/AnMtgsAbsts2009.53781 Biological Interpretation of GE Interactions Using AMMI Analysis in NTEP Trials.

Monday, November 2, 2009: 10:45 AM
Convention Center, Room 316, Third Floor

Jeffrey Ebdon, Univ. of Massachusetts, Amherst, Amherst, MA and Hugh G. Gauch, Cornell Univ., Ithaca, NY
The additive main effect and multiplicative interaction (AMMI) analysis has been shown to be effective in understanding complex genotype-environment (GE) interactions typical of National Turfgrass Evaluation Program (NTEP) variety trials. Interactions in such complex data sets are difficult to understand using ordinary analysis of variance (ANOVA). The objective of this research was to target GE interactions using AMMI and NTEP turfgrass quality data in order to understand why genotypes interact with environments. The 1990 Kentucky bluegrass (Poa pratensis L.) and perennial ryegrass (Lolium perenne L.) variety trials were analyzed. The Kentucky bluegrass (KBG) trial involved 125 genotypes and 69 location-year combinations (environments) while in the perennial ryegrass (PRG) trial 123 genotypes by 60 environments were evaluated. Interaction patterns revealed by AMMI biplots indicated KBG and PRG genotypes are narrowly adapted because no genotype has superior performance in all environments (broad adaptability). Superior performing (winning) genotypes and poor performing (losing) genotypes are among the most narrowly adapted exhibiting large interaction patterns. Interaction biplots indicate that KBG genotypes appear to be more genetically diverse compared to PRG. In both trials, GE interactions could be explained in biologically meaningful terms by cultural intensity level and disease resistance. Genotype scores for the first interaction axis (IAS-1) for KBG were highly correlated with leaf spot (Bipolaris sp., r=0.70, p=0.001) while IAS-1 scores for PRG were correlated with brown patch (Rhizoctonia solani, r=-0.78. p=0.001). Interaction patterns for KBG followed accepted genotype classification categories. Some NTEP locations were highly predictable in year-to-year interaction with genotypes making cultivar recommendations more reliable. Environment IAS-1 scores for KBG were correlated with mowing height of cut (r=-0.52, p=0.001) while environment IAS-1 scores for PRG were correlated with annual fertilizer nitrogen (r=-0.66, p=0.001). Climatic factors were not important in explaining GE interactions.