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FARMIS in brief

FARMIS is a comparative-static programming model for farm groups based on information from the farm accountancy data network (FADN). It provides a detailed reproduction and projection of agricultural production activities at farm level. Competition of farms on important factor markets is modelled endogenously.


Mathematical programming model  

Main field of application

Differentiated analysis of policy impacts on farms of different type, size, etc.


Sector-consistent modelling of policy impacts taking into account farm characteristics as well as ownership and prices of quotas and land for income assessments  


Structural change currently projected exogenously; no single-farm projections; projection of new activities restricted  

Typical applications

Analyses of CAP reforms (e.g. Ecoschemes, CAP after 2020)  


  • Calibration by positive mathematical programming
  • Aggregation of farm group results to sector level
  • Coupling to partial equilibrium market models

Data base

Farm accountancy data network (FADN). The current base year is based on accountancy information from national FADN of the farming years 2018/19, 2019/20 and 2020/21. The stratification by agricultural region, main farm type, farm size and management system provides 601 farm group models (of which 100 farm groups represent organic farming).

Differentiation of production

27 crop und 22 livestock activities  

Policy instruments

Ecoschemes, production quotas, direct payments, modulation, set-aside, stocking rate limits, minimum land use requirements  


Quotas, land, young animals  

Endogenous variables

Factor allocation, supply quantities and income at farm and sector level, prices of quotas, land, young animals  

Exogenous variables

Product prices, policy variables (e.g. area payments, quotas), projection of technical coefficients  




  1. 0

    Rieger J, Freund F, Offermann F, Geibel I, Gocht A (2023) From fork to farm: Impacts of more sustainable diets in the EU-27 on the agricultural sector. J Agric Econ 74(3):764-784, DOI:10.1111/1477-9552.12530

  2. 1

    Haß M, Deblitz C, Freund F, Kreins P, Laquai V, Offermann F, Pelikan J, Sturm V, Wegmann J, Witte T de, Wüstemann F, Zinnbauer M (2022) Thünen-Baseline 2022 - 2032: Agrarökonomische Projektionen für Deutschland. Braunschweig: Johann Heinrich von Thünen-Institut, 126 p, Thünen Rep 100, DOI:10.3220/REP1667811151000

  3. 2

    Braun J (2020) Weiterentwicklung eines sektorkonsistenten Betriebsgruppenmodells um Treibhausgasemissionen und Bewertung von ausgewählten Minderungsstrategien. Aachen: Shaker, 190 p, Berlin, Humboldt-Univ, Diss, 2019, Berliner Schr Agrar Umweltökonomik 23

  4. 3

    Ehrmann M (2017) Modellgestützte Analyse von Einkommens- und Umweltwirkungen auf Basis von Testbetriebsdaten. Braunschweig: Johann Heinrich von Thünen-Institut, 250 p, Thünen Rep 48, DOI:10.3220/REP1493970811000

  5. 4

    Hecht J, Moakes S, Offermann F (2016) Redistribution of direct payments to permanent grassland: intended and unintended impact. EuroChoices 15(3):25-32, DOI:10.1111/1746-692X.12099

  6. 5

    Deppermann A, Grethe H, Offermann F (2014) Distributional effects of CAP liberalisation on western German farm incomes: an ex-ante analysis. Eur Rev Agric Econ 41(4):605-626, DOI:10.1093/erae/jbt034

  7. 6

    Offermann F, Margarian A (2014) Modelling structural change in Ex-Ante-Policy Impact Analysis. In: Zopounidis C, Kalogeras N, Mattas K, Dijk G, Baourakis G (eds) Agricultural cooperative management and policy : new robust, reliable and coherent tools. Cham: Springer International Publ, pp 151-162, DOI:10.1007/978-3-319-06635-6_8

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