Evaluation of resistance and determination of stability of different sugar beet (Beta vulgaris L.) genotypes in rhizomania-infected conditions

Genetic analysis and association detection of agronomic traits in maize genotypes

In maize breeding, enhancing yield through genetic insights is crucial yet challenged by the complex interplay of agronomic traits. This study utilized a diallel mating design involving nine advanced early maize lines to dissect the genetic architecture underlying key agronomic traits and their impact on yield. Over two consecutive years (2018-2019 and 2019-2020), 36 hybrids derived from these lines were grown across two locations, Karaj, Alborz, Iran and Kermanshah (2019-2020), Iran, in a randomized complete block design with three replications. The study aimed to evaluate the general combining ability of the parental lines and the specific combining ability of their hybrids, alongside the mutual influences of critical traits on yield. The analysis of variance revealed significant differences at 1% and 5% probability levels among the hybrids for all traits studied, indicating substantial genetic variability. Diallel analysis suggested that both additive and non-additive genetic effects are crucial in controlling traits such as kernel yield, kernel rows, kernel in row, 1000 kernel weight, plant height, ear height, kernel moisture, and ear wood. Additive effects, as indicated by the Baker's ratio, predominated for these traits. Among the parental lines, KE 79,017/3211 demonstrated the strongest general combining ability for kernel yield. Hybrids K 1264/5-1 × KE 76,009/311, KE 77,005/2 × KE 75,016/321, KE 77,008/1 × KE 77,004/1, and KE 77,008/1 × KE 79,017/3211 exhibited significant and positive specific combining ability effects for kernel yield, highlighting their potential in yield-enhancing breeding programs. Correlation analysis showed no significant association between KY*KIN with the KY*KW. However, there were weak positive correlations between KY*KR with other traits such as KY*PH, KY*KR, and KY*EH. The biplot analyses identified genotypes 4, 12, and 31 as superior across various trait combinations. Genotype 12 emerged as notably high-yielding based on average tester coordinates. Using the multi-trait stability index and imposing a selection pressure of 25%, genotype 10 was ranked highest, followed by genotypes 9, 13, 11, 1, 2, and 16, which were considered the most stable and ideal across all evaluated traits. This comprehensive study underscores the importance of both general combining ability and specific combining ability in maize breeding and highlights specific genotypes and hybrid combinations with promising traits for yield enhancement.

Unraveling genotypic interactions in sugar beet for enhanced yield stability and trait associations

The interaction between genotype and environment (GEI) significantly influences plant performance, crucial for breeding programs and ultimately boosting crop productivity. Alongside GEI, breeders encounter another hurdle in their quest for yield improvement, notably adverse and negative correlations among pivotal traits. This study delved into the stability of white sugar yield (WSY), root yield (RY), sugar content (SC), extraction coefficient of sugar (ECS), and the interplay among essential traits including RY, SC, alpha amino nitrogen (N), sodium (Na+), and potassium (K+) across 15 sugar beet hybrids and three control varieties. The investigation spanned two locations over two consecutive years (2022-2023), employing a randomized complete block design with four replications to comprehensively analyze these factors. The analysis of variance highlighted the significant effects of environment, genotype, and GEI at the 1% probability level. Notably, the AMMI analysis of GEI revealed the significance of the first component for WSY, RY, and SC, with the first two components proving significant for ECS. Within the linear mixed model (LMM), WSY, RY, SC, and ECS demonstrated significant effects from both genotype and GEI. In the WAASB biplot, genotypes 10, 8, 17, 6, 13, 14, 15, 7, 12, and 16 exhibited stability in WSY, while genotypes 9, 10, 6, 14, 7, 8, 13, 12, 18, and 15 displayed stability in RY. Additionally, genotypes 10, 15, 12, 13, 16, 17, 6, and 14 were stable for SC, and genotypes 8, 10, 7, 6, 13, 12, 16, 17, 15, 14, and 18 showcased stability in ECS, boasting above-average yield values. In the genotype by yield × trait (GYT) biplot, genotypes 15, 18, and 16 emerged as top performers when combining RY with SC, Na+, N, and K+, suggesting their potential for inclusion in breeding programs.

