Soil residual water and nutrients explain about 30% of the rotational effect in 4-year pulse-intensified rotation systems
Bibliographic record
Abstract
Diverse crop rotations enable the best use of residual soil water and nutrients, thus decreasing necessary production inputs. Here, we determined the effect of cropping sequences on soil residual water and nutrients and the performance of subsequent wheat (Triticum aestivum L.). Nine rotation systems were evaluated at Swift Current, SK, and Brooks, AB, from 2010 to 2014. Pea (P, Pisum sativum L.) and lentil (L, Lens culinaris Medik.) as preceding crops before wheat (W) or the rotation systems with pea (P–P–P–W) or lentil (L–L–L–W) included more than once in the 4-yr rotations had the highest residual soil water and N in the 30–90 cm depth and continuous wheat (W–W–W–W) had the lowest. Preceding pea and lentil increased the grain yield of the subsequent wheat by 26% and 18%, respectively, as compared with continuous wheat. Variance partitioning of redundancy analysis revealed that soil residual water and residual N explained 12.4%–42.7% (average 30%) of the yield variation observed in the subsequent wheat, with the rest of the rotational benefits unexplainable by soil residual water and residual nutrients. Investigation of the factors other than soil water and nutrients that contribute to the succeeding wheat yield may further enhance the rotational effect.
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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".