Author

G. McLean

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· 3

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Research Focus:benchmarking· 2 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:development & morphogenesis· 1 Research Focus:drug response / drug discovery· 1 Research Focus:ecology & biodiversity· 1 Approach:experimental assay· 1 Research Focus:metabolism & bioenergetics· 1

Frequent Co-Authors

G. Hammer· 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 B. M. Long· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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3 shared papersPlant Biology

G. Hammer

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3 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

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