Abstract
Introduction Predicting, or correlating, soil microbiome metrics with above ground phenotypic plant measurements would enable rapid diagnosis of soil microbiome imbalances. Rapid plant measurements through remote sensing are a leading innovation in agriculture and have reduced the need for labor-intensive plant and soil measurements. In the current study we utilized cotton ( Gossypium hirsutum ) as a plant model whereby stress was induced by drought and root-knot nematode (RKN; Meloidogyne incognita ) infection to induce a change in the soil microbiome which would be reflected as a plant phenotypic response. Methods The experiment was a randomized complete block design with two cotton genotypes (RKN-susceptible or RKN-resistant) and four stress combinations. Rootzone samples were collected upon plant termination and quantified for five soil health genes: 16S rRNA, 18S rRNA, ure C, pho A, and cbb LR. Plant physiology, biomass, and remote sensing hyperspectral readings were previously reported. Results and discussion Overall, RKN infection and plant genotype treatments had little effect on genes. Interestingly, drought stress increased most gene abundances, while plant physiological and biomass measurements decreased, indicating microbiome response to plant stress. Hyperspectral reflectance, through machine learning, accurately predicted the presence of drought stress with an area under the receiver operating characteristic curve value of 0.864. Furthermore, the readings were able to predict the abundance values for all genes except 18S rRNA within one standard deviation of ground truth levels. This study demonstrated that there are key plant characteristics that are registered via hyperspectral wavelengths which can be used to accurately predict soil health gene abundance. While the use of hyperspectral readings and soil microbiome status to inform plant health and vice versa are still in their infancy, the current study provides us with future directions towards this end.
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Claims 5
Claim · model-written summary
Root-knot nematode infection and cotton genotype had little effect on measured soil-health genes.
- Taxon
- cotton
Gossypium hirsutumt0 - Treatment
- RKN infection and plant genotype treatmentse0
- Interactor
- RKNe0pathogen
- Outcome
- genese0
- Direction
- little effecte0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“RKN infection and plant genotype treatments had little effect on genes”
t0“cotton”
Claim · model-written summary
Drought increased most soil-health gene abundances.
- Taxon
- cottonGossypium hirsutumt0
- Treatment
- drought stresse0
- Outcome
- gene abundancese0
- Direction
- increasede0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“drought stress increased most gene abundances”
t0“cotton”
Claim · model-written summary
Drought reduced cotton physiological measurements and biomass.
- Taxon
- cottonGossypium hirsutumt0
- Treatment
- droughtt0
- Outcome
- plant physiological and biomass measurementse0
- Direction
- decreasede0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“plant physiological and biomass measurements decreased”
t0“drought stressed greenhouse cotton”
Claim · model-written summary
Hyperspectral machine learning predicted drought presence with AUROC 0.864.
- Taxon
- cottonGossypium hirsutumt0
- Outcome
- area under the receiver operating characteristic curvee0
- Value
- 0.864e0
- Qualifier
- presence of drought stresse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Hyperspectral reflectance, through machine learning, accurately predicted the presence of drought stress with an area under the receiver operating characteristic curve value of 0.864”
t0“cotton”
Claim · model-written summary
Hyperspectral readings predicted abundance of all measured soil-health genes except 18S rRNA within one standard deviation of ground truth.
- Taxon
- cottonGossypium hirsutumt0
- Outcome
- abundance values for all genes except 18S rRNAe0
- Value
- one standard deviatione0
- Comparator
- ground truth levelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“readings were able to predict the abundance values for all genes except 18S rRNA within one standard deviation of ground truth levels”
t0“cotton”
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