Accounting for soil biotic effects on soil health and crop productivity in the design of crop rotations
Bibliographic record
Abstract
There is an urgent need for novel agronomic improvements capable of boosting crop yields while alleviating environmental impacts. One such approach is the use of optimized crop rotations. However, a set of measurements that can serve as guiding principles for the design of crop rotations is lacking. Crop rotations take advantage of niche complementarity, enabling the optimization of nutrient use and the reduction of pests and specialist pathogen loads. However, despite the recognized importance of plant-soil microbial interactions and feedbacks for crop yield and soil health, this is ignored in the selection and management of crops for rotation systems. We review the literature and propose criteria for the design of crop rotations focusing on the roles of soil biota and feedback on crop productivity and soil health. We consider that identifying specific key organisms or consortia capable of influencing plant productivity is more important as a predictor of soil health and crop productivity than assessing the overall soil microbial diversity per se. As such, we propose that setting up soil feedback studies and applying genetic sequencing tools towards the development of soil biotic community databases has a strong potential to enable the establishment of improved soil health indicators for optimized crop rotations.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".