OPTIMUM ALLOCATION OF TEST RESOURCES IN HYBRID MAIZE BREEDING WITH DOUBLED HAPLOIDS

Optimum allocation of test resources is of fundamental importance for the efficiency of breeding schemes. Our objectives were to (i) investigate the impact of production costs of doubled haploids (DHs) on the optimum allocation of test resources and selection gain, (ii) determine the importance of quantitative-genetic and economic parameters on the optimum allocation of test resources and selection gain, (iii) examine the optimum type and number of testers for selection on general combining ability (GCA), and (iv) assess the importance of line per se evaluation for selection on GCA. We used numerical integration and Monte Carlo simulations for determining selection gain. Increasing production costs of DHs did not affect the optimum number of test locations and reduced selection gain only marginally (Longin et al. 2006). Thus, DHs can easily be integrated into existing breeding schemes simply by compensating the production costs of DHs with a smaller number of initial DH lines. The optimum allocation of test resources and values of selection gain were largely affected by varying budget, number of finally selected DH lines, and quantitative-genetic parameters. For two-stage selection, selection gain was largely increased with a simultaneous reduction of its standard deviation. This is due to th