Utilization of Multiyear Plant Breeding Data to Better Predict Genotype Performance

Crop Science

Desmae, Haile^2¹  DeLacy, Ian H.^1²  Gilmour, Arthur^4³  Hardner, Craig^3⁴  Arief, Vivi N.^1⁵ 

[1] International Crops Research Institute for Semi-Arid Tropics–West & Central Africa (ICRISAT– WCA), Bamako, Mali^2;Statistical and ASReml Consultant, Cargo, NSW 2800, Australia^4;The Climate Corporation, 4 Cityplace Dr., St. Louis, MO 63141, USA^5;The Univ. of Queensland, Queensland Alliance for Agriculture and Food Innovation, Brisbane, QLD 4072, Australia^3;The Univ. of Queensland, School of Agriculture and Food Sciences, St. Lucia, QLD 4072, Brisbane, Australia^1

关键词: BLUE;    best linear unbiased estimate;    BLUP;    best linear unbiased prediction;    MET;    multi-environment trial;    TPE;    target population environments;   

DOI  :  10.2135/cropsci2018.03.0182

学科分类：农业科学（综合）

来源: Crop Science

【 摘 要 】

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 (V_GY) and genotype × year × location variance (V_GYL) 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 (V_G) by the size of the estimate of V_GY, 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.

【 授权许可】

CC BY