Combined application of zinc and silicon alleviates terminal drought stress in wheat by triggering morpho-physiological and antioxidants defense mechanisms

Wheat is an important global staple food crop; however, its productivity is severely hampered by changing climate. Erratic rain patterns cause terminal drought stress, which affect reproductive development and crop yield. This study investigates the potential and zinc (Zn) and silicon (Si) to ameliorate terminal drought stress in wheat and associated mechanisms. Two different drought stress levels, i.e., control [80% water holding capacity (WHC) was maintained] and terminal drought stress (40% WHC maintained from BBCH growth stage 49 to 83) combined with five foliar-applied Zn-Si combinations (i.e., control, water spray, 4 mM Zn, 40 mM Si, 4 mM Zn + 40 mM Si applied 7 days after the initiation of drought stress). Results revealed that application of Zn and Si improved chlorophyll and relative water contents under well-watered conditions and terminal drought stress. Foliar application of Si and Zn had significant effect on antioxidant defense mechanism, proline and soluble protein, which showed that application of Si and Zn ameliorated the effects of terminal drought stress mainly by regulating antioxidant defense mechanism, and production of proline and soluble proteins. Combined application of Zn and Si resulted in the highest improvement in growth and antioxidant defense. The application of Zn and Si improved yield and related traits, both under well-watered conditions and terminal drought stress. The highest yield and related traits were recorded for combined application of Zn and Si. For grain and biological yield differences among sole and combined Zn-Si application were statistically non-significant (p>0.05). In conclusion, combined application of Zn-Si ameliorated the adverse effects of terminal drought stress by improving yield through regulating antioxidant mechanism and production of proline and soluble proteins. Results provide valuable insights for further cross talk between Zn-Si regulatory pathways to enhance grain biofortification.

Evaluation of Ethiopian Common Bean (Phaseolus vulgaris L.) Genotypes Under Terminal Drought Stress Condition

The aim of this study was to evaluate twenty-five common bean genotypes for terminal drought stress. Simple lattice designs were used with two replications under stress and non-stress growing conditions on the field. The experiment was performed using irrigation water during the dry season (December-March). Up to flowering, the stress plots were irrigated and the non-stress plots were provided with water up to physiological maturity. Under both stress and non-stress conditions, several plant characteristics related to yield were assessed. The generated data in this study was subject to analysis of variance (ANOVA) using SAS software version 9.0.0. Data from non-stress (NS) and drought stress (DS) treatments were compared to assess the effect of drought stress or water regime on yield-related traits. In order to perform a combined analysis of variance, the datasets from the two treatments were combined (ANOVA). In this experiment, all the genotypes used showed significant differences in yield and some of the components of yield. For all the characteristics measured, except for flowering days, there were substantial variations between the two water treatments. There was no significant correlation between genotypes and water treatments for almost all the traits tested, with the exception of days to flowering, harvest index and root pulling resistance. Genotypes such as SER 125, MALB-67, MALB-65, MALB-51 and MALB-3 performed better under the two water treatments on the basis of mean productivity (MP) and geometric mean (GM). Understanding the relationships between plant characteristics related to drought stress tolerance and their genetic variability for stress-related grain yield, especially terminal water stress conditions, should prompt common bean breeders to take better measurements of yield and more comprehensive features of drought response.

A classic approach for determining genomic prediction accuracy under terminal drought stress and well-watered conditions in wheat landraces and cultivars

The present study aimed to improve the accuracy of genomic prediction of 16 agronomic traits in a diverse bread wheat (Triticum aestivum L.) germplasm under terminal drought stress and well-watered conditions in semi-arid environments. An association panel including 87 bread wheat cultivars and 199 landraces from Iran bread wheat germplasm was planted under two irrigation systems in semi-arid climate zones. The whole association panel was genotyped with 9047 single nucleotide polymorphism markers using the genotyping-by-sequencing method. A number of 23 marker-trait associations were selected for traits under each condition, whereas 17 marker-trait associations were common between terminal drought stress and well-watered conditions. The identified marker-trait associations were mostly single nucleotide polymorphisms with minor allele effects. This study examined the effect of population structure, genomic selection method (ridge regression-best linear unbiased prediction, genomic best-linear unbiased predictions, and Bayesian ridge regression), training set size, and type of marker set on genomic prediction accuracy. The prediction accuracies were low (-0.32) to moderate (0.52). A marker set including 93 significant markers identified through genome-wide association studies with P values ≤ 0.001 increased the genomic prediction accuracy for all traits under both conditions. This study concluded that obtaining the highest genomic prediction accuracy depends on the extent of linkage disequilibrium, the genetic architecture of trait, genetic diversity of the population, and the genomic selection method. The results encouraged the integration of genome-wide association study and genomic selection to enhance genomic prediction accuracy in applied breeding programs.