Genotype by environment interaction and stability analysis for harvest date in sugar beet cultivars

This research assessed the quantitative and qualitative reactions of commercially grown sugar beets to four different harvest dates and their yield stability. The study followed a split-plot design within a randomized complete block design over 3 years. The main plot involved 10 sugar beet cultivars, while the subplot involved four harvest dates: August 13 (HD1), September 7 (HD2), October 3 (HD3), and November 12 (HD4). The study found that environmental conditions, genotypes, and harvest dates significantly affected various traits of sugar beet. Yearly environmental variations and their interactions with genotypes and harvest dates had substantial impacts on all measured traits at the 1% probability level. Additive main effect and multiplicative interaction analysis based on white sugar yield indicated that genotype and environment's additive effects, as well as the genotype-environment interaction, were significant at 1% probability level. Shokoufa and Arya, which exhibit high white sugar yield (WSY) and low first interaction principal component (IPC1) values, are identified as desirable due to their stability across different environments. Among the harvest dates in different years, the fourth and third dates showed a higher yield than the total average. Perfekta and Ekbatan exhibited high specific adaptability. According to the multi-trait stability index, Arta, Arya and Sina were recognized as stable and superior across all measured traits.

Evaluation of rhizomania infection on sugar beet quality in multi-year field assessment

Rhizomania is one of the most destructive and damaging sugar beet diseases that has spread in different regions of Iran. In order to evaluate the genotypic, environmental, and genotype by environmental variability of sugar beet genotypes under rhizomania infection, variance components were estimated from the trial series in 7 years. Required data, such as yield and quality parameters, were collected from value for cultivation and use trials. Results of analysis of variance showed that the environment was the source that explained most of the variability, except for amino-N and alkalinity. Quality traits were also influenced by the environment × cultivar interaction, so that 4.8% (white sugar content) to 46.1% (alkalinity) variance was observed. In contrast, genetic variation was much lower, between 1.2% (potassium) and 27.4% (amino-N). A strong and negative correlation was found between root yield, sugar yield, and white sugar content with the disease index, which obviously illustrates the negative impact of the rhizomania on root weight and as a consequence on the dependent traits. The cluster analysis of the cultivars based on the quantitative and qualitative traits and the disease index showed that the range of variation in traits, such as the disease index, varied from 6.25 for the susceptible cultivar to 1.25 for the resistant one. This indicates the existence of sufficient genetic diversity among cultivars in terms of this trait. High impurity accumulation was observed in Shiraz region compared with Mashhad. In conclusion, it is observed that rhizomania has a significant effect on the impurity concentration in the root, especially sodium, potassium, and amino-N. This is very important in the sugar industry because sugar extraction depends on the concentration of these impurities, in addition to the sugar content of each cultivar.

Sweet potato (Ipomoea batatas L.) genotype selection using advanced indices and statistical models: A multi-year approach

In Bangladesh, sweet potato holds the fourth position as a crucial carbohydrate source, trailing rice, wheat, and potato. However, locally grown sweet potato varieties often display limited stability and yield. To tackle this challenge, diverse selection methods and statistical models were utilized to pinpoint sweet potato genotypes showcasing both stability and superior yield and quality traits. In the initial two years, multiple selection methods were employed to narrow down the collections based on preferences for yield and its contributing traits. Subsequently, a multi-environment trial (MET) was conducted in the following year to pinpoint superior and stable genotypes with desirable yield and quality characteristics. An integrated approach involving the Multi-Trait Genotype Ideotype Distance Index (MGIDI), Factor Analysis and Ideotype-Design (FAI-BLUP), and Smith-Hazel Index (SH) led to the identification of 71 superior sweet potato genotypes out of a total of 351 in the initial growing season. In the subsequent season, the MGIDI selection index was applied to the 71 genotypes, resulting in the selection of 11 top-performing genotypes. This selection process was complemented by a detailed analysis of the strengths and weaknesses of the selected genotypes. In the MET, the mixed effect model, specifically the linear mixed model (LMM), identified significant genotypic and genotype-environment interaction (GEI) variances. This points to elevated heritability and selection accuracy, ultimately boosting the model's reliability. By combining the strengths of LMM and additive main effects and multiplicative interaction (AMMI), the best linear unbiased prediction (BLUP) index identified H20 as the top-performing genotype for marketable root yield (MRY), H37 for dry weight of root (DW), H8 for beta carotene (BC) and H41 for vitamin c (VC). These genotypes surpassed the overall average in the WAAS index. For simultaneous stability and high performance, the WAASBY index selected H37 for MRY, H6 for DW, H61 for BC, and H3 for VC. Finally, genotypes H3 and H20 were selected using multi-trait stability index (MTSI), as they possessed high performance and stability. Based on the selection sense, the objective has been achieved with regards to the trait MRW, which serves as a major criterion for a superior variety of sweet potato.

