Faba beans are considered one of the most important crops for animal feed. The genotype × environment interaction (GEI) has a considerable effect on faba bean seed production. The objectives of this study included assessing multiple locations and genotypes to understand how various ecosystems and faba bean genotypes relate to one another, and suggesting the ideal climatic conditions, crop management system, and genotypes so that they are carefully chosen for their stability. A 2-year experiment was conducted in order to define the stability across four environments based on stability indices for certain characteristics: moisture (%), ash content (%), crude protein content (%), crude fat (%), total starch (%), and crude fiber content (%). Statistically significant differences indicated that GEIs were present. The heritability was generally high for qualitative traits in comparison with quantitative traits. The crude protein content, plant height, and thousand-seed weight were all positively correlated with the seed yield; however, the other qualitative variables were adversely correlated. The crude protein content of the cultivar Tanagra displayed a high stability index, followed by Ste1. Under conventional management, Tanagra demonstrated high values for the seed yield in Giannitsa and Florina. Ste1 and Ste2 are particularly promising genetic materials that showed high values under low-input conditions. The best genotypes to use and the most favorable environments/types of cultivation were the Tanagra cultivar, followed by the Ste2 genotype, according to the additive main effects and multiplicative interaction (AMMI) and genotype plus genotype-by-environment (GGE) biplot models. Earliness showed significant heritability values and very high stability indices, again indicating qualitative behavior according to genetic parameters. With the exception of the number of pods per plant, which demonstrated low heritability while having excellent index values, traits like seed yield showed relatively low-stability-based heritability values. Global efforts aimed at improving the genetics of faba beans might benefit from genotypes that exhibit consistent yields in various conditions.

Alfalfa (Medicago sativa L.) is used to support livestock. A stability study was carried out over three years. The stability indices for yield and main quality characteristics such as plant height, number of nodes, the yield of green mass and dry matter, crude protein and fiber (%), and ash (%), were examined. Statistical analysis revealed significant differences that indicated the presence of high genotype–year interactions. Heritability was higher in the case of qualitative traits than quantitative traits. The most intriguing correlation was between green mass yield and crude protein content because positive correlations may lead to indirect and simultaneous selection. According to the statistical biplot models AMMI and GGE, the best genotypes for almost all traits to use, regardless of the environment and cultivation type, were the G8 (Population 2) followed by cultivar G3 (Yliki). Despite the high index values shown by the parameter number of nodes, the latter and yield showed low heritability.

One of the main obstacles to finding cultivars with consistent performance across locations and years is the genotype × environment (GE) interaction effect. A new approach to stability analysis for qualitative characteristics in maize was conducted utilizing G × E interactions and further analysis via AMMI and GGE biplots. The study aimed to identify the type of trait inheritance through estimations of the stability index, to evaluate multiple locations and multiple genotypes to determine how different ecosystems and maize genotypes relate to one another, and, finally, to suggest the ideal climatic conditions and genotypes, carefully chosen for their stability. Fifteen F1 commercial maize hybrids comprised the genetic materials tested, along with 15 open-pollination lines created by 4-cycle Honeycomb assessment, at four different environments, Giannitsa, Florina, Trikala, and Kalambaka in Greece. The experiments were conducted in Randomized Complete Block Designs (RCB) with four replications. The tested characteristics were protein content (%), fat (%), ash (%), starch (%), crude fiber (%), moisture (%), seed length, seed thickness, and seed width. All genotypes showed statistically significant differences for all characteristics measured, especially for protein content and size of the kernel. G × E interaction was present only for moisture content and size of the kernel. Environments significantly affected fat, starch content, moisture content, and the kernel’s size (under a multiple G × E interaction). Protein, ash, and fiber content showed no G × E interaction. Further analysis via AMMI and GGE biplots was applied to explore the genotypic stability across all experimental environments for the traits that showed noteworthy G × E interaction. According to our results and approach, protein content is less qualitative than other characteristics like moisture and starch content. Correlations showed that negative selection for the last two characteristics, as well as for ash content, in combination with longer seeds, may lead indirectly to improved stability performance for protein content. Three environments, Giannitsa, Trikala and Kalambaka, exhibited higher stability index values for almost all characteristics measured. Therefore, those environments are perfect for ensuring the stability of the quality characteristics and could be recommended. The best maize hybrids were Mitic, 6818 and 6040, exhibiting high stability indices of quality characteristics and Kermes displaying stability for protein content. Therefore, those should be further tested in multiple environments to confirm the consistency of their high-stability performance.

