Increased nitrogen retention by cover crops: implications of planting date on soil and plant nitrogen dynamics
Bibliographic record
Abstract
Abstract Cover crops are frequently adopted to immobilize residual nitrogen post-harvest, thereby reducing potential N losses. However, the effectiveness of a cover crop depends on the species planting date, and other management practices. Limited information on N dynamics in cover crop systems is available specially in short-season vegetable rotations under temperate climate. From 2008 to 2010, a split-plot field experiment was carried out in a humid, temperate climate with cover crop treatment as the main plot factor [no cover crop control (NoCC), cereal rye, hairy vetch, oat, forage pea, oilseed radish (OSR) and a control with fertilizer N to the cucumber crop (NoCC + N)], and cover crop planting date as the split factor (early and late) to evaluate their impacts on cover crop biomass and N dynamics over the fall and following cucumber crop. All cover crop treatments significantly lowered soil mineral nitrogen (SMN) by 39–87% compared to the NoCC control, which was concomitant with cover crop growth and N accumulation. In the fall, SMN (0–90 cm depth) was less under the early-planted cover crops (avg. 78 kg N ha −1 ) compared to the late-planted (avg. 100 kg N ha −1 ). In April, greater plant available nitrogen (PAN, sum of SMN to 60 cm depth and plant N) with cover crops than without demonstrated N conservation over the winter and into the cucumber crop. Crop yield was equal to or better with a cover crop compared with the NoCC in both years; moreover, compared to the NoCC + N control yields were equivalent with OSR and pea. Oat, vetch and pea cover crops benefited the most by having an earlier planting date, while OSR and rye are recommended if the planting date is delayed. Although an early August planting date significantly increased plant N accumulation and SMN by November, this species-dependent interaction did not persist into the following season in yield and N accounted for in the system.
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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".