Sammanfattning

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.

Nyckelord

Brachiaria; phenotyping; plant breeding; tropical forage grasses

Publicerad i

Crop & Pasture Science
2020, Volym: 71, nummer: 4, sidor: 401-409
Utgivare: CSIRO PUBLISHING

SLU författare

• Leiva, Fernanda

  • Institutionen för växtförädling, Sveriges lantbruksuniversitet
    

UKÄ forskningsämne

Fjärranalysteknik
Jordbruksvetenskap


Publikationens identifierare

DOI: https://doi.org/10.1071/CP19324

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/106460