Application of plant–soil feedbacks in the selection of crop rotation sequences
Bibliographic record
Abstract
Abstract Plant–soil feedback (PSF) can be a major driver of plant performance in communities, and this concept can be used in selecting crop rotation sequences to maximize agricultural yields. Potential benefits of using PSF in this context include nutrient use optimization, pathogen reduction, and enhancement of mutualisms between crops and microbes. Yet the contributions of these combined mechanisms are poorly understood. Here we investigated the relative contributions of these mechanisms using five major crops commonly cultivated in rotation (alfalfa, canola, maize, soybean, and wheat) under controlled conditions. We trained soil by growing each of the five crops in a “training phase,” and then reciprocally planted the five crops in the trained soils in a “feedback phase.” To tease out soil biota from nutrient effects, we established three treatments: “control” (trained unsterilized soil used in the feedback phases), “biota” (sterilized soil in the feedback phase inoculated with soil biota from the control treatment after the training phase), and “nutrient” (sterilized soils in both phases). Plant–soil feedback for each crop was calculated by comparing the total biomass of each crop grown in soils trained by each of the four other crops (i.e., in rotation) against total biomass in self‐trained soil (i.e., monocropping). We found that PSF values varied among crop combinations in all the treatments, but such variation was the greatest in the nutrient treatment. Overall, soil biota feedback tended to be lower, whereas nutrient feedback tended to be greater compared to the unsterilized control soil, suggesting that effects of antagonistic biota outweighed those of beneficial microbes in the biota treatment, and that plants optimized nutrient uptake when the soil microbiome was absent in the nutrient treatment. Furthermore, soils in the nutrient treatment trained by the legume crops (alfalfa and soybean) tended to provide the greatest positive feedback, emphasizing the important legacy of N 2 fixers in crop rotation. Taken together, our data demonstrate how nutrients and soil biota can be integral to PSFs among crops, and that assessing PSFs under controlled conditions can serve as a basis to determine the most productive crop rotation sequences prior to field testing.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".