Increasing crop diversity in wheat rotations increases yields but decreases soil health
Bibliographic record
Abstract
Abstract Demand for food is rising and the ability of any particular soil to support sustainable food production is dependent upon a variety of soil biochemical, chemical and physical soil parameters. However, the challenge is that the impacts of a new management practice on soil properties may take years to assess. In this field study, we investigated the effects of long‐term cropping rotation treatments (established in 2001) on soil health indicators from soils under monoculture, 2‐yr and 3‐yr crop rotations with and without a cover crop. In particular, we compared soil heath indicators under eight cropping sequences including continuous winter wheat ( Triticum aestivum L.) (WW), soybean ( Glycine max L.)–winter wheat (S–WW), corn ( Zea mays L.)–soybean–winter wheat (C–S–WW), and winter wheat–soybean–soybean (WW–S–S) all with and without red clover ( Trifolium pretense L.) (RC) in the wheat phase of the rotation. We measured ten soil health indicators collected from the wheat phase of the rotation. Crop rotation had greater effects on soil health indicators than cover crop. After 17 years, crop yields were 23–28% greater for the 2‐yr and 3‐yr rotations than monoculture WW in the presence of red clover. In the absence of red clover, yields were 32–39% greater for the 2‐yr and 3‐yr rotations than monoculture WW. However, the soil health indicators were significantly greater for monoculture WW than the 2‐yr and 3‐yr rotations. These results suggest WW enhanced soil health while crop rotations with soybean negatively impacted most biochemical soil health parameters.
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 | 0.000 | 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) | 0.002 | 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".