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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1093/jxb/erab115
|2 doi
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|a pubmed25n1075.xml
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|a (DE-627)NLM322702313
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|a (NLM)33713415
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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|a Robles-Zazueta, Carlos A
|e verfasserin
|4 aut
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|a Field-based remote sensing models predict radiation use efficiency in wheat
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 21.05.2021
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|a Date Revised 16.07.2022
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|a published: Print
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|a Citation Status MEDLINE
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|a © The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Experimental Biology.
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|a Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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4 |
|a High-throughput phenotyping
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| 650 |
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4 |
|a RUE
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| 650 |
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4 |
|a hyperspectral reflectance
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| 650 |
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|a partial least squares regression
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| 650 |
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4 |
|a physiological breeding
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| 650 |
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4 |
|a vegetation indices
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| 650 |
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4 |
|a wheat
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| 700 |
1 |
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|a Molero, Gemma
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pinto, Francisco
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Foulkes, M John
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Reynolds, Matthew P
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Murchie, Erik H
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of experimental botany
|d 1985
|g 72(2021), 10 vom: 04. Mai, Seite 3756-3773
|w (DE-627)NLM098182706
|x 1460-2431
|7 nnas
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| 773 |
1 |
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|g volume:72
|g year:2021
|g number:10
|g day:04
|g month:05
|g pages:3756-3773
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| 856 |
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|u http://dx.doi.org/10.1093/jxb/erab115
|3 Volltext
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|d 72
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|h 3756-3773
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