Impact of crop residue removal on crop production, feedstock quality, and theoretical ethanol production in the Mid‐Atlantic United States
Bibliographic record
Abstract
Abstract Cellulosic biomass‐to‐bioenergy systems provide fuel, reduce emissions, and offer economic benefits. Corn (Zea mays L.) and wheat (Triticum aestivum L.) residues could be used as feedstocks for biofuel production. However, the impact of residue removal on crop productivity in the Mid‐Atlantic region has not been thoroughly assessed. A trial was conducted to assess crop yield and quality response to different biomass retention rates in grain cropping systems during 2015–2017. Various combinations of corn stover (0–10 Mg ha−1) and wheat straw (0–3 Mg ha−1) were applied in a corn–wheat/soybean [Glycine max (L.) Merr.] rotation in New Kent, VA. In Blacksburg, VA, corn stover (0–20 Mg ha−1) was applied in the continuous corn system. Residues were applied after grain harvest over two production cycles for each system. Residue retention showed no significant impact on grain or crop residue yields or nutrient uptake in either system. Treatment minimally impacted feedstock quality, except wheat straw's sulfur (S) concentration, optimized at around 70% retention in New Kent. Theoretical ethanol potential (TEP) and yield remained unaffected by total residue rates in New Kent. In Blacksburg, over 2 years, a minimum TEP for corn stover corresponded to a retention rate of approximately 30%. A retention rate of more than 30% increased TEP, likely due to improved feedstock quality. Nutrient replacement costs for primary macronutrients and S uptake ranged from $18.3 to $36.9 ha−1 for corn stover and $6.1 to $11.8 ha−1 for wheat straw. Residue harvest or addition did not harm short‐term biomass yield in Virginia's grain‐based cropping systems.
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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 | 0.000 |
| 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) | 0.001 | 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".