Identification of bread wheat genotypes with superior grain yield and agronomic traits through evaluation under rust epiphytotic conditions in Kenya

Multivariate analysis of phenotypic diversity elite bread wheat (Triticum aestivum L.) genotypes from ICARDA in Ethiopia

Wheat is an important crop for food security, providing a source of protein and energy for the growing population in Ethiopia. However, both biotic and abiotic factors limit national wheat productivity. The availability of genetically diverse wheat genotypes is crucial for developing new wheat varieties that are both high-yielding and resilient to stress. Therefore, this field trial aimed to assess phenotypic variation and relationship among ICARDA-derived bread wheat genotypes using multivariate analysis techniques. The trial was conducted at three locations: Enewari, Wogere, and Kulumsa using an alpha lattice design with two replications during the main cropping seasons of 2022 and 2023. Phenotypic data on eight agronomic traits and the severity of yellow rust were collected and R programming was used for data analysis. Individual and combined location data analysis of variance showed significant differences (p ≤ 0.05) among genotypes for most of the studied traits. The highest heritability and genetic advance as a percentage of the mean were observed in days to heading (90.8, 21.29), plant height (72.4, 28.6), seeds per spike (61.7, 28), thousand kernel weight (61.9, 12), and area under the disease progress curve (67, 39.8), suggesting a predominance of additive gene action. Grain yield showed a strong positive correlation with days to maturity, plant height, spike length, spikelet per spike, and thousand kernel weight for each location. Dendrogram and phylogenetic tree methods were used to group genotypes into four genetically distinct clusters. Cluster II and III had the greatest inter-cluster distance, indicating higher diversity among their genotypes. This study identified new candidate genotypes with superior agronomic performance, high grain yield traits, and robust resistance to yellow rust, making them valuable for both current and future wheat breeding programs. Additionally, the comprehensive dataset produced in this study could facilitate the identification of genetic variations influencing desirable traits through genome-wide association analysis.

Genotype × environment interaction: trade-offs between the agronomic performance and stability of durum (Triticum turgidum) wheat to stem-rust resistance in Kenya

Stem rust significantly threatens durum wheat production, often resulting in substantial yield losses. To better understand resistance mechanisms and the stability of durum lines in stem rust-prone environments, this study evaluated 49 durum genotypes over three seasons at the Kenya Agricultural and Livestock Research Organization in Njoro. Utilizing 7 × 7 alpha lattice design, we assessed adult-plant resistance, monitored disease progression through final disease score (FDS) and area under the disease progress curve (AUDPC), and evaluated agronomic performance. Statistical analyses revealed significant seasonal and genotypic effects on FDS, AUDPC, spike length, and grain yield (p≤0.01; p≤0.001), with important genotype-by-season interactions (p≤0.05; p≤0.001). Broad-sense heritability for AUDPC was high at 0.91 and moderate at 0.35 for kernels per spike, underscoring the genetic basis of these traits. Notably, we observed negative correlations between disease parameters and agronomic traits, suggesting potential trade-offs. GGE biplot analysis singled out the first season (main season of 2019) as crucial for evaluating stem rust resistance and identified several durum lines, such as G45 and G48, as consistently resistant across all conditions. Furthermore, this analysis highlighted G45, G48, G176 and G189 as the highest yielding and most stable lines. The discovery of these resistant and high-performing genotypes is critical for enhancing durum breeding programs, helping to mitigate the impact of stem rust and improve yield stability.

Hyperspectral reflectance and agro-physiological traits for field identification of salt-tolerant wheat genotypes using the genotype by yield*trait biplot technique

Introduction

Salinity is the abiotic obstacle that diminishes food production globally. Salinization causes by natural conditions, such as climate change, or human activities, e.g., irrigation and derange misuse. To cope with the salinity problem, improve the crop environment or utilize crop/wheat breeding (by phenotyping), specifically in spread field conditions. For example, about 33 % of the cropping area in Egypt is affected by salinity.

Methods

Therefore, this study evaluated forty bread wheat genotypes under contrasting salinity field conditions across seasons 2019/20 and 2020/21 at Sakha research station in the north of Egypt. To identify the tolerance genotypes, performing physiological parameters, e.g., Fv/Fm, CCI, Na+, and K+, spectral reflectance indices (SRIs), such as NDVI, MCARI, and SR, and estimated salinity tolerance indices based on grain yield in non-saline soil and saline soil sites over the tested years. These traits (parameters) and grain yield are simultaneously performed for generating GYT biplots.

Results

The results presented significant differences (P≤0.01) among the environments, genotypes, and their interaction for grain yield (GY) evaluated in the four environments. And the first season for traits, grain yield (GY), plant height (PH), harvest index (HI), chlorophyll content index (CCI), chlorophyll fluorescence parameter Fv/Fm, normalized difference vegetation index (NDVI) in contrasting salinity environments. Additionally, significant differences were detected among environments, genotypes, and their interaction for grain yield along with spectral reflectance indices (SRIs), e.g., Blue/Green index (BIG2), curvature index (CI), normalized difference vegetation index (NDVI), Modified simple ratio (MSR). Relying on the genotype plus genotype by environment (GGE) approach, genotypes 34 and 1 are the best for salinity sites. Genotypes 1 and 29 are the best from the genotype by stress tolerance indices (GSTI) biplot and genotype 34. Genotype 1 is the best from the genotype by yield*trait (GYT) method with spectral reflectance indices.

Discussion

Therefore, we can identify genotype 1 as salinity tolerant based on the results of GSTI and GYT of SRIs and recommend involvement in the salinity breeding program in salt-affected soils. In conclusion, spectral reflectance indices were efficiently identifying genotypic variance.