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Record 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 on OpenAlexaboutno aff
Karthik Nadella, Brady J. Deaton, Chad Lawley, Alfons Weersink

Bibliographic record

Venue2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLand tenureProductivityBusinessInvestment (military)Time horizonEconomicsValue (mathematics)Term (time)Public economicsNatural resource economicsAgricultural economicsAgricultureFinanceGeographyEconomic growth
DOInot available

Abstract

fetched live from 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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.247
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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Same venue2014 Annual Meeting, July 27-29, 2014, Minneapolis, MinnesotaSame topicAgricultural Economics and PolicyFrench-language works237,207