Nitrogen Yield and Land Use Efficiency in Annual Sole Crops and Intercrops
Bibliographic record
Abstract
Nitrogen is the most limiting nutrient for crop production on the northern Great Plains of North America. This study was initiated to determine if N yield and land use efficiency for N could be improved by manipulating crop diversity using three annual crops (wheat, Triticum aestivum L.; canola, Brassica napus L.; and field pea, Pisum arvense L.) commonly grown on the Canadian Prairies. The study included all combinations of the crops (sole crops and intercrops) and compared their effects on soil N depletion, plant N concentration, plant N yield, and land equivalent ratios for dry matter and grain N yield (NLER) at two field sites in Manitoba, Canada. The pea sole crop treatment tended to result in higher fall soil nitrate (NO3−)–N concentrations compared to other treatments, indicating greater potential for post‐season NO3− leaching after this treatment. There were often greater N concentrations in wheat, canola, and weeds when grown in association with field pea, suggesting that soil N could have been made available for nonlegume uptake through the NO3−–N sparing effect On average, most intercrop treatments resulted in more efficient land use for N compared to component sole crops, with overall mean intercrop NLER values ranging from 1.10 to 1.20. The wheat–canola–pea and canola–pea intercrop treatments tended to produce the highest and most consistent NLER values for crop dry matter and grain yield, respectively. The results of this study suggest that intercrops could be used for more efficient use of N on a per land area basis.
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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".