Christian R. Werner

The paper proposes sparse testcrossing with 3-5 testers to improve genetic gain in maize hybrid breeding by balancing simplicity, resource efficiency, and GCA prediction accuracy.

This paper shows that early-stage sparse testing strategies increase genetic gain in plant breeding by improving selection accuracy and intensity through broader TPE sampling and reduced replication.

The paper introduces the Desired Gains Index as an optimal method for parent selection in plant breeding, demonstrating its effectiveness through simulations and comparisons with other strategies.

This paper proposes reciprocal recurrent selection based on genetic complementation to enhance dominance-based heterosis in diploids, showing it is cheaper than GCA-based approaches and effective in hybrid maize.

This study compares genomic selection strategies in clonal plant breeding, finding that selecting parents based on genomic predicted cross performance leads to faster genetic gain and reduced inbreeding.

This study compares clonal breeding strategies using stochastic simulation to determine their genetic gain per unit cost, finding that reciprocal recurrent selection (RRS) is optimal in diploids with high genomic selection intensity but not in autopolyploids.

This paper shows genomic selection in intercrop breeding programs leads to greater genetic gain than phenotypic selection, regardless of genetic correlation with monocrop yield.

This study shows that a two-part hybrid crop breeding strategy with genomic selection and a maximum avoidance of inbreeding crossing scheme maximizes genetic gain and long-term genetic variance.