Author

G. Hammer

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Plant Biology· 5

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:ecology & biodiversity· 2 Organism:plant (other)· 2 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:development & morphogenesis· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

G. McLean· 3 shared M. Cooper· 3 shared E. Van Oosterom· 2 shared A. Hajjarpoor· 1 shared A. Potgieter· 1 shared A. Wu· 1 shared B. Collins· 1 shared B. Forster· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersPlant Biology

G. McLean

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5 papers

Recent·Most cited

New algorithm for pearl millet modelling in APSIM allowing a mechanistic simulation of tillers

V. Garin, E. Van Oosterom, G. McLean, G. Hammer, T. Murugesan, S. Kaliamoorthy, M. Diancumba, A. Hajjarpoor, J. Kholova

The paper introduces a new algorithm for pearl millet simulation in APSIM that improves tiller dynamics and genetic control, showing good performance in biomass and tillering but with room for improvement in tiller cessation.

bioRxiv · Feb 12, 2023 · PreprintPlant Biology

A cross-scale analysis to understand and quantify effects of photosynthetic enhancement on crop growth and yield

A. Wu, J. Brider, F. A. Busch, M. Chen, K. Chenu, V. C. Clarke, B. Collins, M. Ermakova, J. R. Evans, G. D. Farquhar, B. Forster, R. T. Furbank, M. Groszmann, M. A. Hernandez, B. M. Long, , G. Hammer

The paper uses a cross-scale model to predict how photosynthetic manipulations affect wheat and sorghum yield across environments, finding modest gains with some strategies and challenges in scaling up.

bioRxiv · Jul 6, 2022 · PreprintPlant Biology

Physiological trait networks enhance understanding of crop growth and water use in contrasting environments

S. M. Gleason, D. M. Barnard, T. R. Green, D. S. Mackay, D. R. Wang, E. A. Ainsworth, J. Altenhofen, T. J. Brodribb, H. Cochard, L. H. Comas, M. Cooper, D. Creek, K. C. DeJonge, S. Delzon, F. B. Fritschi, , H. Zhang

The paper uses a process-based model to identify contrasting trait networks in maize that enhance growth and water use in different environments.

bioRxiv · Mar 11, 2022Plant Biology

Predicting phenotypes from genetic, environment, management, and historical data using CNNs

J. D. Washburn, E. Cimen, G. Ramstein, T. Reeves, P. O'Briant, G. McLean, M. Cooper, G. Hammer, E. S. Buckler

This paper uses CNNs to predict phenotypes from genetic, environmental, and management data, showing improved accuracy with pre-training on historical data.

bioRxiv · May 27, 2021Plant Biology

Two decades of creating drought tolerant maize and underpinning prediction technologies in the US corn-belt: Review and perspectives on the future of crop design

C. Messina, M. Cooper, G. Hammer, D. Berning, I. Ciampitti, R. Clark, C. H. Diepenbrock, C. Gho, M. Jines, T. Lee, R. F. McCormick, E. Mihura, D. Podlich, J. Rotundo, M. Smalley, , F. van Eeuwijk

This paper reviews drought tolerant maize breeding efforts and shows increased crop improvement rates, suggesting future crop design through integrated prediction technologies.

bioRxiv · Oct 29, 2020 · PreprintPlant Biology