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The high rainfall mega-environment [mega environment 2 (ME2)], characterized by more than 500 mm rainfall during wheat season, is one of the 12 mega-environments on which the global wheat breeding program of the International Maize and Wheat Improvement Center (CIMMYT) has been assembling and distributing high rainfall wheat yield trials (HRWYT) since 1992. In this study, our objectives were to evaluate the adaptation and performance of entries included in the 1st through the 12th HRWYT (1992-2004), identify best lines as parents for crossing and recommend for direct release by the National Agricultural Research Systems (NARS), and test the association among sites and determine yield progress in HRWYTs. Combined ANOVA across locations for grain yield showed highly significant difference among genotypes and genotype x environment interactions (p < 0.001) for each of the HRWYTs. At regional level, wide range of yield variability was observed in Africa (0.69-9.614 t [ha.sup.-1]) followed by Asia (0.389-6.93 t [ha.sup.-1]) with the highest mean yields in Europe (4.392 t [ha.sup.-1]). Two widely grown mega-cultivars have been developed from Attila cross: entry number 4 (CM85836-4Y-0M-0Y-8M-0Y) from 1stHRWYT, which is released as PBW343 in India, and entry number 48 from 4thHRWYT (CM85836-50Y-0M-0Y-3M-0Y), which is released in Ethiopia, Tunisia, Iran, Pakistan, and Sudan with local names of Kubsa, Utique96, Chamran, MH97, and Imam, respectively. There is a significant positive association between plant height and grain yield in rain-fed environments whereas the association between heading dates and yield is location specific indicating the importance of selecting broad range of materials for heading date at the main station in Mexico. Correlation analysis shows that the sites giving the best prediction of global performance are Marcos Juarez (Argentina), Pergamino (Argentina), Izmir (Turkey), Bethlehem (South Africa), Sakha (Egypt), and Tassaout (Morocco). In each year, it was possible to find a line out yielding the local check in at least 52% of the sites. However, at the trial level, lines were beating the local checks more frequently in the first trials than in the last trials. Regular assessment of the current and future HRWYTs in terms of adaptation, resistance, diversity, and quality will be of paramount importance in improving the breeding process. doi: 10.2135/cropsci2010.02.0102

In the absence of pollination, female reproductive organs senesce, leading to an irrevocable loss in the reproductive potential of the flower, which directly affects seed set. In self-pollinating crops like wheat (Triticum aestivum), the post-anthesis viability of unpollinated carpels has been overlooked, despite its importance for hybrid seed production systems. To advance our knowledge of carpel development in the absence of pollination, we created a high-throughput phenotyping approach to quantify stigma and ovary morphology. We demonstrate the suitability of the approach, which uses light-microscopy imaging and machine learning, for the analysis of floral organ traits in field-grown plants using fresh and fixed samples. We show that the unpollinated carpel undergoes a well-defined initial growth phase, followed by a peak phase in which stigma area reaches its maximum and the radial expansion of the ovary slows, and a final deterioration phase. These developmental dynamics were consistent across years and could be used to classify male-sterile cultivars. This phenotyping approach provides a new tool for examining carpel development, which we hope will advance research into female fertility of wheat., Competing Interests: Competing interests N.B. is employed by KWS UK and Y.M. is employed by Syngenta, France. The remaining authors declare that they have no conflict of interest., (© 2022. Published by The Company of Biologists Ltd.)

Anther extrusion (AE) is the most important male floral trait for hybrid wheat seed production. AE is a complex quantitative trait that is difficult to phenotype reliably in field experiments not only due to high genotype-by-environment effects but also due to the short expression window in the field condition. In this study, we conducted a genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for AE in the CIMMYT hybrid wheat breeding program. An elite set of male lines ( n = 603) were phenotype for anther count (AC) and anther visual score (VS) across three field experiments in 2017-2019 and genotyped with the 20K Infinitum is elect SNP array. GWAS produced five marker trait associations with small effects. For GP, the main effects of lines (L), environment (E), genomic (G) and pedigree relationships (A), and their interaction effects with environments were used to develop seven statistical models of incremental complexity. The base model used only L and E, whereas the most complex model included L, E, G, A, and G × E and A × E. These models were evaluated in three cross-validation scenarios (CV0, CV1, and CV2). In cross-validation CV0, data from two environments were used to predict an untested environment; in random cross-validation CV1, the test set was never evaluated in any environment; and in CV2, the genotypes in the test set were evaluated in only a subset of environments. The prediction accuracies ranged from -0.03 to 0.74 for AC and -0.01 to 0.54 for VS across different models and CV schemes. For both traits, the highest prediction accuracies with low variance were observed in CV2, and inclusion of the interaction effects increased prediction accuracy for AC only. In CV0, the prediction accuracy was 0.73 and 0.45 for AC and VS, respectively, indicating the high reliability of across environment prediction. Genomic prediction appears to be a very reliable tool for AE in hybrid wheat breeding. Moreover, high prediction accuracy in CV0 demonstrates the possibility of implementing genomic selection across breeding cycles in related germplasm, aiding the rapid breeding cycle., Competing Interests: YM was employed by company Syngenta France S.A.S, France. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Adhikari, Basnet, Crossa, Dreisigacker, Camarillo, Bhati, Jarquin, Manes and Ibrahim.)

In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by environmental covariables. We use five genomic and pedigree models (M1-M5) under four cross-validation (CV) schemes: prediction of hybrids when the training set (i) includes hybrids of all males and females evaluated only in some environments (T2FM), (ii) excludes all progenies from a randomly selected male (T1M), (iii) includes all progenies from 20% randomly selected females in combination with all males (T1F), and (iv) includes one randomly selected male plus 40% randomly selected females that were crossed with it (T0FM). Models were tested on a total of 1888 wheat ( L.) hybrids including 18 males and 667 females in three consecutive years. For grain yield, the most complex model (M5) under T2FM had slightly higher prediction accuracy than the less complex model. For T1F, the prediction accuracy of hybrids for grain yield and other traits of the most complete model was 0.50 to 0.55. For T1M, Model M3 exhibited high prediction accuracies for flowering traits (0.71), whereas the more complex model (M5) demonstrated high accuracy for grain yield (0.5). For T0FM, the prediction accuracy for grain yield of Model M5 was 0.61. Including genomic and pedigree gave relatively high prediction accuracy even when both parents were untested. Results show that it is possible to predict unobserved hybrids when modeling genomic general combining ability (GCA) and specific combining ability (SCA) and their interactions with environments., (Copyright © 2019 Crop Science Society of America.)

While canopy temperature (CT) shows a strong and reliable association with yield under drought and heat stress and is used in wheat breeding to select for yield, little is known of its genetic control. The objective of this study was to determine the gene action controlling CT in five wheat populations grown in diverse environments (heat, drought, and well-irrigated conditions). CT showed negative phenotypic correlations with grain yield under drought and well-irrigated environments. Additive x additive effects were most prevalent and significant for all crosses and environments. Dominance and dominance x dominance gene actions were also found, though the significance and direction was specific for each environment and genotypic cross. The use of CT as a selection criterion to improve tolerance to drought was supported by its significant association with grain yield and the genotype differences observed between cultivars. Our results indicated that genetic gains for CT in wheat could be achieved through conventional breeding. However, given some dominance and epistatic effects, it would be necessary to delay the selection process until the frequency of heterozygous loci within families is reduced.