Evaluating the Effect of Conservation Policies on Agricultural Land Use: A Site‐specific Modeling Approach
Bibliographic record
Abstract
This study evaluates quantitatively the effect of three policies (payments for cropland retirement, fertilizer use taxes and payments for crop rotations) on agricultural land use in the upper Mississippi River basin. This is done by estimating two logit models of land use decisions using data from the 1982, 1987,1992 and 1997 Natural Resource Inventories. The models predict farmers' crop choice, crop rotation and participation in the Conservation Reserve Program (CRP) at more than 48,000 Natural Resource Inventories sites under each of the three policies. Results suggest that an increase in the CRP rental rates would significantly increase the CRP acreage, but most of the acreage increase would come initially from less fertilizer‐intensive crops. In contrast, a fertilizer use tax would significantly reduce acreage planted to more fertilizer‐intensive crops, and thus would likely be cost effective for reducing agricultural chemical use and pollution. Although an incentive payment for a corn‐soybean rotation would raise acreage of this rotation and reduce the acreage of continuous corn, the acreage response is in general quite inelastic. Cette étude évalue quantitativement les effets rovoqués par les trois politiques (paiements pour le retrait des terres cultivables, taxes sur l'utilisation d'engrais et paiements pour l'alternance des cultures) sur les terres agricoles du bassin supérieur du Mississipi. Ceci est obtenu en évaluant deux modéles logit des décisions sur l'utilisation des terres provenant des données des «Natural Resource Inventories» de 1982, 1987, 1992 et 1997. Les modéles prédisent le choix des cultures des agriculteurs, l'alternance des cultures et la participation du «Conservation Reserve Program (CRP)» dans plus de 48 000 Natural Resource Inventories dans le cadre de chacune des trois politiques. Les résultats suggérent qu'une augmentation des taux de location du CRP accroisse de maniére significative la surface de CRP, mais la majeure partie de cet accroissement de surface provenaient initialement de cultures moins intensives sans engrais. Cependant, l'utilisation d'une taxe sur l'utilisation d'engrais pouvait réduire de maniére significative la surface plantée avec des récoltes intensives utilisant plus d'engrais, et ainsi ce serait sans doute plus économique pour réduire la pollution et l'utilisation de produits chimiques en agriculture. Bien que des paiements incitatifs à l'alternance maïs‐soja réduisent la surface d'une culture continue de maïs et augmentaient la surface de l'alternance maïs‐soja, les résultats aux transformations des surfaces des terres seraient tout à fait rigides.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".