Intensified Dryland Cropping Systems for Food and Biofuel Feedstock Production
Bibliographic record
Abstract
Production of biofuels on fallowed land will benefit farmers and environment without creating any "food versus fuel" crisis [1].Camelina has potential to be planted in the fallow period in the predominant wheat-fallow (WW-FAL) cropping system in the Northern Great Plains for annual cropping.In a multi-year field study (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), we evaluated the sustainability of replacing fallow with camelina in WW-FAL rotation with respect to agronomic, economic, and energetic performance [2].We also examined how to improve the sustainability of camelina production via optimization of agronomic practices.Replacing fallow with camelina resulted in 13.2% reduction in wheat yield, but the annual cropping produced 907 kg ha -1 of camelina seed.WW-CAM also outperformed WW-FAL by 30% greater net energy output and similar energy efficiency.Despite agronomic, energetic, and ecological benefits, economic analysis revealed that at existing market prices and production costs, WW-FAL provides greater net returns to growers due to substantially lower variable costs.We found that there is a good potential to curb production costs of camelina through improving nitrogen fertilization use efficiency and reducing herbicide application.Beside lower production cost, higher grain price (the breakeven of $0.358 kg -1 ) and/or greater grain yield are still essential to attract producers to plant camelina.Nevertheless, greater and annually biomass production in WW-CAM system is expected to enhance soil organic matter, higher precipitation use efficiency, and protecting soil against erosion, thus resulting in more agronomic sustainability of the system.
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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 | 0.000 |
| 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 | 0.001 |
| 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".