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Genotype-environment interaction consists of the different response of individual genotypes resulting from changing environmental conditions. Its significance is a phenomenon that makes the breeding process very difficult. On the one hand, the breeder expects stable genotypes, i.e., yielding similarly regardless of environmental conditions. On the other hand, selecting the best genotypes for each region is one of the key challenges for breeders and farmers. The aim of this study was to evaluate genotype-by-environment interaction for grain yield in new maize hybrids developed by Plant Breeding Smolice Co. Ltd., utilizing the additive main effects and multiplicative interaction (AMMI) model. The investigation involved 69 maize ( Zea mays L.) hybrids, tested across five locations in a randomized complete block design with three replications. Grain yield varied from 8.76 t ha –1 (SMH_16417 in Smolice) to 16.89 t ha –1 (SMH_16043 in Płaczkowo), with a mean yield of 13.16 t ha –1 . AMMI analysis identified significant effects of genotype, environment, and their interaction on grain yield. Analysis of variance indicated that 25.12% of the total variation in grain yield was due to environment factor, 35.20% to genotypic differences, and 21.18% to genotype by environmental interactions. Hybrids SMH_1706 and SMH_1707 are recommended for further breeding programs due to their high stability and superior average grain yield.

This study was planned with the purpose of evaluating the drought tolerance of advanced breeding lines of chickpea in natural field conditions. Two methods were employed to impose field conditions; the first: simulating drought stress by growing chickpea genotypes at five rainfed areas, with Faisalabad as the non-stressed control environment; and the second: planting chickpea genotypes in spring to simulate a drought stress environment, with winter-sowing serving as the non-stressed environment. Additive main effects and multiplicative interaction (AMMI) and generalized linear models (GLM) models were both found to be equally effective in extracting main effects in the rainfed experiment. Results demonstrated that environment influenced seed yield, number of primary and secondary branches, number of pods, and number of seeds most predominantly; however, genotype was the main source of variation in 100 seed weight and plant height. The GGE biplot showed that Faisalabad, Kallur Kot, and Bhakkar were contributing the most in the GEI, respectively, while Bahawalpur, Bhawana, and Karor were relatively stable environments, respectively. Faisalabad was the most, and Bhakkar the least productive in terms of seed yield. The best genotypes to grow in non-stressed environments were CH39/08, CH40/09, and CH15/11, whereas CH28/07 and CH39/08 were found suitable for both conditions. CH55/09 displayed the best performance in stress conditions only. The AMMI stability and drought-tolerance indices enabled us to select genotypes with differential performance in both conditions. It is therefore concluded that the spring-sown experiment revealed a high-grade drought stress imposition on plants, and that the genotypes selected by both methods shared quite similar rankings, and also that manually computed drought-tolerance indices are also comparable for usage for better genotypic selections. This study could provide sufficient evidence for using the aforementioned as drought-tolerance evaluation methods, especially for countries and research organizations who have limited resources and funding for conducting multilocation trials, and performing sophisticated analyses on expensive software.

Background The selection of stable, high-yielding pea genotypes is crucial for sustaining production under changing climate conditions. Twenty multi-environment trials (10 locations x 2 years) were conducted to evaluate 11 genotypes during the 2020–2021 and 2021–2022 growing seasons in southern China. Results The Additive Main Effects and Multiplicative Interaction (AMMI) pooled analysis revealed that environmental effects accounted for the largest proportion of total variation (59.62%), followed by genotype-by-environment interaction (GEI) at 30.11%. The factor analysis revealed that plant height, first pod height, number of nodes on the main stem, number of pods/plant, number of seeds/pod, and number of nodes containing pods were the best multi-traits that significantly contributed to high yields. Cluster analysis identified five superior genotypes - Jingwan 8, Yuwan 6, Yunwan 116, 20205, and Suwan 8, with Yunwan 116 also showing disease resistance. The genotype main effects plus GEI (GGE) biplot identified Kunming and Xinxiang locations as the most effective environments for differentiating the genotypes, and Jingwan 8 as the ideal genotype for high yield and stability, particularly in these locations. Further, Yuwan 6 and 20205 also showed wide adaptability across environments. Conclusions Jingwan 8, Yuwan 6, and 20205 pea genotypes are recommended for large-scale cultivation due to their high yield and broad adaptability.

