Fall Rye Reduced Residual Soil Nitrate and Dryland Spring Wheat Grain Yield
Bibliographic record
Abstract
Core Ideas Fall rye reduced pre‐plant nitrate by 2 to 18 times compared with tillage radish. Fall rye reduced dryland spring wheat grain yield by 38 to 58% compared with tillage radish. Pre‐plant soil NH 4 –N plus NO 3 –N explained 93% of spring wheat grain yield variability. Limited information about how cover crop management impacts the agronomic performance of succeeding annual crops in semiarid regions constrains cover crop utilization. Therefore, over 2 yr we quantified how cover crop species (fall rye [ Secale cereale L. ‘AC Remington’] or oilseed radish [ Raphanus sativus L. ‘Tillage radish’]) and nutrient source (compost or inorganic fertilizer) affected cover crop biomass and N uptake, soil nitrate N (NO 3 –N) and ammonium N (NH 4 –N), and the agronomic performance of the succeeding spring wheat ( Triticum aestivum L.) test crop. Fall rye reduced pre‐plant NO 3 –N by 2 to 18 times compared with oilseed radish, and reduced spring wheat grain yields by 38 to 58% compared with amended soils with no cover crop and oilseed radish. Inorganically fertilized soils led to 21% greater pre‐plant soil NO 3 –N concentrations than the compost‐amended soil in 2013–2014 but nutrient source did not significantly affect NO 3 –N concentrations in 2014–2015. A quadratic function explained 93% of the variability between pre‐plant soil NH 4 –N plus NO 3 –N (0–7.5‐cm depth) and spring wheat grain yield in 2014, indicating that the N supply limited spring wheat grain yield. We conclude that fall rye scavenged residual NO 3 –N better than oilseed radish during the non‐growing season, particularly during the spring period when this perennial species assimilates N, but under semiarid conditions it may decompose and mineralize too slowly to supply N at the right time for the subsequent crop, while oilseed radish tended to boost spring wheat grain yield.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".