Variability in crop responses as a function of environment affects the NDVI relationship with grain yield in wheat.

Chamaporn Paiboonvorachat, Kirti Rajagopalan, Michael O Pumphrey, Arron H Carter, Chongyuan Zhang, Worasit Sangjan, Sindhuja Sankaran

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Article in Plant phenomics (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Chamaporn PaiboonvorachatDepartment of Biological Systems Engineering, Washington State University, Pullman, WA, USA.

Kirti RajagopalanDepartment of Biological Systems Engineering, Washington State University, Pullman, WA, USA.

Michael O PumphreyDepartment of Crop and Soil Sciences, Washington State University, Pullman, WA, USA.

Arron H CarterDepartment of Crop and Soil Sciences, Washington State University, Pullman, WA, USA.

Chongyuan ZhangDepartment of Biological Systems Engineering, Washington State University, Pullman, WA, USA.

Worasit SangjanDepartment of Biological Systems Engineering, Washington State University, Pullman, WA, USA.

Sindhuja SankaranDepartment of Biological Systems Engineering, Washington State University, Pullman, WA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advancing wheat breeding requires reliable digital traits that capture genotype × environment interactions and improve yield prediction across diverse growing conditions. Although vegetation indices such as the normalized difference vegetation index (NDVI) are widely used, their performance relative to yield variability and environmental stress remains underexplored in multi-environment trials. This study utilized unmanned aerial vehicle multispectral imagery to derive NDVI and assess its relationship with grain yield in 34 spring and winter wheat variety trials. These trials included data across seven Washington State locations in different precipitation zones, five years (2019 to 2023), and some irrigated trials. Environments were grouped into high-, moderate-, and low-stress clusters based primarily on precipitation and temperature. Variability was quantified using the coefficient of variation, and correlations between grain yield and NDVI were evaluated within and between varieties across environments based on market classes (hard and soft spring and winter wheat). Across all environments and varieties, NDVI strongly correlated with grain yield (

Indexed as

HeritabilityNormalized difference vegetation indexPhenomicsVariabilityWheat breedingWheat yieldYield prediction

Identifiers

PMID42339478

DOI10.1016/j.plaphe.2026.100237

PMCPMC13285712

What Socratic holds

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.