Genetic variability and interrelationships of grain yield and its components of selected bread wheat genotypes

High temperature is one of major constraints of bread wheat production in the Sudan. Evaluation of different genotypes under heat stress conditions is one of the main tasks of breeders for exploiting genetic variations to improve cultivars under heat stress. Also, association of yield and yield contributing traits is important for selection. The objective of this study was to evaluate bread wheat genotypes under the irrigated hot environment of the Gezira, Sudan. Experiments were conducted at Gezira Research Farm, Wad Medani, Sudan for two consecutive seasons 2006/07 and 2007/08. The experiments were arranged in an augmented design with six checks, 4 and 12 blocks in the first and second seasons, respectively. Results showed wide ranges of variations in grain yield among these genotypes in both seasons. Grain yield ranged from 965 to 4019 kg/ha and from 133 to 6258 kg/ha in the first and second seasons, respectively. Similar wide ranges of variations were found in biomass, harvest index, number of spikes m-2, days to heading, days to maturity and plant height. Grain yield showed positive and significant correlation coefficients with biomass and harvest index, in both seasons. Path coefficient analysis indicated that biomass and harvest index were the most directly related parameters to grain yield, in both seasons

Exploitation of Tolerance of Wheat Kernel Weight and Shape-Related Traits from Aegilops tauschii under Heat and Combined Heat-Drought Stresses

Kernel weight and shape-related traits are inherited stably and increase wheat yield. Narrow genetic diversity limits the progress of wheat breeding. Here, we evaluated kernel weight and shape-related traits and applied genome-wide association analysis to a panel of wheat multiple synthetic derivative (MSD) lines. The MSD lines harbored genomic fragments from Aegilops tauschii. These materials were grown under optimum conditions in Japan, as well as under heat and combined heat–drought conditions in Sudan. We aimed to explore useful QTLs for kernel weight and shape-related traits under stress conditions. These can be useful for enhancing yield under stress conditions. MSD lines possessed remarkable genetic variation for all traits under all conditions, and some lines showed better performance than the background parent Norin 61. We identified 82 marker trait associations (MTAs) under the three conditions; most of them originated from the D genome. All of the favorable alleles originated from Ae. tauschii. For the first time, we identified markers on chromosome 5D associated with a candidate gene encoding a RING-type E3 ubiquitin–protein ligase and expected to have a role in regulating wheat seed size. Our study provides important knowledge for the improvement of wheat yield under optimum and stress conditions. The results emphasize the importance of Ae. tauschii as a gene reservoir for wheat breeding

Global Wheat Head Detection 2021: an improved dataset for benchmarking wheat head detection methods

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version

INTERACTIVE EFFECTS OF HEAT STRESS AND WATER DEFICIT ON THE PERFORMANCE OF CHICKPEA (Cicer arietinum L.) GENOTYPES IN SUDAN

Climate variations pose significant challenges for chickpea production. Increases tolerance to heat stress and water deficit is an important option to increase chickpea productivity in Sudan. This study aimed to evaluate the performance, stability and correlation between different traits of nine chickpea genotypes under different conditions. The genotypes were tested at two locations; Hudeiba for three seasons (2007/08, 2009/10 and 2010/11) and Shambat during season 2008/09. To induce stresses, four treatments were used: non-stress, terminal heat stresses, water stress and combination of heat and water stresses. A split-plot design with three replications was used where the stress treatments were assigned to main plots and genotypes to subplots. The results showed that heat, water and combined stresses significantly affected all studied traits. Heat stress induced more reduction than water stress for most of the traits, however combined stress imposed the highest effect. Significant differences between genotypes for all studied traits were also found. The interactions between the genotypes and treatments were significant for most of studied traits. Some genotypes were found tolerant and stable under water deficit (Wad Hamid and Shendi), heat stress (Hwata), or combined stress (Wad Hamid) conditions. Seed yield positively and significantly correlated with number of pods/plant, number of seeds/plant, 100- seed weight and plant height and these traits could be used as selection criteria for breeding high yielding genotypes. It could be concluded that cultivars differentially responded to heat, water and combined stresses, indicating that further improvement in the tolerance of chickpea to these stresses could be achieved

Probing Differential Metabolome Responses among Wheat Genotypes to Heat Stress Using Fourier Transform Infrared-Based Chemical Fingerprinting

Heat stress is one of the major environmental constraints for wheat production; thus, a comprehensive understanding of the metabolomic responses of wheat is required for breeding heat-tolerant varieties. In this study, the metabolome responses of heat-tolerant genotypes Imam and Norin 61, and susceptible genotype Chinese Spring were comparatively analyzed using Fourier transform infrared (FTIR) spectroscopy in combination with chemometric data mining techniques. Principal component analysis of the FTIR data suggested a spectral feature partially overlapping between the three genotypes. FTIR spectral biomarker assay showed similar heat responses between the genotypes for markers Fm482 and Fm1502, whereas genotype-dependent variations were observed for other markers. The markers Fm1251 and Fm1729 showed contrasting behaviors between heat-tolerant and susceptible genotypes, suggesting that these markers may potentially serve as a tool for distinguishing heat-tolerant genotypes. Linear discriminant analysis (LDA) of the spectra demonstrated a clear separation between the three genotypes in terms of the heat stress effect. Analysis of LDA coefficients identified several spectral regions that were potentially responsible for the discrimination of FTIR spectra between different genotypes and environments. These results suggest that a combination of FTIR and chemometrics can be a useful technique for characterizing the metabolic behavior of diverse wheat genotypes under heat stress

