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2025 Journal Article Integrating yield gap analysis to capture genotype by environment by management interactions for Australian broadacre sorghum cropping systemsGarba, Ismail I., Fernandez, Javier A., Chen, Qiaomin, Gho, Carla, deVoil, Peter, Cooper, Mark and Chapman, Scott C. (2025). Integrating yield gap analysis to capture genotype by environment by management interactions for Australian broadacre sorghum cropping systems. Field Crops Research, 326 109858, 109858. doi: 10.1016/j.fcr.2025.109858 |
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2025 Other Outputs Envirotyping Facilitates Understanding of Genotype x Environment Interactions and Highlights the Potential of Stay-green Traits in WheatAmin, Asad, Christopher, Jack, Forknall, Clayton, Cooper, Mark, Macdonald, Bethany, Collins, Brian, Voss-Fels, Kai, Hickey, Lee and Chenu, Karine (2025). Envirotyping Facilitates Understanding of Genotype x Environment Interactions and Highlights the Potential of Stay-green Traits in Wheat. doi: 10.1101/2025.03.11.642575 |
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2025 Journal Article Improved Genomic Prediction Performance with Ensembles of Diverse ModelsTomura, Shunichiro, Wilkinson, Melanie J., Cooper, Mark and Powell, Owen (2025). Improved Genomic Prediction Performance with Ensembles of Diverse Models. G3: Genes, Genomes, Genetics jkaf048, 1-15. doi: 10.1093/g3journal/jkaf048 |
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2025 Journal Article Transgene effects vary among maize populations with implications for improving quantitative traitsLinares, Julien F., Coles, Nathan D., Mo, Hua, Habben, Jeffrey E., Humbert, Sabrina, Messina, Carlos, Tang, Tom, Cooper, Mark, Gho, Carla, Carrasco, Ricardo, Carter, Javier, Flounders, Jillian Wicher and Brummer, E. Charles (2025). Transgene effects vary among maize populations with implications for improving quantitative traits. Crop Science, 65 (1) e21408. doi: 10.1002/csc2.21408 |
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2024 Other Outputs Improvements in prediction performance of ensemble approaches for genomic prediction in crop breedingTomura, Shunichiro, Cooper, Mark and Powell, Owen (2024). Improvements in prediction performance of ensemble approaches for genomic prediction in crop breeding. doi: 10.1101/2024.09.06.611589 |
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2024 Conference Publication Prediction of non-additive genetic effects with hierarchical genomic prediction modelsPowell, Owen, McLean, Greg, Brider, Jason, Saddigh, Joe, Technow, Frank, Tang, Tom, Totir, Radu, Messina, Carlos D., Hammer, Graeme and Cooper, Mark (2024). Prediction of non-additive genetic effects with hierarchical genomic prediction models. International Conference of Quantitative Genetics (ICQG) 7, Vienna, Austria, 22-26 July 2024. doi: 10.6084/m9.figshare.26425735.v1 |
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2024 Other Outputs Molecular Breeding MethodsCooper, Mark, Messina, Carlos, Technow, Frank and Totir, Radu (2024). Molecular Breeding Methods. 11985930. |
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2024 Journal Article Contrasting leaf‐scale photosynthetic low‐light response and its temperature dependency are key to differences in crop‐scale radiation use efficiencyWu, Alex, Truong, Sandra Huynh, McCormick, Ryan, van Oosterom, Erik J., Messina, Carlos D., Cooper, Mark and Hammer, Graeme L. (2024). Contrasting leaf‐scale photosynthetic low‐light response and its temperature dependency are key to differences in crop‐scale radiation use efficiency. New Phytologist, 241 (6), 2435-2447. doi: 10.1111/nph.19537 |
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2024 Journal Article Methods for evaluating effects of transgenes for quantitative traitsLinares, Julien F., Coles, Nathan D., Mo, Hua, Habben, Jeffrey E., Humbert, Sabrina, Messina, Carlos, Tang, Tom, Cooper, Mark, Gho, Carla, Carrasco, Ricardo, Carter, Javier, Flounders, Jillian Wicher and Brummer, E. Charles (2024). Methods for evaluating effects of transgenes for quantitative traits. Crop Science, 64 (1), 141-148. doi: 10.1002/csc2.21141 |
