Simulation study for assessment of FITCON in the presence of bienniality in MLT data of mango (Mangifera indica)

Abstract:

Mango is an important perennial fruit crop of India which exhibits bienniality in fruiting. In the analysis of Multi-Location Trials (MLTs) data, it is noticed that a few entries in the genotype-environmental table are missing. Such missing values leads to incomplete data in genotype × environment analysis. Due to incompleteness, MLT data becomes unbalanced data which is a bit challenging task for the analysis of Genotype ´ Environment Interaction (GEI). This challenge gates intensified when crop exhibits bienniality like mango fruit crop.  Fitting constant (FITCON) is being used for imputing missing observations in incomplete MLT data.  In present study performance of FITCON method has been assessed considering different rates of missing observations in the presence of bienniality on the basis of ranking of genotypes based on different stability parameters as well as simultaneous selection indices in mango crop. MLTs data on mango fruit yield has been collected from All India co-ordinated research of Tropical and sub-tropical fruits, (CISH) Lucknow for 16 genotypes for fourteen years i.e. from 1990 to 2005.  Box plot of distribution correlation coefficient yield and different measures of stability as well as indices for simultaneous selection for yield and stability (SISGYS) has been used to assess the performance of FITCON on simulated data under four different empirical situations. It has been found that FITCON terminated after few iteration when imputation was done for more that 10% of missing observations after eliminating the bienniality. Thus, FITCON is recommended for imputation up to 10% of missing observations of MLT data in mango.