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

文献类型： 外文期刊

作者： Luisa Buchaillot, Ma 1 ; Soba, David 3 ; Shu, Tianchu 4 ; Liu, Juan 5 ; Aranjuelo, Iker 3 ; Araus, Jose Luis 1 ; Runion, G. Brett 6 ; Prior, Stephen A. 6 ; Kefauver, Shawn C. 1 ; Sanz-Saez, Alvaro 4 ;

作者机构： 1.Univ Barcelona, Fac Biol, Plant Physiol Sect, Integrat Crop Ecophysiol Grp, E-08028 Barcelona, Spain

2.AGROTECNIO Ctr Res Agrotechnol, Av Rovira Roure 191, Lleida 25198, Spain

3.Consejo Super Invest Cient CSIC Gobierno Navarra, Inst Agrobiotecnol IdAB, Av Pamplona 123, Mutilva 31192, Spain

4.Auburn Univ, Dept Crop Soil & Environm Sci, Auburn, AL 36849 USA

5.Henan Acad Agr Sci, Ind Crops Res Inst, Zhengzhou, Henan, Peoples R China

6.USDA ARS, Natl Soil Dynam Lab, Auburn, AL 36832 USA

关键词： Advanced regression models; ARDR; Bayesian ridge model; High-throughput phenotyping; J(max); Lasso; Leaf reflectance; Peanut; Photosynthesis; PLS; Soybean; V-c,V-max

期刊名称：PLANTA （ 影响因子：4.54； 五年影响因子：4.689 ）

ISSN： 0032-0935

年卷期： 2022 年 255 卷 4 期

页码：

收录情况： SCI

摘要： 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 (V-c,V-max) and maximum electron transport rate supporting RuBP regeneration (J(max)), 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 V-c,V-max and J(max) 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 V-c,V-max (R-2 = 0.70) and J(max) (R-2 = 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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