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ABMSim - A flexible framework for Agent Based Models to simulate spatially explicit structural change in agriculture

Overview:

(network)

Example from a landscape near Cologne in the ABM,
with agricultural plots, settlements and forest cover

ABMSim is a research activity to analyze farm structural change in a spatial explicit setting. It combines FARMDyn, a detailed single farm model based on a meta-modeling approach with an Agent Based Model (ABM) which covers both farmers and potentially dairies.

ABMSIM generates a population of farmers differentiated by farm specialization and size based on census data a comune level which can compete in land-markets, in markets for manure rights and for milk delivery rights.

The resarch activity is receiving since November 2014 funds from the German Science foundation in the context of the project Understanding spatial interactions and structural change in the dairy production chain and is used in the project "Modeling structural change and agricultural nutrient flows across scales in regions of North Rhine-Westphalia", 2016-2019, financed by MKULNV (Ministry for environment and agriculture, state of North Rhine-Westphalia).

Core model characteristics:

  • Allows for simulations with thousands of farmers and large landscapes with several 10 thousands plots
  • Farm population differentiated by specialisation and size based on farm structural statistics at commune level
  • Generic auction module with different options to e.g. determine auction order, selection of the winning bid and price determination
  • Framing population can be dis-aggregated further by behavior, e.g. with regard to farm exit decisions or bidding behavior in land markets
  • Considers different plot sizes and a differentiation between different types of land cover (arable and grass lands,forest, housing, other urban fabric, water bodies, other types of land)
  • Landscape generated from CORINE land cover data, resolution 100x100m, landscape sizes >> 5000 sq km possible
  • Comprises a simple cellular automaton based land use cover change module to account for urbanization
  • Bidding behavior of farmers for plots, manure rights and milk delivery contracts can be based on a dual profit function
  • Dual profit functions by farm type are estimated from simulation experiments with FARMDYN
  • Farm structural change module, considering relative profits, age of farmer, distance to potential work places and a stochastic component
  • Java based (1.8 using parallelism, own development, no ABM specific libraries) with a Graphical User Interface implemented in GGIG, to allow for a flexible steering of the modeling system and use a well developed set of exploitation tools (maps, graphs, tables)
  • Input and output with GDX files to interact with GAMS / R

Documents related to the project

Methodological and technical documentation of the prototype version

ODD+D based documentation of the main characteristica of the model

Documentation of the java code (javadoc)

FARMDYN model documentation

Staff at ILR working on the project:

Publications related to the project

    Seidel, C., Britz, W. (2017): Can we derive plausible land rents based on Mathematical Programming? A critical assessment of a dual profit function estimation from simulated farm programs, selected paper presented at the EAAE XVth Congress, Parma (Italy).

    Schäfer, D., Seidel, C., Britz, W. (2016): Estimating Dual Profit Functions to Depict Farmer Behavior in Agent-Based Models – a Meta-Modelling Approach, poster presented at the 56th Annual Conference of the German Association of Agricultural Economists (GEWISOLA), Bonn, 28-30 September 2016.

    Seidel, C., Britz, W. (2016): Agent-Based Modeling of Structural Change in Agriculture: Does the Plot Auction Matter?, poster presented at the 56th Annual Conference of the German Association of Agricultural Economists (GEWISOLA), Bonn, 28-30 September 2016.

    Britz, W., Wieck, C. (2014): Analyzing structural change in dairy farming based on an Agent Based Model Download....



Last updated: Friday, November 09, 2018