Microbial indicators are better predictors of wheat yield and quality than N fertilization
Bibliographic record
Abstract
In view of their key roles in many soil- and plant-related processes, we hypothesized that soil microorganisms could play a larger role in determining wheat baking quality than nitrogen fertilization. A field experiment was conducted under bread wheat production conditions, where different fertilization treatments, ranging from 0-120 kg/ha NH4NO3, were applied. Soil samples were taken in May, June and July. Functional genes in the nitrogen cycle were quantified and amplicons of the 16S rRNA gene and the ITS region were sequenced. Wheat yields were measured, and the grain baking quality was analysed for each plot. Fertilisation did not significantly influence the yields and the grain quality. Many bacterial and fungal Amplicon Sequence Variants showed significant positive or negative correlations with yield and grain baking quality parameters. Among the functional gene quantified, the archaeal amoA showed strong negative correlations with the wheat yields and many grain and flour quality parameters. Regression models were able to explain up to 81% of the variability in grain quality based on the microbial data from the May sampling. A better understanding of the microbiology of wheat fields could lead to an optimized management of the N fertilization to maximize yields and grain quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".