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Buy, sell or rent the farm: succession planning and the future of farming on the Great Plains

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Abstract

With deep agricultural roots, Western Canada is a major farmland market. Historically, farmland was retained within families by succession. But with strong personal and economic linkages, succession can create a difficult situation for retiring farmers trying to make objective decisions about the future of their (land) assets. Some offer that increased farmland ownership by external investors has the potential to negatively affect farm structure. Yet there are sound financial reasons for institutional investors to want farmland as an investment, including diversification benefits for investment portfolios. Due to the complexity and heterogeneity of individual agricultural land market decisions, we build upon prior related models to develop an agent-based simulation framework designed to highlight the interplay between these potentially conflicting factors. To this end, we endogenize both farm succession and the presence of institutional investors who desire to purchase regional farmland as a financial asset. Under conservative assumptions, we find that the presence of institutional investors elevates farmland prices in the study region by approximately 15% to 40%, depending on scenario. With the presence of investors, farmers tend to lease more land over time to support their arable land base. Through the duration of our simulation, the total number of farms in the study region drops over time, while larger individual farms emerge. We conclude that institutional investors will affect future farming structure in the region, but their presence could manifest in very subtle ways. Most critically, farming success with institutional investors operating in this land market is contingent on future farmers being willing to rely on land rental rather than ownership in the process of farm maintenance or expansion.

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(Source: 2011 Census, Statistics Canada)

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Notes

  1. This paper is abridged from the thesis of Su (2017) at the University of Saskatchewan.

  2. Carter (2010) maintained that farm real estate has gone through a “financialization” process, where profit is generated from financial rather than productive channels.

  3. We need to thank a referee for pointing out a related point about institutional investor objectives and that this might affect the portability of the model to other regions. In some regions like the EU or possibly Africa, these types of investors might also choose to invest in extant farm operations, but that is not the case currently in North America. External investors that we are aware of use land investments in the way we specify in this simulation.

  4. The very broad definition of “winner’s curse” in an auction is “the person who bids the most and wins the auction may ultimately regret the bid since it often exceeds the value of the object being auctioned” (Foreman and Murnighan 1996).

  5. While somewhat non-standard in the auction literature, this method of computing transacted land prices is Pareto improving. In addition, some of the land auctions had a single farm agent bidder, so the auctioneer agent could not always reliably use a second price/bid to generate a transacted land price.

  6. Regarding the succession process, we found that a linear probability model was a much better fit and the coefficients produced were more sensible than those generated under logit estimation.

  7. As pointed out by a referee, we have assumed investors will purchase as much land as they can across a given set of land auctions, subject to their computed bid value for individual land parcels, as well as the upper bounds on their bid valuations (see below). As in reality, investors are assumed to have minimal (no) budget constraints, unlike their farm agent competitors.

  8. Su (2017, Ch. 4) computes autoregressive-based forecasts of all the key variables used in the simulation over a 30-year time span. Much of this information is assumed to be available to the investor agent for internal decision-making.

  9. Su (2017, Ch. 3) details farm agents land price expectation formulation. These are based on a basic adaptive specification that is also a function of past land parcel prices.

  10. Since we computed a 30-year forecast, we offer that we generated a large enough sample size to include all possible scenarios along with our price and yield time paths.

  11. The 2011 National Agriculture Census result was the latest available data to use, as this part of the study was conducted between 2014 and 2016.

  12. Starting debt level and distribution is based on a farm survey (Anderson 2021).

  13. Farm size is a long-run strategic decision and is an important management variable in the integer programming model. Each machinery package has an associated investment requirement, annual cost and a maximum acreage constraint. There are no lower bounds, however. What this means is that changes in farm size may require an accompanying shift to a different least cost machinery package.

  14. As pointed out by a referee, there could be other plausible reasons for this finding, including the manner in which we measured farm debt.

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Funding

Funding was provided by the Farm Credit Canada (Grant No. 413787).

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  1. Department of Agricultural and Resource Economics, University of of Saskatchewan, Saskatoon, SK, Canada

    Chi Su, Richard A. Schoney & James F. Nolan

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  1. Chi Su
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Correspondence to James F. Nolan.

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Su, C., Schoney, R.A. & Nolan, J.F. Buy, sell or rent the farm: succession planning and the future of farming on the Great Plains. J Econ Interact Coord 18, 627–669 (2023). https://doi.org/10.1007/s11403-023-00381-0

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