Background Sweet potato ( Ipomoea batatas L.) is a vital root crop valued for its nutritional quality, yield potential, and adaptability; however, the effects of early harvesting on yield, root size distribution, and marketable value are not well understood. This study evaluated nine genotypes to compare genotype performance at two harvest times, classify roots by economic value, and assess consumer-preferred traits. Methods The experiment was conducted at 90 and 120 days after transplanting (DAT). Morphological, yield, and nutritional traits were measured, including SPAD value, vine and leaf characteristics, shoot weight, root number and mass, economic root number and mass, soluble sugars, and total yield. Roots were classified into six size classes based on mass and number. Results More than 40% of root mass per plot consisted of roots weighing 30–100 g, with the remainder distributed among five other size classes. Genotypes G-89 and BARI Sweetpotato-15 produced 150% and 65% higher yields at 120 DAT compared with 90 DAT. At 120 DAT, BARI Sweetpotato-17, G-193, G-89, and BAU Sweetpotato-5 produced roots across all six size classes. Sensory evaluation at 90 DAT indicated that G-54 and G-193 were preferred for texture, boiling, and baking, while G-89 and BAU Sweetpotato-5 were favored for aroma, indicating potential traits associated with earlier maturity under the study conditions. Conclusions Genotypes G-89, G-184, BARI Sweetpotato-15, and G-138 exhibited comparatively higher yields and larger storage roots at 120 days after planting (DAT) under char land conditions, while several other genotypes including G-54 produced satisfactory yields and acceptable root quality at 90 DAT. This indicates their comparatively earlier performance within the evaluated harvest times under the specific char land agro-ecological conditions of the study. Broader applicability and definitive classification would require further validation across additional harvest stages and agro-ecological zones.

Foeniculum vulgare L. (fennel), a member of the Apiaceae family, is a widely cultivated spice plant valued for its aromatic fruits and medicinal properties. This study aimed to evaluate the agro-morphological characteristics, yield potential, essential oil content and components, as well as elemental profiles of twenty genetically diverse fennel genotypes under identical agro-climatic conditions during the 2019 and 2020 growing seasons. Significant phenotypic variation was observed among the genotypes, with fruit yields ranging from 183.78 to 1682.77 kg/ha. Essential oil content varied between 1.80% and 4.11%, with Ames23130 and Ames30693 genotypes exhibiting the highest oil yields. Also, essential oil yield values were found between 3.92 and 55.74 L/ha, and Ames23130 genotype had the highest essential oil yield. Gas chromatography-mass spectrometry (GC-MS) analysis identified 17 essential oil components, five of which trans-anethole (54.14–90.44%), estragole (2.38–28.75%), p-cymene (0.10-39.63%), limonene (0.13–7.94%), and α-fenchone (0.47–8.44%) were classified as major components. Among these, trans-anethole consistently dominated across all genotypes and both years, reflecting a stable chemotypic profile. Elemental analysis performed via inductively coupled plasma optical emission spectrometry (ICP-OES) revealed that fennel fruits are rich in potassium, calcium and magnesium, with negligible levels of toxic metals such as cadmium and lead, affirming the samples’ nutritional quality and food safety. Cluster analysis grouped the genotypes based on integrated yield, phytochemical, and mineral traits, with Ames23130 emerging as the most promising genotype for both fruit and essential oil production. Additionally, PI649471 and NSL6409 stood out for their distinct essential oil profiles, while PI414189 was notable for its superior potassium accumulation. The PCA analysis showed 42.9% of total variation, and correlation analysis revealed that highly significant positive correlation was found between Mn and Ca mineral contents with r  = 0.749** These findings provide valuable insights for fennel breeding programs and support the selection of elite genotypes for both commercial cultivation and functional food applications.

