Productivity, adaptability, and stability in Guazuma crinita progeny tests across three environments in the Aguaytia River Basin, Ucayali, Peru

• Autor(a) principal: Revilla-Chavez, Jorge Manuel
• Data de Publicação: 2022
• Outros Autores: Moraes, Marcela Aparecida de [UNESP], Pinchi-Ramirez, Mack Henry, Sebbenn, Alexandre Magno [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.2478/sg-2022-0009; http://hdl.handle.net/11449/237869
• topic: Biomass; G x E interaction; Native tree species; Plantations; Tree improvement; White Bolaina

Guazuma crinita is a fast-growing tree with potential for use in agroforestry systems, due to its rapid wood production, which can contribute significantly to the livelihoods of small-scale farmers in the Peruvian Amazon. However, indiscriminate logging due to high demand is leading to the disappearance of natural forests. As such, the International Council for Research in Agroforestry (ICRAF) began a domestication program to reduce pressure on natural populations of the species. The objective of the present study was to use analyses of genetic parameters, adaptability (PRVG), productivity (MHPRVG), and stability (MHVG), to select G. crinita genotypes from a three-year-old progeny test established in the Aguaytia River Basin, in Ucayali, Peru. The test was established in three different sites, with three blocks, 200 progeny per block, and two individuals per plot. The measured traits were diameter at breast height (DBH), total height (H), and total aerial biomass (B). Significant differences in traits between progenies were detected, but with no genotype x environment interaction (GxE). However, the genotypic correlation among sites was important (> 0.702), suggesting that genetic improvement is possible by selecting the same progeny across sites. The mean heritability among progenies was moderate for all traits (0.34-0.369) and selective precision through combined site analysis was relatively high (0.583-0.608). Based on selection for DBH through combined analysis, MHVG, PRVG, and MHPRVG, 50 superior progenies (25.9 %) were identified for all environments. These should be prioritized in breeding programs as they can offer stable genetic variability for future selection cycles.