IDEAS home Printed from https://ideas.repec.org/p/ags/cfcp15/344233.html
   My bibliography  Save this paper

How can South Africa’s land redistribution succeed? An agent-based modelling approach for assessing structural and economic impacts

Author

Listed:
  • Zantsi, Siphe
  • Mack, Gabriele
  • Möhring, Anke
  • Cloete, Kandas
  • Greyling, Jan C
  • Mann, Stefan

Abstract

This paper wants to make the case that agent-based modelling may contribute to provide support for the difficult process of South Africa’s land reform by running scenarios that then do not need to be explored in practice. An agent-based model (ILUPSA) was developed from a database of 605 commercial farmers and 833 commercially oriented smallholders, which are the potential land redistribution beneficiaries. Three scenarios are simulated (1) when a willing buyer- willing seller mechanism (WB-WS) is used to acquire land (baseline scenario), (2) WB- WS whereas redistributed land is subdivided into viable emerging farm parcels and (3) when less productive farms are expropriated. Simulation results shows that under WB-WS only 14% of commercial farmland becomes available for redistribution. Ninety-nine percent of this land is for grazing and the remainder is field crop and horticultural land. The redistribution becomes even more marginal when only farmland with low productivity is expropriated (less than a quarter of the land that becomes available in the baseline scenario). An estimated amount of R50 billion will be required to implement land redistribution.

