Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models
Fecha
2022Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Impacto
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10.1007/s00425-022-03867-6
Resumen
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 a ...
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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. [--]
Materias
Advanced regression models,
ARDR,
Bayesian ridge model,
High-throughput phenotyping,
Jmax,
Lasso,
Leaf reflectance,
Peanut,
Photosynthesis,
PLS,
Soybean,
Vc,max
Editor
Springer
Publicado en
Planta, 2022, vol. 255 (4)
Departamento
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. IdAB. Instituto de Agrobiotecnología / Agrobioteknologiako Institutua
Versión del editor
Entidades Financiadoras
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