Extraction of Subsoil Nitrogen by Alfalfa, Alfalfa–Wheat, and Perennial Grass Systems
Bibliographic record
Abstract
The role of alfalfa ( Medicago sativa L.) in extracting NO 3 –N from deep soils of areas with cold, short growing seasons, such as western Canada, is not well understood. A study was established in 1990 to determine NO 3 –N extraction ability to 300 cm; initial soil NO 3 –N concentrations were high (>8 mg kg −1 ). Systems included continuous alfalfa; annual rotations of spring wheat ( Triticum aestivum L.), field pea ( Pisum sativum L.), and barley ( Hordeum vulgare L.); a native‐grass system [big bluestem ( Andropogon gerardi Vitman) and western wheatgrass ( Agropyron smithii Rydb.)]; and continuous fallow. The annual rotation effectively lowered NO 3 –N to <2.3 mg kg −1 in the 30‐ to 90‐cm depth. By the 4th yr, alfalfa had reduced NO 3 –N concentrations to <3.8 mg kg −1 for the 30‐ to 240‐cm increment. The greatest NO 3 –N extraction benefits of alfalfa were realized in the 4th yr at a maximum soil depth of 270 cm. Subsoil NO 3 –N concentration increased in the continuous alfalfa between the 4th and 6th yr. Greater NO 3 –N extraction occurred with the native‐grass treatment compared with continuous alfalfa in the 0‐ to 120‐cm soil depth; however, similar extraction patterns existed below 120 cm. A system involving 4 yr of alfalfa followed by two wheat crops resulted in the lowest subsoil NO 3 –N concentration, even lower than the continuous alfalfa and native‐grass systems. It was concluded that subsoil NO 3 –N extraction with alfalfa was maximized when alfalfa was rotated with annual crops.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".