Predication of Hybrid Performance Among USDA Alfalfa Core Collection Using AFLP Genetic Distances, Combining Ability Parameters and Physiological Parameters

Utilization of nuclear genetic distance (GD) information based on molecular markers such as AFLP has been proposed to help reduce the time required, and resources needed, for selecting proper parents that produce heterosis (Hybrid vigor) among their F₁ progenies. The purpose of this study was to evaluate if regression models based on GD alone or in various combinations with the variables of GCA, mid-parent performance (VE), physiological parameters such as fall regrowth (FR) and winter-hardiness (WH), of nine parental populations were able to predict the performance and heterosis of their 36-diallel hybrids in semi-arid region of Southern New Mexico State (U.S.A). The results revealed that AFLP genetic distance was able to predict yield performance for 63% of 19 superior hybrids, superior than best check cultivars. The best single model that significantly predicts hybrid performance was GCA. It was able to identify 79% of superior hybrids followed by WH (74%), VE (68%), and FR (68%).

Heterosis was predicted by GCA (r=0.44), VE (r=0.71), FR (r=0.51) and WH (r=0.46), but not by GD (r=0.18). The implication of these results is that in abiotic stressed environment (average annual extreme minimum temperature of -12 to -9 C) evaluation of physiological parameters such as FR or WH, could offer a practical approach to predict hybrid performance and heterosis among the parents used in this study, because these parameters are critical indicators for recovering from winter freezing temperature. Thus, it is very important for evaluating plant genetic resources for crop improvement to evaluate physiological parameters that help to select parents/populations for crop improvement.