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Robert Jackson

Researcher at National Institute of Agricultural Botany

Publications -  9
Citations -  268

Robert Jackson is an academic researcher from National Institute of Agricultural Botany. The author has contributed to research in topics: Trait & Computer science. The author has an hindex of 3, co-authored 5 publications receiving 181 citations.

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A Two-Part Strategy for Using Genomic Selection to Develop Inbred Lines

TL;DR: A strategy for implementing genomic selection in plant breeding programs for developing inbred lines that reorganizes traditional breeding programs into two distinct components is proposed, indicating that the two-part strategy is a cost-effective strategy.
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The effects of training population design on genomic prediction accuracy in wheat

TL;DR: The results of this study emphasise the importance of the training panel design in relation to the genetic material to which the resulting prediction model is to be applied, and the design of training sets is in turn central to achieving sufficient levels of accuracy.
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A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders.

TL;DR: A pedigree resource of 2,657 wheat genotypes originating from 38 countries, representing more than a century of breeding and variety development is presented, highlighting the benefits of generating detailed pedigree resources for crop species.
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AirMeasurer: open‐source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice

TL;DR: AirMeasurer as discussed by the authors is an open source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice trials.
Posted ContentDOI

The effects of training population design on genomic prediction accuracy in wheat

TL;DR: Small numbers of close relatives and very large numbers of distant relatives are expected to enable accurate predictions of genomic selection in crops, and the results emphasize the importance of the training set design in relation to the genetic material to which the resulting prediction model is to be applied.