| 基于无人机多光谱的湿地松生长性状遗传评价 |
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| 引用本文： | 彭叶青,洪大伟,产启福,卜良高,李彦杰,栾启福,武浩然.基于无人机多光谱的湿地松生长性状遗传评价[J].安徽农业大学学报,2023,50(5):758. |
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| 作者姓名： | 彭叶青 洪大伟 产启福 卜良高 李彦杰 栾启福 武浩然 |
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| 作者单位： | 泾县马头国有林场，宣城 242500;泾县林业局，宣城 242500;中国林业科学研究院亚热带林业研究所，杭州 311400;中国林业科学研究院亚热带林业研究所，杭州 311400; 河北农业大学园林与旅游学院，保定 071000 |
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| 基金项目： | 安徽省 2022 年林业碳汇自筹科技攻关项目（No. 01）和浙江省农业（林木）新品种选育重大科技专项（2021C02070-1）共同资助。 |
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| 摘 要： | 为了选育生长性状优良的湿地松家系，基于无人机多光谱技术，对不同家系湿地松的生长性状进行遗传变异分析。以8年生湿地松的20个半同胞家系测定林为研究对象，利用无人机多光谱快速提取其2021年11个月份（2月份除外）的树高和冠幅面积，并根据实测胸径数据构建胸径预测模型；估算不同月份的每个家系湿地松树高、冠幅面积和胸径的遗传力和育种值。结果表明：基于冠幅面积和树高建立的深度学习模型的胸径预测值与测量的胸径真实值之间具有较强的相关性，其中R2为0.70，RMSE为1.83 cm；湿地松的3个生长性状的遗传力在0.00 ~ 0.40之间；以10%入选率进行家系选择时获得了较好的遗传增益，3个生长性状遗传增益范围为0.21 ~ 0.79（11月份冠幅面积的遗传增益接近于0.00）。根据冠幅面积和树高的育种值进行家系选择，最终应考虑1、6、8、9、10、16、18和20号家系作为备选家系。基于湿地松的冠幅面积和树高的深度学习模型可应用于预测湿地松的胸径。湿地松的3个生长性状受中等遗传力控制，10%选择强度获得了较好的遗传增益。8个家系被选择出来用于冠幅面积和树高的同步遗传改良。研究结果可为选育生长性状优良的湿地松家系提供参考依据。
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| 关 键 词： | 湿地松 无人机多光谱 生长性状 遗传分析 |
Genetic evaluation of Pinus elliottii growth traits based on UAV multispectral |
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| PENG Yeqing,HONG Dawei,CHAN Qifu,BU Lianggao,LI Yanjie,LUAN Qifu,WU Haoran.Genetic evaluation of Pinus elliottii growth traits based on UAV multispectral[J].Journal of Anhui Agricultural University,2023,50(5):758. |
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| Authors: | PENG Yeqing HONG Dawei CHAN Qifu BU Lianggao LI Yanjie LUAN Qifu WU Haoran |
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| Affiliation: | Matou State-owned Forest Farm of Jingxian, Xuancheng 242500;Forestry Bureau of Jingxian County, Xuancheng 242500;Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400; Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400; College of Landscape Architecture and Tourism, Hebei Agricultural University, Baoding 071000 |
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| Abstract: | In order to breed Pinus elliottii families with excellent growth traits, based on the UAV multi-spectral technology, the genetic variation analysis of the growth traits of different Pinus elliottii families was carried out. Taking 8-year-old P. elliottii forests of 20 half-sib families as the research objects, the tree height and canopy area of 11 months (except for February) in 2021 were quickly extracted by drone multi-spectroscopy, and DBH prediction model was built based on the actual measurement DBH data; the heritability and breeding values of the tree height, crown area and diameter at the breast height were estimated for each lineage of P. elliottii in different months. The results showed that: there was a strong correlation between the predicted DBH value of the established deep learning model based on the canopy area and tree height and the measured DBH true values, where R2 was 0.70 and RMSE was 1.83 cm; the heritability of the three growth traits of P. elliottii was between 0.00 and 0.40; a good genetic gain was obtained when the pedigree was selected with a 10% selection rate, and the genetic gain of the three growth traits ranged from 0.21 to 0.79 (the genetic gain of crown area in November was 0.00). According to the breeding values of the crown area and tree height, family selection should be carried out. Finally, families of 1, 6, 8, 9, 10, 16, 18 and 20 should be considered as candidate families. The deep learning model based on the crown area and tree height of P. elliottii could be applied to predict the DBH of slash pine. Three growth traits of slash pine were controlled by moderate heritability. A good genetic gain was obtained with 10% selection |
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| Keywords: | Pinus elliottii UAV multispectral growth traits genetic analysis |
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