Browsing by Author "Jose Crossa"

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Generalizing the sites regression model to three-way interaction including multi-attributes
(2009) Mario Varela; Jose Crossa; Arun Kumar Joshi; Paul L. Cornelius; Yann Manes
When a multienvironment trial (MET) is established across several locations and years, the interaction is referred to as a three-way array. Three-way interaction can be studied by means of three-way principal components analysis. In this study, the three-way principal components analysis is adapted to the sites regression model (three-way SREG). The three-way SREG with location and year combines the effects of genotype, genotype × location, genotype × year, and genotype × location × year. The objective of this study is to show how the three-way SREG can be put to practical use in agriculture and breeding. We utilized two wheat (Triticum aestivum L) data sets that have already been used for fitting a three-way additive main effects and multiplicative interaction model. One data set had genotype (25) × location (4) × sowing times (4) and eight attributes, and the other data set included genotype (20) × irrigation level × year on grain yield. The three-way SREG applied simultaneously to eight attributes facilitates the interpretation of genotypic performance for all traits in specific locations and across locations for a selected sowing time. Results of the threeway SREG for both data sets show the different response patterns of genotypes for locations and sowing dates (Data Set 1), as well as genotypic responses across irrigation levels in different years (Data Set 2). Using Data Set 1, we show that fitting a three-way data structure to a three-way SREG model is more effective for detecting important interaction patterns than using the two-way SREG. © Crop Science Society of America.
Genomic prediction for grain zinc and iron concentrations in spring wheat
(Springer Verlag, 2016) Govindan Velu; Jose Crossa; Ravi P. Singh; Yuanfeng Hao; Susanne Dreisigacker; Paulino Perez-Rodriguez; Arun K. Joshi; Ravish Chatrath; Vikas Gupta; Arun Balasubramaniam; Chhavi Tiwari; Vinod K. Mishra; Virinder Singh Sohu; Gurvinder Singh Mavi
Key message: Predictability estimated through cross-validation approach showed moderate to high level; hence, genomic selection approach holds great potential for biofortification breeding to enhance grain zinc and iron concentrations in wheat. Abstract: Wheat (Triticum aestivum L.) is a major staple crop, providing 20 % of dietary energy and protein consumption worldwide. It is an important source of mineral micronutrients such as zinc (Zn) and iron (Fe) for resource poor consumers. Genomic selection (GS) approaches have great potential to accelerate development of Fe- and Zn-enriched wheat. Here, we present the results of large-scale genomic and phenotypic data from the HarvestPlus Association Mapping (HPAM) panel consisting of 330 diverse wheat lines to perform genomic predictions for grain Zn (GZnC) and Fe (GFeC) concentrations, thousand-kernel weight (TKW) and days to maturity (DTM) in wheat. The HPAM lines were phenotyped in three different locations in India and Mexico in two successive crop seasons (2011–12 and 2012–13) for GZnC, GFeC, TKW and DTM. The genomic prediction models revealed that the estimated prediction abilities ranged from 0.331 to 0.694 for Zn and from 0.324 to 0.734 for Fe according to different environments, whereas prediction abilities for TKW and DTM were as high as 0.76 and 0.64, respectively, suggesting that GS holds great potential in biofortification breeding to enhance grain Zn and Fe concentrations in bread wheat germplasm. © 2016, Springer-Verlag Berlin Heidelberg.
Performance of yield and stability of advanced wheat genotypes under heat stress environments of the indo-gangetic plains
(2007) Jagadish Rane; Raj Kumar Pannu; Virinder Singh Sohu; Ran Singh Saini; Banwari Mishra; Jag Shoran; Jose Crossa; Mateo Vargas; Arun Kumar Joshi
A set of 25 advanced breeding lines and released varieties of wheat (Triticum aestivum L.) developed by different breeding centers in India were assessed for their adaptation in 18 different environments across the Indo-Gangetic plains. The study was aimed at identifying genotype(s) with high yield stability across the environments in general and heat stress environments in particular. Jaipur and Varanasi were hotter than any other locations considered in this study. Considerable intralocation variation in genotypic response pattern was observed over the years and dates of sowing, and this was more conspicuous at Varanasi. Longer crop duration and short grain growth duration at Varanasi were in contrast to shorter crop duration and relatively longer grain growth period that supported better grain growth at Jaipur. The genotype x environment interaction biplots for grain yield revealed that genotypes Raj 3765 and Raj 4027, developed at Jaipur, were more stable across all environments. This was due to their adaptability to high-temperature environments, and hence they are being proposed as promising germplasm sources for late-sown and/or warmer environments. Since the pattern of genotypic response observed at Jaipur was not similar to that observed at Varanasi, it is suggested that a common breeding strategy, if any, should emphasize grain yield stability for breeding for high-temperature tolerance. This can also take care of intralocation variation in genotypic response over the years and dates of sowing at Varanasi. © Crop Science Society of America.