Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ags/aesc22/321199.html
   My bibliography  Save this paper

Evaluating environmental effects of the adoption of automatic milking systems in Norway

Author

Listed:
  • Martinsson, Elin
  • Storm, Hugo
Abstract
In Norwegian dairy farming, the usage of automatic milking systems (AMS) has increased during the last decades. AMS is primarily a productivity increasing and labor reducing technology, but previous research shows that AMS can have other (secondary) effects that could impact the environmental performance of farms. Effects of AMS-adoption found, include increased total production, increased farm size, changes in grazing patterns and feed mix and changes in energy consumption. This paper aims to study the hypothesis that AMS-adoption, through these secondary effects, affect farm-level GHG-emissions. Using a difference-in-difference approach, we provide evidence of the presence of secondary effects and shows that AMS-adoption negatively affects farms’ eco-efficiency, particularly by increasing enteric fermentation. The causal effect is identified by considering adopting farms and non-adopting farms observed at two periods in time. Apart from providing this empirical result, the paper also presents a general procedure of how to go about evaluating farm-level effects of technology adoption.

Suggested Citation

  • Martinsson, Elin & Storm, Hugo, 2022. "Evaluating environmental effects of the adoption of automatic milking systems in Norway," 96th Annual Conference, April 4-6, 2022, K U Leuven, Belgium 321199, Agricultural Economics Society - AES.
    • Handle: RePEc:ags:aesc22:321199
    DOI: 10.22004/ag.econ.321199
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/321199/files/Elin_Martinsson_aes_submission.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.321199?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Stræte, Egil Petter & Vik, Jostein & Hansen, Bjørn Gunnar, 2017. "The Social Robot: A Study of the Social and Political Aspects of Automatic Milking Systems," International Journal on Food System Dynamics, International Center for Management, Communication, and Research, vol. 2017(1), June.
    2. Laure Latruffe & Yann Desjeux, 2016. "Common Agricultural Policy support, technical efficiency and productivity change in French agriculture," Review of Agricultural, Food and Environmental Studies, Springer, vol. 97(1), pages 15-28, June.
    3. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    4. Gjalt Huppes & Masanobu Ishikawa, 2005. "A Framework for Quantified Eco‐efficiency Analysis," Journal of Industrial Ecology, Yale University, vol. 9(4), pages 25-41, October.
    5. Henry Tulkens & Philippe Eeckaut, 2006. "Nonparametric Efficiency, Progress and Regress Measures For Panel Data: Methodological Aspects," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 395-429, Springer.
    6. Rebecca Schewe & Diana Stuart, 2015. "Diversity in agricultural technology adoption: How are automatic milking systems used and to what end?," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 32(2), pages 199-213, June.
    7. Hoff, Ayoe, 2007. "Second stage DEA: Comparison of approaches for modelling the DEA score," European Journal of Operational Research, Elsevier, vol. 181(1), pages 425-435, August.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    10. Simar, Leopold & Wilson, Paul W., 1999. "Estimating and bootstrapping Malmquist indices," European Journal of Operational Research, Elsevier, vol. 115(3), pages 459-471, June.
    11. Md. Sariful Islam & Sabiha Ferdousy & Sonia Afrin & Md. Nasif Ahsan & Mohammed Ziaul Haider & Debasish Kumar Das, 2020. "How does farmers' field schooling impact eco-efficiency? Empirical evidence from paddy farmers in Bangladesh," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 12(3), pages 527-552, July.
    12. Robert Finger & Scott M. Swinton & Nadja El Benni & Achim Walter, 2019. "Precision Farming at the Nexus of Agricultural Production and the Environment," Annual Review of Resource Economics, Annual Reviews, vol. 11(1), pages 313-335, October.
    13. Avkiran, Necmi K., 2009. "Removing the impact of environment with units-invariant efficient frontier analysis: An illustrative case study with intertemporal panel data," Omega, Elsevier, vol. 37(3), pages 535-544, June.
    14. Laure Latruffe & Sophia Davidova & Kelvin Balcombe, 2008. "Application of a double bootstrap to investigation of determinants of technical efficiency of farms in Central Europe," Journal of Productivity Analysis, Springer, vol. 29(2), pages 183-191, April.
    15. Boris E. Bravo‐Ureta & Mario González‐Flores & William Greene & Daniel Solís, 2021. "Technology and Technical Efficiency Change: Evidence from a Difference in Differences Selectivity Corrected Stochastic Production Frontier Model," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 362-385, January.
    16. Stræte, Egil Petter & Vik, Jostein & Hansen, Bjørn Gunnar, 2017. "The Social Robot: A Study of the Social and Political Aspects of Automatic Milking Systems," 2018 International European Forum (163rd EAAE Seminar), February 5-9, 2018, Innsbruck-Igls, Austria 276909, International European Forum on System Dynamics and Innovation in Food Networks.
    17. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    18. Kortelainen, Mika, 2008. "Dynamic environmental performance analysis: A Malmquist index approach," Ecological Economics, Elsevier, vol. 64(4), pages 701-715, February.
