Phenotypic Variation and Relationships between Grain Yield, Protein Content and Unmanned Aerial Vehicle-Derived Normalized Difference Vegetation Index in Spring Wheat in Nordic–Baltic Environments

Abstract

Accurate and robust methods are needed to monitor crop growth and predict grain yield and quality in breeding programs, particularly under variable agrometeorological conditions. Field experiments were conducted during two successive cropping seasons (2021, 2022) at four trial locations (Estonia, Latvia, Lithuania, Norway). The focus was on assessment of the grain yield (GY), grain protein content (GPC), and UAV-derived NDVI measured at different plant growth stages. The performance and stability of 16 selected spring wheat genotypes were assessed under two N application rates (75, 150 kg N ha−1) and across different agrometeorological conditions. Quantitative relationships between agronomic traits and UAV-derived variables were determined. None of the traits exhibited a significant (p < 0.05) genotype-by-nitrogen interaction. High-yielding and high-protein genotypes were detected with a high WAASB stability, specifically under high and low N rates. This study highlights the significant effect of an NDVI analysis at GS55 and GS75 as key linear predictors, especially concerning spring wheat GYs. However, the effectiveness of these indices depends on the specific growing conditions in different, geospatially distant locations, limiting their universal utility.

Keywords

• genotype
• meteorological parameters
• nitrogen fertilization rate
• multispectral vegetation index
• correlations