Satellite-enabled Enviromics to Enhance Crop Improvement.

Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., classifying environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management (G × E × M). To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, radar, and LiDAR sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for sustainable breeding, a few obstacles remain, such as the need for 1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; 2) state-of-the-art artificial intelligence (AI) models for data integration, simulation, and prediction; 3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and 4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.

Enviromics in breeding: applications and perspectives on envirotypic-assisted selection.

KEY MESSAGE: We propose the application of enviromics to breeding practice, by which the similarity among sites assessed on an "omics" scale of environmental attributes drives the prediction of unobserved genotype performances. Genotype by environment interaction (GEI) studies in plant breeding have focused mainly on estimating genetic parameters over a limited number of experimental trials. However, recent geographic information system (GIS) techniques have opened new frontiers for better understanding and dealing with GEI. These advances allow increasing selection accuracy across all sites of interest, including those where experimental trials have not yet been deployed. Here, we introduce the term enviromics, within an envirotypic-assisted breeding framework. In summary, likewise genotypes at DNA markers, any particular site is characterized by a set of "envirotypes" at multiple "enviromic" markers corresponding to environmental variables that may interact with the genetic background, thus providing informative breeding re-rankings for optimized decisions over different environments. Based on simulated data, we illustrate an index-based enviromics method (the "GIS-GEI") which, due to its higher granular resolution than standard methods, allows for: (1) accurate matching of sites to their most appropriate genotypes; (2) better definition of breeding areas that have high genetic correlation to ensure selection gains across environments; and (3) efficient determination of the best sites to carry out experiments for further analyses. Environmental scenarios can also be optimized for productivity improvement and genetic resources management, especially in the current outlook of dynamic climate change. Envirotyping provides a new class of markers for genetic studies, which are fairly inexpensive, increasingly available and transferable across species. We envision a promising future for the integration of enviromics approaches into plant breeding when coupled with next-generation genotyping/phenotyping and powerful statistical modeling of genetic diversity.

Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding.

Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.

Genome-Wide Association and Regional Heritability Mapping of Plant Architecture, Lodging and Productivity in Phaseolus vulgaris.

The availability of high-density molecular markers in common bean has allowed to explore the genetic basis of important complex agronomic traits with increased resolution. Genome-Wide Association Studies (GWAS) and Regional Heritability Mapping (RHM) are two analytical approaches for the detection of genetic variants. We carried out GWAS and RHM for plant architecture, lodging and productivity across two important growing environments in Brazil in a germplasm of 188 common bean varieties using DArTseq genotyping strategies. The coefficient of determination of G × E interaction (c2int ) was equal to 17, 21 and 41%, respectively for the traits architecture, lodging, and productivity. Trait heritabilities were estimated at 0.81 (architecture), 0.79 (lodging) and 0.43 (productivity), and total genomic heritability accounted for large proportions (72% to ≈100%) of trait heritability. At the same probability threshold, three marker-trait associations were detected using GWAS, while RHM detected eight QTL encompassing 145 markers along five chromosomes. The proportion of genomic heritability explained by RHM was considerably higher (35.48 to 58.02) than that explained by GWAS (28.39 to 30.37). In general, RHM accounted for larger fractions of the additive genetic variance being captured by markers effects inside the defined regions. Nevertheless, a considerable proportion of the heritability is still missing (∼42% to ∼64%), probably due to LD between markers and genes and/or rare allele variants not sampled. RHM in autogamous species had the potential to identify larger-effect QTL combining allelic variants that could be effectively incorporated into whole-genome prediction models and tracked through breeding generations using marker-assisted selection.

Effect of Pre-weaning Diet on the Ruminal Archaeal, Bacterial, and Fungal Communities of Dairy Calves.

At birth, calves display an underdeveloped rumen that eventually matures into a fully functional rumen as a result of solid food intake and microbial activity. However, little is known regarding the gradual impact of pre-weaning diet on the establishment of the rumen microbiota. Here, we employed next-generation sequencing to investigate the effects of the inclusion of starter concentrate (M: milk-fed vs. MC: milk plus starter concentrate fed) on archaeal, bacterial and anaerobic fungal communities in the rumens of 45 crossbred dairy calves across pre-weaning development (7, 28, 49, and 63 days). Our results show that archaeal, bacterial, and fungal taxa commonly found in the mature rumen were already established in the rumens of calves at 7 days old, regardless of diet. This confirms that microbiota colonization occurs in the absence of solid substrate. However, diet did significantly impact some microbial taxa. In the bacterial community, feeding starter concentrate promoted greater diversity of bacterial taxa known to degrade readily fermentable carbohydrates in the rumen (e.g., Megasphaera, Sharpea, and Succinivribrio). Shifts in the ruminal bacterial community also correlated to changes in fermentation patterns that favored the colonization of Methanosphaera sp. A4 in the rumen of MC calves. In contrast, M calves displayed a bacterial community dominated by taxa able to utilize milk nutrients (e.g., Lactobacillus, Bacteroides, and Parabacteroides). In both diet groups, the dominance of these milk-associated taxa decreased with age, suggesting that diet and age simultaneously drive changes in the structure and abundance of bacterial communities in the developing rumen. Changes in the composition and abundance of archaeal communities were attributed exclusively to diet, with more highly abundant Methanosphaera and less abundant Methanobrevibacter in MC calves. Finally, the fungal community was dominated by members of the genus SK3 and Caecomyces. Relative anaerobic fungal abundances did not change significantly in response to diet or age, likely due to high inter-animal variation and the low fiber content of starter concentrate. This study provides new insights into the colonization of archaea, bacteria, and anaerobic fungi communities in pre-ruminant calves that may be useful in designing strategies to promote colonization of target communities to improve functional development.