CGIAR Barley Breeding Toolbox: A diversity panel to facilitate breeding and genomic research in the developing world

Breeding programs in developing countries still cannot afford the new genotyping technologies, hindering their research. We aimed to assemble an Association Mapping panel to serve as CGIAR Barley Breeding Toolbox (CBBT), especially for the Developing World. The germplasm had to be representative of the one grown in the Developing World; with high genetic variability and be of public domain. For it, we genotyped with the Infinium iSelect 50K chip, a Global Barley Panel (GBP) of 530 genotypes representing a wide range of row-types, end-uses, growth habits, geographical origins and environments. 40,342 markers were polymorphic with an average polymorphism information content of 0.35 and 66% of them exceeding 0.25. The analysis of the population structure identified 8 subpopulations mostly linked to geographical origin, four of them with significant ICARDA origin. The 16 allele combinations at 4 major flowering genes (HvVRN-H3, HvPPD-H1, HvVRN-H1 and HvCEN) explained 11.07% genetic variation and were linked to the geographic origins of the lines. ICARDA material showed the widest diversity as revealed by the highest number of polymorphic loci (99.76% of all polymorphic SNPs in GBP), number of private alleles and the fact that ICARDA lines were present in all 8 subpopulations and carried all 16 allelic combinations. Due to their genetic diversity and their representativity of the germplasm adapted to the Developing World, ICARDA-derived lines and cultivated landraces were pre-selected to form the CBBT. Using the Mean of Transformed Kinships method, we assembled a panel capturing most of the allelic diversity in the GBP. The CBBT (N=250) preserves good balance between row-types and good representation of both phenology allelic combinations and subpopulations of the GBP. The CBBT and its genotypic data is available to researchers worldwide as a collaborative tool to underpin the genetic mechanisms of traits of interest for barley cultivation

Phytochemical Profiling and Untargeted Metabolite Fingerprinting of the MEDWHEALTH Wheat, Barley and Lentil Wholemeal Flours

An important research target is improving the health benefits of traditional Mediterranean, durum wheat-based foods using innovative raw materials. In this study, we characterised wholemeal flours obtained from a traditional durum wheat cv. Svevo, two innovative durum wheat varieties (Svevo-High Amylose and Faridur), the naked barley cv. Chifaa and the elite lentil line 6002/ILWL118/1-1, evaluating them for targeted phytochemicals, untargeted metabolomics fingerprints and antioxidant capacity. To this aim, individual phenolic acids, flavonoids, tocochromanols and carotenoids were identified and quantified through HPLC-DAD, and the antioxidant capacities of both the extracts and whole meals were detected by ABTS assays. An untargeted metabolomics fingerprinting of the samples was conducted through NMR spectroscopy. Results showed that the innovative materials improved phytochemical profiles and antioxidant capacity compared to Svevo. In particular, Svevo-HA and Faridur had higher contents of ferulic and sinapic acids, β-tocotrienol and lutein. Moreover, Chifaa is a rich source of phenolic acids, β-tocopherols, lutein and zeaxanthin whereas lentil of flavonoids (i.e., catechin and procyanidin B2). The NMR profiles of Svevo-HA and Faridur showed a significant reduction of sugar content, malate and tryptophan compared to that of Svevo. Finally, substantial differences characterized the lentil profiles, especially for citrate, trigonelline and phenolic resonances of secondary metabolites, such as catechin-like compounds. Overall, these results support the potential of the above innovative materials to renew the health value of traditional Mediterranean durum wheat-based products

High accuracy of genome-enabled prediction of belowground and physiological traits in barley seedlings

In plants, the study of belowground traits is gaining momentum due to their importance on yield formation and the uptake of water and nutrients. In several cereal crops, seminal root number and seminal root angle are proxy traits of the root system architecture at the mature stages, which in turn contributes to modulating the uptake of water and nutrients. Along with seminal root number and seminal root angle, experimental evidence indicates that the transpiration rate response to evaporative demand or vapor pressure deficit is a key physiological trait that might be targeted to cope with drought tolerance as the reduction of the water flux to leaves for limiting transpiration rate at high levels of vapor pressure deficit allows to better manage soil moisture. In the present study, we examined the phenotypic diversity of seminal root number, seminal root angle, and transpiration rate at the seedling stage in a panel of 8-way Multiparent Advanced Generation Inter-Crosses lines of winter barley and correlated these traits with grain yield measured in different site-by-season combinations. Second, phenotypic and genotypic data of the Multiparent Advanced Generation Inter-Crosses population were combined to fit and cross-validate different genomic prediction models for these belowground and physiological traits. Genomic prediction models for seminal root number were fitted using threshold and log-normal models, considering these data as ordinal discrete variable and as count data, respectively, while for seminal root angle and transpiration rate, genomic prediction was implemented using models based on extended genomic best linear unbiased predictors. The results presented in this study show that genome-enabled prediction models of seminal root number, seminal root angle, and transpiration rate data have high predictive ability and that the best models investigated in the present study include first-order additive × additive epistatic interaction effects. Our analyses indicate that beyond grain yield, genomic prediction models might be used to predict belowground and physiological traits and pave the way to practical applications for barley improvement

