Proximal sensing of Urochloa grasses increases selection accuracy
In the American tropics, livestock production is highly restricted by forage availability. In addition, the breeding and development of new forage varieties with outstanding yield and high nutritional quality is often limited by a lack of resources and poor technology. Non-destructive, high-throughp...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Commonwealth Scientific and Industrial Research Organisation
2020
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/111686 |
| _version_ | 1855538222404206592 |
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| author | Jiménez, Juan de la Cruz Leiva Sandoval, Luisa Fernanda Cardoso, Juan Andrés French, Andrew N. Thorp, Kelly R. |
| author_browse | Cardoso, Juan Andrés French, Andrew N. Jiménez, Juan de la Cruz Leiva Sandoval, Luisa Fernanda Thorp, Kelly R. |
| author_facet | Jiménez, Juan de la Cruz Leiva Sandoval, Luisa Fernanda Cardoso, Juan Andrés French, Andrew N. Thorp, Kelly R. |
| author_sort | Jiménez, Juan de la Cruz |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | In the American tropics, livestock production is highly restricted by forage availability. In addition, the breeding and development of new forage varieties with outstanding yield and high nutritional quality is often limited by a lack of resources and poor technology. Non-destructive, high-throughput phenotyping offers a rapid and economical means of evaluating large numbers of genotypes. In this study, visual assessments, digital colour images, and spectral reflectance data were collected from 200 Urochloa hybrids in a field setting. Partial least-squares regression (PLSR) was applied to relate visual assessments, digital image analysis and spectral data to shoot dry weight, crude protein and chlorophyll concentrations. Visual evaluations of biomass and greenness were collected in 68 min, digital colour imaging data in 40 min, and hyperspectral canopy data in 80 min. Root-mean-squared errors of prediction for PLSR estimations of shoot dry weight, crude protein and chlorophyll were lowest for digital image analysis followed by hyperspectral analysis and visual assessments. This study showed that digital colour image and spectral analysis techniques have the potential to improve precision and reduce time for tropical forage grass phenotyping. |
| format | Journal Article |
| id | CGSpace111686 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Commonwealth Scientific and Industrial Research Organisation |
| publisherStr | Commonwealth Scientific and Industrial Research Organisation |
| record_format | dspace |
| spelling | CGSpace1116862025-12-08T10:29:22Z Proximal sensing of Urochloa grasses increases selection accuracy Jiménez, Juan de la Cruz Leiva Sandoval, Luisa Fernanda Cardoso, Juan Andrés French, Andrew N. Thorp, Kelly R. brachiaria phenotypes plant breeding forage grasses fenotipos fitomejoramiento In the American tropics, livestock production is highly restricted by forage availability. In addition, the breeding and development of new forage varieties with outstanding yield and high nutritional quality is often limited by a lack of resources and poor technology. Non-destructive, high-throughput phenotyping offers a rapid and economical means of evaluating large numbers of genotypes. In this study, visual assessments, digital colour images, and spectral reflectance data were collected from 200 Urochloa hybrids in a field setting. Partial least-squares regression (PLSR) was applied to relate visual assessments, digital image analysis and spectral data to shoot dry weight, crude protein and chlorophyll concentrations. Visual evaluations of biomass and greenness were collected in 68 min, digital colour imaging data in 40 min, and hyperspectral canopy data in 80 min. Root-mean-squared errors of prediction for PLSR estimations of shoot dry weight, crude protein and chlorophyll were lowest for digital image analysis followed by hyperspectral analysis and visual assessments. This study showed that digital colour image and spectral analysis techniques have the potential to improve precision and reduce time for tropical forage grass phenotyping. 2020-04-18 2021-03-01T13:46:35Z 2021-03-01T13:46:35Z Journal Article https://hdl.handle.net/10568/111686 en Open Access application/pdf Commonwealth Scientific and Industrial Research Organisation Jiménez, Juan de la Cruz; Leiva, L.; Cardoso, J.A.; French, A.N.; Thorp, K.R. (2020) Proximal sensing of Urochloa grasses increases selection accuracy. Crop and Pasture Science 71(4) p. 401-409. ISSN: 1836-0947 |
| spellingShingle | brachiaria phenotypes plant breeding forage grasses fenotipos fitomejoramiento Jiménez, Juan de la Cruz Leiva Sandoval, Luisa Fernanda Cardoso, Juan Andrés French, Andrew N. Thorp, Kelly R. Proximal sensing of Urochloa grasses increases selection accuracy |
| title | Proximal sensing of Urochloa grasses increases selection accuracy |
| title_full | Proximal sensing of Urochloa grasses increases selection accuracy |
| title_fullStr | Proximal sensing of Urochloa grasses increases selection accuracy |
| title_full_unstemmed | Proximal sensing of Urochloa grasses increases selection accuracy |
| title_short | Proximal sensing of Urochloa grasses increases selection accuracy |
| title_sort | proximal sensing of urochloa grasses increases selection accuracy |
| topic | brachiaria phenotypes plant breeding forage grasses fenotipos fitomejoramiento |
| url | https://hdl.handle.net/10568/111686 |
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