Background Drought stress markedly constrains soybean yields; however, progress in breeding has been limited by reliance on single-trait selection. Effective drought adaptation likely requires coordinated responses across root architecture, antioxidant defense, and osmotic adjustment, yet the relationships among these traits complexes across diverse germplasm remain poorly understood. Methods A comprehensive assessment of drought tolerance was conducted, evaluating 301 soybean genotypes over 2 years under both well-watered and drought-stressed conditions at 40% field capacity, using 11 morphological and physiological traits. Genetic variation was characterized through analysis of variance, broad-sense heritability estimation, and genetic and phenotypic coefficients of variation. Multivariate approaches, including principal component analysis, hierarchical clustering, and five machine learning classifiers, were used to identify trait interactions and classify drought responses. The Multi-trait Genotype-Ideotype Distance Index (MGIDI) and Stress Tolerance Index (STI) were applied for multi-trait genotype selection. Results Root traits demonstrated high broad-sense heritability (0.74–0.77), indicating substantial genetic potential for selection. Drought conditions reduced root parameters by 10–20%, whereas catalase activity increased by 24.3%. Proline content exhibited extreme genotypic variation (50–1,500 µg g⁻¹), with the highest accumulation associated with the poorest performance, characterized by severe membrane damage and limited root development, a pattern consistent with a stress-injury profile rather than effective tolerance. Hierarchical clustering revealed that superior genotypes attained drought tolerance through coordinated, moderate responses maintaining extensive root systems (total root length 1,239 mm) and high catalase activity (156 µmol min⁻¹ g⁻¹) rather than through extreme expression of individual traits. Machine learning variable importance analysis identified catalase activity, total root length, malondialdehyde, and proline as the most discriminative traits. Six elite genotypes (Jiyu 92; Dengke No. 1; Kennong 57; Mengdou 28; Ronda 130; Jinong SB 2012 − 136) were consistently identified as top performers across MGIDI, STI, cluster membership, and drought-to-control trait ratios. Conclusions Effective drought adaptation necessitates balanced, multi-trait coordination rather than the maximization of individual traits. Concentrating early-stage phenotyping on the four most important traits identified by machine learning would substantially reduce per-genotype resource requirements while retaining 70–80% of the discriminative information. The six identified elite genotypes are high-priority candidates for field-based validation and potential incorporation into drought-resilient soybean breeding programs.

The complexity of selecting for drought tolerance in cassava, influenced by multiple factors, demands innovative approaches to plant selection. This study aimed to identify cassava clones with tolerance to water stress by employing truncated selection and selection based on genomic values for population improvement and genotype evaluation per se . The Best Linear Unbiased Predictions (BLUPs), Genomic Estimated Breeding Values (GEBVs), and Genomic Estimated Genotypic Values (GETGVs) were obtained based on different prediction models via genomic selection. The selection intensity ranged from 10 to 30%. A wide range of BLUPs for agronomic traits indicate desirable genetic variability for initiating genomic selection cycles to improve cassava’s drought tolerance. SNP-based heritability ( h 2 ) and broad-sense heritabilities ( H 2 ) under water deficit were low magnitude (<0.40) for 8 to 12 agronomic traits evaluated. Genomic predictive abilities were below the levels of phenotypic heritability, varying by trait and prediction model, with the lowest and highest predictive abilities observed for starch content (0.15 – 0.22) and root length (0.34 – 0.36). Some agronomic traits of greater importance, such as fresh root yield (0.29 – 0.31) and shoot yield (0.31 – 0.32), showed good predictive ability, while dry matter content had lower predictive ability (0.16 – 0.22). The G-BLUP and RKHS methods presented higher predictive abilities, suggesting that incorporating kinship effects can be beneficial, especially in challenging environments. The selection differential based on a 15% selection intensity (62 genotypes) was higher for economically significant traits, such as starch content, shoot yield, and fresh root yield, both for population improvement (GEBVs) and for evaluating genotype’s performance per (GETGVs). The lower costs of genotyping offer advantages over conventional phenotyping, making genomic selection a promising approach to increasing genetic gains for drought tolerance in cassava and reducing the breeding cycle to at least half the conventional time.

