Using logistic regression models for selection in non-replicated sugarcane breeding populations

Autor:	Marvellous M. Zhou
Rok vydání:	2013
Předmět:	biology business.industry Plant Science Horticulture Stage ii Wald test biology.organism_classification Logistic regression Biotechnology Path coefficient Genetics Trait Gene–environment interaction Cane business Sugar yield Agronomy and Crop Science
Zdroj:	Euphytica. 191:415-428
ISSN:	1573-5060 0014-2336
DOI:	10.1007/s10681-013-0899-x
Popis:	Increasing selection efficiency in un-replicated populations has remained a challenge to sugarcane breeders due to the effects of genotype by environment interaction and competition among plots. Therefore studies aimed at exploring models to improve selection efficiency are required. At the South African Sugarcane Research Institute, the Stage II selection populations are planted to un-replicated plots. Visual selection, which is known to be subjective, is used to determine the genotypes advanced. Although path coefficient analysis has identified important yield components, there is little use of that knowledge to enhance selection. This study demonstrates the use of logistic regression model as a statistical decision support tool to enhance selection in un-replicated populations. The logistic regression model used stalk number, stalk height, stalk diameter and estimable recoverable crystal (ERC) % cane to predict sugar yield. The data were collected from Stage II populations across regional breeding programs and analysed using the logistic procedure of Statistical Analysis System. The Likelihood Ratio, Score and Wald statistics were highly significant (P
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b55a9a69185459ef990f2b91d3b32164 https://doi.org/10.1007/s10681-013-0899-x Zobrazit plný text záznamu Plný text ve formátu PDF