Effect of tillage and crop on arbuscular mycorrhiza colonization of winter wheat and triticale under Mediterranean conditions
Bibliographic record
Abstract
Abstract Large‐scale inoculation with arbuscular mycorrhizal fungi (AMF) is generally impractical in most regions and we have little understanding of the factors that determine inoculation success. Nevertheless, the ability to take full advantage of indigenous AMF for sustainable production needs to be developed within cropping systems. We used part of a long‐term field experiment to understand the influence of tillage and the preceding crop on AMF colonization over the growing season. Arbuscular mycorrhiza colonization rate was more affected by treatment (tillage or the combination of crop and preceding crop) than by the total number of AMF spores in the soil. Conventional tillage (CT) had a statistically significant negative effect ( P ≤ 0.05) on spore numbers isolated from the soil, but only in the first year of study. However, the AMF colonization rate was significantly reduced by CT, and the roots of wheat, Triticum aestivum , L, cv. Coa after sunflower, Helianthus annuus L., were less well colonized than were those of triticale, X Triticosecale Wittmack, cv. Alter after wheat, but the affect of tillage was more pronounced than was the effect of crop combination. Under no‐till there was a significant increase in AMF colonization rate throughout the sampling period in both wheat and triticale, indicating that the extraradical mycelium previously produced acted as a source of inoculum. In general, triticale showed greater AMF colonization than wheat, despite the preceding crop being less mycotrophic. Under these experimental conditions, typical of Mediterranean agricultural systems, AMF colonization responded more strongly to tillage practices than to the combination of crop and preceding crop.
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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 | 0.001 |
| 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 | 0.000 | 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".