Nitrogen Yield and Land Use Efficiency in Annual Sole Crops and Intercrops
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 (NO 3 − )–N concentrations compared to other treatments, indicating greater potential for post‐season NO 3 − 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 NO 3 − –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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it