An Agent Based Simulation of Farm Succession and Farmland Valuation
Bibliographic record
Abstract
The impact of widespread farm ownership by large investors is influential and uncertain. There are various reasons for buying farmland as an investment, and one of them diversification benefits for financial portfolios because many studies have found that the correlation between farmland price yield and the yields of major financial assets, such as stocks, bonds and real estates are negative. On the other hand, the prime objectives of many farm family businesses are “to maintain control and pass on a secure and sound business to the next generation” (Hay and Morris 1984, Errington 2002). Farmland is generally retained within the family by succession due to the strong emotional and economic linkage. However, very little literature has studied the interaction between these two types of farmland transitions. Given limited data on this research topic, an agent based simulation model (ABSM) is one of the few practical solutions for the study of this area. The model in this study is built upon Anderson’s (2012) model that simulates farming activities in Canadian Agricultural Region 1A in Saskatchewan in Repast©. Two modules, farm succession and institutional investors who purchase farmland as a financial asset to diversify the aggregate risk of the portfolio, are added. Further, 30-year farming and investing performances are simulated in four different scenarios to test the impacts on different activeness of institutional investments. They are assumed to lease the farmland they own back to the local farmers for the rent as their dividends. Conclusively, it is found the participation of institutional investors in farmland purchase market can push up the farmland price for 15% to 40% in different scenarios, and the farmers tend to lease slightly more. Meanwhile, the number of farms constantly decrease and more and more large farms emerge in the simulation period with or without investors. Finally, the results show that the existence of institutional investors would not negatively impact the industry health. Les impacts de l'achat en masse de terres agricoles par de grands investisseurs sont à la fois influents et incertains. L'achat de terres agricoles comme investissement se fait pour diverses raisons. L'une d'entre elles est l'avantage que représente la diversification des portefeuilles financiers parce que de nombreuses études démontrent le lien négatif entre la valeur du rendement agricole et les rendements d'avoirs financiers d'importance comme les actions, obligations et investissements immobiliers. D'autre part, les objectifs principaux de nombreuses entreprises agricoles sont de « maintenir le contrôle et de transmettre à la prochaine génération une entreprise solide et saine » [traduction libre] (Hay and Morris 1984, Errington 2002). Les terres agricoles sont ainsi conservées au sein d'une même famille par succession grâce aux liens émotifs et économiques forts. Par contre, peu d'études portent sur cette interaction entre ces deux types de transitions agricoles. Étant donné le peu de données à ce sujet, le modèle de simulation à base d'agents représente l'une des solutions les plus pratiques pour étudier ce sujet. Le modèle utilisé pour cette étude se base sur le modèle d'Anderson (2012) qui simule les activités agricoles de la région agricole canadienne 1A en Saskatchewan dans Repast©. Deux modules sont ajoutés : succession agricole et investisseurs institutionnels qui font l'acquisition de terres agricoles comme avoir financier pour diversifier le risque global au portefeuille. De plus, les performances agricoles et d'investissement sur une période de 30 ans sont simulées dans quatre scénarios distincts afin d'évaluer les impacts sur différents niveaux d'activités des investissements institutionnels. L'on suppose que ces derniers louent les terres agricoles aux fermiers locaux pour le loyer comme dividendes. Il est donc trouvé définitivement que la participation des investisseurs institutionnels dans l'achat de terres agricoles peut faire augmenter le prix de ces terres de 15 % à 40 $ selon le scénario, et les fermiers ont légèrement plus tendance à louer. Parallèlement, le nombre de fermes diminue constamment et de plus en plus de grandes fermes apparaissent dans la période de simulation avec ou sans investisseurs. Finalement, les résultats démontrent que l'existence d'investisseurs institutionnels n'aurait pas d'impact sur la santé de l'industrie.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".