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This report constitutes a comprehensive overview of the state of approaches and modelling platforms currently employed for micro-level policy analysis at farm-level, representing a specific response to, and consideration of, recent policy developments within the evolving CAP context. Taking into consideration the increasing need for more targeted i...
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Citations
... Detailed farm-level models (Richardson et al., 2014;Weersink et al., 2002), usually implemented as optimisation models, are capable of representing individual decision-making with a rich representation of input choices, investments and environmental indicators. However, those farm-level models usually do not account for interaction among farmers, market feedback, or environmental feedback on larger scales (Heckelei, 2013;Shang et al., 2021). Here, agent-based models (ABMs) (Gilbert, 2007) can be used to model endogenous market feedback and to capture the dynamic interaction of heterogeneous farms (Kremmydas et al., 2018;Müller et al., 2020;Rasch et al., 2017). ...
Technological change co‐determines agri‐environmental performance and farm structural transformation. Meaningful impact assessment of related policies can be derived from farm‐level models that are rich in technology details and environmental indicators, integrated with agent‐based models capturing dynamic farm interaction. However, such integration faces considerable challenges affecting model development, debugging and computational demands in application. Surrogate modelling using deep learning techniques can facilitate such integration for simulations with broad regional coverage. We develop surrogates of the farm model FarmDyn using different architectures of neural networks. Our specifically designed evaluation metrics allow practitioners to assess trade‐offs among model fit, inference time and data requirements. All tested neural networks achieve a high fit but differ substantially in inference time. The Multilayer Perceptron shows almost top performance in all criteria but saves strongly on inference time compared to a Bi‐directional Long Short Term Memory.
... In the last decade, the development and use of farm-level models have become an important activity of agricultural economists (Ciaian et al., 2013). Decision makers at various levels urgently need data, models, and knowledge products that provide userfriendly data collection and analysis capabilities. ...
... Thus are unable to fully capture the impact of new policies at the farm level (Louhichi et al. 2015). Namely, there are obvious changes toward more outcomeoriented agricultural policies and a clear dedication to policies based on evidence and proven intervention logic (Lovec et al., 2020), which is linked to the increasing demand for micro-level policy analysis tools and methods and a better understanding of farm-level decision-making (Ciaian et al., 2013). ...
... Farm typology is important for its utility in effective agricultural policy planning and for discussion and support in finding appropriate solutions for developing multifunctional and sustainable agricultural and rural areas (Mądry et al., 2016). Numerous operational models based on different techniques have been developed to answer various questions in agricultural systems (Ciaian et al., 2013). Various approaches have been used for this purpose. ...
Background: Farm-level models have become an important tool for agricultural economists as there is a growing demand for microsimulation and analysis of farms at the individual level.
Objectives: In this paper, we present a mathematical model with the main objective of assessing the effectiveness of production and various possible strategies for agricultural holdings by reducing risks. At the same time, we were also interested in the environmental impacts of such strategies. The latter was measured using the indicator of GHG emissions.
Methods/Approach: The model applied is based on linear programming and upgraded with QRP for risk analysis. The approach was tested on medium size mixed agricultural holding, which often faces challenges in light of the structural changes taking place in Slovenia.
Results: The results suggest that such a farm could improve financial results with a more efficient risk management strategy. With a slightly modified production plan, the expected gross margin (EGM) can be increased by up to 10% at more or less the same risk. However, if the farmer is willing to diversify the production plan and take a higher risk (+23%), the farm’s EGM could increase by up to 18%. This kind of change in the production plan would also generate 17% more GHG emissions in total, calculated as kg equivalent of CO2 at the farm level, as both BL and C scenarios have the same relative ratio at 3.12 GHG CO2 eq. /EUR.
Conclusions: Through this research, we concluded that diversification has a positive potential on a mixed farm, and the farm could achieve better financial results. With flexibility in management, the farmer could also achieve higher risk management efficiency and better farm results.
... Detailed farm-level models (Richardson et al., 2014;Weersink et al., 2002), usually implemented as optimisation models, are capable of representing individual decision-making with a rich representation of input choices, investments, and environmental indicators. However, those farmlevel models usually do not account for interaction among farmers, market feedback, or environmental feedback on larger scales (Heckelei, 2013;Shang et al., 2021). Here, Agent-based Models (ABMs) (Gilbert, 2007) step in to model endogenous market feedback and to capture the dynamic interaction of heterogeneous farms (Müller et al., 2020;Kremmydas et al., 2018;Rasch et al., 2017). ...
