Changes in Soil Phosphorus Fractions for a Long‐Term Corn‐Soybean Rotation with Tillage and Phosphorus Fertilization
Bibliographic record
Abstract
Determining how agricultural management practices affect soil P over time could further our understanding of soil P cycling and, thereby, improve P fertilizer use. This study assessed the effects of tillage and P fertilization on soil P fractions over 10‐ and 16‐yr periods of cultivation. In 1992, a long‐term corn ( Zea mays L.) and soybean [ Glycine max (L.) Merr.] rotational experiment was established in eastern Canada. Soil samples (0–15 cm) were collected in spring 2002 and 2008 before corn was planted in plots cultivated under moldboard plow (MP) and no till (NT) management and fertilized with 0, 17.5, or 35 kg P ha ‐1 and 160 kg N ha ‐1 (applied only during the corn phase). Soil samples were analyzed for different attributes including P fractions, Mehlich‐3 P (P M3 ), P saturation index (PSI), and other chemical properties. Results show that P M3 and PSI were greater under NT compared with MP and also increased with increasing P application. Resin‐P was greater under fertilized NT compare with MP after the 10‐yr period. Similar results were obtained with NaHCO 3 –inorganic P (Pi) after the 16‐yr period. The P fertilization increased labile P fractions and HCl‐P after the 10‐yr period but increased all Pi fractions after the 16‐yr period. This indicates that the effects of P fertilization increased over time. For the two periods of cultivation, total P declined regardless of treatment; resin‐P, HCl‐P, NaOH‐organic P (Po), and residual‐P were the main contributors and accounted for 83 to 98% of the decrease in total P. Regressions between total P and cumulative P budget indicate that the cumulative P budget variations account for 61 and 43% of total P variations after a 10‐ and 16‐yr period, respectively. Positive correlations between all Pi fractions and cumulative P budget indicate that combined NT and P fertilization enhanced soil Pi fractions, thereby improving soil P supplying capacity and P balance.
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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 | 0.000 |
| 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 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".