with a common designation “diversification”. The predominant economic incentive for the diversification of agricultural holding is the expected increased income. This action, however, does not influence positively only a farming establishment but also offers numerous advantages for the broader region: the quality of life in the countryside (to improve the quality of life is also one of a strategic priority for the European Union), culture, tradition, and, last but not least, employment. Due to all the specifics of the agrarian structures according to Groot et al. (2009) these are mountainous and diverse terrain, the high proportion of karst areas) and lowering the factor incomes per employee in the agriculture (SORS, 2017), the farmers have to think hard about all the factors, not only economic ones, when they think about the step of diversification, especially if an investment would require more significant financial input. In the case of farm tourism as a method of diversification, thus, in addition to economic factors, neither environmental nor social nor socio-cultural factors are negligible (Žibert et al., 2020). Not all farming establishments are appropriate for a step of this type of diversification. Žibert et al. (2020) researched the attributes of farming establishments for diversification to non-agricultural industries. Sharpley (2002) addresses the importance of long-term financial-technical aids and subsidies when developing farm tourism. Arroyo et al. (2013), Kheiri and Nasihatkon (2016) and Su et al. (2019) also addressed the importance of support services in their works. Muresan et al. (2016), Sharpley and Vass (2006) and Xue et al. (2017) study social context of the farmer’s attitude in connection with the diversification of the primary activity in tourism. 66000 68000 70000 72000 74000 76000 78000 2003 2005 2007 2010 2013 2016 No. of Farms Year 135 Business Systems Research | Vol. 11 No. 2 |2020 The development of supplementary activities at farm establishment related to tourism bears different advantages and disadvantages. That is why there exists a clear tendency of the use of modern supports in decision-making by which the proper directives can be ensured before bigger investment and activities affecting the environment. Using system dynamics, we can test different alternatives over time. System dynamics methodology Sterman (2000) says that systems thinking is necessary for efficient decision-making. Richmond (1993) speaks about systems thinking as about a multidimensional system where:  We can think with models, which mean the ability to build a model and transfer the acquired knowledge into a real circumstance.  We speak about dynamic thinking which enables anticipation of future behaviour of systems with all the delays, fluctuations, and feedback loops.  We can understand a system as interrelated thinking where a single cause does not mean a single consequence. Consequences depend on a multitude of indirect influences.  The system management – we understand the dimension of systems thinking as the most pragmatic component.  Systems thinking and system dynamics observe the same types of problems. Contrary to the systems thinking, the system dynamics enables us – utilizing computer simulations of the models – a depiction of the behaviour of the real system when testing the effects of alternative decisions through time (Brailsford et al., 2014). Forrester (1994) described the methodology of system dynamics by which we have followed in this research. The idea of such modelling is based on the presumptions that every real system, as well as business systems, can be described by a whole of equations which are interconnected (Rozman et al., 2013; Rozman, et. al., 2015). Meanwhile, Sterman (2000) discussed modelling similarly in his work and described it as an iterative and standing part of the process of learning which is intended to setting the hypotheses and testing the formal and mental models. He described it with the following steps: Determination of a problem, Setting a dynamic hypothesis, Setting a simulation model, Testing, and Design of the strategy. Results Casual loop model The development of complementary activities on the farm, especially if these are such where bigger financial investment (e.g. the development of tourism) is necessary not only on the level of farming establishment but also on the level of national and international policies, represents a dynamic and complex system which demands a developed ability of system thinking and the use of methods of system dynamics in order to determine and control the important issues. Figure 2 represents the causal loop diagram of system structure – diversification of farming establishments in tourist farms with important consequences for the region and the farming establishment (Žibert et al., 2019). 