Previous legume identity influence wheat rhizosphere microbial communities and grain protein content
Bibliographic record
Abstract
Abstract In crop rotation systems, plant-soil feedback (PSF) effects can modulate nutrient cycling in soil, like organic carbon (C) and nitrogen (N) cycling. Organic nitrogen is present in plant and microbial necromass, and it must be depolymerized by microbes to become available for crops. Since plant identity can modulate microbial diversity and community composition, we thought that previous crop identity and its residue management would also change the microbial functional capacity, and thereby impact soil N availability, and the quality and yield of the following crop. To test this, two legumes ( Vicia faba L., i.e. faba bean, and Pisum sativum L., i.e., yellow pea) were grown in two fields (Cloutier, and Palmarolle) in Abitibi-Témiscamingue, Québec, Canada (n=3 for each field). At the end of the growing season, we harvested peas and faba beans in both fields. During the following growing season, spring wheat ( Triticum aestivum L.) was sown and received, or not granulated chicken manure as an organic fertilizer. We determined the diversity and composition of the microbial communities and their enzymatic depolymerization capacity in the soil and the rhizosphere each growing season. During wheat growth, previous legumes shaped bacterial (p-value = 0.006) and fungal (p-value = 0.001) communities without modulating the enzymatic activity of wheat-associated rhizosphere microbes. However, faba bean as a previous crop increased soil ammonium and wheat grain protein content at harvest as compared to peas at Cloutier. Altogether, our results show that faba beans can enhance wheat N nutrition, without a concomitant increase in potential protein or cellulose depolymerization, suggesting more mineralization due to increases in fungal: bacterial ratio or in the availability of substrates. Understanding plant-soil feedback in crop rotation systems is crucial to improve our practices and sustainably meet crops’ nutritional needs. Highlights Previous crop identity distinctively shaped wheat rhizosphere microbial communities Previous legume did not altered the enzymatic activity of wheat-associated rhizosphere microbes, but increased soil ammonium Wheat grain protein content was higher following faba bean than peas
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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".