Biochar–manure impacts wheat and canola grain productivity, dry matter partitioning, and protein content in western Canada
Bibliographic record
Abstract
Abstract Amending soil with manure from cattle fed biochar (BC) (biochar–manure [BM]) is a potential best management practice to improve plant nutrition in the circular economy. Yet, information concerning the agronomic performance of BM under temperate field conditions is scarce. A 2‐year study on a Gray Luvisol was conducted to determine the effect of BM on the crop growth of spring wheat ( Triticum aestivum L.), followed by canola ( Brassica napus L.), soil fertility, and microbial function. Treatments included (i) no amendments (control), (ii) BC at 5 and 10 Mg·ha −1 (BC5 and BC10), (iii) regular stockpiled manure (RM) at 100 kg total N·ha −1 , (iv) stockpiled BM at 100 kg total N·ha −1 , and (v) BC and RM (BC+RM) or BC and BM (BC+BM) at the aforementioned rates. During the wheat growing season in 2020, which had high precipitation, grain yield was 2.4 times greater in BM+BC10 than in BM alone (1416 vs. 579 kg·ha −1 , p < 0.001), highlighting synergistic effects of BM together with BC application on agronomic performance. Conversely, lower precipitation and warmer temperatures in the canola growing season in 2021 hampered any statistical differences among treatments. While soil microbial biomass did not change by the end of the experiment, shifts toward amino acid utilization with BC additions in both crops potentially influenced crop growth and nitrogen‐use efficiency. In summary, BM+BC at 10 Mg·ha −1 performed best in this study during the first cropping season under cold and rainy conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".