136 Business Systems Research | Vol. 11 No. 2 |2020 Figure 2 Causal loop diagram of system structure – diversification of farming establishments in tourist farms Source: Author’s illustration According to an analysis of the environment (the study of practice) and following the previous research in this field (Sharpley, 2002) the simulation model should consider the key variables we have identified:  The number of tourist overnight stays in the area,  The number of agricultural holdings,  The transition of the holding into a tourist farm,  Subsidies,  General organization and affection for tourism,  The promotion of tourism,  Environmental acceptability of rural tourism development A key variable in the model is the number of tourist farms. This is a form with supplementary activity (which is catering activity) on the farm. From 2018 to 2019, the number of tourist farms in Slovenia increased from 1075 to 1126, although the number of farms has decreased. During the development of casual loop diagram, the key variables were identified:  The number of potential farms (agricultural holdings) for diversification to farm tourism  The number of tourist farms with  The flow between them In the system dynamics model (Figure 2), we can see several main feedback loops which represent reinforcing (R1, R2 and R3) and balance (B1 and B2). The loops R1, R2, and R3 indicate the developmental activity. agricultural holdings diversification tourist farms “catering activity” decision concentration of potential farms for diversification GDP investment per unit investment in infrastructure preserving the cultural landscape atractive environment demand subsidies + + + - promotion promotion factors number of tourist overnight stays price + + + + + - +++ + + + + + + + B1 R1 R3 B2 R2 137 Business Systems Research | Vol. 11 No. 2 |2020 In reinforcing loop R1 increasing or growth of GDP influences investments in infrastructure directly, which has positive consequences on the environment mostly, as this is the way it is preserved more easily. Besides, the destinations are more easily accessible. At the same time, it influences environmental attractiveness. This attractiveness of the environment increases the demand for lodging and/or visiting destinations (reinforcing loop R2). Increasing the demand influences positively the decision of farming establishment whether it will diversify its primary industry. As already mentioned, this diversification influences economic effects, employability, and the quality of life positively. By the development of supplementary activity – farm tourism, the opportunities emerge for the development of other supplementary activities related to the cultivation of primarily agricultural crops, the sales of agricultural crops and products of farms, activities which are connected to traditional knowledge on farms, and social security services. Not all the farms' establishments are suitable for the diversifying of tourism activities. However, as they expand their activities, there remains a smaller number of agricultural holdings that would exclusively deal with agriculture (B1). An important variable of system structure is also the promotion factor (reinforcing loop R3). Not in the sense of promotion of the industry that tourism is the catalyst which would help in economic challenges of the countryside (Hoggart et al., 1995; Williams & Shaw, 1998), but in the sense of the promotion of tourist farms, destinations, the tradition of cultural habits, events, and environment whose part is the farming establishment itself. These are, therefore, the tools which are available to farms or broader groups of entities, and through which they communicate with their target publics about all the matters which influence the profitability and, primarily, the decision for the step of diversification (Podnar & Golob, 2001). Despite everything, however, the share of GDP cannot entirely cover the investments in infrastructure which helps in the development of the tourist industry. Gartner (2004) reports on numerous support rates in the development of the industry. Despite that, however, the share of investments in infrastructure per unit shows one of the decisive equalization loops (B2). An important