Technological change co-determines agri-environmental performance and farm structural transformation. Meaningful impact assessment of related policies requires farm-level models to be rich in technology details and environmental indicators, integrated with agent-based models capturing dynamic farm interaction. However, such integration faces considerable computational challenges affecting model development, debugging and application. Surrogate modelling using machine learning techniques may enable such integration for simulations with broad regional coverage. We develop surrogates of the farm model FarmDyn using state-of-the-art neural networks. All tested neural networks achieve a high fit but differ substantially in inference time. We develop evaluation metrics allowing practitioners to assess trade-offs among model fit, inference time and data requirements. The Multilayer Perceptron shows almost equal performance in all criteria but saves strongly on inference time compared to a Bi-directional Long Short Term Memory.
... In Uruguay, Righi et al. (2011) quantitatively defined seven TAHs based on data on available labor, level of mechanization, and proportion of land under irrigation to achieve sustainable development. Kuivanen et al. (2016) To support planning, farm models have been developed for almost two decades, progressively complementing the previously dominant sectoral models based on partial and general equilibria (Van Tongeren et al., 2001;Langrell et al., 2013). Farm-level modeling requires comprehensive farm-level data sources. ...
... Breen et al. (2019) Examples of modeling in agriculture in Slovenia include fertilizer planning (Žgajnar and Kavčič, 2011), feed ration optimization (Žgajnar et al., 2007), emergy analysis (Kocjančič et al., 2018), and economic analysis of the equestrian center (Žgajnar, 2015). To analyze the impact of agricultural policies, the European Commission uses the IMF CAP model, which is also based on a mathematical programming approach (Langrell et al., 2013). Žgajnar et al. (2020) present an example of a future CAP scenario analysis that uses the same farm model tool as our study. ...
In this study, the resilience of farm production plan through different management adjustments was analyzed. For this purpose, a farm model based on mathematical programming was applied. Through organized workshops typical farms focusing on dairy production were defined through qualitative and quantitative classification. Data were obtained from various databases and expert assessments from the agricultural sector. Analysis of resilience was carried out for three of these typical dairy farms. Using the farm model, the production plan of each farm was reconstructed in the first step and then tested for possible deviations from the baseline. Gross margin was used as the main economic indicator. The results show that the typical farms have very different levels of efficiency and potential for improvement. Furthermore, it was found that all farms can achieve significantly higher gross margin only with improved feed quality, which indirectly leads to a lower need for purchased feed and consequently to lower variable costs and higher gross margin. The level of the latter is also significantly affected by the milk yield achieved, especially on larger farms. However, on smaller farms they can improve profitability more significantly by keeping dairy cows on pasture to a greater extent, which results in a reduction in harvesting costs.
... This type of approach has several advantages that justify its recent increased use. For example, the approach allows the explicit representation of behavior that facilitates interdisciplinary research on agri-environmental interactions and agricultural systems' environmental assessment (Ciaian et al. 2013). ...
This study presents the environmental impacts of agricultural policy instruments as evidence from an ex-ante farm-level policy simulation model in Japan. Simulations did indicate that all types of agri-environmental payments achieved the environmental benefit for the land studied. Conversely, market price support does not inevitably increase nitrogen runoff or greenhouse gas emissions at any time since paddy fields themselves have the function of purifying water pollution and work as a biodiversity nursery. The direction and magnitude of the policy impacts are an empirical matter that should be considered carefully at a local level.
... In support of planning, farm models have been developing for almost two decades, gradually complementing the previously prevailing sectoral models based on partial and general equilibria (Van Tongeren et al., 2001;Langrell et al., 2013). Farm level modelling requires comprehensive data sources at farm level. ...
... This becomes a central issue for both EU policy makers and researchers. Langrell et al. (2013) mention (i) data availability and (ii) quality as the key challenges in using farm models for policy analysis at EU level. The effects of changed income conditions are then habitually monitored at the level of average farms that use accountancy (e.g. in the EU FADN system). ...