    19. McDonald, John, 2009. "Using least squares and tobit in second stage DEA efficiency analyses," European Journal of Operational Research, Elsevier, vol. 197(2), pages 792-798, September.
    20. Shakeeb Khan & Elie Tamer, 2010. "Irregular Identification, Support Conditions, and Inverse Weight Estimation," Econometrica, Econometric Society, vol. 78(6), pages 2021-2042, November.
    21. Lindlbauer, Ivonne & Schreyögg, Jonas & Winter, Vera, 2016. "Changes in technical efficiency after quality management certification: A DEA approach using difference-in-difference estimation with genetic matching in the hospital industry," European Journal of Operational Research, Elsevier, vol. 250(3), pages 1026-1036.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muhammet Enis Bulak & Murat Kucukvar, 2022. "How ecoefficient is European food consumption? A frontier‐based multiregional input–output analysis," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 817-832, October.
    2. Boris Radovanov & Branislav Dudic & Michal Gregus & Aleksandra Marcikic Horvat & Vincent Karovic, 2020. "Using a Two-Stage DEA Model to Measure Tourism Potentials of EU Countries and Western Balkan Countries: An Approach to Sustainable Development," Sustainability, MDPI, vol. 12(12), pages 1-12, June.
    3. Kaffash, Sepideh & Azizi, Roza & Huang, Ying & Zhu, Joe, 2020. "A survey of data envelopment analysis applications in the insurance industry 1993–2018," European Journal of Operational Research, Elsevier, vol. 284(3), pages 801-813.
    4. Li, Hai-ling & Zhu, Xue-hong & Chen, Jin-yu & Jiang, Fei-tao, 2019. "Environmental regulations, environmental governance efficiency and the green transformation of China's iron and steel enterprises," Ecological Economics, Elsevier, vol. 165(C), pages 1-1.
    5. Sinuany-Stern, Zilla, 2023. "Foundations of operations research: From linear programming to data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1069-1080.
    6. Bernardino Benito & José Solana & María-Rocío Moreno, 2014. "Explaining efficiency in municipal services providers," Journal of Productivity Analysis, Springer, vol. 42(3), pages 225-239, December.
    7. Banker, Rajiv & Natarajan, Ram & Zhang, Daqun, 2019. "Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using Data Envelopment Analysis: Second stage OLS versus bootstrap approaches," European Journal of Operational Research, Elsevier, vol. 278(2), pages 368-384.
    8. Rishabh Goswami & Farah Hussain & Manish Kumar, 2019. "Banking Efficiency Determinants in India: A Two-stage Analysis," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 13(4), pages 361-380, November.
    9. Meike Weltin & Silke Hüttel, 2023. "Sustainable Intensification Farming as an Enabler for Farm Eco-Efficiency?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(1), pages 315-342, January.
    10. Alexander Cotte Poveda, 2012. "Estimating Effectiveness of the Control of Violence and Socioeconomic Development in Colombia: An Application of Dynamic Data Envelopment Analysis and Data Panel Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 105(3), pages 343-366, February.
    11. Amar Oukil & Slim Zekri, 2021. "Investigating farming efficiency through a two stage analytical approach: Application to the agricultural sector in Northern Oman," Papers 2104.10943, arXiv.org.
    12. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    13. Sai, Rockson & Lin, Boqiang, 2022. "Productivity assessment of power generation in Kenya: What are the impacts?," Energy, Elsevier, vol. 254(PA).
    14. ChuangLin Fang & XingLiang Guan & ShaSha Lu & Min Zhou & Yu Deng, 2013. "Input–Output Efficiency of Urban Agglomerations in China: An Application of Data Envelopment Analysis (DEA)," Urban Studies, Urban Studies Journal Limited, vol. 50(13), pages 2766-2790, October.
    15. Laure Latruffe & Yann Desjeux, 2016. "Common Agricultural Policy support, technical efficiencyand productivity change in French agriculture," Review of Agricultural, Food and Environmental Studies, INRA Department of Economics, vol. 97(1), pages 15-28.
    16. Mohammad Izadikhah & Reza Farzipoor Saen, 2020. "Ranking sustainable suppliers by context-dependent data envelopment analysis," Annals of Operations Research, Springer, vol. 293(2), pages 607-637, October.
    17. Oleg Badunenko & Harald Tauchmann, 2019. "Simar and Wilson two-stage efficiency analysis for Stata," Stata Journal, StataCorp LP, vol. 19(4), pages 950-988, December.
    18. Chen Kaihua & Kou Mingting, 2014. "Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 627-657, March.
    19. Haitao Li & Jie Xiong & Jianhui Xie & Zhongbao Zhou & Jinlong Zhang, 2019. "A Unified Approach to Efficiency Decomposition for a Two-Stage Network DEA Model with Application of Performance Evaluation in Banks and Sustainable Product Design," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    20. Hongli Liu & Xiaoyu Yan & Jinhua Cheng & Jun Zhang & Yan Bu, 2021. "Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.

    More about this item

    Keywords

    Livestock Production/Industries; Production Economics; Environmental Economics and Policy;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aesc22:321199. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aesukea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.