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2024 Book Chapter Transforming food production with AICooper, Mark, Hickey, Lee, Jiang, Xianxian, La Fata, Giorgio, Lomas, Harold, Miller, Tim, O’Brien, Susan, Patel, Parth and Tomarchio, Samuel (2024). Transforming food production with AI. Food AI: A game changer for Australia’s food and beverage sector. (pp. 5-13) edited by Janet R. McColl-Kennedy and Damian Hine. Brisbane, QLD, Australia: The University of Queensland, Australia's Food and Beverage Accelerator (FaBA). |
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2023 Conference Publication APSIM-WGP: a software platform to predict crop GxExM interactionsPowell, Owen, McLean, Greg, Brider, Jason, Hammer, Graeme and Cooper, Mark (2023). APSIM-WGP: a software platform to predict crop GxExM interactions. GxExM Symposium II, Gainesville, FL USA, 6-7 November 2023. |
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2023 Journal Article Genetic modification can improve crop yields — but stop overselling itKhaipho-Burch, Merritt, Cooper, Mark, Crossa, Jose, de Leon, Natalia, Holland, James, Lewis, Ramsey, McCouch, Susan, Murray, Seth C., Rabbi, Ismail, Ronald, Pamela, Ross-Ibarra, Jeffrey, Weigel, Detlef and Buckler, Edward S. (2023). Genetic modification can improve crop yields — but stop overselling it. Nature, 621 (7979), 470-473. doi: 10.1038/d41586-023-02895-w |
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2023 Other Outputs Crop improvement can accelerate agriculture adaptation to societal demands and climate changeMessina, Carlos D., Borras, Lucas, Tang, Tom and Cooper, Mark (2023). Crop improvement can accelerate agriculture adaptation to societal demands and climate change. |
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2023 Journal Article Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maizeMessina, Carlos D., Gho, Carla, Hammer, Graeme L., Tang, Tom and Cooper, Mark (2023). Two decades of harnessing standing genetic variation for physiological traits to improve drought tolerance in maize. Journal of Experimental Botany, 74 (16), 4847-4861. doi: 10.1093/jxb/erad231 |
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2023 Other Outputs In Silico Sorghum GxExM Dataset For Prediction Algorithm ComparisonsOwen Powell, Greg McLean, Jason Brider, Mark Cooper and Graeme Hammer (2023). In Silico Sorghum GxExM Dataset For Prediction Algorithm Comparisons. figShare. (Dataset) doi: 10.6084/m9.figshare.23789685.v2 |
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2023 Conference Publication Random Forest Importance Diagnostics can Capture Quantitative Genetic Properties of Markers for Genomic PredictionTomura, Shunichiro, Powell, Owen and Cooper, Mark (2023). Random Forest Importance Diagnostics can Capture Quantitative Genetic Properties of Markers for Genomic Prediction. International Congress of Genetics, Melbourne, VIC Australia, 16-21 July 2023. figShare. doi: 10.6084/m9.figshare.24211230.v1 |
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2023 Other Outputs Stochastic Simulation of Divergent Selection Experiment on a Gene-Phenotype Network: A Case Study of Shoot Branching in PlantsPowell, Owen and Cooper, Mark (2023). Stochastic Simulation of Divergent Selection Experiment on a Gene-Phenotype Network: A Case Study of Shoot Branching in Plants. figShare. (Dataset) doi: 10.6084/m9.figshare.23590083 |
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2023 Conference Publication GPU can Accelerate the Prediction of Complex PhenotypesTomura, Shunichiro, Powell, Owen and Cooper, Mark (2023). GPU can Accelerate the Prediction of Complex Phenotypes. Australasian Leadership Computing Symposium, Canberra, ACT Australia, 14-16 June 2023. doi: 10.6084/m9.figshare.24484831.v1 |
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2023 Journal Article Inclusive collaboration across plant physiology and genomics: Now is the time!Interdisciplinary Plant Science Consortium and Cooper, Mark (2023). Inclusive collaboration across plant physiology and genomics: Now is the time!. Plant Direct, 7 (5) e493. doi: 10.1002/pld3.493 |
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2023 Book Chapter Incorporating genetics into crop models to identify new phenotypes adapted to climate changeMessina, Carlos D. and Cooper, Mark (2023). Incorporating genetics into crop models to identify new phenotypes adapted to climate change. Modelling climate change impacts on agricultural systems. (pp. 75-104) Cambridge, United Kingdom: Burleigh Dodds Science Publishing. doi: 10.19103/AS.2022.0115.04 |
