文献类型：期刊文献

英文题名：Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery

作者：Li, Yanjie[1,2,3,4] Yang, Xinyu[5] Tong, Long[6] Wang, Lingling[7] Xue, Liang[1,2,3,4] Luan, Qifu[1,2,3,4] Jiang, Jingmin[2,3,4]

第一作者：Li, Yanjie;李彦杰

通信作者：Luan, QF[1];Luan, QF[2];Luan, QF[3];Luan, QF[4]

机构：[1]Chinese Acad Forestry, State Key Lab Tree Genet & Breeding, Beijing, Peoples R China;[2]Chinese Acad Forestry, Res Inst Subtrop Forestry, Hangzhou, Zhejiang, Peoples R China;[3]State Forestry & Grassland Adm Subtrop Forest Cult, Key Lab, Hangzhou, Zhejiang, Peoples R China;[4]Key Lab Tree Breeding Zhejiang Prov, Hangzhou, Zhejiang, Peoples R China;[5]Nanjing Agr Univ, Jiangsu Collaborat Innovat Ctr Modern Crop Prod, State Key Lab Crop Genet & Germplasm Enhancement, Coll Agr,Soybean Res Inst,Natl Ctr Soybean Improve, Nanjing, Peoples R China;[6]Chongqing Acad Forestry, Chongqing, Peoples R China;[7]Forestry & Water Conservancy Bur Fuyang Dist Hangz, Hangzhou, Peoples R China

年份：2023

卷号：14

外文期刊名：FRONTIERS IN PLANT SCIENCE

收录：;Scopus(收录号：2-s2.0-85169707261);WOS:【SCI-EXPANDED(收录号:WOS:001057386200001)】；

基金：This research was supported by the cooperation projects between the People's Government of Zhejiang Province and the Chinese Academy of Forestry, No. 2023SY10 and the Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding, No. 2021C02070-7-3.

语种：英文

外文关键词：phenomic selection; forest phenomics; PBWAS; high throughput; time-series

摘要：Genomic selection (GS) is an option for plant domestication that offers high efficiency in improving genetics. However, GS is often not feasible for long-lived tree species with large and complex genomes. In this paper, we investigated UAV multispectral imagery in time series to evaluate genetic variation in tree growth and developed a new predictive approach that is independent of sequencing or pedigrees based on multispectral imagery plus vegetation indices (VIs) for slash pine. Results show that temporal factors have a strong influence on the h2 of tree growth traits. High genetic correlations were found in most months, and genetic gain also showed a slight influence on the time series. Using a consistent ranking of family breeding values, optimal slash pine families were selected, obtaining a promising and reliable predictive ability based on multispectral+VIs (MV) alone or on the combination of pedigree and MV. The highest predictive value, ranging from 0.52 to 0.56, was found in July. The methods described in this paper provide new approaches for phenotypic selection (PS) using high-throughput multispectral unmanned aerial vehicle (UAV) technology, which could potentially be used to reduce the generation time for conifer species and increase the genetic granularity independent of sequencing or pedigrees.