Author

S. Hiremath

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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Research Focus:benchmarking· 2 Approach:statistical model· 2 Approach:benchmark study· 1 Research Focus:software / tool· 1

Frequent Co-Authors

A. Souissi· 3 shared B. Dumont· 3 shared B. Qian· 3 shared D. Wallach· 3 shared E. Justes· 3 shared E. Lewan· 3 shared F. Andrianasolo· 3 shared G. Hoogenboom· 3 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersPlant Biology

A. Souissi

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3 papers

Recent·Most cited

Proposal and extensive test of a calibration protocol for crop phenology models

D. Wallach, T. Palosuo, P. Thorburn, H. Mielenz, S. Buis, Z. Hochman, E. Gourdain, C. Garcia, F. Andrianasolo, B. Dumont, R. Ferrise, T. Gaiser, S. Gayler, S. Hiremath, H. Horan, , S. Seidel

This paper proposes and tests a calibration protocol for crop phenology models, reducing prediction error and team variability by 22%.

bioRxiv · Jun 8, 2022Plant Biology

The chaos in calibrating crop models

D. Wallach, T. Palosuo, P. Thorburn, Z. Hochman, E. Gourdain, F. Andrianasolo, S. Asseng, B. Basso, S. Buis, N. Crout, C. Dibari, B. Dumont, R. Ferrise, T. Gaiser, C. Garcia, , Srivas

The paper proposes a method to develop calibration guidelines for system models by analyzing multi-model simulations, applying it to crop model phenology calibration and providing recommendations on objective functions, parameters, and software.

bioRxiv · Sep 12, 2020 · PreprintPlant Biology

Multi-model evaluation of phenology prediction for wheat in Australia

D. Wallach, T. Palosuo, P. Thorburn, Z. Hochman, F. Andrianasolo, S. Asseng, B. Basso, S. Buis, N. Crout, B. Dumont, R. Ferrise, T. Gaiser, S. Gayler, S. Hiremath, S. Hoek, , L. Weihermüller

This study evaluates wheat phenology prediction models in Australia, finding that multi-model averaging performs nearly as well as the best single model, with an average error of 9 days.

bioRxiv · Jun 6, 2020Plant Biology