MIND STEP model toolbox: integration of IDM models, ABMs,
and large-scale models

Figure 1 MIND STEP model toolbox: integration of IDM models, ABMs, and large-scale models

• Model Name: GLOBIOM (Global Biosphere Management Model)
• Model Type: Global recursive dynamic bottom-up partial equilibrium bioeconomic model
• Sectors: Agriculture, forestry and bioenergy
• Geographical Areas: Supply side – spatially explicit, starting from 5 arcmin grid globally, NUTS2 level EU, Demand side – 53 world regions, incl. all EU member states individually
• Purpose: Initially developed for impact assessment of climate change mitigation policies in land-based sectors, incl. biofuels, and nowadays widely used for agricultural and timber markets foresight, social and economic impact analyses of climate change and adaptation, and a wide range of land related sustainability dimensions, incl. food security, biodiversity, nitrogen and water

• Model Name: MAGNET (Modular Applied GeNeralEquilbrium Tool)
• Model Type: general equilbrium mode macro-evel mode
• Sector: Global Economy. Quantative analyses related to bio-economy and food secur
• Geographical Area: 141 regions and countries
• Purpose: t decries the development of prices, production and rade at an individual country level and it calculates the fects of changes in the work economy and poly changes.
   

• Model name: Supply chain mechanism model
• Model Type: Industrial organization structural econometric model
• Sector: Agricultural
• Geographical Area: N/A
• Purpose: It describes price transmission dynamics in the three-stage supply chain. Farmers supply agricultural raw commodities to food manufacturers, which, in turns, sell food products to the retail sector, that delivers final products to consumers.

• Model name: Land market model
• Model Type: Agent-based model, crop production choice model
• Sector: Agricultural
• Geographical Area: N/A
• Purpose: It handles competition among farms as well as agriculture and other uses for land and land-transformation. Regional land market agents allow interactions in-between farms or between farms and other sectors supplying or demanding land

• Model name: CoESM (Collective Ecosystem Services Management)
• Model Type: Agent-based model
• Sector: Agricultural, decision-making
• Geographical Area: N/A
• Purpose: It models farmers’ decision-making towards implementing flower strips at the farm level. Farmers decision making rely on profit maximization, recurring habits, imitating peers or role models, making deliberate comparisons and asking friends or colleagues for advice.

• Model name: Crop production choice models
• Model Type: Farm-level econometric model
• Sector: Agricultural
• Geographical Area: N/A
• Purpose: These models mostly describe how farmers’ choices respond to input and output prices (in the short-medium run) and, deliver price elasticities of yield, input use and acreage choices, including the decision to produce the considered crops or not.

• Model name: Structural change model
• Model Type: Farm-level econometric model
• Sector: Agricultural, financial
• Geographical Area: Europe
• Purpose: It is of a multinomial logit-type, featuring spatial dependencies. It features the explained variable: the land-use and production system composition of each NUTS-2 regions; the explanatory variables: GLOBIOM outputs on agricultural prices, previous land-use and cropping allocations, as well as exogeneous observations on sectoral composition, educational attainment, economic impact, and other biophysical factors.

• Model name: Farm exit model
• Model Type: Farm-level econometric model
• Sector: Agricultural, financial
• Geographical Area: Germany
• Purpose: It analyses the probability of a farm exiting or staying in business taking into account certain parametres such as the farm holder, the farm type, profits, rental area payments and agricultural support payments.

• Model name: Risk management model
• Model Type: Farm-level econometric model
• Sector: Agricultural, risk management
• Geographical Area: N/A
• Purpose: It informs GLOBIOM to translate the revealed risk preference related to the experiment to a crop-specific risk aversion parameter that takes the form of a cost.

• Model name: Cost estimation model
• Model Type: farm-level micro-econometric model
• Sector: Agricultural
• Geographical Area: European Union
• Purpose: It provides farm-specific estimates of costs per farm activity (fertilizer and pesticide costs, labour costs, and other inputs ) across different agricultural outputs.

• Model Name: Agrispace
• Model Type: Multi-Commodity Market Model; individual decision- making model
• Sectors: Agricultural
• Geographical Areas: Norway
• Purpose: It analyses impacts of market and policy changes on the agricultural sector and farm structural change in Norway.

• Model Name: IFM CAP
• Model Type: positive mathematical programming farm level model ; individual decision- making model
• Sectors: Agricultural
• Geographical Areas: European Union. All farms in EU FADN.
• Purpose: It assesses a wide range of farm-specific policies while capturing the heterogeneity of EU commercial farms.

• Model Name: FarmDyn
• Model Type: bio-economic technology rich mathematical programming farm level model ; individual decision-making model
• Sectors: Agricultural
• Geographical Areas: European Union
• Purpose: to assess individual, average or representative farm level impacts of agricultural and environmental policies, examining a multitude of farm management, environmental and economic indicators given boundary conditions such as available endowments, yields and prices.

• Model Name: FADN spatial (FARMtogrid)
• Model Type: modules providing spatial disaggregation of the macro-level models output data
• Sectors: Agricultural
• Geographical Areas: EU
• Purpose: It links economic and bio-physical data to overcome the shortcoming of not knowing the spatial location of individual observations

• Model Name: DownScale
• Model Type: modules providing spatial disaggregation of the macro-level models output data
• Sectors: land-use
• Geographical Areas: N/A
• Purpose: It provides consistent high-resolution land-use change projections based on the dynamic nature of future land-use change; reproduction of observed land-use change patterns while still taking the dynamic nature of future land-use change into account.

• Model Name: CAPDIS (Spatial dis-aggregation module of CAPRI results)
• Model Type: modules providing spatial disaggregation of the macro-level models output data
• Sectors: agricultural
• Geographical Areas: N/A
• Purpose: It provides a successive disaggregation of agricultural information from the NUTS2 regional level to high-resolution spatial units.

• Model Name: EU Forestry and Agriculture Land-use and Management COsting (FALMCO) Module
• Model Type: Micro-econometric farm-level model
• Sectors: Agriculture and Forestry
• Geographical Areas: NUTS2 with EU wide coverage
• Purpose: It allocates farm-level inputs and production costs to individual farm activities   for  a  range  of  land uses, (i.e., major  crops)  and  management systems. This module provides cost estimates that are integrated into GLOBIOM.

• Model Name: CAPRI (Common Agricultural Policy Regionalised Impact)
• Model Type: bio-economic comparative static partial equilibrium model; Macro-level model
• Sectors: agricultural
• Geographical Areas: 45 world-wide regions, EU agricultural supply at NUTS2 level
• Purpose: It assesses the economic and environmental impacts of agricultural and international trade policies with a focus on the European Union.

• Model name: AgriPoliS
• Model Type: Agent-based model
• Sector: Agricultural, economic
• Geographical Area: N/A
• Purpose: It allows to perform experiments with artificial economic agents interacting in a dynamic and spatially explicit manner, especially focusing on structural change and land markets.

• Model name: FarmAgriPoliS
• Model Type: Agent-based model
• Sector: Agricultural, economic
• Geographical Area: N/A
• Purpose: It allows to experimentally test certain behavioral assumptions e.g. factors that influence  participation in collective AES. FarmAgriPoliS provide insights into how human participants behave in competitive situations compared to computerized optimizing agents as used in AgriPoliS.