Suggested Citation

  • Zantsi, Siphe & Mack, Gabriele & Möhring, Anke & Cloete, Kandas & Greyling, Jan C & Mann, Stefan, 2024. "How can South Africa’s land redistribution succeed? An agent-based modelling approach for assessing structural and economic impacts," IAAE 2024 Conference, August 2-7, 2024, New Delhi, India 344233, International Association of Agricultural Economists (IAAE).
  • Handle: RePEc:ags:cfcp15:344233
    DOI: 10.22004/ag.econ.344233
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/344233/files/20260.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.344233?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. Happe, Kathrin & Balmann, Alfons & Kellermann, Konrad & Sahrbacher, Christoph, 2008. "Does structure matter? The impact of switching the agricultural policy regime on farm structures," Journal of Economic Behavior & Organization, Elsevier, vol. 67(2), pages 431-444, August.
    2. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    3. Beatrice Conradie, 2019. "Land Use and Redistribution in the Arid West: The case of Laingsburg Magisterial District," Agrekon, Taylor & Francis Journals, vol. 58(3), pages 281-291, July.
    4. Julius Mukarati & Itumeleng P. Mongale & Godswill Makombe, 2020. "Land redistribution and the South African economy," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(1), pages 46-54.
    5. Cooper, Rachel & Jarre, Astrid, 2017. "An Agent-based Model of the South African Offshore Hake Trawl Industry: Part I Model Description and Validation," Ecological Economics, Elsevier, vol. 142(C), pages 268-281.
    6. Cooper, Rachel & Jarre, Astrid, 2017. "An Agent-based Model of the South African Offshore Hake Trawl Industry: Part II Drivers and Trade-offs in Profit and Risk," Ecological Economics, Elsevier, vol. 142(C), pages 257-267.
    7. Mack, Gabriele & Ferjani, Ali & Möhring, Anke & von Ow, Albert & Mann, Stefan, 2019. "How did farmers act? Ex-post validation of linear and positive mathematical programming approaches for farm-level models implemented in an agent-based agricultural sector model," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 8(1), April.
    8. Hans P. Binswanger-Mkhize & Camille Bourguignon & Rogier van den Brink, 2009. "Agricultural Land Redistribution : Toward Greater Consensus," World Bank Publications - Books, The World Bank Group, number 2653.
    9. J. van Zyl & C. J. van Rooyen & J. F. Kirsten & H. D. van Schalkwyk, 1994. "Land reform in south africa: Options to consider for the future," Journal of International Development, John Wiley & Sons, Ltd., vol. 6(2), pages 219-239, March.
    10. McCarl, Bruce A., 1984. "Model Validation: An Overview with some Emphasis on Risk Models," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 52(03), pages 1-21, December.
    11. 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.
    12. Ruth Hall & Thembela Kepe, 2017. "Elite capture and state neglect: new evidence on South Africa’s land reform," Review of African Political Economy, Taylor & Francis Journals, vol. 44(151), pages 122-130, January.
    13. Andrew Graham & Michael Lyne, 1999. "Land redistribution in KwaZulu-Natal: An analysis of farmland transactions in 1997," Development Southern Africa, Taylor & Francis Journals, vol. 16(3), pages 435-445.
    14. Reidsma, Pytrik & Janssen, Sander & Jansen, Jacques & van Ittersum, Martin K., 2018. "On the development and use of farm models for policy impact assessment in the European Union – A review," Agricultural Systems, Elsevier, vol. 159(C), pages 111-125.
    15. Albert Zimmermann & Anke Möhring & Gabriele Mack & Ali Ferjani & Stefan Mann, 2015. "Pathways to Truth: Comparing Different Upscaling Options for an Agent-Based Sector Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-11.
    16. Olubode-Awosola, O.O. & van Schalkwyk, H.D. & Jooste, A., 2008. "Mathematical modeling of the South African land redistribution for development policy," Journal of Policy Modeling, Elsevier, vol. 30(5), pages 841-855.
    17. Happe, Kathrin & Kellermann, Konrad & Balmann, Alfons, 2006. "Agent-based analysis of agricultural policies: An illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 11(1).
    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. Diego Ferraro & Daniela Blanco & Sebasti'an Pessah & Rodrigo Castro, 2021. "Land use change in agricultural systems: an integrated ecological-social simulation model of farmer decisions and cropping system performance based on a cellular automata approach," Papers 2109.01031, arXiv.org, revised Sep 2021.
    2. 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.
    3. Sardorbek Musayev & Jonathan Mellor & Tara Walsh & Emmanouil Anagnostou, 2022. "Application of Agent-Based Modeling in Agricultural Productivity in Rural Area of Bahir Dar, Ethiopia," Forecasting, MDPI, vol. 4(1), pages 1-22, March.
    4. Robert Huber & Hang Xiong & Kevin Keller & Robert Finger, 2022. "Bridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling framework," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 35-63, February.
    5. 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.
    6. Mössinger, Johannes & Troost, Christian & Berger, Thomas, 2022. "Bridging the gap between models and users: A lightweight mobile interface for optimized farming decisions in interactive modeling sessions," Agricultural Systems, Elsevier, vol. 195(C).
    7. Linmei Shang & Jifeng Wang & David Schäfer & Thomas Heckelei & Juergen Gall & Franziska Appel & Hugo Storm, 2024. "Surrogate modelling of a detailed farm‐level model using deep learning," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(1), pages 235-260, February.
    8. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    9. Sorda, G. & Sunak, Y. & Madlener, R., 2013. "An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany," Ecological Economics, Elsevier, vol. 89(C), pages 43-60.
    10. Djanibekov, Utkur & Finger, Robert, 2018. "Agricultural risks and farm land consolidation process in transition countries: The case of cotton production in Uzbekistan," Agricultural Systems, Elsevier, vol. 164(C), pages 223-235.
    11. 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.
    12. 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.
    13. Yamashita, Ryohei & Hoshino, Satoshi, 2018. "Development of an agent-based model for estimation of agricultural land preservation in rural Japan," Agricultural Systems, Elsevier, vol. 164(C), pages 264-276.
    14. 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.
    15. Marius Eisele & Christian Troost & Thomas Berger, 2021. "How Bayesian Are Farmers When Making Climate Adaptation Decisions? A Computer Laboratory Experiment for Parameterising Models of Expectation Formation," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(3), pages 805-828, September.
    16. Dimitris Kremmydas, 2012. "Agent based modeling for agricultural policy evaluation: A review," Working Papers 2012-3, Agricultural University of Athens, Department Of Agricultural Economics.
    17. Carrella, Ernesto & Saul, Steven & Marshall, Kristin & Burgess, Matthew G. & Cabral, Reniel B. & Bailey, Richard M. & Dorsett, Chris & Drexler, Michael & Madsen, Jens Koed & Merkl, Andreas, 2020. "Simple Adaptive Rules Describe Fishing Behaviour Better than Perfect Rationality in the US West Coast Groundfish Fishery," Ecological Economics, Elsevier, vol. 169(C).
    18. Hugo Storm & Klaus Mittenzwei & Thomas Heckelei, 2015. "Direct Payments, Spatial Competition, and Farm Survival in Norway," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(4), pages 1192-1205.
    19. Ostermeyer, Arlette & Schonau, Franziska, 2012. "Effects of biogas production on inter- and in-farm competition," 131st Seminar, September 18-19, 2012, Prague, Czech Republic 135772, European Association of Agricultural Economists.
    20. Britz, Wolfgang & van Ittersum, Martin K. & Oude Lansink, Alfons G.J.M. & Heckelei, Thomas, 2012. "Tools for Integrated Assessment in Agriculture. State of the Art and Challenges," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(2), pages 1-26, August.

    More about this item

    Keywords

    Agricultural and Food Policy; Farm Management;

    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:cfcp15:344233. 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://iaae-agecon.org/ .

    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.