Abstract

OSTENGO, Santiago; CUENYA, María I.  and  BALZARINI, Mónica. A biplot-based analysis for exploring interactions in sugarcane multienvironment trials with multiple harvests. Rev. ind. agric. Tucumán [online]. 2014, vol.91, n.1, pp.43-46. ISSN 1851-3018.

Sugarcane breeding programs involve multienvironment trials (MET), where genotype yields are compared at different crop ages within and across locations. Temporal correlation among yield data from consecutive harvests per genotype and spatial correlations among data from neighboring plots are expected in each trial. It is also expected that the residual variances are not equal in different environments. Mixed Linear Models (MLM) based on the lack of independence and homogeneity of variance turn out to be more appropriate than classical analysis of variance of fixed effect models (ANOVA) to compare genotype performance and analyze genotype-location, genotype-age, and genotype-location-age interactions. MLM allow obtaining predictors of random terms (BLUPs) of interactions, which are useful to understand genotype-environment association (GE). This work proposes a graphical MLM-based approach to study interactions between genotypes and locations considering different crop ages. Data from MET of the Sugarcane Breeding Program of Estación Experimental Agroindustrial Obispo Colombres (Tucumán, Argentina) were analyzed. Twenty clones were compared with respect to cane production (tons per hectare) on six locations through three crop ages. An MLM was fitted with heterogeneous residual variances among locations and temporal and spatial correlations (based on a two-dimensional coordinate plot position). To model these correlations, a first order autoregressive model in three dimensions was applied, deriving from the direct product of temporal and spatial correlation matrixes. Location effect and genotype-location-age (GLA) interaction were considered random. Triple interaction BLUPs (GLA) were subjected to principal component and biplot analysis. Results were compared with the ones obtained with an additive main effect and multiplicative interaction (AMMI) biplot of GE interaction, where E is held as the combination of location and age factors. According to the Akaike Information Criterion and the likelihood-based test, the MLM was better than the ANOVA model assuming independent data and homogeneous variances. The biplot obtained from the triple interaction BLUPs facilitated the interpretation of genotype-age interactions for each location, and genotype-location interactions for different ages.

Keywords : Predictors; Triple interaction; TUC 95-10.

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