Research on Influencing Factors of Land Rental Prices for Alfalfa Planting in Minnesota
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".