IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i3p590-d1083617.html
   My bibliography  Save this article

Differential Assessment of Strategies to Increase Milk Yield in Small-Scale Dairy Farming Systems Using Multi-Agent Modelling and Simulation

Author

Listed:
  • Devotha G. Nyambo

    (Information and Communication Science and Engineering, Nelson Mandela African Institution of Science and Technology, Arusha P.O. Box 447, Tanzania)

  • Thomas Clemen

    (Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany)

Abstract

Multi-agent-based modelling and simulation provides an adequate environment to study the real world. This paper presents the use of a multi-agent research and simulation (MARS) framework and model design based on the overview, design concepts, design (ODD) protocol to model and simulate small-scale management strategies that are important for increased milk yield per cow. In reality, strategies for farm management at a small-scale level are purely based on heuristics that cost farmers and lead to inadequate milk yields. A differential assessment of the farming strategies was conducted to yield a data-driven approach for selection of the best strategies, which in turn will optimize investments and increase milk yield. The agent-based modelling and simulation revealed that, the studied strategies based on income, farm, and farmer-based characteristics influenced an increase of up to 7.72 L of milk above the average (12.7 ± 4.89). Generally, there was an increase in milk yield based on the identified evolvement strategies; from a baseline data average milk yield of 12.7 ± 4.89 to simulated milk yield average of 17.57 ± 0.72. Evaluating the agent-based models in real-world scenarios will strengthen the assurance that the identified strategies can move small-scale dairy farmers from low to higher milk producers.

Suggested Citation

  • Devotha G. Nyambo & Thomas Clemen, 2023. "Differential Assessment of Strategies to Increase Milk Yield in Small-Scale Dairy Farming Systems Using Multi-Agent Modelling and Simulation," Agriculture, MDPI, vol. 13(3), pages 1-13, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:590-:d:1083617
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/3/590/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/3/590/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schilling, Chris & Kaye-Blake, William & Post, Elizabeth & Rains, Scott, 2012. "The importance of farmer behaviour: an application of Desktop MAS, a multi-agent system model for rural New Zealand communities," 2012 Conference, August 31, 2012, Nelson, New Zealand 136070, New Zealand Agricultural and Resource Economics Society.
    2. Oudendag, Diti & Hoogendoorn, Mark & Jongeneel, Roel, 2014. "Agent-Based Modeling of Farming Behavior: A Dutch Case Study on Milk Quota Abolishment and Sustainable Dairying," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182700, European Association of Agricultural Economists.
    3. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    4. Mack, Gabriele & Huber, Robert, 2017. "On-farm compliance costs and N surplus reduction of mixed dairy farms under grassland-based feeding systems," Agricultural Systems, Elsevier, vol. 154(C), pages 34-44.
    5. Fust, Pascal & Schlecht, Eva, 2018. "Integrating spatio-temporal variation in resource availability and herbivore movements into rangeland management: RaMDry—An agent-based model on livestock feeding ecology in a dynamic, heterogeneous, ," Ecological Modelling, Elsevier, vol. 369(C), pages 13-41.
    6. James Nolan & Dawn Parker & G. Cornelis Van Kooten & Thomas Berger, 2009. "An Overview of Computational Modeling in Agricultural and Resource Economics," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 417-429, December.
    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. Huber, Robert & Bakker, Martha & Balmann, Alfons & Berger, Thomas & Bithell, Mike & Brown, Calum & Grêt-Regamey, Adrienne & Xiong, Hang & Le, Quang Bao & Mack, Gabriele & Meyfroidt, Patrick & Millingt, 2018. "Representation of decision-making in European agricultural agent-based models," Agricultural Systems, Elsevier, vol. 167(C), pages 143-160.
    2. 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.
    3. Rianne Duinen & Tatiana Filatova & Wander Jager & Anne Veen, 2016. "Going beyond perfect rationality: drought risk, economic choices and the influence of social networks," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 57(2), pages 335-369, November.
    4. Carrasco, L. Roman & Cook, David & Baker, Richard & MacLeod, Alan & Knight, Jon D. & Mumford, John D., 2012. "Towards the integration of spread and economic impacts of biological invasions in a landscape of learning and imitating agents," Ecological Economics, Elsevier, vol. 76(C), pages 95-103.
    5. Troost, Christian & Huber, Robert & Bell, Andrew R. & van Delden, Hedwig & Filatova, Tatiana & Le, Quang Bao & Lippe, Melvin & Niamir, Leila & Polhill, J. Gareth & Sun, Zhanli & Berger, Thomas, 2023. "How to keep it adequate: A protocol for ensuring validity in agent-based simulation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 159, pages 1-21.
    6. Ran Sun & James Nolan & Suren Kulshreshtha, 2022. "Agent-based modeling of policy induced agri-environmental technology adoption," SN Business & Economics, Springer, vol. 2(8), pages 1-26, August.
    7. Brinkmann, Katja & Kübler, Daniel & Liehr, Stefan & Buerkert, Andreas, 2021. "Agent-based modelling of the social-ecological nature of poverty traps in southwestern Madagascar," Agricultural Systems, Elsevier, vol. 190(C).
    8. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    9. Gawith, David & Hodge, Ian & Morgan, Fraser & Daigneault, Adam, 2020. "Climate change costs more than we think because people adapt less than we assume," Ecological Economics, Elsevier, vol. 173(C).
    10. Grovermann, Christian & Schreinemachers, Pepijn & Riwthong, Suthathip & Berger, Thomas, 2017. "‘Smart’ policies to reduce pesticide use and avoid income trade-offs: An agent-based model applied to Thai agriculture," Ecological Economics, Elsevier, vol. 132(C), pages 91-103.
    11. Bert, Federico E. & Podestá, Guillermo P. & Rovere, Santiago L. & Menéndez, Ángel N. & North, Michael & Tatara, Eric & Laciana, Carlos E. & Weber, Elke & Toranzo, Fernando Ruiz, 2011. "An agent based model to simulate structural and land use changes in agricultural systems of the argentine pampas," Ecological Modelling, Elsevier, vol. 222(19), pages 3486-3499.
    12. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    13. Vimercati, Giovanni & Hui, Cang & Davies, Sarah J. & Measey, G. John, 2017. "Integrating age structured and landscape resistance models to disentangle invasion dynamics of a pond-breeding anuran," Ecological Modelling, Elsevier, vol. 356(C), pages 104-116.
    14. Jagadish, Arundhati & Dwivedi, Puneet & McEntire, Kira D. & Chandar, Mamta, 2019. "Agent-based modeling of “cleaner” cookstove adoption and woodfuel use: An integrative empirical approach," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    15. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    16. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    17. Jascha-Alexander Koch & Jens Lausen & Moritz Kohlhase, 2021. "Internalizing the externalities of overfunding: an agent-based model approach for analyzing the market dynamics on crowdfunding platforms," Journal of Business Economics, Springer, vol. 91(9), pages 1387-1430, November.
    18. Crevier, Lucas Phillip & Salkeld, Joseph H & Marley, Jessa & Parrott, Lael, 2021. "Making the best possible choice: Using agent-based modelling to inform wildlife management in small communities," Ecological Modelling, Elsevier, vol. 446(C).
    19. Qingxu Huang & Dawn C Parker & Tatiana Filatova & Shipeng Sun, 2014. "A Review of Urban Residential Choice Models Using Agent-Based Modeling," Environment and Planning B, , vol. 41(4), pages 661-689, August.
    20. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.

    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:gam:jagris:v:13:y:2023:i:3:p:590-:d:1083617. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.