Dataset Information

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

ABSTRACT:

Main conclusion

By combining hyperspectral signatures of peanut and soybean, we predicted V_cmax and J_max 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 (V_c,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,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,max (R² = 0.70) and J_max (R² = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.

SUBMITTER: Buchaillot ML

PROVIDER: S-EPMC8948130 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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

Buchaillot Ma Luisa ML Soba David D Shu Tianchu T Liu Juan J Aranjuelo Iker I Araus José Luis JL Runion G Brett GB Prior Stephen A SA Kefauver Shawn C SC Sanz-Saez Alvaro A

Planta 20220324 4

<h4>Main conclusion</h4>By combining hyperspectral signatures of peanut and soybean, we predicted V<sub>cmax</sub> and J<sub>max</sub> 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 ...[more]

PMID: 35325309

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Project description:The use of hydroponic systems for cultivation in controlled climatic conditions and the selection of suitable genotypes for the specific environment help improving crop growth and yield. We hypothesized that plant performance in hydroponics could be further maximized by exploiting the action of plant growth-promoting organisms (PGPMs). However, the effects of PGPMs on plant physiology have been scarcely investigated in hydroponics. Within a series of experiments aimed to identify the best protocol for hydroponic cultivation of soybean [Glycine max (L.) Merr.], we evaluated the effects of a PGPMs mix, containing bacteria, yeasts, mycorrhiza and trichoderma beneficial species on leaf anatomy, photosynthetic activity and plant growth of soybean cv. 'Pr91m10' in closed nutrient film technique (NFT). Plants were grown in a growth chamber under semi-aseptic conditions and inoculated at seed, seedling and plant stages, and compared to non-inoculated (control) plants. Light and epi-fluorescence microscopy analyses showed that leaves of inoculated plants had higher density of smaller stomata (297 vs. 247 n/mm2), thicker palisade parenchyma (95.0 vs. 85.8 μm), and larger intercellular spaces in the mesophyll (57.5% vs. 52.2%), compared to non-inoculated plants. The modifications in leaf functional anatomical traits affected gas exchanges; in fact starting from the reproductive phase, the rate of leaf net photosynthesis (NP) was higher in inoculated compared to control plants (8.69 vs. 6.13 μmol CO2 m-2 s-1 at the beginning of flowering). These data are consistent with the better maximal PSII photochemical efficiency observed in inoculated plants (0.807 vs. 0.784 in control); conversely no difference in leaf chlorophyll content was found. The PGPM-induced changes in leaf structure and photosynthesis lead to an improvement of plant growth (+29.9% in plant leaf area) and seed yield (+36.9%) compared to control. Our results confirm that PGPMs may confer benefits in photosynthetic traits of soybean plants even in hydroponics (i.e., NFT), with positive effects on growth and seed production, prefiguring potential application of beneficial microorganisms in plant cultivation in hydroponics.

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Dataset Information

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

Main conclusion

Publications

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

Similar Datasets

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