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Enregistrement W3124193886

Do farmers treat rented land differently than the land they own? A fixed effects model of farmer’s decision to adopt conservation practices on owned and rented land

2014· article· en· W3124193886 sur OpenAlexaboutno aff
Karthik Nadella, Brady J. Deaton, Chad Lawley, Alfons Weersink

Notice bibliographique

Revue2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota · 2014
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueAgricultural Economics and Policy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLand tenureProductivityBusinessInvestment (military)Time horizonEconomicsValue (mathematics)Term (time)Public economicsNatural resource economicsAgricultural economicsAgricultureFinanceGeographyEconomic growth
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Approximately 40% of farmland is rented in Canada (Statistics Canada, 2011). In our survey, approximately ninety-five percent of the farmers that rent land also farm on their own land. This provides a unique empirical opportunity to assess the influence of ownership on farmers’ decision to adopt conservation practices. This empirical assessment is important given the conflicting results in previous literature. One group of studies find that owner-operators are more likely to adopt conservation practices than tenants (Belknap and Saupe, 1988; Lynne et al., 1988). This finding is consistent with the idea that owner-operators have a longer planning horizon than tenants and are therefore in a better position to realize the long-term benefits of current investment in conservation practices. However, another set of studies (Rahm and Huffman, 1984; Norris and Batie, 1987) find no differences between owner-operators and tenants with respect to the adoption of conservation practices like conservation tillage. This conflict in literature can be potentially explained by the differences in the present value of expected returns across conservation practices. The adoption of cover crops, for example, involves a tradeoff between costs which occur in the short term and increases in the productivity of the land which occur in the longer term. In our theoretical model the expected returns of long-term investments are influenced by a tenure security measure. Farmers are expected to face a lower level of tenure security on the land they rent relative to the land that they own and are therefore hypothesized to be less likely to plant cover crops on rented land. On the other hand, the adoption of conservation tillage could be profitable in the short-term once the farmer has acquired the machinery and might not be necessarily influenced by tenure status. The empirical analysis (fixed effects) regresses the adoption of conservation practices against a number of explanatory variables. Our data set allows us to examine this decision for the same farmer and thereby eliminates differences that may be explained by characteristics of the farmer. The key explanatory variable of interest is land tenure, which is modeled by observations regarding whether the land is owned or rented. We examine the sensitivity of this to alternative specifications of the model by accounting for the length of time the farmer has rented the land. The data for this analysis comes from a survey of 425 farmers who operate on both owned and rented land in Ontario and Manitoba. The data was collected over a two-week period in April 2013. Farmers provided information about their production practices on both owned and rented properties. (We also gather information from farmers who farm only their own land. This expands the data set to 810 observations.) The key dependent variable is the adoption of conservation practices: i.e., cover crops and conservation tillage. For example, 26% of farmers adopt cover crops on their own land while 15 % adopt cover crops on rented land. The key explanatory variables are measures of land tenure: e.g., whether the land is owned or rented. Additional explanatory variables include measures, which account for variation in land characteristics and crop choice. The data also allows for exploration of a number of important issues, for example, we are able to gather data on characteristics of the farmland owner. For instance, approximately 40% of the landlords in Ontario can be characterized as Non-Farmer Investors while approximately 11% can be characterized as widows or widowers. In total we group landlords into seven categories. In the linear probability model with fixed effects, tenure status is not found to be a statistically significant factor on the probability of the adoption of conservation tillage. However, tenure status is found to be a statistically significant factor on the probability of the adoption of cover crops. These results confirm the hypotheses generated by our theoretical model, which suggests that the influence of tenure status varies on the adoption of conservation practices varies depending on the type of practice that is being considered. These results are also found to be robust under different model specifications.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,198
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,247
Écart entre enseignants0,228 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2014
Routes d'admission1
Résumé présentoui

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