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

Authors: BUCHAILLOT, MA. LUISA - University Of Barcelona; SOBA, DAVID - Spanish National Research Council; SHU, TIANCHU - Auburn University; LIU, JUAN - Academy Of Agricultural Science; ARANJUELO, IKER - Spanish National Research Council; ARAUS, JOSE - University Of Barcelona; Runion, George; Prior, Stephen - Steve; KEFAUVER, SHAWN - University Of Barcelona; SANZ-SAEZ, ALVARO - Auburn University

Submitted to: Planta
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/3/2022
Publication Date: 3/23/2022
Citation: Buchaillot, M., Soba, D., Shu, T., Liu, J., Aranjuelo, I., Araus, J.L., Runion, G.B., Prior, S.A., Kefauver, S.C., Sanz-Saez, A. 2022. Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models. Planta. 255:93. https://doi.org/10.1007/s00425-022-03867-6.
DOI: https://doi.org/10.1007/s00425-022-03867-6

Interpretive Summary: One means of ensuring gains in future crop yield could be examining genotypic variability in photosynthesis by improved phenotyping methods. Some photosynthetic parameters that vary in response to climate have been identified for improvement. Direct measurements are very time-consuming, but parameters could be estimated using rapid and non-destructive leaf spectroscopy. We compared four advanced regression models (PLS, BR, ARDR, and LASSO) to estimate parameters based on leaves measured with a field spectroradiometer. Diverse genotypes of two legume crops (soybean and peanut) were examined under different environments (watering level, atmospheric CO2 level, and night temperature). Model sensitivities were assessed for crops and treatments separately and in combination to identify strengths and weaknesses. Regardless of model, robust predictions were achieved for photosynthesis parameters when considering both species. Field spectroscopy shows promising results for estimating variations in photosynthetic capacity based on leaf and canopy spectral properties.

Technical 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), vary in response to climatic conditions and have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming (20 to 60 minutes per evaluation); 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 spectroradiometer. Diverse genotypes of 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 modelling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.55) when considering both species. Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.