High-throughput, image-based phenotyping reveals nutrient-dependent growth facilitation in a grass-legume mixture.

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dc.contributor.authorBall, K.R.
dc.contributor.authorPower, S.A.
dc.contributor.authorBrien, C.
dc.contributor.authorWoodin, S.
dc.contributor.authorJewell, N.
dc.contributor.authorBerger, B.
dc.contributor.authorPendall, E.
dc.contributor.editorPapa, R.
dc.date.issued2020
dc.description.abstractThis study used high throughput, image-based phenotyping (HTP) to distinguish growth patterns, detect facilitation and interpret variations to nutrient uptake in a model mixed-pasture system in response to factorial low and high nitrogen (N) and phosphorus (P) application. HTP has not previously been used to examine pasture species in mixture. We used redgreen- blue (RGB) imaging to obtain smoothed projected shoot area (sPSA) to predict absolute growth (AG) up to 70 days after planting (sPSA, DAP 70), to identify variation in relative growth rates (RGR, DAP 35–70) and detect overyielding (an increase in yield in mixture compared with monoculture, indicating facilitation) in a grass-legume model pasture. Finally, using principal components analysis we interpreted between species changes to HTPderived temporal growth dynamics and nutrient uptake in mixtures and monocultures. Overyielding was detected in all treatments and was driven by both grass and legume. Our data supported expectations of more rapid grass growth and augmented nutrient uptake in the presence of a legume. Legumes grew more slowly in mixture and where growth became more reliant on soil P. Relative growth rate in grass was strongly associated with shoot N concentration, whereas legume RGR was not strongly associated with shoot nutrients. High throughput, image-based phenotyping was a useful tool to quantify growth trait variation between contrasting species and to this end is highly useful in understanding nutrient-yield relationships in mixed pasture cultivations.
dc.description.statementofresponsibilityKirsten Rae Ball, Sally Anne Power, Chris Brien, Sarah Woodin, Nathaniel Jewell, Bettina Berger, Elise Pendall
dc.identifier.citationPLoS ONE, 2020; 15(10):e0239673-1-e0239673-18
dc.identifier.doi10.1371/journal.pone.0239673
dc.identifier.issn1932-6203
dc.identifier.issn1932-6203
dc.identifier.orcidBrien, C. [0000-0003-0581-1817]
dc.identifier.orcidBerger, B. [0000-0003-1195-4478]
dc.identifier.urihttps://hdl.handle.net/2440/146323
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)
dc.rights© 2020 Ball et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.source.urihttps://doi.org/10.1371/journal.pone.0239673
dc.subjectLegumes; Grasses; Monoculture cropping; Fertilizers; Principal component analysis; Experimental design; Plant growth and development; Phenotypes;
dc.subject.meshFabaceae
dc.subject.meshPoaceae
dc.subject.meshNitrogen
dc.subject.meshPhosphorus
dc.subject.meshSoil
dc.subject.meshBiomass
dc.subject.meshAgriculture
dc.subject.meshHigh-Throughput Screening Assays
dc.subject.meshGrassland
dc.subject.meshBiological Variation, Population
dc.subject.meshNutrients
dc.titleHigh-throughput, image-based phenotyping reveals nutrient-dependent growth facilitation in a grass-legume mixture.
dc.typeJournal article
pubs.publication-statusPublished

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