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Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with genomic data.
Adak, Alper; Kang, Myeongjong; Anderson, Steven L; Murray, Seth C; Jarquin, Diego; Wong, Raymond K W; Katzfuß, Matthias.
Affiliation
  • Adak A; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA.
  • Kang M; Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
  • Anderson SL; Syngenta, Naples, FL 34114, USA.
  • Murray SC; Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-2474, USA.
  • Jarquin D; Agronomy Department, University of Florida, Gainesville, FL 32611, USA.
  • Wong RKW; Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
  • Katzfuß M; Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
J Exp Bot ; 74(17): 5307-5326, 2023 09 13.
Article in En | MEDLINE | ID: mdl-37279568
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Zea mays / Phenomics Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: J Exp Bot Journal subject: BOTANICA Year: 2023 Document type: Article Affiliation country: United States Country of publication: United kingdom
Fulltext
Add to My VHL
Print
XML
PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Zea mays / Phenomics Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: J Exp Bot Journal subject: BOTANICA Year: 2023 Document type: Article Affiliation country: United States Country of publication: United kingdom