Soil microbial carbon and phosphorus as influenced by phosphorus fertilization and tillage in a maize-soybean rotation in south-western Quebec
Bibliographic record
Abstract
The management of the soil microbial P pool could improve system sustainability. The long-term impact of inorganic P inputs (0, 40 and 80 kg P2O5 ha-1) and tillage (conventional and ridge tillage) on soil microbial biomass P (SMB-P) was defined in the soybean phase of a 10-yr-old maize-soybean rotation, on a Gleysolic clay-loam. Soil microbial biomass C (SMB-C) and SMB-P were determined four times in the growing season. Yearly applications of 40 kg P2O5 ha-1 increased soil organic carbon level, partly explaining the increase in SMB-P measured at this rate. Results suggest that P-mediated modification of soil microbial community structure also contributed to increase SMB-P at this P rate. The increase of application of P rate (80 kg P2O5 ha-1) produced the largest soybean yield, but generally decreased SMB-P. Our results and those of others suggest that balanced soil fertility (corresponding to fertilizer recommendations in our case) promotes soil microbial development. The use of ridge tillage did not increase the soil organic carbon level, but did increase SMB-P. The SMB-P pool was large (equivalent to 24.4kg P2O5 ha-1) in the 0- to 20-cm soil layer, but unrelated to yield. Improving the ability of crops to access this pool of soil P would increase the value of its management. Key words: Conventional tillage, conservation tillage, P fertilization, soil microbial biomass C, P, and C to P ratio
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".