Predicting agronomic performance in Canadian winter wheat using...


Humphreys G, Gahagan G, Kalikililo A, Sadeghi-Tehran P, Hawkesford M, Morrison M (2018) Predicting agronomic performance in Canadian winter wheat using high-throughput phenotyping and plant pixel area. Poster and abstract presented at 4th Canadian Wheat Symposium, Nov. 19-22, 2018 in Winnipeg, MB.


Effective high-throughput phenotyping of wheat lines is desirable to improve breeding selection efficiency and to facilitate the use of whole genome based selection methods. Green pixel area (GPA), expressed as a proportion of the total pixel number in photographs of yield plots, has been used previously as a measure of plant establishment, growth and vigor. In this study, three phenomics factors were used: (1) plant pixel area (PPA) which is a measure of green pixel area in a yield plot filtered to remove weed plants, (2) active canopy coverage (ACC) which is defined as mean growing degree days above 50% canopy, (3) linear senescence rate (LSR) which is the rate of loss of PPA after maximum canopy coverage is attained. The purpose of this research was to investigate the relationships between PPA, AAC and LSR with important agronomic traits. In 2017, mean phenomics parameters were determined from weekly green pixel area estimates in two advanced Canadian winter wheat yield trials (EA and MT). In both trials, ACC and LSR was significantly (P<0.05) correlated with grain yield. LSR was also significantly correlated with test weight and seed mass. AAC was significantly correlated with test weight and seed mass for MT. PPA was significantly correlated with heading date and plant height for MT but was not significantly correlated with grain yield in either trial. In this study, LSR which is possibly an estimate of “stay-green” potential was the most promising predictor of grain yield, test weight and seed mass.

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