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Simulation modelling to investigate nutrient loss mitigation practices

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  • Baillie, Sarah
  • Kaye-Blake, William
  • Smale, Paul
  • Dennis, Samuel

Abstract

One important way that agriculture influences water quality is through nitrogen (N) losses from farmland, and several variables affect the extent of losses. The present research used RF-MAS (Rural Futures Multi-Agent Simulation), a system model of New Zealand agricultural industries that incorporates natural resources, land use, policies, prices and farmer behaviour. Scenarios included several levels of on-farm mitigation activities, changes in information available and the willingness of farmers to change practices, and different social parameters. The outputs from the modelling included changes in N losses from farms, changes in total regional agricultural revenue and impact per kilogram of N loss reduction. Results suggested that nutrient caps applied by industry appeared the most profitable for farmers, and the result was insensitive to the size of a farmer-agent’s peer network. Less effective were nutrient caps applied uniformly to all farms or caps set according to Land Use Capability class. Assumptions about famer behaviours had considerable impacts on model outputs. The assumed level of profit-maximising behaviour among farmers affected the amount of mitigation achieved. Calibrating models to observed economic objectives of farmers may be important for generating accurate water and nutrient policy modelling.

Suggested Citation

  • Baillie, Sarah & Kaye-Blake, William & Smale, Paul & Dennis, Samuel, 2016. "Simulation modelling to investigate nutrient loss mitigation practices," Agricultural Water Management, Elsevier, vol. 177(C), pages 221-228.
    • Handle: RePEc:eee:agiwat:v:177:y:2016:i:c:p:221-228
    DOI: 10.1016/j.agwat.2016.07.018
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    References listed on IDEAS

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    1. Bill Kaye-Blake & Chris Schilling & Elizabeth Post, 2014. "Validation of an Agricultural MAS for Southland, New Zealand," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(4), pages 1-5.
    2. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    3. A. Hernando & D. Villuendas & C. Vesperinas & M. Abad & A. Plastino, 2010. "Unravelling the size distribution of social groups with information theory in complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 76(1), pages 87-97, July.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    5. David Tilman & Kenneth G. Cassman & Pamela A. Matson & Rosamond Naylor & Stephen Polasky, 2002. "Agricultural sustainability and intensive production practices," Nature, Nature, vol. 418(6898), pages 671-677, August.
    6. Kaye-Blake, Bill & Schilling, Chris & Nixon, Chris & Destremau, Killian, 2014. "Water management in New Zealand : A road map for understanding water value," NZIER Working Paper 2014/1, New Zealand Institute of Economic Research.
    7. Dimitris Kremmydas, 2012. "Agent based modeling for agricultural policy evaluation: A review," Working Papers 2012-3, Agricultural University of Athens, Department Of Agricultural Economics.
    8. Land and Water Forum, 2010. "Report of the Land and Water Forum: A Fresh Start for Freshwater," Reports 128675, Land and Water Forum.
    9. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
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    Cited by:

    1. Kremmydas, Dimitris & Athanasiadis, Ioannis N. & Rozakis, Stelios, 2018. "A review of Agent Based Modeling for agricultural policy evaluation," Agricultural Systems, Elsevier, vol. 164(C), pages 95-106.
    2. Kaye-Blake, William & Schilling, Chris & Monaghan, Ross & Vibart, Ronaldo & Dennis, Samuel & Post, Elizabeth, 2019. "Quantification of environmental-economic trade-offs in nutrient management policies," Agricultural Systems, Elsevier, vol. 173(C), pages 458-468.

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