Maturity Classification of Sugarcane (Saccharum officinarum L) Genotypes Grown Under Different Production Environments of Ethiopia

Abstract

Sugarcane (Saccharum officinarum L.) yield productivity is potentially affected by maturity which is genotypic and environment dependent. The limited information about the effects of genotypes, maturity and climate on sugarcane yield has been the main concern in sugarcane production of Ethiopia. The objectives of this study were to characterize sugarcane production environments of Ethiopia using meteorological and maturity data, classify genotypes using maturity data collected over growing seasons and across locations (Sugar Estates) and identify the major climatic conditions affecting the variability in sugarcane maturity. The design of the experiment was simple lattice design replicated across five locations and three seasons. Recoverable sucrose percentage and Brix Data of 49 genotypes evaluated across environments were collected over crop ages and subjected to ANOVA, Principal Component Analyses (PCA) and AMMI2 analyses. Results from ANOVA revealed brix percentage measurements were highly significantly (P<0.01) affected by environment, genotype and their interaction effects. Both principal component and additive main and multiplicative interaction (AMMI2) bi-plots generated similar genotype adaptations to maturity and successfully classify the genotypes in to early, medium and late under specific environments. Most of the CIRAD advanced lines were early maturing with wide range of maturing periods while those genotypes that were introduced at mid-way selection state were medium maturity. The correlation analysis made between environmental covariates and environmental IPCA scores demonstrated mean, maximum and minimum temperature and relative humidity regimes were the major climatic conditions affecting genotype × location interactions for brix percentage within 14-16 months of crop ages while minimum relative humidity was the major environmental factor that affect maturity across locations within 10-18 months crop ages. Further applications of these techniques will involve the inclusion of covariates derived from soil, climatic and management factors over diverse regions.