Long-term rotation impacts soil total macronutrient levels and wheat response to applied nitrogen, phosphorus, potassium, sulfur in a Luvisolic soil
Bibliographic record
Abstract
Over the last 20–30 yr, increased intensification and diversity of crop rotations, along with increasingly higher yielding crop cultivars on the Northern Great Plains, has increased nutrient removal from cropping systems, but also increased crop residues returned to the soil, affecting soil nutrient cycling, soil carbon (C) and nutrient balances. The University of Alberta Breton Classical Plots, established in 1929, consist of two crop rotations of varying diversity and intensity: (1) wheat–fallow (WF); and (2) 5 yr, cereal–forage. Superimposed on these rotations are eight fertility treatments, including a check (control), manure, balanced (NPKS), and nutrient exclusion treatments. Soil total C, nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) levels were measured on soil samples (0–15 cm) collected from both rotations in 2013. Wheat yields and N uptake for the 2007–2018 growing seasons from both rotations were compared. In the 5 yr rotation, soil total C, N, and S, wheat yield and wheat N uptake were greater than the WF rotation. Soil total P levels were not different between the two rotations, but soil total K was higher in the WF rotation. Despite higher soil S and comparable soil P, wheat yield and N uptake response to applied P and S was greater in the 5 yr rotation compared with the WF rotation. Response to applied N in the 5 yr rotation was muted because of significant inputs of biologically fixed N. Wheat also responded to applied K in the 5 yr rotation. These results highlight the need to replace exported nutrients.
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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) | 0.001 | 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".