CAP post 2022 scenarios and income impacts-a case analysis for selected typical farms in Slovenia Abstract: assessment based on representative farms is an established approach in the modern assessment of the effects of changes in agricultural policy. In line with previous CAP reforms , we can expect income redistribution impacts also with the implementation of the legislative and financial framework of the CAP for the next period. This paper discusses a scenario analysis using the farm model. The model is based on linear programming, which enables to address various technological challenges at farm level. We formed the scenarios for the analysis following the example of the scenarios contained in the impact assessment that the European Commission prepared for the CAP after 2020. The analysis involves selected farm types from selected sectors. The results suggest that the expected reduction in the envelope will generally lead to lower farm-level revenues from CAP direct payments. Consequently, economic performance will deteriorate, what is likely to be amplified in some sectors by the abolition of historical payments. The range of consequences at farm level will likely be considerable, especially for sectors and production types with a high share of CAP payments in the structure of total farm income. In certain sectors, however, there is even an improvement regarding the current situation.
... However, the modular setup of FARMIND allows the first-tier strategic decision to be combined with different types of bio-economic optimisation models containing income and farming activities as output. Consequently, FARMIND can be linked to numerous bio-economic farm models (see e.g., Ciaian et al., 2013;Reidsma et al., 2018;Shrestha et al., 2016;van Wijk et al., 2012 for reviews) depending on the research question at hand. It would even be possible to link the first strategic decision-level to existing agricultural ABMs, that is, MP-MAS (Schreinemachers & Berger, 2011) or AgriPoliS (Brady et al., 2017;Brady et al., 2009), which usually apply a complex, but uniform, type of decision-making. ...
Agent-based models are important tools for simulating farmers’ behaviour in response to changing environmental, economic or institutional conditions and policies. This article introduces an agent-based modelling approach that combines behavioural factors with standard bio-economic modelling of agricultural production. More specifically, our framework integrates the cumulative prospect theory and social interactions with constrained optimisation decisions in agricultural production. We apply our modelling approach to an exemplary bio-economic model on the assessment of weed control decisions. Results show the effects of heterogeneous farm decision-making and social networks on mechanical weed control and herbicide use. This framework provides a generic and conceptually sound approach to improve the scope for representing farmers’ decision-making and allows the simulation of their decisions and recent advances in behavioural economics to be aligned with existing bio-economic models of agricultural systems.
... A análise política à escala das explorações agrícolas tem sido realizada mediante a utilização dos ABM, conforme exposto no trabalho de Kremmydas et al. (2018), dedicado a uma revisão sistemática de documentos publicados entre 2000 e 2016, e na análise de 184 documentos desenvolvida por Reidsma et al. (2018). Porém, se, por um lado, o uso dos ABM permite obter conhecimentos em nível desagregado e em escala espacial, proporcionando importantes aditamentos aos modelos tradicionais desenvolvidos no âmbito das explorações agrícolas (Ciaian et al., 2013), por outro, os ABM são frequentemente utilizados nos casos em que os agentes econômicos enfrentam capacidade limitada de informação e/ou processamento de informação e recursos finitos . Tal situação torna vantajosa a modelização empírica por meio dos ABM, a qual reside na flexibilidade de especificação e concepção, na sua principal lacuna que exige procedimentos de verificação e validação (Parker et al., 2003). ...
Resumo: Em Portugal, a disponibilidade de mão de obra tem-se revelado um fator-chave para a viticultura de montanha. Estatísticas mais recentes denotam valores preocupantes que poderão colocar em causa a produção de vinho de qualidade e o atraente conjunto de paisagens vitivinícolas consideradas um recurso potencial para o desenvolvimento do turismo. Por a região duriense ser uma das principais regiões vitivinícolas portuguesas, caracterizada por proeminente e acentuada viticultura de montanha, pretende-se, neste trabalho, simular o comportamento das suas explorações perante alterações do preço de mão de obra, recorrendo-se a modelos baseados em agentes (ABM). Foi ainda usado o software MATLAB para obter funções periódicas ajustadas aos dados caracterizadores das variáveis consideradas pertinentes, obtidas de inquéritos presenciais a 110 explorações e atendendo aos dados disponibilizados pela Rede de Informação de Contabilidades Agrícolas (RICA). Posteriormente, o software ABM (NETLOGO) foi selecionado para simular os próximos 100 anos, familiarizando a dinâmica real baseada nos dados anteriormente considerados. Dependendo do preço da mão de obra, no final do horizonte de simulação, com o preço da uva a 0,77€ /kg, das 300 explorações existentes inicialmente, sobrevivem entre 127 e 231 (42,3% a 77%). Num cenário mais otimista, com o preço da uva a 1,17 €/kg, a taxa de sobrevivência oscila entre 72,1% e 93,2%.
