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Local adaptation and genotype by environment (G×E) interactions affect the expression of phenotypes in crop species. An investigation on the interplay between adaptation and G×E on sorghum heterosis phenotypes is lacking. To address this question, a set of 112 diverse grain sorghum hybrids and their 22 inbred parents of local and exotic origins, representing the primary female and male heterotic groups, were tested in five contrasting sorghum‐growing environments across 2 years in Queensland, Australia. Plant height, days to flowering, seed yield, grain weight, and grain number were measured and used in the estimation of heterosis. Midparent heterosis for yield ranged from –25% to 217% and was highly influenced by grain number. In contrast to observations in maize, the magnitude of heterosis for yield was not associated with site mean. Striking differences were observed in heterosis in hybrids from locally developed inbred parents compared with hybrids that were developed from exotic inbred parents developed in other countries. Heterosis in the latter combination was higher on average across all the test environments for the majority of traits. We hypothesize maladaptive phenotypic plasticity in the exotic parents contributed to the observed differences in heterosis estimates. These data confirm that heterosis estimates in sorghum must be obtained and interpreted in relevant genetic and environmental contexts. Breeders in developing countries with low sorghum hybrid seed uptake will find these insights useful when selecting hybrids for broader adaptation, improving efficiency of their breeding programs. Core Ideas: Adaptation of sorghum inbred parents to test environment impacts the estimates of heterosis.Heterosis for yield in sorghum was not associated with mean site yields.Grain number in sorghum drives heterosis for yield. [ABSTRACT FROM AUTHOR]

Management and distribution of experimental data from prebreeding projects is important to ensure uptake of germplasm into breeding and research programs. Being able to access and share this data in standard formats is essential. The adoption of a common informatics platform for crops that may have limited resources brings economies of scale, allowing common informatics components to be used across multiple species. The close integration of such a platform with commonly used breeding software, visualization, and analysis tools reduces the barrier for entry to researchers and provides a common framework to facilitate collaborations and data sharing. This work presents significant updates to the Germinate platform and highlights its value in distributing prebreeding data for 14 crops as part of the project 'Adapting Agriculture to Climate Change: Collecting, Protecting and Preparing Crop Wild Relatives' (hereafter Crop Trust Crop Wild Relatives project) led by the Crop Trust (https://www.cwrdiversity.org). The addition of data on these species compliments data already publicly available in Germinate. We present a suite of updated Germinate features using examples from these crop species and their wild relatives. The use of Germinate within the Crop Trust Crop Wild Relatives project demonstrates the usefulness of the system and the benefits a shared informatics platform provides. These data resources provide a foundation on which breeding and research communities can develop additional online resources for their crops, harness new data as it becomes available, and benefit collectively from future developments of the Germinate platform. [ABSTRACT FROM AUTHOR]

One response to mitigate the impact of climate change on agricultural systems is to develop new varieties that are tolerant to the new range of biotic and abiotic challenges this change causes. This requires access to novel variants of genes for complex adaptive traits. Crop wild relatives (CWRs) are a potentially valuable source of these genes; however, these materials are often difficult to work with and identifying valuable alleles is difficult without substantial investment in prebreeding. In this study, we describe the development of a nested association mapping population for sorghum [Sorghum bicolor (L.) Moench] using two cultivated grain sorghum reference parents and nine wild and exotic sorghum accessions as donors. The donor parents come from the S. bicolor (L.) Moench subsp. verticilliflorum (Steud.) de Wet ex Wiersema & J. Dahlb., S. bicolor (L.) Moench subsp. drummondii (Steud.) de Wet ex Davidse, and S. bicolor (L.) Moench subsp. bicolor margaritiferum taxa and were sampled from a range of environments across Africa. In total, the resource consists of 13 populations and a total of 1,224 lines. The population has been genotyped with diversity array technology (DArT) markers that produced 42,372 unique single nucleotide polymorphism (SNP) markers covering the genome. We determine the utility of the resource for high resolution mapping of complex traits by demonstrating that the exotics contain unique alleles for some example adaptive trait loci and by using the population for genome‐wide association study (GWAS). The resource should provide useful material for plant breeders attempting to deal with the challenges generated by climate change. [ABSTRACT FROM AUTHOR]

Genomic selection can increase the rate of genetic gain in plant breeding programs by shortening the breeding cycle. Gain can also be increased through higher selection intensities, as the size of the population available for selection can be increased by predicting performance of nonphenotyped, but genotyped, lines. This paper demonstrates the application of genomic prediction in a sorghum [Sorghum bicolor (L.) Moench] breeding program and compares different genomic prediction models incorporating relationship information derived from molecular markers and pedigree information. In cross-validation, the models using marker-based relationships had higher selection accuracy than the selection accuracy for models that used pedigree-based relationships. It was demonstrated that genotypes that have not been included in the trials could be predicted quite accurately using marker information alone. The accuracy of prediction declined as the genomic relationship of the predicted individual to the training population declined. We also demonstrate that the accuracy of genomic breeding values from the prediction error variance derived from the mixed model equations is a useful indicator of the accuracy of prediction. This will be useful to plant breeders, as the accuracy of the genomic predictions can be assessed with confidence before phenotypes are available. Four distinct environments were studied and shown to perform very differently with respect to the accuracy of predictions and the composition of estimated breeding values. This paper shows that there is considerable potential for sorghum breeding programs to benefit from the implementation of genomic selection. [ABSTRACT FROM AUTHOR]