Theobroma grandiflorum breeding optimization based on repeatability, stability and adaptability information

Abstract

The cultivation of Theobroma grandiflorum in the Brazilian Amazon is mainly conducted by family farmers who use a range of different management strategies. Thus, breeding programs of the species must address the challenge of developing cultivars that are adapted to and stable in a variety of cultivation environments. In this context, this study aimed to estimate the optimum number of harvests for genetic selection of T. grandiflorum progenies and identify the most promising ones in terms of productivity, stability, and adaptability. The trials were implemented in three environments, using a randomized complete block design, with 25 full-sib progenies, five replications, and three plants per plot. The traits mean number of fruits/plant, mean fruit production/plant, and rate of infection with witches broom (Moniliophthora perniciosa) were evaluated over 11 harvests. The Restricted Maximum Likelihood/Best Linear Unbiased Prediction (REML/BLUP) mixed model method was used to estimate genetic parameters and predict genetic values, which were then applied to assess stability and adaptability. The results show that there is genetic variability among the studied T. grandiflorum progenies and that accurate genetic selection aiming at recombination is effective after three harvests, for recombination, or eleven harvests for identification of recommended progenies. Six progenies were selected that met the requirements for productivity, stability, and adaptability to different cultivation environments. These results can be used to optimize and advance T. grandiflorum breeding programs.