Research on Influencing Factors of Land Rental Prices for Alfalfa Planting in Minnesota
Why this work is in the frame
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Bibliographic record
Abstract
Agriculture is essential for human beings to survive.It not only provides food to eat and feed but also brings profits through exportation.Not all people own their lands, so they have to rent for planting.This study aims to analyze the factors contributing to the overall rental prices for alfalfa planting.It investigated the average rental prices of lands planting alfalfa in Minnesota under R package alr4 with 67 observations in the 1970s.Based on the pairwise correlation and scatterplot matrix, this paper suggested a simple linear regression model as a startup.After analyzing four diagnosis plots, the initial model failed the constant variance assumption.Then this paper built a new linear model containing all variables and their interactions.This new model produced the exact model under backward elimination AIC and BIC methods.A comparison of the initial model to the final model under ANOVA also had evidence supporting the final model.The average specialization rent is positively associated with the average rent for all tillable lands, density of cattle and pasture percentage; negatively associated with the interactions between the tillable and pastures as well as between the cattle and the fields.This study demonstrates a model available projecting the future rents as the changes in its predictors.It brings out an overview to farmers for budget preparation and land allocations.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it