factor that decides whether a farm will diversify its primary activity and spread to the field of tourism is the revenue the farm receives. The latter is divided into several types. We speak about the profit the farm creates with its primary activity and about subventions that the farm can receive with the purpose to spread its activity. Subventions are a financial aid which farms receive from different sources (local, national or international). They can be onetime or long-lasting. Farms do not have to repay them. In the programing period 2014-2020 (RDP, 2014), the Rural Development Programme (it is a programing document of an individual member state of the EU which is the basis for the absorption of the financial funds from the European Agricultural Fund for Rural Development) anticipates some changes in a way that app. The amount of 10 million Euros of subventions would be allocated to farms in the Republic of Slovenia for the development of tourism in the countryside. The next step in the system dynamics modelling process shows the system dynamics model for farm tourism development (as supplemented activities which are catering activities). It was based on the casual loop model presented in Figure 2. Model development A system dynamics model structure is shown in Figure 3. The methodology of system dynamics was defined by Forrester (1997) and Sterman (2000). The models of the system dynamics are composed of level elements that represent flows, stock, and levels of the system and auxiliary elements. 138 Business Systems Research | Vol. 11 No. 2 |2020 Figure 3 System dynamics model of tourist farms Source: Author’s illustration There are two levels present in the developed to the elements of the model. The variable “potentialFarmsForDiversification” represents the number of farms that are suitable for the transition. Farms in Slovenia with supplementary activity – tourism (which is catering activity) use 11. 60 ha of utilized agricultural area (CAFS, 2017). We have identified the potential farm with acreage between 10 and 15 ha of utilized agricultural area. By the flow “Transition” the “potentialFarmsForDiversification” become “diversedFarms”. In 2018 there were 1,075 tourist farms (which was a catering activity) in Slovenia. One of the most important problems is the decreasing number of farms in Slovenia. On average, each year in the last period 1% of farms is lost (SORS, 2020). “PotentialFarmsForDiversification” has been influenced by the element “closingFarms”, which has been gotten by multiplying “ratioOfClosing” with the function”impactOfDiversificationOnClosing” and “potentialFarmsForDiversification”. Average yearly closing of farms in Slovenia from 2003 to 2016 is 2%. That presents an element “ratioOfClosing”. In the model, we have set it as a constant 0.02. If the proportion diversification is larger, we expect, that the income will improve and fewer farms will close their business. When the ratio is present as potential: diver = 5:1, 2% of potential farms leave the business. If income would be higher none would leave. That is what we present with the function “impactOfDiversificationOnClosing«. An element “RatioPotentialVsDiversed” represent input to the graph function “impactOfDiversificationOnClosing«. We get this ration by subtracting the number of farms that have diversified from farms that are suitable for the transition. The flow "transition" in the model is associated with “noOfDecided”, which represents the number of farms that decide to make a transition. That number has been gotten by multiplying “concentrationOfPotentialFarms” with concentrationOfPotentialFarms farmsThinkingAboutTransition Transition contactsToThink atractivenesDueToSubsidies noOfDecided totalNoOfFarms yearlyGrowthOfTourism percentageOfDetermined diversedFarms potentialFarmsForDiversification diversedFarms ratioPotentialVsDiversed impactOfDiversificationOnClosing ratioOfClosing closingFarms potentialFarmsForDiversification percentageOfSubsidies 139 Business Systems Research | Vol. 11 No. 2 |2020 “farmsThinkingAboutTransition”, “percentageOfDetermined” and “atractivenesDueToSubsidies”. The” concentrationOfPotentialfarms” is obtained by dividing the number of “potentialFarmsForDiversification« by the »totalNoOfFarms«, where “totalNoOfFarms “ present the sum of potential farms for diversification and diverse farms. The variable “FarmsThinkingAboutTransition” represents several farms, that think about transition due to information spread. This variable has been getting by multiplying “contactsToThink« and »diversedFarms«. The element “contactsToThink« represents a constant. It has been set as a constant of 5. One new diverse farm triggers 2 other farms to consider the transition. Yearly, 1% out of 5415 potential farms for diversification turns to diversified. That would mean 54 farms. “YearlyGrowthOTtourism” is input to the graph function “percentageOfDetermined«. Effect of subsidies on the transitions represents the function “atractivenesDueToSubsidies” where an element “percentageOfSubsidies” represents its input. The proportion of diversification investment coverage represents a constant – “percentageOfSubsidies”, where its value in the model is 0.85. Equations and parameters, as well as user defined functions, are quantified; the model is mathematically formulated (appendix). Model calibration and validation Validation of the model is an important part of the methodology (Forrester, 1994; Pejić-Bach& Čerić, 2007; Rahmandad & Sterman, 2012; Sterman, 2000). To perform the validation the model was upgraded with the Mean Squared Error auxiliary variable and Cumulative Mean Squared Error level element which is shown in Figure 4. Figure 4 A model with added Mean Squared Error auxiliary element and Cumulative Mean Squared Error level element Source: Author’s illustration concentrationOfPotentialFarms farmsThinkingAboutTransition Transition atractivenesDueToSubsidies noOfDecided totalNoOfFarms percentageOfDetermined potentialFarmsForDiversification diverFarms ratioPotentialVsDiversed impactOfDiversificationOnClosing closingFarms potentialFarmsForDiversification CMSE diverFarms percentageOfSubsidies yearlyGrowthOfTourism contactsToThink ratioOfClosing MSE realData 146 Business Systems Research | Vol. 11 No. 2 |2020 45. Su, M. M., Wall, G., Wang, Y., Jin, M. (2019), “Livelihood sustainability in a rural tourism destination-Hetu Town, Anhui Province, China”, Tourism Management, Vol. 71, pp. 272281. 46. Tew, C., Barbieri, C. (2012), “The perceived benefits of agritourism: The provider’s perspective”, Tourism Management, Vol. 33, No. 1, pp. 215-224. 47. Williams, A., Shaw, G. (1998). Tourism and economic development: European experiences (3rd ed.), Wiley, Chichester. 48. Xue, L., Kerstetter, D., Hunt, C. (2017), “Tourism development and changing rural identity in China”, Annals of Tourism Research, Vol. 66, pp. 170-182. 49. Žibert, M., Rozman, Č., Prevolšek, B., Škraba, A. (2019). “The system dynamics model for diversification of agricultural holdings into farm tourism“, in Zadnik Stirn, L. (Ed.), et al., SOR '19 proceedings. Ljubljana: Slovenian Society Informatika, Section for Operational Research. 2019, pp. 34-38. 50. Žibert, M., Rozman, Č., Prevolšek, B., Škraba, A. (2020). “Attributes of the agricultural holding for diversification of farm activities”, in Celec, R. (Ed.), Ecological Influences on Different Life Aspects, Dr. Kovač, Hamburg, pp. 105-122. About the authors Maja Žibert achieved her master study at the University of Maribor, Faculty of Tourism. Currently, she is a PhD student in agrarian economics (Faculty of Agriculture and Life Sciences, University of Maribor). She is active as a teaching assistant at the Faculty of the Tourism University of Maribor. Her research includes papers in destination management, wine tourism, rural tourism area and system dynamics. In year 2014/2015 she was regional Wine Queen of Slovenia and in 2017 she was Slovenian Wine Queen. The author can be contacted at [email protected] Črtomir Rozman received his PhD at the University of Maribor, Faculty of Agriculture. He is active as a full professor of farm management in the Department for Agriculture Economics and Rural Development. His research includes the development of decision-support systems for farm management (simulation modelling, multi-criteria decision analysis, machine learning) and the economics of agricultural production. He is the author or co-author 86 scientific papers, 43 with journal citation report impact factor. He is also an author or co-author of 7 scientific books and 25 book chapters. The author can be contacted at [email protected] Andrej Škraba obtained his BSc, MSc and PhD in the field of Organizational Sciences – Informatics from the University of Maribor in 1995, 1998, and 2000, respectively. He works as a professor and researcher in the Cybernetics & Decision Support Systems Laboratory at the University of Maribor, Faculty of Organizational Sciences. His research interests cover systems theory, modelling and simulation, cyber-physical systems, the