Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models

Buchaillot, María Luisa; Soba Hidalgo, David; Shu, Tianchu; Liu, Juan; Aranjuelo Michelena, Iker; Araus, José Luis; Runion, G. Brett; Prior, Stephen A.; Kefauver, Shawn C.; Sanz Sáez, Álvaro

Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models

dc.contributor.author	Buchaillot, María Luisa
dc.contributor.author	Soba Hidalgo, David
dc.contributor.author	Shu, Tianchu
dc.contributor.author	Liu, Juan
dc.contributor.author	Aranjuelo Michelena, Iker
dc.contributor.author	Araus, José Luis
dc.contributor.author	Runion, G. Brett
dc.contributor.author	Prior, Stephen A.
dc.contributor.author	Kefauver, Shawn C.
dc.contributor.author	Sanz Sáez, Álvaro
dc.contributor.department	IdAB. Instituto de Agrobiotecnología / Agrobioteknologiako Institutua	es_ES
dc.date.accessioned	2022-09-26T08:24:13Z
dc.date.available	2022-09-26T08:24:13Z
dc.date.issued	2022
dc.date.updated	2022-09-26T08:06:09Z
dc.description.abstract	One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.	en
dc.description.sponsorship	This research was supported by the Action CA17134 SENSECO (Optical Synergies for Spatiotemporal Sensing of Scalable Ecophysiological Traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu ). This research was also supported by Auburn University and Alabama Agricultural Experimental Station Seed Grant. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature	en
dc.format.mimetype	application/pdf	en
dc.format.mimetype	application/msword	en
dc.identifier.citation	Buchaillot, M.L., Soba, D., Shu, T. et al. Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models. Planta 255, 93 (2022).	en
dc.identifier.doi	10.1007/s00425-022-03867-6
dc.identifier.issn	0032-0935
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/44117
dc.language.iso	eng	en
dc.publisher	Springer	en
dc.relation.ispartof	Planta, 2022, vol. 255 (4)	en
dc.relation.publisherversion	https://doi.org/10.1007/s00425-022-03867-6
dc.rights	© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	Advanced regression models	en
dc.subject	ARDR	en
dc.subject	Bayesian ridge model	en
dc.subject	High-throughput phenotyping	en
dc.subject	Jmax	en
dc.subject	Lasso	en
dc.subject	Leaf reflectance	en
dc.subject	Peanut	en
dc.subject	Photosynthesis	en
dc.subject	PLS	en
dc.subject	Soybean	en
dc.subject	Vc,max	en
dc.title	Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models	en
dc.type	info:eu-repo/semantics/article
dc.type.version	info:eu-repo/semantics/publishedVersion
dspace.entity.type	Publication
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