Author

C. D. Messina

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

Signature Assays, Organisms & Methods

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Organism:plant (other)· 3 Approach:statistical model· 3 Approach:deep learning· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:ecology & biodiversity· 1 Research Focus:genome assembly· 1 Research Focus:variant calling· 1

Frequent Co-Authors

M. Cooper· 2 shared A. P. Gaspar· 1 shared B. M. Stevens· 1 shared C. H. Diepenbrock· 1 shared C. Hunter· 1 shared D. Creek· 1 shared D. Kyle· 1 shared D. Lombardozzi· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersPlant Biology

M. Cooper

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Author profile →

3 papers

Recent·Most cited

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

Can we harness digital technologies and physiology to hasten genetic gain in U.S. maize breeding?

C. H. Diepenbrock, T. Tang, M. Jines, F. Technow, S. Lira, D. Podlich, M. Cooper, C. D. Messina

This study uses digital technologies and physiology to improve genetic gain in maize breeding by enhancing predictive models and breeding objectives.

bioRxiv · Feb 23, 2021 · PreprintPlant Biology

Intercontinental prediction of soybean phenology via hybrid ensemble of knowledge-based and data-driven models

R. F. McCormick, S. K. Truong, J. Rotundo, A. P. Gaspar, D. Kyle, F. van Eeuwijk, C. D. Messina

The paper develops a hybrid model combining knowledge-based and data-driven approaches to predict soybean phenology across continents, achieving lower error than existing benchmarks.

bioRxiv · Sep 22, 2020Plant Biology