Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars
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 prog...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524002314 |
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| author | Ramon Gonçalves de Paula Martha Freire da Silva Cibele Amaral Guilherme de Sousa Paula Laércio Junio da Silva Herika Paula Pessoa Felipe Lopes da Silva |
| author_facet | Ramon Gonçalves de Paula Martha Freire da Silva Cibele Amaral Guilherme de Sousa Paula Laércio Junio da Silva Herika Paula Pessoa Felipe Lopes da Silva |
| author_sort | Ramon Gonçalves de Paula |
| collection | DOAJ |
| description | 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 R2 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. |
| format | Article |
| id | doaj-art-7d3ac07b8f7a41f4ae2eaa2dbc57ab8d |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-7d3ac07b8f7a41f4ae2eaa2dbc57ab8d2024-12-13T11:08:06ZengElsevierSmart Agricultural Technology2772-37552024-12-019100626Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivarsRamon Gonçalves de Paula0Martha Freire da Silva1Cibele Amaral2Guilherme de Sousa Paula3Laércio Junio da Silva4Herika Paula Pessoa5Felipe Lopes da Silva6Department of General Biology, Federal University of Viçosa, Viçosa, MG, Brazil; Corresponding author.Department of Plant Science, São Paulo State University Julio de Mesquita Filho - Campus Ilha Solteira, SP, BrazilDepartment of Forestry, Federal University of Viçosa, Viçosa, MG, Brazil; Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO 80303, United StatesDepartment of Agronomy, Federal University of Viçosa, Viçosa, MG, BrazilDepartment of Agronomy, Federal University of Viçosa, Viçosa, MG, BrazilDepartment of Horticulture Science, University of Minnesota, Saint Paul, MN 55108, United StatesDepartment of General Biology, Federal University of Viçosa, Viçosa, MG, Brazil; Department of Agronomy, Federal University of Viçosa, Viçosa, MG, BrazilUnderstanding 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 R2 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.http://www.sciencedirect.com/science/article/pii/S2772375524002314Drought stressGlycine maxPhysiological selectionHigh-throughput phenotypingRemote sensingPartial least square |
| spellingShingle | Ramon Gonçalves de Paula Martha Freire da Silva Cibele Amaral Guilherme de Sousa Paula Laércio Junio da Silva Herika Paula Pessoa Felipe Lopes da Silva Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars Smart Agricultural Technology Drought stress Glycine max Physiological selection High-throughput phenotyping Remote sensing Partial least square |
| title | Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars |
| title_full | Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars |
| title_fullStr | Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars |
| title_full_unstemmed | Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars |
| title_short | Leveraging leaf spectroscopy to identify drought-tolerant soybean cultivars |
| title_sort | leveraging leaf spectroscopy to identify drought tolerant soybean cultivars |
| topic | Drought stress Glycine max Physiological selection High-throughput phenotyping Remote sensing Partial least square |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524002314 |
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