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.

Wheat (Triticum aestivum L.) has become the most widely grown cereal crop in the Nordic-Baltic region, but due to climate change, its yields are under increasing risk. Here we present results of an international effort to use available wheat germplasm from the region to identify tools and genotypes for breeding wheat varieties with improved stability. We formed a panel of 300 spring wheat genotypes from the Nordic-Baltic region and studied their phenotypic diversity across environments to identify genotypes with high potential for adaptation to a changing climate. Field experiments were carried out in 2021 and 2022 in Estonia, Latvia, Lithuania and Norway. The performance and stability of yield (GY), protein content (PC), thousand kernel weight (TKW), test weight (TW), length of growing period (GP), and nine other traits were studied. Drought and excessive rainfall occurred in the Baltic countries in 2021–2022, whereas the weather in Norway was more stable. High variability for most traits, and significant GxE effects for all studied traits, were identified. We identified stable genotypes combining yield and quality using the AMMI model-based Weighted Average of Absolute Scores index (WAASB). Finally, we selected nineteen superior genotypes that combined high yield with high values of important quality traits. Thus, combining broad Nordic-Baltic wheat germplasm with extensive field phenotyping, we identified promising breeding material to develop climate-ready spring wheat varieties for the region.

Wheat (Triticum aestivum L.) has become the most widely grown cereal crop in the Nordic-Baltic region, however, due to climate change its yields are under increasing risk. Here we present results of international effort harnessing available wheat germplasm in the region for identification of tools and genotypes for breeding wheat varieties with improved yield stability. We formed a panel comprising 300 spring wheat genotypes from Nordic-Baltic region and studied their phenotypic diversity across different environments to identify genotypes with high potential for adaptation to changing climate. Field experiments were carried out in 2021 and 2022 in Estonia, Latvia, Lithuania and Norway. The performance and stability of yield (GY), protein content (PC), thousand kernel weight (TKW), test weight (TW), length of growing period (GP), and nine other traits – were studied. Drought and excessive rainfall occurred in the Baltic countries in 2021–2022, whereas weather was more stable in Norway. High variability for most, and significant GxE effect for all the studied traits was identified. We found out genotypes combining yield and quality with stability using the AMMI model-based Weighted Average of Absolute Scores index (WAASB). Finally, we selected nineteen superior genotypes that combined high yield with high values of important quality traits. Thus, combining broad Nordic-Baltic wheat germplasm with extensive field phenotyping, we identified promising breeding material to develop climate ready spring wheat varieties for the region.

Spring barley yields are highly variable, depending mostly on early season precipitation and temperature. Stomatal traits (stomatal conductance, size, density, adaxial to abaxial stomatal density ratio and conductance ratio) are important determinants of plant production and stress tolerance. Here, we determined stomatal traits of penultimate and flag leaves and grain yields of thirteen barley genotypes during a 3‐year field experiment in Estonia. We found that adaxial to abaxial stomatal density ratio was mostly determined by environment in penultimate leaves. Grain yields ranged from 2400 to 6100 kg ha ⁻¹ in our study. Correlations between stomatal traits and grain yield were found in the pooled dataset due to strong effects of annual abiotic conditions on studied traits. Penultimate leaf ratios of adaxial to abaxial stomatal density and conductance correlated positively with grain yield in the pooled dataset. In 2022 and 2023, the stomatal conductance ratio also correlated with grain yield, however, no other stomatal trait correlated with grain yield in any individual year, although stomatal traits differed significantly between genotypes. Thus, stomatal traits of barley penultimate leaves and their association with yield are largely driven by environmental variation and coordinated stomatal trait responses of leaf surfaces to abiotic environment are important for barley adaptation.

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.