Malting Quality of ICARDA Elite Winter Barley (Hordeum vulgare L.) Germplasm Grown in Moroccan Middle Atlas

The use of barley (Hordeum vulgare L.) in Morocco is still limited to food and feed despite the amplified demand by local industries for imported malt. This study aims to evaluate 36 barley elite lines for major grain physicochemical parameters and malt quality traits. Analysis of variance, Pearson correlation, principal component analysis (PCA), and hierarchical cluster analysis (HCA) were performed. The results showed significant genotypic variation among genotypes for individual grain and malt traits. High broad sense heritability was obtained for all traits except for plump grain percentage, malt friability, and germination capacity. Starch, malt extract, Kolbach index, grain area, and test weight correlated significantly and negatively with barley protein. Malt extract correlated positively with Kolbach index and starch, but a negative correlation with soluble protein and malt protein was found. Based on 12 characters, 77% of the total genotypic variation was explained by the three first principal components following PCA and four clusters were depicted based on HCA. Genotypes of high interest with desirable levels of quality standards were identified to be used as a malt quality traits donor while designing crossing programs

The influence of mixing on the stratospheric age of air changes in the 21st century

Climate models consistently predict an acceleration of the Brewer–Dobson circulation (BDC) due to climate change in the 21st century. However, the strength of this acceleration varies considerably among individual models, which constitutes a notable source of uncertainty for future climate projections. To shed more light upon the magnitude of this uncertainty and on its causes, we analyse the stratospheric mean age of air (AoA) of 10 climate projection simulations from the Chemistry-Climate Model Initiative phase 1 (CCMI-I), covering the period between 1960 and 2100. In agreement with previous multi-model studies, we find a large model spread in the magnitude of the AoA trend over the simulation period. Differences between future and past AoA are found to be predominantly due to differences in mixing (reduced aging by mixing and recirculation) rather than differences in residual mean transport. We furthermore analyse the mixing efficiency, a measure of the relative strength of mixing for given residual mean transport, which was previously hypothesised to be a model constant. Here, the mixing efficiency is found to vary not only across models, but also over time in all models. Changes in mixing efficiency are shown to be closely related to changes in AoA and quantified to roughly contribute 10 % to the long-term AoA decrease over the 21st century. Additionally, mixing efficiency variations are shown to considerably enhance model spread in AoA changes. To understand these mixing efficiency variations, we also present a consistent dynamical framework based on diffusive closure, which highlights the role of basic state potential vorticity gradients in controlling mixing efficiency and therefore aging by mixing.Helmholtz Association | Ref. VH-NG-1014Australian Research Council’s Centre of Excellence for Climate System Science | Ref. CE110001028Australian Government’s National Computational Merit Allocation Scheme | Ref. FUERZAS 4012Ministerio de Ciencia e Innovación | Ref. CGL2015-71575-PNew Zealand Royal Society Marsden Fund | Ref. 12-NIW-00

Genomic Prediction of Grain Yield in a Barley MAGIC Population Modeling Genotype per Environment Interaction

18 Pags.- 7 Figs.- 4 Tabls. © 2021 Puglisi, Delbono, Visioni, Ozkan, Kara, Casas, Igartua, Valè, Piero, Cattivelli, Tondelli and Fricano. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.This research was carried out in the framework of the iBarMed project, which has been funded through the ARIMNet2 initiative and the Italian “Ministry of Agricultural, Food and Forestry Policies” under grant agreement “DM n. 20120.” ARIMNet2 has received funding from the EU 7th Framework Programme for research, technological development and demonstration under grant agreement no. 618127. The work was also supported by YSTEMIC_1063 (An integrated approach to the challenge of sustainable food systems: adaptive and mitigatory strategies to address climate change and malnutrition: From cereal diversity to plant breeding), a research project funded by Italian “Ministry of Agricultural, Food and Forestry Policies” in the frame of the Knowledge Hub on Food and Nutrition Security.Peer reviewe

