Soil health improvements under cover crops are associated with enhanced soil content of cytokinins
Bibliographic record
Abstract
Cytokinins (CKs) are phytohormones produced by plants and other soil life. including bacteria, fungi, insects, and earthworms. These organisms can release CKs to the soil, which may have positive implications for soil health and plant growth. However, no studies have examined phytohormones as soil health indicators. In custom-designed rhizo-pots that separated rhizosphere and bulk soils, the cover crops tillage radish and cereal rye were used to manipulate soil health parameters: soil pH, soil organic matter, soil active carbon, soil microbial community diversity, and extracellular enzyme activities involved in C, N and P cycling. Data were compared to impacts of cover crops on CKs that were purified from the complex soil and measured with HPLC-HRMS/MS. From soil we detected free base-CKs (trans-zeatin (tZ), isopentenyladenine (iP)), riboside-CKs (RB-CKs), cis-zeatin riboside (cZR), isopentenyladenosine (iPR) and four methylthiolated CKs: 2-methylthio-zeatin (2MeSZ), 2-methylthio-zeatin ribosides (2MeSZR), 2-methylthio-isopentenyladenine (2MeSiP), and 2-methylthio-isopentenyladenine riboside (2MeSiPR). These CK levels were significantly enhanced in cover cropped soil compared to uncultivated soil, and reflect a positive relationship between soil CK profiles and other soil health parameters - notably, between total CK and active C levels and soil microbial community diversity. This is the first detailed soil CK analysis and assessment of its potential use as a novel, reliable, short-term soil health parameter. The increased CK concentrations in cover cropped soils likely reflects the activity levels of soil life (plants, microbes, animals) and provides a rationale to use CKs as tools to evaluate soil health as influenced by agricultural management strategies.
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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".