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  • Correction
  • Open Access

Correction to: Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture

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  • 2Email author,
  • 3Email author,
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Plant Methods201814:57

  • Published:

The original article was published in Plant Methods 2018 14:46

Correction to: Plant Methods (2018) 14:46

Unfortunately, in the original version [1] of this article, a funder note was missed out in the acknowledgement. The corrected acknowledgement is given below:


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.



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Authors’ Affiliations

Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430 Guadalajara, Jalisco, Mexico
Facultad de Telemática, Universidad de Colima, 28040 Colima, Colima, Mexico
Epidemiology and Biostatistics and Statistics and Probability Departments, Michigan State University, 909 Fee Road, East Lansing, MI 48824, USA
Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico City, Mexico


  1. 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.View ArticlePubMedPubMed CentralGoogle Scholar


© The Author(s) 2018