Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 +/- 0.13) and the fixed effects model (0.62 +/- 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 +/- 0.11), GBLUP (0.55 +/- 0.1), and ABLUP (0.48 +/- 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical

Genetic sources and loci for wheat head blast resistance identified by genome-wide association analysis

The emergence and spread of wheat blast caused by fungal pathogen Magnaporthe oryzae pathotype Triticum is a threat to global wheat production. The resistance level and genetic loci for blast resistance in Chinese germplasm remain unknown. A panel of 266 bread wheat accessions from China, CIMMYT-Mexico and other countries was screened for head blast resistance under 12 field experiments in Bolivia and Bangladesh. Subsequently, a genome-wide association study was performed to understand the genetic basis of wheat blast resistance. The average blast index of all the accessions was 53.7% ± 12.7%, and 10 accessions including Chinese accessions Yumai 10 and Yu 02321 showed moderate to high levels of blast resistance, accounting for only 3.8% in the panel. Fifty-eight significant SNPs clustered in a 28.9 Mb interval on the 2AS/2NS translocation region, explaining phenotypic variation between 10.0% and 35.0%. The frequency of the 2AS/2NS translocation in the Chinese accessions was as low as 4.5%. These results indicated that the 2NS fragment was the only major locus conferring resistance to wheat blast in this panel, and the resistant and moderately resistant lines identified could be deployed in breeding

Understanding photothermal interactions will help expand production range and increase genetic diversity of lentil (Lens culinaris Medik.)

Lentil is a staple in many diets around the world and growing in popularity as a quick-cooking, nutritious, plant-based source of protein in the human diet. Lentil varieties are usually grown close to where they were bred. Future climate change scenarios will result in increased temperatures and shifts in lentil crop production areas, necessitating expanded breeding efforts. We show how we can use a daylength and temperature model to identify varieties most likely to succeed in these new environments, expand genetic diversity, and give plant breeders additional knowledge and tools to help mitigate these changes for lentil producers.This research was conducted as part of the ‘Application of Genomics to Innovation in the Lentil Economy (AGILE)' project funded by Genome Canada and managed by Genome Prairie. We are grateful for the matching financial support from the Saskatchewan Pulse Growers, Western Grains Research Foundation, the Government of Saskatchewan, and the University of Saskatchewan. We acknowledge the support from our international partners: University of Basilicata (UNIBAS) in Italy; Institute for Sustainable Agriculture (IAS) in Spain; Center for Agriculture Research in the Dry Areas (ICARDA) in Morocco, India and Bangladesh; Local Initiatives for Biodiversity, Research and Development (LI-BIRD) in Nepal; and United States Department of Agriculture (USDA CRIS Project 5348-21000-017-00D) in the USA, for conducting field experiments in their respective countries

Genomic selection for wheat blast in a diversity panel, breeding panel and full-sibs panel

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical

QTL mapping for field resistance to wheat blast in the Caninde#1/Alondra population

Key message Wheat blast resistance in Caninde#1 is controlled by a major QTL on 2NS/2AS translocation and multiple minor QTL in an additive mode. Wheat blast (WB) is a devastating disease in South America, and it recently also emerged in Bangladesh. Host resistance to WB has relied heavily on the 2NS/2AS translocation, but the responsible QTL has not been mapped and its phenotypic effects in different environments have not been reported. In the current study, a recombinant inbred line population with 298 progenies was generated, with the female and male parents being Caninde#1 (with 2NS) and Alondra (without 2NS), respectively. Phenotyping was carried out in two locations in Bolivia, namely Quirusillas and Okinawa, and one location in Bangladesh, Jashore, with two sowing dates in each of the two cropping seasons in each location, during the years 2017-2019. Genotyping was performed with the DArTseq (R) technology along with five previously reported STS markers in the 2NS region. QTL mapping identified a major and consistent QTL on 2NS/2AS region, explaining between 22.4 and 50.1% of the phenotypic variation in different environments. Additional QTL were detected on chromosomes 1AS, 2BL, 3AL, 4BS, 4DL and 7BS, all additive to the 2NS QTL and showing phenotypic effects less than 10%. Two codominant STS markers,WGGB156andWGGB159, were linked proximally to the 2NS/2AS QTL with a genetic distance of 0.9 cM, being potentially useful in marker-assisted selection

