Author

T. Streck

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

Biology Disciplines

Click to filter author papers

Plant Biology· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:benchmarking· 2 Organism:plant (other)· 2 Approach:Bayesian inference· 1 Approach:benchmark study· 1 Approach:statistical model· 1

Frequent Co-Authors

S. Gayler· 3 shared A. K. Srivastava· 2 shared A. Poyda· 2 shared A. Souissi· 2 shared B. Basso· 2 shared B. Dumont· 2 shared B. Maestrini· 2 shared D. Wallach· 2 shared

Activity by Year

2020-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersPlant Biology

S. Gayler

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Bayesian multi-level calibration of a process-based maize phenology model

M. Viswanathan, A. Scheidegger, T. Streck, S. Gayler, T. Weber

This paper improves phenology model calibration by using a Bayesian multi-level approach to account for cultivar and ripening group differences in maize, enhancing accuracy and identifying model limitations.

bioRxiv · Jun 3, 2022Plant 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

How well do crop modeling groups predict wheat phenology, given calibration data from the target population?

D. Wallach, T. Palosuo, P. Thorburn, E. Gourdain, S. Asseng, B. Basso, S. Buis, N. Crout, C. Dibari, B. Dumont, R. Ferrise, T. Gaiser, C. Garcia, S. Gayler, A. Ghahramani, , J. Wang

This study evaluates how well 27 crop modeling groups predict wheat phenology in Northern France using calibration data from the same target population, finding mean absolute errors comparable to measurement error.

bioRxiv · Mar 30, 2020Plant Biology