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What Drives Marginal Abatement Costs of Greenhouse Gases on Dairy Farms? A Meta-modelling Approach

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  • Bernd Lengers
  • Wolfgang Britz
  • Karin Holm-Müller

Abstract

type="main" xml:id="jage12057-abs-0001"> This paper examines the relationships between the marginal abatement costs (MAC) of greenhouse gas (GHG) emissions on dairy farms and factors such as herd size, milk yield and available farm labour, on the one hand, and prices, GHG indicators and GHG reduction levels, on the other. A two-stage Heckman procedure is used to estimate these relationships from a systematically designed set of simulations with a highly detailed mixed integer bio-economic farm-level model. The resulting meta-models are then used to analyse how MAC vary across farm-level conditions and GHG measures. We find that simpler GHG indicators lead to significantly higher MAC, and that MAC strongly increase beyond a 1–5% emission reduction, depending on farm attributes and the chosen indicator. MAC decrease rapidly with increasing farm size, but the effect levels off beyond a herd size of 40 cows. As expected, the main factors driving gross margins per dairy cow also significantly influence mitigation costs. Our results indicate high variability of MAC on real life farms. In contrast to time consuming simulations with the complex mixed integer bio-economic programming model, the meta-models allow the distribution of MAC in a farm population to be efficiently derived and thus could be used to upscale to regional or sector level.

Suggested Citation

  • Bernd Lengers & Wolfgang Britz & Karin Holm-Müller, 2014. "What Drives Marginal Abatement Costs of Greenhouse Gases on Dairy Farms? A Meta-modelling Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 65(3), pages 579-599, September.
    • Handle: RePEc:bla:jageco:v:65:y:2014:i:3:p:579-599
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    File URL: http://hdl.handle.net/10.1111/jage.2014.65.issue-3
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    Citations

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    Cited by:

    1. Schaefer, David & Britz, Wolfgang & Kuhn, Till, 2020. "Modelling policy induced manure transports at large scale using an agent-based simulation model," Discussion Papers 305270, University of Bonn, Institute for Food and Resource Economics.
    2. Christian Troost & Julia Parussis-Krech & Matías Mejaíl & Thomas Berger, 2023. "Boosting the Scalability of Farm-Level Models: Efficient Surrogate Modeling of Compositional Simulation Output," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 721-759, October.
    3. Kokemohr, Lennart & Mittenzwei, Klaus, 2022. "Governance for Greenhouse Gas Abatement in Norwegian Agriculture," 62nd Annual Conference, Stuttgart, Germany, September 7-9, 2022 329613, German Association of Agricultural Economists (GEWISOLA).
    4. Kuhn, T. & Enders, A. & Gaiser, T. & Schäfer, D. & Srivastava, A.K. & Britz, W., 2020. "Coupling crop and bio-economic farm modelling to evaluate the revised fertilization regulations in Germany," Agricultural Systems, Elsevier, vol. 177(C).
    5. Kuhn, Till & Schäfer, David & Holm-Müller, Karin & Britz, Wolfgang, 2019. "On-farm compliance costs with the EU-Nitrates Directive: A modelling approach for specialized livestock production in northwest Germany," Agricultural Systems, Elsevier, vol. 173(C), pages 233-243.
    6. Mosnier, Claire & Duclos, Anne & Agabriel, Jacques & Gac, Armelle, 2017. "What prospective scenarios for 2035 will be compatible with reduced impact of French beef and dairy farm on climate change?," Agricultural Systems, Elsevier, vol. 157(C), pages 193-201.
    7. Huber, Robert & Tarruella, Marta & Schäfer, David & Finger, Robert, 2023. "Marginal climate change abatement costs in Swiss dairy production considering farm heterogeneity and interaction effects," Agricultural Systems, Elsevier, vol. 207(C).
    8. Schmidt, Alena & Necpalova, Magdalena & Mack, Gabriele & Möhring, Anke & Six, Johan, 2021. "A food tax only minimally reduces the N surplus of Swiss agriculture," Agricultural Systems, Elsevier, vol. 194(C).

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