Biochar-induced soil stability influences phosphorus retention in the agricultural field in Quebec
Bibliographic record
Abstract
Surface runoff from agricultural fields is the largest non-point source of phosphorus (P) that pollutes surface water in humid temperate regions. Best management practices have attempted to reduce P loading and improve P retention in agricultural soils but significant losses continue to occur, emphasizing the need for novel solutions. The objective of this research project was to determine whether biochar amendments in an agricultural soil could reduce P loss in surface runoff by increasing water infiltration or by improving soil stability. Experimental plots were established in St-Francois-Xavier-de-Brompton, Quebec, Canada on an agricultural field amended with three types of biochar (Dynamotive, Pyrovac, and Basques) applied at two application rates (5 and 10 t ha-1), and one unamended control plot. First, a 30-minute rainfall simulation was conducted using the Cornell Sprinkle Infiltrometer to assess runoff volume, time-until-ponding, infiltration rate, and water holding capacity (WHC), as well as P concentration and load in runoff. Second, soil samples from the experimental plots were fractionated using a wet-sieve method to determine the proportion of macro- and micro-aggregates. Each fraction was analyzed for total organic C and total P to locate biochar presence and determine whether additional P was retained in macro- or micro-aggregate fractions of biochar-amended soils. Water dynamics in the rainfall simulation showed no significant differences, however, runoff contained significantly less ortho-P in soil amended with Dynamotive biochar at 5 t ha-1 (p=0.048) and significantly less particulate P from soil amended with Pyrovac biochar at 5 t ha-1 (p=0.012) and Dynamotive and Basques biochars at 10 t ha-1 (p=0.024 and p=0.047, respectively). Soils amended with biochar at 5 t ha-1 and 10 t ha-1 also had significantly greater microaggregate stability (p=0.032 and p=0.046, respectively), which corresponded to significantly more organic C content (p=0.013 and p<0.01, respectively). Macroaggregates from biochar-amended soils also contained significantly higher organic C and total P concentrations (p<0.05 for both biochar rates) than the control soil. This suggests that the reduction in particulate P concentration in runoff is the result of biochar integration within the microaggregate structure, which indirectly promotes P retention in macroaggregates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".