... First, decisions at the farm level are based on a multi-input and multi-output production functions (e.g. Ciaian et al., 2013;Shrestha et al., 2016). For example, farms often include crop and livestock production activities, which are linked via manure or fodder balances. ...
The use of agent-based modelling approaches in ex-post and ex-ante evaluations of agricultural policies has been progressively increasing over the last few years. There are now a sufficient number of models that it is worth taking stock of the way these models have been developed. Here, we review 20 agricultural agent-based models (ABM) addressing heterogeneous decision-making processes in the context of European agriculture. The goals of this review were to i) develop a framework describing aspects of farmers' decision-making that are relevant from a farm-systems perspective, ii) reveal the current state-of-the-art in representing farmers' decision-making in the European agricultural sector, and iii) provide a critical reflection of underdeveloped research areas and on future opportunities in modelling decision-making. To compare different approaches in modelling farmers' behaviour, we focused on the European agricultural sector, which presents a specific character with its family farms, its single market and the common agricultural policy (CAP). We identified several key properties of farmers' decision-making: the
multi-output nature of production; the importance of non-agricultural activities; heterogeneous household and family characteristics; and the need for concurrent short- and long-term decision-making. These properties were then used to define levels and types of decision-making mechanisms to structure a literature review. We find most models are sophisticated in the representation of farm exit and entry decisions, as well as the representation of long-term decisions and the consideration of farming styles or types using farm typologies. Considerably fewer attempts to model farmers' emotions, values, learning, risk and uncertainty or social interactions occur in the different case studies. We conclude that there is considerable scope to improve diversity in representation of decision-making and the integration of social interactions in agricultural agent-based modelling approaches by combining existing modelling approaches and promoting model inter-comparisons. Thus, this review provides a valuable entry point for agent-based modellers, agricultural systems modellers and data driven social scientists for the re-use and sharing of model components, code and data. An intensified dialogue could fertilize more coordinated and purposeful combinations and comparisons of ABM and other modelling approaches as well as better reconciliation of empirical data and theoretical foundations, which ultimately are key to developing improved models of agricultural
systems.
... Consequently impacts of policy measures depend on the specific farm characteristics. So getting insights at disaggregated level and spatial scale becomes relevant for both policymakers and researchers; consequently farm scale policy analysis is receiving increased attention (Langrell et al., 2013). Berger and Troost (2014) summarized the requirements that farmscale models need to fulfill in order to provide useful insights within this new policy context: sufficient detail of farm management and agronomic conditions; model the heterogeneity in behavioral constraints and behaviors; include farm interactions; incorporate spatial dimension; consider farm-environment interactions and feedback; move from a comparative-static to a comparative-dynamic analysis; moderate data requirements connected to existing data sources; employ comprehensive sensitivity and uncertainty analysis. ...
... In a 2007 review, Matthews et al. note that "there is an increasing pressure from funding agencies to develop (Agent Based Land Use Models) tools that are of practical use by end-users and other stakeholders". Later in a methodological overview of agricultural and farm level modeling development and implementation, Langrell et al. (2013) found that although there is a substantial increase of ABMs models over time, "a large number of existing farm level models are developed for specific purposes and locations and are not easily adaptable and reusable (for policy evaluation)". ...
... In the second category the models are based on qualitative information and second order data (stylized facts) and are used for exploring questions in principle, e.g. looking for emerging properties like resilience, etc. Ex-ante policy evaluation is pursued by means of farm models that simulate an actual farming system (Reidsma et al., 2018;Langrell et al., 2013). Due to the empirical policy orientation of the paper, we focus on data-driven ABM. ...
Farm level scale policy analysis is receiving increased attention due to a changing agricultural policy orientation. Agent based models (ABM) are farm level models that have appeared in the end of 1990's, having several differences from traditional farm level models, like the consideration of interactions between farms, the way markets are simulated, the inclusion of agents' bounded rationality, behavioral heterogeneity, etc. Considering the potential of ABMs to complement existing farm level models and that they are a relatively recent approach with a growing demand for new models and modelers, we perform a systematic literature review to (a) consolidate in a consistent and transparent way the literature status on policy evaluation ABMs; (b) examine the status of the literature regarding model transparency; the modeling of the agents' decision processes; and the creation of the initial population.