Groundnut mutants with end-of-season drought tolerance for the marginal dry lands of North Kordofan State, Sudan.

Groundnut (Arachis hypogaea L.), produced in the traditional small-scale rainfed sector of Western Sudan, accounts for 80% of the total annual groundnut acreage, producing 70% of the total production. Low productivity of groundnut is a characteristic feature in North Kordofan State, which is characterized as the most vulnerable state to the impact of climate change. Terminal drought stress resulting from reduction in rainfall amount and distribution at the end of the season is the most deleterious drought period, as it coincides with groundnut pod filling and maturation periods. High and stable yields under subsistence farming conditions in North Kordofan State could be realized only by using adapted high-yielding, drought-tolerant genotypes. Mutation induction by gamma-rays of 200 and 300 Gy was utilized to irradiate 500 dry seeds of the Spanish-type groundnut genotypes, Barberton, Sodari, ICGV 89104, ICGV 86743, ICGV 86744 and ICG 221, aiming at increasing the chances of obtaining genotypes with the desired drought-tolerant traits. Mutants were selected from the M3 plants using visual morphological traits. Groundnut mutants at the M4 and M5 generations, advanced by single seed descent, were evaluated for end-of-season drought tolerance. A terminal drought period of 25 days was imposed after 60 days from planting, using a rainout shelter. Mutants that survived 25 days of terminal drought stress were further evaluated for agronomic performance under rainfed field conditions. The groundnut mutant, Barberton-b-30-3-B, produced 1024 kg/ha, a significantly higher mean pod yield over 12 seasons compared with 926 kg/ha for 'Gubeish', the widely grown released check cultivar, showing overall yield advantage of 11%. Under 5 years of participatory research, Barberton-b-30-3-B was ranked the best with yield increment of 21% over 'Gubeish' under the mother trials. The GGE biplot analysis for 12 and five seasons, respectively, showed that Barberton-b-30-3-B was stable and produced a good yield in both high and low rainfall situations. Hence, Barberton-b-30-3-B was found to be a suitable mutant for sustainable profitable yields in the marginal dry lands of North Kordofan State and was officially released as 'Tafra-1' by the National Variety Release Committee during its second meeting of April 2018.

A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat

Background High-throughput phenotyping and genomic selection accelerate genetic gain in breeding programs by advances in phenotyping and genotyping methods. This study developed a simple, cost-effective high-throughput image analysis pipeline to quantify digital images taken in a panel of 286 Iran bread wheat accessions under terminal drought stress and well-watered conditions. The color proportion of green to yellow (tolerance ratio) and the color proportion of yellow to green (stress ratio) was assessed for each canopy using the pipeline. The estimated tolerance and stress ratios were used as covariates in the genomic prediction models to evaluate the effect of change in canopy color on the improvement of the genomic prediction accuracy of different agronomic traits in wheat. Results The reliability of the high-throughput image analysis pipeline was proved by three to four times of improvement in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covariates in the univariate genomic selection models. The higher prediction accuracies were attained for days to maturity when both tolerance and stress ratios were used as fixed effects in the univariate models. The results of this study indicated that the Bayesian ridge regression and ridge regression-best linear unbiased prediction methods were superior to other genomic prediction methods which were used in this study under terminal drought stress and well-watered conditions, respectively. Conclusions This study provided a robust, quick, and cost-effective machine learning-enabled image-phenotyping pipeline to improve the genomic prediction accuracy for days to maturity in wheat. The results encouraged the integration of phenomics and genomics in breeding programs.