Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.

We validated the efficiency of genomic predictions calibrated on sparse factorial training sets to predict the next generation of hybrids and tested different strategies for updating predictions along generations. Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question.

Genetic regions determine tolerance to nitrogen deficiency in European elite bread wheats grown under contrasting nitrogen stress scenarios.

Clustering 24 environments in four contrasting nitrogen stress scenarios enabled the detection of genetic regions determining tolerance to nitrogen deficiency in European elite bread wheats. Increasing the nitrogen use efficiency of wheat varieties is an important goal for breeding. However, most genetic studies of wheat grown at different nitrogen levels in the field report significant interactions with the genotype.

Identification of QTLs affecting post-anthesis heat stress responses in European bread wheat.

The response of a large panel of European elite wheat varieties to post-anthesis heat stress is influenced by 17 QTL linked to grain weight or the stay-green phenotype. Heat stress is a critical abiotic stress for winter bread wheat (Triticum aestivum L.) especially at the flowering and grain filling stages, limiting its growth and productivity in Europe and elsewhere.

Using crop growth model stress covariates and AMMI decomposition to better predict genotype-by-environment interactions.

We propose new methods to predict genotype × environment interaction by selecting relevant environmental covariates and using an AMMI decomposition of the interaction. Farmers are asked to produce more efficiently and to reduce their inputs in the context of climate change. They have to face more and more limiting factors that can combine in numerous stress scenarios.

Using environmental clustering to identify specific drought tolerance QTLs in bread wheat (T. aestivum L.).

Environmental clustering helps to identify QTLs associated with grain yield in different water stress scenarios. These QTLs could be useful for breeders to improve grain yields and increase genetic resilience in marginal environments. Drought is one of the main abiotic stresses limiting winter bread wheat growth and productivity around the world.