Proximal sensing of Urochloa grasses increases selection accuracy
Juan de la Cruz Jiménez A E , Luisa Leiva B , Juan A. Cardoso C , Andrew N. French D and Kelly R. Thorp D
+ Author Affiliations
- Author Affiliations
A UWA School of Agriculture and Environment, Faculty of Science, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia.
B Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
C Tropical Forage Program, International Center for Tropical Agriculture (CIAT), Km 17 Recta Cali, Palmira, Colombia.
D USDA-ARS, U.S. Arid Land Agricultural Research Center, 21881 North Cardon Lane, Maricopa, AZ 85138, USA.
E Corresponding author. Email: juan.jimenezserna@research.uwa.edu.au
Crop and Pasture Science 71(4) 401-409 https://doi.org/10.1071/CP19324
Submitted: 7 August 2019 Accepted: 10 February 2020 Published: 18 April 2020
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 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.
Additional keywords: Brachiaria, phenotyping, plant breeding, tropical forage grasses.
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