Predicting growth, water-use efficiency and drought response through machine learning, GWAS and differential expression in Ponderosa pine.

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Bibliographic Details
Title: Predicting growth, water-use efficiency and drought response through machine learning, GWAS and differential expression in Ponderosa pine.
Authors: Collins SM; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Cathey MJ; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Barrera M; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Harris B; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Baesen K; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA., Lincoln A; Department of Interior, Bureau of Land Management Grand Junction Field Office, Grand Junction, CO, 81506, USA., Dixit A; Department of Natural Resource Ecology & Management, Oklahoma State University Stillwater, Stillwater, OK, USA., De La Torre AR; School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA. Amanda.de-la-torre@nau.edu.
Source: BMC plant biology [BMC Plant Biol] 2026 May 29. Date of Electronic Publication: 2026 May 29.
Publication Type: Journal Article
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 100967807 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1471-2229 (Electronic) Linking ISSN: 14712229 NLM ISO Abbreviation: BMC Plant Biol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1471-2229
DOI:10.1186/s12870-026-09113-5