Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery

Yanjie Li; Xinyu Yang; Long Tong; Lingling Wang; Liang Xue; Qifu Luan; Jingmin Jiang

doi:10.3389/fpls.2023.1156430

Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery

Front Plant Sci. 2023 Aug 21:14:1156430. doi: 10.3389/fpls.2023.1156430. eCollection 2023.

Authors

Yanjie Li^{1

2

3

4}, Xinyu Yang⁵, Long Tong⁶, Lingling Wang⁷, Liang Xue^{1

2

3

4}, Qifu Luan^{1

2

3

4}, Jingmin Jiang^{2

3

4}

Affiliations

¹ State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China.
² Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China.
³ Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China.
⁴ Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China.
⁵ Soybean Research Institute, National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China.
⁶ Chongqing Academy of Forestry, Chongqing, China.
⁷ Forestry and Water Conservancy Bureau of Fuyang District in Hangzhou, Hangzhou, China.

Abstract

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.

Keywords: PBWAS; forest phenomics; high throughput; phenomic selection; time-series.

Grants and funding

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.