eprintid: 11076
rev_number: 12
eprint_status: archive
userid: 1305
dir: disk0/00/01/10/76
datestamp: 2019-03-07 04:02:45
lastmod: 2020-01-17 03:08:51
status_changed: 2019-03-07 04:02:45
type: article
metadata_visibility: show
creators_name: Arief, V N
creators_name: Desmae, H
creators_name: Hardner, C
creators_name: DeLacy, I H
creators_name: Gilmour, A
creators_name: Bull, J K
creators_name: Basford, K E
creators_id: female
creators_id: female
creators_gender: Female
creators_gender: Female
icrisatcreators_name: Desmae, H
affiliation: The University of Queensland, School of Agriculture and Food Sciences (Brisbane)
affiliation: ICRISAT (Bamako)
affiliation: The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (Brisbane)
affiliation: Statistical and ASReml Consultant (Cargo)
affiliation: The Climate Corporation (St. Louis)
country: Australia
country: Mali
country: USA
title: Utilization of Multiyear Plant Breeding Data to Better Predict Genotype Performance
ispublished: pub
subjects: PLB1
subjects: s2.13
subjects: s2.6
divisions: CRPS1
full_text_status: public
keywords: Plant Breeding Data, Prediction, Genotype Performance, Genotype advancement, Maize, Variance Components, phenotypic variance, Genetics, Genomics, Breeding
abstract: Despite the availability of multiyear, multicycle, and multiphase data in plant breeding programs for annual crops, selection is often based on single-year, single-cycle, and single-phase data. As genotypes in the same fields are usually grown under the same management practice, data from these fields can and should be analyzed together. In Monsanto’s North American maize (Zea mays L.) breeding program, this approach enables a spatial model to be fitted in each field, providing an estimate of spatial trend and a better estimate of residual variance in each field. Multiyear, multicycle analysis showed that the estimates of genotype × year variance (VGY) and genotype × year × location variance (VGYL) were still the largest components of the estimated phenotypic variance. Analysis of any single-year subset of the data inflated the estimate of genotypic variance (VG) by the size of the estimate of VGY, resulting in potential bias in the estimates of genotype performance. These results demonstrate the advantage of a combined analysis of data across years and cycles to make selection decisions for genotype advancement.
date: 2019
date_type: published
publication: Crop Science (TSI)
volume: 59
publisher: Crop Science Society of America
pagerange: 1-11
id_number: 10.2135/cropsci2018.03.0182
refereed: TRUE
issn: 0011-183X
official_url: http://dx.doi.org/10.2135/cropsci2018.03.0182
related_url_url: https://scholar.google.co.in/scholar?hl=en&as_sdt=0%2C5&q=Utilization+of+Multiyear+Plant+Breeding+Data+to+Better+Predict+Genotype+Performance&btnG=
related_url_type: pub
citation:   Arief, V N and Desmae, H and Hardner, C and DeLacy, I H and Gilmour, A and Bull, J K and Basford, K E  (2019) Utilization of Multiyear Plant Breeding Data to Better Predict Genotype Performance.  Crop Science (TSI), 59.  pp. 1-11.  ISSN 0011-183X     
document_url: http://oar.icrisat.org/11076/1/cs-0-0-cropsci2018.03.0182.pdf