Chemical Fingerprinting of Heat Stress Responses in the Leaves of Common Wheat by Fourier Transform Infrared Spectroscopy

Wheat (Triticum aestivum L.) is known to be negatively affected by heat stress, and its production is threatened by global warming, particularly in arid regions. Thus, efforts to better understand the molecular responses of wheat to heat stress are required. In the present study, Fourier transform infrared (FTIR) spectroscopy, coupled with chemometrics, was applied to develop a protocol that monitors chemical changes in common wheat under heat stress. Wheat plants at the three-leaf stage were subjected to heat stress at a 42 °C daily maximum temperature for 3 days, and this led to delayed growth in comparison to that of the control. Measurement of FTIR spectra and their principal component analysis showed partially overlapping features between heat-stressed and control leaves. In contrast, supervised machine learning through linear discriminant analysis (LDA) of the spectra demonstrated clear discrimination of heat-stressed leaves from the controls. Analysis of LDA loading suggested that several wavenumbers in the fingerprinting region (400–1800 cm−1) contributed significantly to their discrimination. Novel spectrum-based biomarkers were developed using these discriminative wavenumbers that enabled the successful diagnosis of heat-stressed leaves. Overall, these observations demonstrate the versatility of FTIR-based chemical fingerprints for use in heat-stress profiling in wheat

Multi-Locational Evaluation of Forage-Suited Selected Sudan Pearl Millet [<i>Pennisetum glaucum</i> (L.) R. Br.] Accessions Identified High-Yielding and Stable Genotypes in Irrigated, Arid Environments

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a subtropical grain and forage crop. It is privileged with several desirable forage attributes. Nevertheless, research on pearl millet is limited, especially as a forage crop, in developing countries. Therefore, the objectives of this study were to investigate the field performance and stability of pearl millet genotypes for forage yield across seven environments. The study was conducted in seven environments (combination of locations and seasons) during the 2016/2017–2018/2019 seasons. Twenty-five pearl millet genotypes, selected based on forage yield from a core collection of 200 accessions, were arranged in an alpha lattice design with three replications. The parameters measured were fresh forage yield, days to flowering, plant height, number of culms m−2, leaf-to-stem ratio, and stem girth. The combined analysis revealed that environments, genotypes, and their interaction had significant effects on all traits studied except the genotypic effect on stem girth. Across the seven environments, four genotypes (G14, G01, G12, and G22) outyielded the check genotype in fresh matter yield by 20.7, 16.5, 11.0 and 9.8%, respectively. The additive main effects and multiplicative interaction (AMMI) analysis showed that the genotype, environment, and their interaction were highly significant (p ≤ 0.001) for fresh matter yield. The results of AMMI stability values (ASVs) and the genotype selection index (GSI) combined with the AMMI estimate-based selection showed that genotypes G14, G22 and G01 were the most stable and adapted genotypes and were superior to the check genotype. These results indicate that forage pearl millet varieties could be developed directly through evaluating the wealth of available collections or indirectly through hybridization in crop breeding programs

Durum Wheat Field Performance and Stability in the Irrigated, Dry and Heat-Prone Environments of Sudan

Developing climate-resilient crop varieties with better performance under variable environments is essential to ensure food security in a changing climate. This process is significantly influenced, among other factors, by genotype × environment (G × E) interactions. With the objective of identifying high-yielding and stable genotypes, 20 elite durum wheat lines were evaluated in 24 environments (location–season combination) during 5 crop seasons (2010/11–2014/15). The REML (residual maximum likelihood)-predicted means of grain yield of 16 genotypes that were common across all environments ranged from 3522 kg/ha in G201 to 4132 kg/ha in G217. Results of additive main effect and multiplicative interaction (AMMI) analysis showed that genotypes (G), environments (E), and genotype × environment interaction (GEI) significantly affected grain yield. From the total sum of squares due to treatments (G + E + GEI), E attributed the highest proportion of the variation (90.0%), followed by GEI (8.7%) and G (1.3%). Based on the first four AMMI selections for grain yield in the 24 environments, genotypes G217, G219, G211, and G213 were selected in 23, 12, 11, and 9 environments, respectively. The genotype and genotype × environment biplot (GGE) biplot polygon view showed that the environments were separated into three mega-environments. The winning genotypes in these mega-environments were G217, G214, and G204. Genotypes G212, G220, G217, G215, and G213 showed low AMMI stability values (ASV), whereas genotypes G217, G220, G212, G211, and G219 showed low genotype selection index (GSI), indicating their better stability and adaptability to the test environments. The results indicated that genotypes G217, G219, G211, G213, and G220 combined both high grain yield and stability/adaptability under dry but irrigated and heat-prone environments. An in-depth analysis of the superior genotypes could help better understand the stress-adaptive traits that could be targeted to further increase durum wheat yield and stability under the changing climate