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Analysis of environmental and economic efficiency using a farm population micro-simulation model

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  • Ramilan, Thiagarajah
  • Scrimgeour, Frank
  • Marsh, Dan

Abstract

New Zealand's success in raising agricultural productivity has been accompanied by higher input use, leading to adverse effects on the environment. Until recently, analysis of farm performance has tended to ignore such negative externalities. The current emphasis on environmental issues has led dairy farmers to target improvements in both environmental performance and productivity. Therefore, measuring the environmental performance of farms and integrating this information into farm productivity calculations should assist in making informed policy decisions which promote sustainable development. However, this is a challenging process since conventional environmental efficiency measures are usually based on simple input and output flows but nitrogen discharge is a complex process affected by climate, pasture composition, cow physiology and geophysical variability. Furthermore, the outdoor pastoral nature of New Zealand farming means that it is difficult to control input and output flows, particularly of nitrogen. We present a novel approach to measure the environmental and economic efficiency of farms, using the Overseer nutrient budget model and spatially micro-simulated virtual population data. The empirical analysis is based on dairy farms in the Karapiro catchment, where nitrogen discharge from dairy farming is a major source of nonpoint pollution.

Suggested Citation

  • Ramilan, Thiagarajah & Scrimgeour, Frank & Marsh, Dan, 2011. "Analysis of environmental and economic efficiency using a farm population micro-simulation model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1344-1352.
  • Handle: RePEc:eee:matcom:v:81:y:2011:i:7:p:1344-1352
    DOI: 10.1016/j.matcom.2010.04.018
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    2. Zhong, Shen & Li, Junwei & Qu, Yi, 2022. "Green total factor productivity of dairy cow in China: Key facts from scale and regional sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    3. K Hervé Dakpo & Philippe Jeanneaux & Laure Latruffe, 2014. "Inclusion of undesirable outputs in production technology modeling:The case of greenhouse gas emissions in French meat sheep farming," Working Papers SMART 14-08, INRAE UMR SMART.
    4. Mehdi Toloo & Madjid Tavana & Francisco J. Santos-Arteaga, 2019. "An integrated data envelopment analysis and mixed integer non-linear programming model for linearizing the common set of weights," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(4), pages 887-904, December.
    5. Doole, Graeme J. & Kingwell, Ross, 2015. "Efficient economic and environmental management of pastoral systems: Theory and application," Agricultural Systems, Elsevier, vol. 133(C), pages 73-84.
    6. Jason Loughrey & Fiona Thorne & Thia Hennessy, 2016. "A Microsimulation Model for Risk in Irish Tillage Farming," International Journal of Microsimulation, International Microsimulation Association, vol. 9(2), pages 41-76.
    7. Ebrahimnejad, Ali & Tavana, Madjid & Santos-Arteaga, Francisco J., 2016. "An integrated data envelopment analysis and simulation method for group consensus ranking," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 119(C), pages 1-17.
    8. Xin ZHAO & Yong PENG & Yuemei XUE & Shun YUAN, 2016. "Spatial Patterns of Ocean Economic Efficiency and their Influencing Factors in Chinese Coastal Regions," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 35-49, December.

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