Asad, Numan Ibne; Wang, Xiao-Bo; Dozois, Jessica; Azarbad, Hamed; Constant, Philippe 
ORCID: https://orcid.org/0000-0003-2739-2801 et Yergeau, Étienne ORCID: https://orcid.org/0000-0002-7112-3425
(2022).
Early Season Soil Microbiome Best Predicts Wheat Grain Quality
FEMS Microbiology Ecology
, vol. 99
, nº 1:fiac14.
pp. 1-13.
DOI: 10.1093/femsec/fiac144.
Résumé
Previous studies have shown that it is possible to accurately predict wheat grain quality and yields using microbial indicators. However, it is uncertain what the best timing for sampling is. For optimal usefulness of this modeling approach, microbial indicators from samples taken early in the season should have the best predictive power. Here, we sampled a field every two weeks across a single growing season and measured a wide array of microbial parameters (amplicon sequencing, abundance of N-cycle related functional genes, and microbial carbon usage) to find the moment when the microbial predictive power for wheat grain baking quality is highest. We found that the highest predictive power for wheat grain quality was for microbial data derived from samples taken early in the season (May-June) which coincides roughly with the seedling and tillering growth stages, that are important for wheat N nutrition. Our models based on LASSO regression also highlighted a set of microbial parameters highly coherent with our previous surveys, including alpha- and beta-diversity indices and N-cycle genes. Taken together, our results suggest that measuring microbial parameters early in the wheat growing season could help farmers better predict wheat grain quality.
| Type de document: | Article |
|---|---|
| Mots-clés libres: | Amplicon sequencing; Community level physiological profiling; Grain quality; LASSO regression; Nitrogen cycle; Wheat mi- crobiome |
| Centre: | Centre INRS-Institut Armand Frappier |
| Date de dépôt: | 29 avr. 2023 20:34 |
| Dernière modification: | 29 avr. 2023 20:34 |
| URI: | https://espace.inrs.ca/id/eprint/13333 |
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