Banca de DEFESA: Marcos Corbellini

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : Marcos Corbellini
DATA : 27/07/2023
HORA: 09:00
LOCAL: Videoconferência
TÍTULO:

Geographic adaptability of soybean to soy macro region 4 from Brazil

PALAVRAS-CHAVES:

Regression-Krigagem; Environmental covariates; Geostatistic

PÁGINAS: 18
GRANDE ÁREA: Ciências Agrárias
ÁREA: Agronomia
SUBÁREA: Fitotecnia
ESPECIALIDADE: Melhoramento Vegetal
RESUMO:

Brazil is the most soybean producer, account 30% of global production, estimated on season 2019/20, considered the main oilseed cultivated in the world. Currently, soybean production abroad all regions of the country, resulting high environmental diversity to the crop. Environment has great influence on phenotypic behavior of species, specially by factors like water availability, temperature, photoperiod and altitude. Multiambiental experiments associated to geostatistics techniques, with environmental variables help, may subsidize the understanding of interaction genotype ambient and adaptative thresholds. The most laborious step inside a breeding program is the genotypes evaluation in different ambients, limiting the size of the breeding program to its capacity of experimental evaluation. Studying adaptability and stability contributes to identification of previsible behavior genotypes, that suffer little changes according to environmental variations, ensuring the selection of materials with better stability and yield previsibility inside target region. The objective of present study was understand the adaptation of soybean cultivars in the soy macro region 4 from Brazil by spatialization of yield, to know the adaptatives geographic thresholds. It was perform yield trials from soybean breeding program of Syngenta Seeds, conducted in 40 places belong to soy macro region 4, on seasons 2018/2019, 2019/2020 and 2020/2021. Experimental design was completely randomized blocks with three repetitions, in 30 genotypes. Data were submitted to means adjustment and yield predition to obtaining the genotypic and ambient effects. After the model adjustment, obtaining the genotypic effects and means adjustment, was applied the method of regression Krigagem.

MEMBROS DA BANCA:
Interno - 985.174.497-20 - FLAVIO DESSAUNE TARDIN - UENF
Interno - 285519001 - THIAGO ALEXANDRE SANTANA GILIO
Externo à Instituição - LENIO URZEDA FERREIRA - UFG