Maximizing efficiency in sunflower breeding through historical data optimization.

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program.

Genomic selection in plant breeding: Key factors shaping two decades of progress.

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy.

The Relevance of Life Cycle Assessment Tools in the Development of Emerging Decarbonization Technologies.

The development of emerging decarbonization technologies requires advanced tools for decision-making that incorporate the environmental perspective from the early design. Today, Life Cycle Assessment (LCA) is the preferred tool to promote sustainability in the technology development, identifying environmental challenges and opportunities and defining the final implementation pathways. So far, most environmental studies related to decarbonization emerging solutions are still limited to midpoint metrics, mainly the carbon footprint, with global sustainability implications being relatively unexplored.

A comparison of methods for training population optimization in genomic selection.

Maximizing CDmean and Avg_GRM_self were the best criteria for training set optimization. A training set size of 50-55% (targeted) or 65-85% (untargeted) is needed to obtain 95% of the accuracy.  With the advent of genomic selection (GS) as a widespread breeding tool, mechanisms to efficiently design an optimal training set for GS models became more relevant, since they allow maximizing the accuracy while minimizing the phenotyping costs.