Genotype by environment and genotype by yield*trait interactions in sugar beet: analyzing yield stability and determining key traits association

The genotype by environment interaction significantly influences plant yield, making it imperative to understand its nature for the creation of breeding programs to enhance crop production. However, this is not the only obstacle in the yield improvement process. Breeders also face the significant challenge of unfavorable and negative correlations among key traits. In this study, the stability of root yield and white sugar yield, and the association between the key traits of root yield, sugar content, nitrogen, sodium, and potassium were examined in 20 sugar beet genotypes. The study was conducted using a randomized complete block design with four replications over two consecutive years across five locations. The combined analysis of variance results revealed significant main effects of year, location, and genotype on both root yield and white sugar yield. Notably, two-way and three-way interactions between these main effects on root yield and white sugar yield resulted in a significant difference. The additive main effect and multiplicative interaction analysis revealed that the first five interaction principal components significantly impacted both the root yield and white sugar yield. The linear mixed model results for root yield and white sugar yield indicated that the genotype effect and the genotype by environment interaction were significant. The weighted average absolute scores of the best linear unbiased predictions biplot demonstrated that genotypes 20, 4, 7, 2, 16, 3, 6, 1, 14, and 15 were superior in terms of root yield. For white sugar yield, genotypes 4, 16, 3, 7, 5, 1, 10, 20, 2, and 6 stood out. These genotypes were not only stable but also had a yield value higher than the total average. All key traits, which include sugar content, sodium, potassium, and alpha amino nitrogen, demonstrated a negative correlation with root yield. Based on the genotype by yield*trait analysis results, genotypes 20, 19, and 16 demonstrated optimal performance when considering the combination of root yield with sugar content, sodium, alpha amino nitrogen, and potassium. The multi-trait stability study, genotype 13 ranked first, and genotypes 10, 8, and 9 were identified as the most ideal stable genotypes across all traits. According to the multi-trait stability index, genotype 13 emerged as the top-ranking genotype. Additionally, genotypes 10, 8, and 9 were recognized as the most stable genotypes.

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

The methods utilized to analyze genotype by environment interaction (GEI) and assess the stability and adaptability of genotypes are constantly changing and developing. In this regard, often instead of depending on a single analysis, it is better to use a combination of several methods to measure the nature of the GEI from various dimensions. In this study, the GEI was investigated using different methods. For this purpose, 18 sugar beet genotypes were evaluated in randomized complete block design in five research stations over 2 years. The additive effects analysis of the additive main effects and multiplicative interaction (AMMI) model showed that the effects of genotype, environment and GEI were significant for root yield (RY), white sugar yield (WSY), sugar content (SC), and extraction coefficient of sugar (ECS). The multiplicative effect's analysis of AMMI into interaction principal components (IPCs) showed that the number of significant components varies from one to four in the studied traits. According to the biplot of the mean yield against the weighted average of absolute scores (WAAS) of the IPCs, G2 and G16 for RY, G16 and G2 for WSY, G6, G4, and G1 for SC and G8, G10 and G15 for ECS were identified as stable genotypes with optimum performance. The likelihood ratio test showed that the effects of genotype and GEI was significant for all studied traits. In terms of RY and WSY, G3 and G4 had high mean values of the best linear unbiased predictions (BLUP), so they were identified as suitable genotypes. However, in terms of SC and ECS, G15 obtained high mean values of the BLUP. The GGE biplot method classified environments into four (RY and ECS) and three (WSY and SC) mega-environments (MEs). Based on the multi-trait stability index (MTSI), G15, G10, G6, and G1 were the most ideal genotypes.