Stability for yield and seed quality across environments are desirable traits for varieties used for the support of livestock, and such specific varieties of common vetch (Vicia sativa L.) and peas (Pisum sativum L.) are highly demanded from farmers. The objective of this study was to investigate the stability performance of seed quality attributes on six common vetch genotypes and five pea genotypes. The genotypes’ stability traits were based on seed quality characteristics of peas and common vetch under low-input vs. conventional cultivation systems. Significantly positive or negative correlations between the main traits in all cultivation schemes were found. Based on these findings, improving certain traits that exhibit qualitative inheritance is expected to be an efficient indirect way to improve seed quality stability, more easily in the case of peas. It was evident from comparisons that even in low-input farming systems, varieties showed stable performance. Analysis of variance (ANOVA), GGE biplot on main traits, and AMMI analysis all resulted in statistically significant variations between genotypes, environments, and farming practices. This analysis resulted in specific pea varieties and vetch cultivars that were stable for various regions and farming systems on seed quality traits.

It is generally recognized that clovers represent a major nutrient used in ruminants’ diets due to their composition, which is high in protein content and low in fiber content. Investigating the nutritional quality of red and white clover genotypes, and classifying the genetic materials according to their primary quality attributes were the main goals of the current study. During a two-year experiment, we assessed their performance stability. Twelve red clover (Trifolium pratense L.) and twelve white clover (Trifolium repens L.) genetic materials were cultivated in a randomized complete block experimental design. Crude protein (CP%), acid detergent fiber (ADF%) and neutral detergent fiber (NDF%) concentrations were measured, and the relative feed value (RFV) was calculated using the estimates of digestible dry matter (DDM%) and potential dry matter intake (DMI% of body weight) of the forage. The average CP% content for white clover varied between 17.18% (REP-4) and 20.55% (REP-9) during the two years of testing. Regarding the red clover, the populations PRA-4, PRA-3, PRA-5, and PRA-10 exhibited the highest CP% content (19.98%) and the lowest ADF% (less than 29%). Although the variation was stable across the years, the profile of this variation of CP, ADF, and NDF for each clover species differs. The CP% was related negatively to ADF% and thus positively to DDM in both species but not with the same allocation, forming different clusters. PRA-3 and PRA-4 for the red clover and REP-5 for the white clover genetic materials exhibited characteristics belonging to the other species studied regarding their content in protein and fiber. Because of their relationship, CP and DDM could be improved simultaneously by breeders. This study revealed that ROZETA and PRA-4 were more stable genetic materials in red clover, and GRASSLANDS HUIA, RIVENDEL, and REP-5 in white clover.

The authenticity of Protected Designation of Origin (PDO) Feta cheese is critical for consumer confidence and market integrity, particularly in light of widespread concerns over economically motivated adulteration. This study evaluated the potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometric modeling to differentiate authentic Feta from non-Feta white brined cheeses. A total of 90 cheese samples, consisting of verified Feta and cow milk cheeses, were analyzed in both freeze-dried and fresh forms. Spectral data from raw, first derivative, and second derivative spectra were analyzed using principal component analysis–linear discriminant analysis (PCA-LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) to distinguish authentic Feta from non-Feta cheese samples. Derivative processing significantly improved classification accuracy. All classification models performed relatively well, but the PLS-DA model applied to second derivative spectra of freeze-dried samples achieved the best results, with 95.8% accuracy, 100% sensitivity, and 90.9% specificity. The most consistently highlighted discriminatory regions across models included ~2920 cm⁻¹ (C–H stretching in lipids), ~1650 cm⁻¹ (Amide I band, corresponding to C=O stretching in proteins), and the 1300–900 cm⁻¹ range, which is associated with carbohydrate-related bands. These findings support ATR-FTIR spectroscopy as a rapid, non-destructive tool for routine Feta authentication. The approach offers promise for enhancing traceability and quality assurance in high-value dairy products.