Field Trial Analyses of Wheat and Cassava Benefit from Spatial Correction

Authors

• Tesfahun Alemu Setotaw, Cornell UniversityFollow
• Christine Mwende Nyaga, Cornell University
• David James Waring, Cornell University
• Jianli Chen, University of IdahoFollow
• Chiedozie Egesi, International Institute of Tropical AgricultureFollow
• Katherine Frels, University of Nebraska-LincolnFollow
• Lucia Gutierrez, University of Wisconsin-MadisonFollow
• Jinha Jung, Purdue UniversityFollow
• Michael Kanaabi, National Agricultural Research Organization, Entebbe, Uganda
• Margaret Krause, Oregon State UniversityFollow
• R. Esten Mason, Colorado State University, Fort CollinsFollow
• Mohsen Mahammadi, Purdue UniversityFollow
• Eder Jorge de Oliveira, Embrapa Mandioca e Fruticultura, Cruz das Almas, BrazilFollow
• Eric Olson, Michigan State UniversityFollow
• Jessica E. Rutkoski, University of Illinois at Urbana-ChampaignFollow
• Nicholas Santantonio, Virginia TechFollow
• Clay Sneller, Ohio State UniversityFollow
• Mark E. Sorrells, Cornell UniversityFollow
• Lukas A. Mueller, Boyce Thompson InstituteFollow
• Jean-Luc Jannink, Cornell UniversityFollow

ORCID IDs

Setotaw https://orcid.org/0000-0002-3794-8829

Gutierrez https://orcid.org/0000-0002-2957-3086

Kanaabi https://orcid.org/0000-0002-8162-6640

Document Type

Article

Date of this Version

8-19-2026

Citation

Plos ONE (2026) 21: e0354968

doi: 10.1371/journal.pone.0354968

Comments

United States government work. Public domain

Abstract

Spatial variation is a major source of error in agricultural field experiments affecting genotype performance prediction. Implementing statistical models that account for spatial effects can improve the prediction of genotype performance. This study evaluated the impact of the P-spline spatial correction method on the estimation of genetic parameters and AIC values in two distinct crops, wheat and cassava, using four models: Block, Block + Spatial, Block + Marker, and Block + Marker + Spatial. Analyses were performed on data from 115 and 68 trials obtained from the T3/WheatCAP and Cassavabase databases, respectively. As assessed using Cullis heritability estimates and AIC values, the results demonstrated that correcting for spatial variation improved analyses of grain yield, test weight, plant height, powdery mildew, stripe rust, and bacterial streak disease in wheat. Similar improvements were observed in cassava for dry matter content, dry yield, and plant height. However, no improvement was observed for cassava mosaic disease or bacterial blight. These results were consistent whether or not marker effects were fitted in the models. This study demonstrates that incorporating spatial correction into statistical analyses substantially improves the precision of variety evaluation. By accounting for field heterogeneity, spatial modeling complements experimental design and enhances the accuracy of treatment comparisons. Therefore, integrating robust experimental designs with appropriate spatial analyses is essential for achieving optimal precision and reliability in field trial evaluations.