internet of things and decision processes. Prof. Škraba has received a Bronze Medal of University of Maribor, for successful research and pedagogical work in the field of Systems Modeling and Simulation in 2003. He is a member of the System Dynamics Society (SDS) and the Slovenian Society for Simulation and Modelling (SLOSIM). The author can be contacted at andrej.skrab[email protected] Boris Prevolšek is a teaching assistant and lecturer at the Faculty of Tourism, the University of Maribor in Brežice. He received his Master of Science in 2012 from the Faculty of Economics and is currently enrolled in his PhD Studies at the Faculty of Agriculture and Life Sciences of the University of Maribor. As a researcher, he is interested in various topics concerning tourism, also safety in tourism and rural development. He co-authored 6 scientific papers, 2 scientific books and 4 book chapters. The author can be contacted at [email protected] 147 Business Systems Research | Vol. 11 No. 2 |2020 Appendix 1. Model Equations init diversedFarms = 1075 flow diversedFarms = +dt*Transition doc diversedFarms = Farms that have diversified. unit diversedFarms = farm init potentialFarmsForDiversification = 5415 flow potentialFarmsForDiversification = -dt*closingFarms -dt*Transition doc potentialFarmsForDiversification = Farms that are suitable for transition, ackreage between 10ha and 15ha. unit potentialFarmsForDiversification = farm aux closingFarms = ratioOfClosing*impactOfDiversificationOnClosing*potentialFarmsForDiversification aux Transition = noOfDecided doc Transition = Transition from conventional farm to diversified. unit Transition = farm/year aux atractivenesDueToSubsidies = GRAPH(percentageOfSubsidies,0,0.1,[0,0,0.04,0.28,0.77,1,1.08,1.13,1.23,1.4,2"Min:0;Ma x:2"]) doc atractivenesDueToSubsidies = Effect of subsidies on the transitions aux concentrationOfPotentialFarms = potentialFarmsForDiversification/totalNoOfFarms aux farmsThinkingAboutTransition = contactsToThink*diversedFarms doc farmsThinkingAboutTransition = Number of farms, that think about transition due to information spread. unit farmsThinkingAboutTransition = contact/year aux impactOfDiversificationOnClosing = GRAPH(ratioPotentialVsDiversed,0,1,[0,0.08,0.14,0.28,0.62,1,1.33,1.55,1.76,1.92,2"Min:0; Max:2"]) doc impactOfDiversificationOnClosing = If the proportion diversification is larger, we expect, that the income will improve and less farms will close theri business. When ratio is present as potential:diver=5:1, the 2% of potential farms leaves the business. If income would be higher none would leave. unit impactOfDiversificationOnClosing = dmnl aux noOfDecided = concentrationOfPotentialFarms*farmsThinkingAboutTransition*percentageOfDetermi ned*atractivenesDueToSubsidies doc noOfDecided = Number of farms that decided to make a transition. unit noOfDecided = farms/year aux percentageOfDetermined = GRAPH(yearlyGrowthOfTourism,- 0.02,0.02,[0,0.0012,0.0014,0.0025,0.0055,0.01,0.0146,0.02,0.0243,0.0263,0.027"Min:0;Max :0.03"]) doc percentageOfDetermined = Yearly growth of toursim si input to the graph function. In the case, that the yearly growth of tourism branch is 8%, the growth in new diversified farms is aproximately 1%. This can be also used, with come 148 Business Systems Research | Vol. 11 No. 2 |2020 correction, as the part, which determines how many of those who thing about transition will actually perform the transition. unit percentageOfDetermined = dmnl aux ratioPotentialVsDiversed = potentialFarmsForDiversification/diversedFarms aux totalNoOfFarms = potentialFarmsForDiversification+diversedFarms doc totalNoOfFarms = Sum of potential farms for diversificatin and diversed farms. unit totalNoOfFarms = farm const contactsToThink = 5 doc contactsToThink = One new diversed farm triggers 2 other farms to consider transition. Yearly, 1% out of 5415 potential farms for diversification turns to diversified. That would mean 54 farms. unit contactsToThink = contacts/farm/year const percentageOfSubsidies = 0.85 doc percentageOfSubsidies = Proportion of diversification investment coverage. unit percentageOfSubsidies = dmnl const ratioOfClosing = 0.02 doc ratioOfClosing = Average yearly closing of farms in Slovenia from 2003 to 2016 is 2%. unit ratioOfClosing = dmnl const yearlyGrowthOfTourism = 0.08 doc yearlyGrowthOfTourism = Average yearly growth of tourism in Slovenia from 2010 to 2018 is 8%. unit yearlyGrowthOfTourism = dmnl spec start = 0.00000 spec stop = 50.00000 spec dt = 1.00000 spec method = Euler (fixed step)