Correction to: Plant Methods (2018) 14:46 https://doi.org/10.1186/s13007-018-0314-7
Unfortunately, in the original version [1] of this article, a funder note was missed out in the acknowledgement. The corrected acknowledgement is given below:
Acknowledgements
The authors thank all the field and lab assistants of CIMMYT’s Global Wheat Breeding Program who collected and processed the agronomic and breeding field data as well as the image data. The data used in this study was collected under projects supported by Bill and Melinda Gates Foundation and USAID.
Reference
Montesinos-Lopez A, Montesinos-Lopez OA, de los Campos G, Crossa J, Burgueño J, Luna‑Vazquez FJ. Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture. Plant Methods. 2018;14:46.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Montesinos-López, A., Montesinos-López, O.A., de los Campos, G. et al. Correction to: Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture. Plant Methods 14, 57 (2018). https://doi.org/10.1186/s13007-018-0321-8
Published:
DOI: https://doi.org/10.1186/s13007-018-0321-8