A comprehensive study of spike fruiting efficiency in wheat

Spike fruiting efficiency (FE), defined as grains per unit of spike dry weight at anthesis (SDWa) is a promising trait for improving grain number (GN) in wheat (Triticum aestivum L.). It is often estimated at maturity as the grains per unit of chaff or FE at maturity (FEm). The fertile floret efficiency (FFE), defined as fertile florets per unit of SDWa, and grain set (GST), or the number of grains per floret, were studied to better understand FE determination for the first time. Two double haploid populations designed by crossing modern cultivars contrasting for FE [‘Baguette 19’ and ‘Baguette Premium 11’(high FE) × ‘BioINTA2002’ (low FE)] were sown in five environments. The FE and FEm showed an unstable correlation (low or high) among genotypes within environments (caused by variable SDWa–chaff associations), resulting in a worse correlation between GN and FEm than between GN and FE. Therefore, the use of FEm as a surrogate for FE to improve GN may yield lower gains than those expected if FE were used. The narrow-sense heritability of FFE was high but the variability in fertile florets per spike among genotypes within environments was correlated with FFE only in the environments with high SDWa. Despite the close association between FE and FFE, the former was not totally set at anthesis, as GST greatly affected FE and GN. Selecting for higher FFE and GST, where genotype × environment effects determine heavy spikes at anthesis, is an alternative to breeding for improved GN.Fil: Pretini, Nicole. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; ArgentinaFil: Terrile, Ignacio Ismael. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Gazaba, Luciana N.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Donaire, Guillermo M.. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Córdoba. Estación Experimental Agropecuaria Marcos Juárez; ArgentinaFil: Arisnabarreta Dupuy, Sebastián. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Norte. Estación Experimental Agropecuaria Pergamino; ArgentinaFil: Vanzetti, Leonardo Sebastián. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Córdoba. Estación Experimental Agropecuaria Marcos Juárez; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: González, Fernanda Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Centro de Investigaciones y Transferencia del Noroeste de la Provincia de Buenos Aires; Argentin

Tracing the ancestry of modern bread wheats

For more than 10,000 years, the selection of plant and animal traits that are better tailored for human use has shaped the development of civilizations. During this period, bread wheat (Triticum aestivum) emerged as one of the world’s most important crops. We use exome sequencing of a worldwide panel of almost 500 genotypes selected from across the geographical range of the wheat species complex to explore how 10,000 years of hybridization, selection, adaptation and plant breeding has shaped the genetic makeup of modern bread wheats. We observe considerable genetic variation at the genic, chromosomal and subgenomic levels, and use this information to decipher the likely origins of modern day wheats, the consequences of range expansion and the allelic variants selected since its domestication. Our data support a reconciled model of wheat evolution and provide novel avenues for future breeding improvement.</p

Boosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models.

Genomic selection has been implemented in several plant and animal breeding programs and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of grain yield was measured in 147 maize (Zea mays L.) singlecross hybrids at 12 environments. Single-cross hybrids genotypes were inferred based on their parents (inbred lines) via single nucleotide polymorphism (SNP) markers obtained from genotyping-by-sequencing (GBS). Factor analytic multiplicative genomic best linear unbiased prediction (GBLUP) models, in the framework of multienvironment trials, were used to predict grain yield performance of unobserved tropical maize single-cross hybrids. Predictions were performed for two situations: untested hybrids (CV1), and hybrids evaluated in some environments but missing in others (CV2). Models that borrowed information across individuals through genomic relationships and within individuals across environments presented higher predictive accuracy than those models that ignored it. For these models, predictive accuracies were up to 0.4 until eight environments were considered as missing for the validation set, which represents 67% of missing data for a given hybrid. These results highlight the importance of including genotype-by-environment interactions and genomic relationship information for boosting predictions of tropical maize single-cross hybrids for grain yield