Soil–Plant Indices Help Explain Legume Response to Crop Rotation in a Semiarid Environment
Bibliographic record
Abstract
Crop productivity is typically affected by various soil-plant factors systematically as they influence plant photosynthesis, soil fertility, and root systems. However, little is known about how the productivity of legumes is related to crop rotation systems. The objectives of this study were to determine the effect of rotation systems on legume productivity and the relationships among legume productivity and soil-plant factors. Three annual legumes - Chickpea (Cicer arietinum L.), pea (Pisum sativum L.), and lentil (Lens culinaris Medikus), were included in various diversified rotation systems and compared with legume monoculture in the 8-yr rotation study. Soil N and water conditions, and canopy and root systems were evaluated at the end of 8-year rotation in the semiarid Canadian prairies. Results showed that diversified rotation systems improved leaf greenness by 4%, shoot biomass by 25%, nodule biomass by 44%, and seed yield by 95% for chickpea and pea, but such effects were not found for lentil. Pea monocultures increased root rot severity by 3-fold compared with diversified rotations, and chickpea monoculture increased shoot rot severity by 23%, root rot severity by 96% and nodule damage by 219%. However, all the legume monocultures improved soil N accumulation by an average 38% compared to diversified systems. Pea and chickpea displayed considerable sensitivity to plant biotic stresses, whereas lentil productivity had a larger dependence on initial soil N content. The 8-yr study concludes that the rotational effect on legume productivity varies with legume species, the frequency of a legume appearing in the rotation, and the integration of relevant soil and plant indices.
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.
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 | 0.001 | 0.001 |
| 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".