Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Phenotypic Variation and Relationships of Grain Yield, Protein Content and Uav-Derived Ndvi in Spring Wheat under Nordic-Baltic Environments

Zaiga Jansone
,
Zigmārs Rendenieks
,
Andris Lapāns
,
Ilmar Tamm
ORCID logo ,
Anne Ingver
,
Andrii Gorash
,
Andrius Aleliūnas
,
Gintaras Brazauskas
ORCID logo ,
Sahameh Shafiei
,
Tomasz Mroz
,
Morten Lillemo
ORCID logo ,
Hannes Kollist
,
Māra Bleidere
* ORCID logo
Version 1 : Received: 11 December 2023 / Approved: 11 December 2023 / Online: 12 December 2023 (04:35:04 CET)

A peer-reviewed article of this Preprint also exists.

Jansone, Z.; Rendenieks, Z.; Lapāns, A.; Tamm, I.; Ingver, A.; Gorash, A.; Aleliūnas, A.; Brazauskas, G.; Shafiee, S.; Mróz, T.; Lillemo, M.; Kollist, H.; Bleidere, M. 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. Agronomy 2024, 14, 51. Jansone, Z.; Rendenieks, Z.; Lapāns, A.; Tamm, I.; Ingver, A.; Gorash, A.; Aleliūnas, A.; Brazauskas, G.; Shafiee, S.; Mróz, T.; Lillemo, M.; Kollist, H.; Bleidere, M. 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. Agronomy 2024, 14, 51.

Abstract

Accurate and robust methods are needed to monitor crop growth and predict grain yield and quality in breeding programs 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 grain yield (GY), grain protein content (GPC), and UAV-derived NDVI measured at different plant growth stages. Performance and stability of 16 selected spring wheat genotypes were assessed under two N application rates (75, 150 kg N ha−1) and over contrasted agrometeorological conditions were assessed, and the quantitative relationships between agronomic traits and UAV-derived variables were figured out. None of the traits were subject to a significant (p<0.05) genotype by nitrogen interaction. High-yielding and high-protein genotypes were detected with high WAASB stability, specifically under high and low N rates. This study highlights the significant effect of NDVI analysis on growth stages GS55 and GS75 as key linear predictors, especially in the context of spring wheat GY. However, the effect of these indices depends on the specific growing conditions in the respective locations, thus limiting their universal utility.

Keywords

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

Subject

Biology and Life Sciences, Agricultural Science and Agronomy

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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