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Buchaillot ML, Soba D, Shu T, Liu J, Aranjuelo I, Araus JL, Runion GB, Prior SA, Kefauver SC, Sanz-Saez A. Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models. Planta 2022; 255:93. [PMID: 35325309 PMCID: PMC8948130 DOI: 10.1007/s00425-022-03867-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
MAIN CONCLUSION By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. 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.
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Affiliation(s)
- Ma Luisa Buchaillot
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - David Soba
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - Tianchu Shu
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA
| | - Juan Liu
- Industrial Crops Research Institute, Henan Academy of Agricultural Sciences, Henan, China
| | - Iker Aranjuelo
- Instituto de Agrobiotecnología (IdAB), Consejo Superior de Investigaciones Científicas (CSIC)-Gobierno de Navarra, Av. Pamplona 123, 31192, Mutilva, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain
| | - G Brett Runion
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Stephen A Prior
- U.S. Department of Agriculture-Agricultural Research Service, National Soil Dynamics Laboratory, Auburn, AL, 36832, USA
| | - Shawn C Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, 08028, Barcelona, Spain.
- AGROTECNIO (Center for Research in Agrotechnology), Av. Rovira Roure 191, 25198, Lleida, Spain.
| | - Alvaro Sanz-Saez
- Department of Crop, Soil, and Environmental Sciences, Auburn University, Alabama, USA.
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