|JIMENEZ, JUAN - University Of Western Australia|
|LEIVA, LUISA - International Center For Tropical Agriculture (CIAT)|
|CARDOSO, JUAN - International Center For Tropical Agriculture (CIAT)|
Submitted to: Crop and Pasture Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/10/2020
Publication Date: 4/18/2020
Citation: Jimenez, J., 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):401-409. https://doi.org/10.1071/CP19324.
Interpretive Summary: Livestock production is dependent on breeding of improved forage varieties that increase forage availability. To facilitate development of new forage varieties, sensing tools are needed for rapid phenotyping of large numbers of plant genotypes. This study compared several phenotyping methods for estimating shoot dry weight, nitrogen content, and chlorophyll content in a forage grass population in Columbia. Results showed that proximal digital color imaging can improve estimates of plant traits and can evaluate more varieties per hour as compared to traditional phenotyping approaches. Results are useful for scientists engaged in the area of high-throughput phenotyping and for growers who received improved plant varieties from the breeding programs that adopt these technologies.
Technical Abstract: 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 200Urochloahybrids in afield 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.