In the present study, using the BLUP-GGE approach, southern-type (ST) Populus deltoides genotypes were analyzed and evaluated, and variations in growth traits, seedling height (H), and ground diameter (GD) were studied in various climatic regions, which could facilitate the increase of the breeding range of ST. The test materials were 119 one-year-old ST genotypes, and the test sites were Ningyang (NY) and Hainan (HN). A linear mixed-effects model was constructed, and the BLUP values of H and GD were obtained using the linear unbiased prediction (BLUP) method. GGE-Biplots were generated. The H variation was greater than the GD variation. The effects of environment, block, and genotype–environment interaction (G×E) were highly significant. The goodness of fit of the GGE-Biplots obtained by extracting the BLUP values was >95%. According to the GGE-biplot results, the performance of each genotype varied considerably. The genotype No. 13 had the highest average GD and the highest average H. In NY, the genotypes No. 93 and 115 had the highest H and GD, and in HN, the genotype No. 9 had the highest H and GD. ST had a better second-year survival rate in NY than in HN. The hybridization of tropical Populus deltoides can be performed using the No. 13 and 117 genotypes, which grow rapidly and have high yields.

Increasing wheat ( Triticum aestivum L.) yield and grain protein concentration (GPC) without excessive nitrogen (N) inputs requires understanding the genotypic variations in N accumulation, partitioning, and utilization strategies. This study evaluated whether high protein genotypes exhibit increased N accumulation (herein also expressed as N nutrition index, NNI) and partitioning (including remobilization from vegetative organs) compared to low-protein genotypes under low and high N conditions. Four winter wheat genotypes with similar yields but contrasting GPC were examined under two N rates (0 and 120 kg N ha -1 ) across two environments and four growing seasons in Oklahoma, US. As expected, the high-protein genotypes Doublestop CL+ (Dob) and Green Hammer (Grn) had greater GPC than the medium- (Gallagher, Gal) and low-protein genotypes (Iba), without any difference in grain yield. Total plant N accumulation at maturity showed diminishing increases for greater grain yield, and low-protein genotype showed greater N utilization efficiency (NUtE) than high-protein genotypes. The high-protein genotype Grn tended to achieve higher GPC by increasing total N uptake, while Dob exhibited a tendency towards higher N partitioning to grain (NHI). The allometric relationship between total N accumulation and biomass remained unchanged for both high- and low-protein genotypes. The N remobilization patterns differed between high- and low-protein genotypes. As N conditions improved, the proportional contributions of remobilized N from leaves tended to increase, while contributions from stems and chaff tended to decrease or remained unchanged for high-protein genotypes. This study highlights the importance of both N uptake capacity and efficient N partitioning to the grain as critical traits for realizing wheat’s dual goals of higher yield and protein. Leaf N remobilization plays a critical role during grain filling, sustaining plant N status and contributing to protein levels. The higher NUtE observed in the low-protein genotype Iba likely contributed to its lower GPC, emphasizing the trade-off between NUtE and GPC. The physiological strategies employed by high-protein genotypes, such as genotype Grn’s tendency for increased N uptake and Dob’s efficient N partitioning, provide a foundation for future breeding efforts aimed at developing resource-efficient and nutritionally superior wheat genotypes capable of achieving both increased yield and protein.

The main approach for improving multiple traits simultaneously is the selection index. The most widely used selection indices are those based on factor analysis, which overcome statistical limitations such as multicollinearity and the reliance on arbitrary weights of the classical Smith–Hazel approach and support multi-environment trials. Nevertheless, the efficiency indices are affected by factors such as genotype number, environment and trait correlation, and heritability. In this study, we simulated different scenarios varying the mentioned factors to evaluate the performance of the Factor-Analysis and Ideotype-Design-Based Index (FAI-BLUP), Multi-trait Genotype–Ideotype Distance Index (MGIDI), and Multi-Trait Stability Index (MTSI). All correlations were positive and constant within each scenario, while the ideotype sought genetic gains for traits in opposite directions. Simulations were conducted using AlphaSimR and FieldSimR, and indices were implemented via the metan package. Results showed that index efficiency was higher in scenarios with larger numbers of genotypes, low-to-moderate trait correlations, and moderate-to-high inter-environment correlations. However, strong correlations among traits, particularly when combined with high heritability, compromise selection index efficiency in scenarios with antagonistic trait objectives. Despite that, the MGIDI consistently outperformed the other indices across most scenarios. Therefore, we emphasize accounting for trait genetic architectures, genotype–trait correlations, and target environment correlations.