| Accelerating genetic gain in sugarcane breeding using genomic selection S Yadav, P Jackson, X Wei, EM Ross, K Aitken, E Deomano, F Atkin, ... Agronomy 10 (4), 585, 2020 | 79 | 2020 |
| Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects S Yadav, X Wei, P Joyce, F Atkin, E Deomano, Y Sun, LT Nguyen, ... Theoretical and Applied Genetics 134 (7), 2235-2252, 2021 | 40 | 2021 |
| A linkage disequilibrium-based approach to position unmapped SNPs in crop species S Yadav, EM Ross, KS Aitken, LT Hickey, O Powell, X Wei, KP Voss-Fels, ... BMC genomics 22 (1), 773, 2021 | 10 | 2021 |
| Use of continuous genotypes for genomic prediction in sugarcane S Yadav, EM Ross, X Wei, S Liu, LT Nguyen, O Powell, LT Hickey, ... The Plant Genome 17 (1), e20417, 2024 | 1 | 2024 |
| Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non‐additive variation for key traits C Chen, O Powell, E Dinglasan, EM Ross, S Yadav, X Wei, F Atkin, ... The Plant Genome 16 (4), e20390, 2023 | 1 | 2023 |
| Optimising genomic selection for sugarcane S Yadav | 1 | 2023 |
| Optimising Clonal Performance in Sugarcane: Leveraging Non-Additive Effects via Mate-Allocation Strategies S Yadav, E Ross, X Wei, O Powell, V Hivert, L Hickey, F Atkin, E Deomano, ... Frontiers in Plant Science 14, 1260517, 2023 | 1 | 2023 |
| Genomic mate-allocation strategies exploiting additive and non-additive genetic effects to maximise total clonal performance in sugarcane S Yadav, EM Ross, X Wei, O Powell, V Hivert, LT Hickey, F Atkin, ... bioRxiv, 2022.12. 19.521119, 2022 | | 2022 |
Ben HayesProfessor of AgriGenomics, University of QueenslandVerified email at uq.edu.au
