The genotypes from the International Bread Wheat Screening Nursery (IBWSN) multiplication trial, which is designed to rapidly assess a large number of advanced generation (F3-F7) lines of spring bread wheat under Mega-environment 1 (ME1) that represents diversity for a wide range of latitudes, climates, day lengths, fertility conditions, water management, and (most importantly) disease conditions, were used for this study.
In genomic prediction deep learning artificial neural network are part of machine learning methods that incorporate parametric, non-parametric and semi-parametric statistical models. Kernel methods are seeing more flexible, and easier to interpret than neural networks. Kernel methods used in genomic predictions comprise the linear genomic best linear unbiased predictor (GBLUP) kernel (GB) and the Gaussian kernel (GK). These kernels have being used with two statistical models, single environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has being used as phenotype method for prediction of unobserved line performance in plant breeding trials. In this study, we used a non-linear Arc-cosine kernel (AK) that emulates deep learning artificial neural network. We compared AK prediction accuracy with GB and GK kernel methods in four genomic data sets one of them including also pedigree (ABLUP) and NIR (NBLUP) information. Results show that for all four data sets AK and GK kernels gave higher prediction accuracy than the linear GB kernel for single environment as well as GE multi-environment models. In addition, AK gave similar or slightly higher prediction accuracy than the GK kernel.
For the first time in history, the emergence of wheat-blast in Bangladesh has generated major food security concerns. The Bangladesh Agricultural Research Institute (BARI) together with the International Maize and Wheat Improvement Center (CIMMYT) developed and released the wheat variety BARI Gom 33 that is resistant to wheat blast and other common diseases. The new variety provides a 5-8% yield gain over the available popular varieties, as well as being zinc enriched. This study examines the potential economic benefits of BARI Gom 33 in Bangladesh. First, applying a climate analogue model, this study identified that more than 55% of the total wheat area in Bangladesh (across 45 districts) is vulnerable to wheat blast. Second, applying an ex-ante impact assessment framework, this study shows that with an assumed cumulative adoption starting from 2019-20 increasing up to 30% by 2027, the potential economic benefits of the newly developed wheat variety by 2029-30, far exceeds its dissemination costs. Even if dissemination of the new wheat variety is limited to only the ten currently blast-affected districts, the yearly average net benefits amount to USD 0.23-1.6 million. Based on the findings, the international donor agencies are urged to support the national system in scaling out the new wheat variety and wheat research in general to ensure overall food security in South Asia.
Early generation germplasm developed by the IWYP Hub and containing IWYP traits of interest. This germplasm is made avaiable in addtion to the available International Wheat Improvement Network (IWIN) Nurseries from CIMMYT. All germplasm requests requires a signed sMTA.
The genotypes from the International Bread Wheat Screening Nursery (IBWSN) multiplication trial, which is designed to rapidly assess a large number of advanced generation (F3-F7) lines of spring bread wheat under Mega-environment 1 (ME1) that represents diversity for a wide range of latitudes, climates, day lengths, fertility conditions, water management, and (most importantly) disease conditions, were used for this study.
We studied a RIL population of 276 entries derived from a cross between SYN-D × Weebill 1. SYN-D (Croc 1/Aegilops Squarrosa (224)//Opata) is a synthetic derived hexaploid wheat with dark green broad leaves without wax. The RILs did not segregate for Rht-B1, Rht-D1, Ppd-A1, Ppd-D1, Vrn-A1, Vrn-A1, Vrn-D1, and Eps-D1 genes and showed a narrow range of phenology, which avoids the confounding effect of phenology to identify QTL that may otherwise be masked by crop development. The RILs population was phenotyped in a randomized lattice design with two replications under four environments -drought (2009-2010, D10), heat (2009-2010, H10), heat + drought (2011-2012 and 2012-2013, HD12 and HD13)- at the Campo Experimental Norman E. Borlaug (CENEB), CIMMYT’s experimental station at Ciudad Obregón, Sonora, Northwest Mexico (27.20°N, 109.54°W, 38 masl). Drought stress (D) was applied by normal planting (late November) with significantly reduced irrigation (total water supply 700 mm) to avoid the effect of drought; the combined stress (H+D) was applied by delayed planting date (late February) with reduced irrigation (total water supply < 200 mm).
In this study, we evaluated genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information in two different validation schemes. All models included main effects, and others also considered interactions between the different types of covariates via Hadamard products of similarity structures. The pedigree models always gave better results predicting new lines in observed environments than the genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, markers and environments were included. When new lines were predicted in unobserved environments in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design of future breeding programs.
This file provides supporting material for the manuscript entitled ' Estimation of Physiological Genomic Estimated Breeding Values (PGEBV) Combining full Hyperspectral and Marker Data Across Environments for Grain Yield Under Combined Heat and Drought Stress in Tropical Maize (Zea mays L.)'. The file includes spreadsheets containing information on the experimental structure ('Experimental structure' spreadsheet), agronomic data ('Agronomic data' spreadsheet), hyperspectral data ('Hyperspectral data' spreadsheet) and molecular marker information ('markerdata' spreadsheet). A separate self explanatory summary of the information contained in the file can be found the the 'Read me first' spreadhsheet.
Since 2012, Australian support has resulted in the release of 23 wheat varieties by Afghanistan’s National Varietal Release Committee (NVRC). These varieties deliver both improved yield and disease tolerance. A 2015/16 farmer survey confirmed that farmers were indeed sharing seed with an average of three relatives/neighbours – although with no indication of how much seed was shared. This uncertainty regarding the adoption of new varieties has been intended to resolve by a recent DNA assessments of seed collected in 2015/16 that shows the general prevalence of the new varieties across 600 surveyed farms
