Crop Residues Stimulate Yield-Scaled Greenhouse Gas Emissions In Maize-Wheat Cropping Rotation In A Semi-Arid Climate
Bibliographic record
Abstract
Mitigating yield-scaled greenhouse gas emissions (YSE) is beneficial for enhancing crop yield, reducing greenhouse gas (GHG) emissions, and advancing climate-smart agronomic management practices. This study aims to evaluate the impact of different crop residue rates– 100% (R100), 50% (R50), and residue removal (R0) – on the YSE indicator within a maize-wheat cropping rotation under both conventional tillage (CT) and no-tillage (NT) systems in a semi-arid region. In the NT system, crop residues had a notable effect on the YSE indicator for wheat. Specifically, R0 exhibited a 39% and 20% decrease in YSE for wheat compared to R100 and R50, respectively. Interestingly, crop residue did not significantly influence YSE for maize under the NT system. On the other hand, in the CT system, YSE for maize in R0 was 33% and 25% lower than that in R100 and R50, respectively. Additionally, compared to R0, there were observed increases of 28% and 20% in YSE for wheat in R100 and R50 under the CT system, respectively. Our findings show that crop residue removal decreases YSE under both CT and NT systems. However, given that this practice degrades soil quality and results in lower yields, it is not considered a sustainable management practice compared to residue retention options. This research highlights the importance of evaluating GHG mitigation strategies by concurrently considering both emissions and crop production. Nevertheless, it is essential to conduct off-site assessments of GHG emissions from crop residue application and also engage in long-term studies to comprehend the full potential of crop residue management on YSE.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".