The Influence of Mixing on Stratospheric Age of Air Changes in the 21st Century

Climate models consistently predict an acceleration of the BrewerDobson circulation (BDC) due to climate change in the 21st century. However, the strength of this acceleration varies considerably among individual models, which constitutes a notable source of uncertainty for future climate projections. To shed more light upon the magnitude of this uncertainty and on its causes, we analyse the stratospheric mean age of air (AoA) of 10 climate projection simulations from the Chemistry-Climate Model Initiative phase 1 (CCMI-I), covering the period between 1960 and 2100. In agreement with previous multi-model studies, we find a large model spread in the magnitude of the AoA trend over the simulation period. Differences between future and past AoA are found to be predominantly due to differences in mixing (reduced aging by mixing and recirculation) rather than differences in residual mean transport. We furthermore analyse the mixing efficiency, a measure of the relative strength of mixing for given residual mean transport, which was previously hypothesised to be a model constant. Here, the mixing efficiency is found to vary not only across models, but also over time in all models. Changes in mixing efficiency are shown to be closely related to changes in AoA and quantified to roughly contribute 10 % to the long-term AoA decrease over the 21st century. Additionally, mixing efficiency variations are shown to considerably enhance model spread in AoA changes. To understand these mixing efficiency variations, we also present a consistent dynamical framework based on diffusive closure, which highlights the role of basic state potential vorticity gradients in controlling mixing efficiency and therefore aging by mixing

Revisiting the Mystery of Recent Stratospheric Temperature Trends

Simulated stratospheric temperatures over the period 1979–2016 in models from the Chemistry-Climate Model Initiative are compared with recently updated and extended satellite data sets. The multimodel mean global temperature trends over 1979–2005 are -0.88 ± 0.23, -0.70 ± 0.16, and -0.50 ± 0.12 K/decade for the Stratospheric Sounding Unit (SSU) channels 3 (~40–50 km), 2 (~35–45 km), and 1 (~25–35 km), respectively (with 95% confidence intervals). These are within the uncertainty bounds of the observed temperature trends from two reprocessed SSU data sets. In the lower stratosphere, the multimodel mean trend in global temperature for the Microwave Sounding Unit channel 4 (~13–22 km) is -0.25 ± 0.12 K/decade over 1979–2005, consistent with observed estimates from three versions of this satellite record. The models and an extended satellite data set comprised of SSU with the Advanced Microwave Sounding Unit-A show weaker global stratospheric cooling over 1998–2016 compared to the period of intensive ozone depletion (1979–1997). This is due to the reduction in ozone-induced cooling from the slowdown of ozone trends and the onset of ozone recovery since the late 1990s. In summary, the results show much better consistency between simulated and satellite-observed stratospheric temperature trends than was reported by Thompson et al. (2012, https://doi.org/10.1038/nature11579) for the previous versions of the SSU record and chemistry-climate models. The improved agreement mainly comes from updates to the satellite records; the range of stratospheric temperature trends over 1979–2005 simulated in Chemistry-Climate Model Initiative models is comparable to the previous generation of chemistry-climate models

Deriving Global OH Abundance and Atmospheric Lifetimes for Long-Lived Gases: A Search for CH 3 CCl 3 Alternatives

An accurate estimate of global hydroxyl radical (OH) abundance is important for projections of air quality, climate, and stratospheric ozone recovery. As the atmospheric mixing ratios of methyl chloroform (CH₃CCl₃) (MCF), the commonly used OH reference gas, approaches zero, it is important to find alternative approaches to infer atmospheric OH abundance and variability. The lack of global bottom‐up emission inventories is the primary obstacle in choosing a MCF alternative. We illustrate that global emissions of long‐lived trace gases can be inferred from their observed mixing ratio differences between the Northern Hemisphere (NH) and Southern Hemisphere (SH), given realistic estimates of their NH‐SH exchange time, the emission partitioning between the two hemispheres, and the NH versus SH OH abundance ratio. Using the observed long‐term trend and emissions derived from the measured hemispheric gradient, the combination of HFC‐32 (CH₂F₂), HFC‐134a (CH₂FCF₃, HFC‐152a (CH₃CHF₂), and HCFC‐22 (CHClF₂), instead of a single gas, will be useful as a MCF alternative to infer global and hemispheric OH abundance and trace gas lifetimes. The primary assumption on which this multispecies approach relies is that the OH lifetimes can be estimated by scaling the thermal reaction rates of a reference gas at 272 K on global and hemispheric scales. Thus, the derived hemispheric and global OH estimates are forced to reconcile the observed trends and gradient for all four compounds simultaneously. However, currently, observations of these gases from the surface networks do not provide more accurate OH abundance estimate than that from MCF