Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars

• Breeding for drought tolerance is becoming a necessity for most of the main crops, including soybean. • Phenotyping for physiological traits is considered unfeasible in breeding strategy focuses on abiotic stress tolerance. • Spectroscopy data can be used for high-throughput phenotyping methodology for hard-to-measure traits. • PLSR models successfully predicted physiological parameters at leaf-level. • Hyperspectral data can be used as a selection methodology for physiological traits in soybean cultivars under drought conditions. Understanding cultivars' physiological traits variations under abiotic stresses is critical to improve phenotyping and selections of resistant crop varieties. Traditional methods of accessing physiological traits in plants are costly and time consuming, which prevents their use in breeding programs. Spectroscopy data and statistical approaches such as partial least square regression could be applied to rapidly collect and predict several physiological parameters at leaf-level, allowing phenotyping several genotypes in a high-throughput manner. We collected spectroscopy data of twenty soybean cultivars planted under well-watered and drought conditions during the reproductive phase. At 20 days after drought was imposed, we measured leaf pigments content (chlorophyll a and b, and carotenoids), specific leaf area, electrons transfer rate, and photosynthetic active radiation. At 28 days after drought imposition, we measured leaf pigments content, specific leaf area, relative water content, and leaf temperature. Partial least square regression models accurately predicted leaf pigments content, specific leaf area, and leaf temperature (cross-validation R 2 ranging from 0.56 to 0.84). Discriminant analysis using 54 wavelengths was able to select the best-performance cultivars regarding all evaluated physiological traits. We showed the great potential of using spectroscopy as a feasible, non-destructive, and accurate method to estimate physiological traits and screening of superior genotypes.

Authors

Institutions

Publication Details

Journal
Smart Agricultural Technology
Published
2024-10-28
DOI
https://doi.org/10.1016/j.atech.2024.100626
Citations
4
Primary Topic
Soybean genetics and cultivation
Type
article
Field-Weighted Citation Impact
2.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars

Herika Paula Pessoa, Ramon Gonçalves de Paula, Cibele Hummel do Amaral, Felipe Lopes da Silva et al.
4 citations
Smart Agricultural Technology
Soybean genetics and cultivation
2.40
article

Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars

Herika Paula Pessoa, Ramon Gonçalves de Paula, Cibele Hummel do Amaral, Felipe Lopes da Silva, Laércio Junio da Silva, Martha Freire da Silva
article en
4 citations

Abstract

• Breeding for drought tolerance is becoming a necessity for most of the main crops, including soybean. • Phenotyping for physiological traits is considered unfeasible in breeding strategy focuses on abiotic stress tolerance. • Spectroscopy data can be used for high-throughput phenotyping methodology for hard-to-measure traits. • PLSR models successfully predicted physiological parameters at leaf-level. • Hyperspectral data can be used as a selection methodology for physiological traits in soybean cultivars under drought conditions. Understanding cultivars' physiological traits variations under abiotic stresses is critical to improve phenotyping and selections of resistant crop varieties. Traditional methods of accessing physiological traits in plants are costly and time consuming, which prevents their use in breeding programs. Spectroscopy data and statistical approaches such as partial least square regression could be applied to rapidly collect and predict several physiological parameters at leaf-level, allowing phenotyping several genotypes in a high-throughput manner. We collected spectroscopy data of twenty soybean cultivars planted under well-watered and drought conditions during the reproductive phase. At 20 days after drought was imposed, we measured leaf pigments content (chlorophyll a and b, and carotenoids), specific leaf area, electrons transfer rate, and photosynthetic active radiation. At 28 days after drought imposition, we measured leaf pigments content, specific leaf area, relative water content, and leaf temperature. Partial least square regression models accurately predicted leaf pigments content, specific leaf area, and leaf temperature (cross-validation R 2 ranging from 0.56 to 0.84). Discriminant analysis using 54 wavelengths was able to select the best-performance cultivars regarding all evaluated physiological traits. We showed the great potential of using spectroscopy as a feasible, non-destructive, and accurate method to estimate physiological traits and screening of superior genotypes.

Smart Agricultural TechnologyVol. 9
University of Minnesota (US), Universidade Federal de Viçosa (BR), Cooperative Institute for Research in Environmental Sciences (US), University of Colorado Boulder (US), Universidade Estadual Paulista (Unesp) (BR)
Zero hunger
Openalex Percentile: Top 11%
Soybean genetics and cultivation
2.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.