Impact of manure and biochar additions on annual crop growth, nutrient uptake, and fate of <sup>15</sup>N-labelled fertilizer in two contrasting temperate prairie soils after four years
Bibliographic record
Abstract
Liquid hog manure (LHM) and solid cattle manure (SCM) co-applied with biochar could beneficially influence soil nitrogen (N) and phosphorus (P) dynamics. A split-plot design was used at two sites (Brown and Black) in Saskatchewan to assess LHM and SCM (100 kg N·ha−1) applied alone or in combination with biochars (8 Mg carbon·ha−1) produced using slow or fast pyrolysis. Crop growth and nutrient uptake, along with fertilizer 15N recovery, were followed over four years in a cereal–oilseed rotation. Crop growth on the Brown soil was more responsive to the treatments than the Black soil, reflecting lower fertility of the Brown soil. The manure and biochar, applied alone or in combination, had little impact on available soil P. Manure and biochar effects on crop growth and nutrient uptake were attributed to temporal effects on soil N immobilization–mineralization influencing plant-available soil N. A negative impact of the fast pyrolysis biochar on growth and plant uptake was observed and attributable to its greater labile-carbon content, which likely promoted soil N immobilization. Synergism observed between SCM and the slow pyrolysis biochar may reflect enhanced net SCM-N mineralization and increased water-holding capacity. The majority (55%–80%) of plant 15N recovery occurred during the first year, with 86% of fertilizer 15N conserved within the soil–plant system after four years. Greater (40%) plant 15N recovery without biochar addition, coupled with increased (38%) soil 15N recovery with added biochar, suggests biochar-related immobilization and (or) sorption in the biochar-amended soils.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".