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 NH4–N plus NO3–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 cerealeL. ‘AC Remington’] or oilseed radish [Raphanus sativusL. ‘Tillage radish’]) and nutrient source (compost or inorganic fertilizer) affected cover crop biomass and N uptake, soil nitrate N (NO3–N) and ammonium N (NH4–N), and the agronomic performance of the succeeding spring wheat (Triticum aestivumL.) test crop. Fall rye reduced pre‐plant NO3–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 NO3–N concentrations than the compost‐amended soil in 2013–2014 but nutrient source did not significantly affect NO3–N concentrations in 2014–2015. A quadratic function explained 93% of the variability between pre‐plant soil NH4–N plus NO3–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 NO3–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 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".