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Two-Line Elements (TLEs) issued by the space-track catalog are still the most extensive public data source for space debris tracking to date. However, TLEs accuracy at epoch is typically larger than 1 km in Low Earth Orbit (LEO) and 3 km in Medium Earth Orbit (MEO). Therefore, TLEs are too coarse to enable collision avoidance maneuvers. The present work aims at correcting TLEs orbits at epoch to enable operational conjunction assessment and help satellite operators better protect their assets at moderate cost. Using only Moon-Earth and Sun-Earth distance time series, as well as 2018 TLE data for 14 GPS satellites and SGP4 propagation over a short period, we were able to correct the TLEs of the 29 operational GPS satellites by 65% on average for year 2019, reducing the 3D RMS error at epoch from 2.2 km to 680 m. In other words, we improved TLEs accuracy from 5 km to 1.5 km with a 95% confidence level.

International audience; Plant breeding has always sought to develop crops able to withstand environmental stresses, but this is all the more urgent now as climate change is affecting the agricultural regions of the world. It is currently difficult to screen genetic material to determine how well a crop will tolerate various stresses. Multi-environment trials (MET) which include a particular stress condition could be used to train a genomic selection model thanks to molecular marker information that is now readily available. Our study focuses on understanding how and predicting whether a plant is adapted to a particular environmental stress. We propose a way to use genomic random regression, an extension of factorial regression, to model the reaction norms of a genotype to an environmental stress: the factorial regression genomic best linear unbiased predictor (FR-gBLUP). Twenty-eight wheat trials in France (3 years, 12 locations, nitrogen or water stress treatments) were split into two METs where different stresses limited grain number and yield. In MET1, drought at flowering was responsible for 46.7% of the genotype-by-environment (G x E) interactions for yield while in MET2, heat stress during booting was identified as the main factor responsible for G x E interactions, but that explained less of the interaction variance (33.6%). Since drought at flowering explained a fairly large variance in G x E in MET1, the FR-gBLUP model was more accurate than the additive gBLUP across all types of cross validation. Accuracy gains varied from 2.4% to 12.9% for the genomic regression to drought. In MET2 accuracy gains were modest, varying from 5.7% to 2.4%. When a major stress influencing G x E is identified, the FR-gBLUP strategy makes it possible to predict the level of adaptation of genotyped individuals to varying stress intensities, and thus to select them in silico. Our study demonstrates how genome-wide selection can facilitate breeding for adaptation.

Cassava (Manihot esculenta L.) is one of the most important food crops in the tropics, but yields are far below their potential. The gene pool of cas- sava contains natural genetic diversity relevant to many important breeding goals, but breeding progress has been slow, partly because of insuf- ficient genomic resources. As a first step toward implementing genomewide genetic studies that will facilitate rapid genetic gain through breed- ing, we genotyped-by-sequencing a set of 182 full-sibs population of cassava that segregated in several traits: resistance to the cassava mosaic disease (CMD) and yield under CMD pressure; increased carotenoid content in storage roots; color of stem exterior and anthocyanin pigmen- tation in the petioles, inner root skin, and api- cal leaves. Employing a rare-cutting restriction enzyme, PstI, in a genotyping-by-sequencing (GBS) library preparation, we obtained 2478 segregating single nucleotide polymorphisms (SNPs), of which 1257 passed standard filter - ing for missing genotypes and deviation from expected genotypic frequencies. We mapped 772 SNPs across 19 linkage groups and anchored 313 unique scaffolds from the version 4.1 of the cassava genome assembly. Most of the stud- ied morphological traits as well as resistance to CMD and root carotenoid content showed quali- tative inheritance. As expected, quantitative trait loci analysis for these traits revealed single loci surrounded by small confidence intervals. Yield under CMD was associated with the CMD resis- tance locus. We show that GBS is a powerful genotyping tool that provides a sufficient number of markers for unraveling the genetic architecture of Mendelian traits in cassava in addition to the development of a robust genetic map that can help anchor unassembled genomic scaffolds.

Before implementation of genomic selection, evaluation of the potential accuracy of prediction can be obtained by cross-validation. In this procedure, a population with both phenotypes and genotypes is split into training and validation sets. The prediction model is fi tted using the training set, and its accuracy is calculated on the validation set. The degree of genetic relatedness between the training and validation sets may infl uence the expected accuracy as may the genotype × environment (G×E) interaction in those sets. We developed a method to assess these effects and tested it in cassava (Manihot esculenta Crantz). We used historical phenotypic data available from the International Institute of Tropical Agriculture Genetic Gain trial and performed genotyping by sequencing for these clones. We tested cross-validation sampling schemes preventing the training and validation sets from sharing (i) genetically close clones or (ii) similar evaluation locations. For 19 traits, plot-basis heritabilities ranged from 0.04 to 0.66. The correlation between predicted and observed phenotypes ranged from 0.15 to 0.47. Across traits, predicting for less related clones decreased accuracy from 0 to 0.07, a small but consistent effect. For 17 traits, predicting for different locations decreased accuracy between 0.01 and 0.18. Genomic selection has potential to accelerate gains in cassava and the existing training population should give a reasonable estimate of future prediction accuracies.

In plant breeding, one of the major challenges of genomic selection is to account for genotype-by-environment (G × E) interactions, and more specifically how varieties are adapted to various environments. Crop growth models (CGM) were developed to model the response of plants to environmental conditions. They can be used to characterize eco-physiological stresses in relation to crop growth and developmental stages, and thereby help to dissect G × E interactions. Our study aims at demonstrating how environment characterization using crop models can be integrated to improve both the understanding and the genomic predictions of G × E interactions. We evaluated the usefulness of using CGM to characterize environments by comparing basic and CGM-based stress indicators, to assess how much of the G × E interaction can be explained and whether gains in prediction accuracy can be made. We carried out a case study in wheat (Triticum aestivum) to model nitrogen stress in a CGM in 12 environments defined by year × location × nitrogen treatment. Interactions between 194 varieties of a core collection and these 12 different nitrogen conditions were examined by analyzing grain number. We showed that (i) CGM based indicators captured the G × E interactions better than basic indicators and that (ii) genomic predictions were slightly improved by modeling the genomic interaction with the crop model based characterization of nitrogen stress. A framework was proposed to integrate crop model environment characterization into genomic predictions. We describe how this characterization promises to improve the prediction accuracy of adaptation to environmental stresses.