Application of zinc, boron, and molybdenum in soil increases lentil productivity, nutrient uptake, and apparent balance

In severely deficit soil, lentil (Lens culinaris Medic) crop requires micronutrients for increased production. Micronutrient management is, therefore, very important for lentil productivity but mostly ignored. This study was carried out from 2014–2015 to 2016–2017 to understand the effects of zinc (Zn), boron (B), and molybdenum (Mo) on lentil productivity, nodulation, and nutrient uptake and how these elements improve soil micronutrient fertility. The experiment was laid out in randomized complete block design, and the treatments were replicated thrice. Different combinations of Zn, Mo, and B were contrasted with no application of micronutrients. The treatments were Zn alone (Zn), B alone (B), Mo alone (Mo), Zn combined with B (ZnB), Zn with Mo (ZnMo), B with Mo (BMo), and Zn combined with B and Mo (ZnBMo). Doses of Zn, B, and Mo were 3, 2, and 1 kg ha−1, respectively. In this trial, the highest average seed yield (1807 kg ha−1) and yield increment (44%) was obtained in ZnBMo combined application with macronutrients. Single, dual, and combined application of Zn, B, and Mo had significant effects on yield parameters and yield of lentil (P < 0.05). The highest nutrient uptake, maximum nodulation (63.5 plant−1), and the highest protein content (26.6%) in seed were recorded from the treatment receiving all three micronutrients. The increased lentil yield might be associated with increased nodulation and nutrient uptake by the crop under micronutrient-applied treatments. The results suggest that combination of Zn, B, and Mo could be applied for increased lentil production in micronutrient deficit soils.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Modification of Nutrient Requirements for a Four Crop-Based Cropping System to Increase System Productivity, Maintain Soil Fertility, and Achieve Sustainable Intensification

Sustainable and resilient cropping intensity is now a global focus to address the food demand and nutrition security of the growing population. For sustainable intensification, maintaining soil fertility is a key concern. The nutrient management for the recently developed four crop-based cropping system in Bangladesh has not yet been studied. Hence, field experiments were conducted on the nutrient management of the four crop-based cropping system [Aus (pre-monsoon rice), Aman (monsoon rice), lentil, and mungbean] in calcareous soil in Bangladesh during the years of 2016/17 and 2017/18 to determine the appropriate fertilizer management package to improve crop productivity and sustain soil fertility. The experiment had six treatments assigned in a randomized complete block design with three replications. The treatments included T1 = control (without synthetic fertilizer), T2 = 50% recommended dose of fertilizer (RDF), T3 = 75% RDF, T4 = 100% RDF, T5 = 125% RDF, and T6 = farmers&rsquo; practice (FP). The results revealed that the 125% RDF significantly contributed to higher yields of all four crops. The rice equivalent yield (REY) was the highest for the fertilizer management of 125% RDF, which was 45.5%, 9.4%, and 12.2% higher than the control (T1), 100% RDF (T4), and FP, respectively. Considering the uptake of nutrients (N, P, K, S, Zn, and B) by the crops in the cropping system, the 125% RDF was superior to the other treatments. The nutrient management practices had a positive influence on the apparent nutrient recovery (ANR) efficiency of the cropping system. The fertilizer management of 125% RDF was also economically more profitable due to the increment in the cost&ndash;benefit ratio of 26.8%, 4.4%, and 4.9% over the control, 100% RDF, and FP, respectively. The results indicate that the current fertilizer recommendations and FP for aus, aman, lentil, and mungbean are not adequate for the change from the three crop to the four crop-based pattern, and an increased dose of fertilizer is required to increase the yield of each individual crop as well as the total system&rsquo;s productivity. The fertilizer use efficiency is also higher for 125% RDF than the 100% RDF and FP indicating that to sustain the soil fertility in the four crop-based system, the current RDF and FP are not sufficient. This finding will help intensive cropping areas in preventing nutrient deficiencies that would lead to a reduction in the crop yield