Estimating Available Abandoned Cropland in the United States: Possibilities for Energy Crop Production
Bibliographic record
Abstract
Abandoned cropland (ACL) is often cited as a land resource on which to produce energy crops while reducing the negative impacts of broad-scale energy crop production; for example, carbon emissions from land-cover change and competition with food production. In contrast to marginal land, which refers to a set of biophysical and economic criteria usually imposed by experts or policymakers, the designation of ACL refers to a land-use decision by a land owner. As such, ACL is argued to be a more appropriate indication of land availability for dedicated energy crop production. Prevailing estimates of ACL in the United States vary widely due to inconsistent treatment of land-use conversions away from cropland and overreliance on remote sensing methods that measure land cover, even though ACL is a category of land use. This article develops and applies a replicable and flexible methodology to estimate available abandoned cropland (AACL) at the county level in the United States, which accounts for conversion of ACL to forest cover, urban development, or permanent pasture. Estimates of AACL are derived for two scenarios: (1) land abandoned between 1978 and 2012, which excludes lands with meaningful forest regrowth, and (2) land abandoned between 2007 and 2012, which corresponds to land-use constraints imposed by the Renewable Fuel Standard. Results show that 15.0 and 4.9 Mha of AACL exist in the United States in the two scenarios, respectively, amounting to between only 3 and 8 percent of total light-duty gasoline consumption in the United States. The policy implications of these findings and the need for future research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".