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Open Access

Peer-reviewed

Research Article

Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis

Zhanyou Xu,

Roles Conceptualization, Data curation, Formal analysis, Investigation, Software, Writing – original draft

Affiliation Plant Science Research Unit, USDA, Agricultural Research Service, Saint Paul, MN, United States of America

https://orcid.org/0000-0002-7633-036X

⨯
Andreomar Kurek,

Roles Data curation, Software, Validation

Affiliation Department of Agronomy, Iowa State University, Ames, IA, United States of America
⨯
Steven B. Cannon,

Roles Conceptualization, Methodology, Validation, Writing – review & editing

Affiliation Corn Insects, and Crop Genetics Research Unit, USDA, Agricultural Research Service, Ames, IA, United States of America
⨯
William D. Beavis

Roles Conceptualization, Project administration, Resources, Supervision, Writing – review & editing

* E-mail: wdbeavis@iastate.edu

Affiliation Department of Agronomy, Iowa State University, Ames, IA, United States of America
⨯

Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis

Zhanyou Xu,
Andreomar Kurek,
Steven B. Cannon,
William D. Beavis

x

Published: July 9, 2021
https://doi.org/10.1371/journal.pone.0240948

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