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Analyzing and optimizing yield formation of tomato introgression lines using plant model | |
Kang, Mengzhen1,2; Wang, Xiujuan1,3; Qi, Rui4; Jia, Zhi-Qi5; de Reffye, Philippe6; Huang, San-Wen7 | |
发表期刊 | EUPHYTICA |
ISSN | 0014-2336 |
2021-06-01 | |
卷号 | 217期号:6页码:17 |
摘要 | Generally, the relation between quantitative trait loci (QTLs) and yield is empirical, and their roles in source-sink dynamics are unclear. A tomato introgression line (IL) population (S. pennellii ILs) was applied to analyze the effect of chromosome segment from wild cultivar on numerous yield-related phenotypes, including plant yield, the weight of vegetative part, the number and weight of individual fruits. A functional-structural plant model was applied to analyze the difference in yield formation of tomato ILs. Measurements on organ biomass were performed at four stages during the growth period of plants. Source and sink parameters were estimated from the experimental measurements of different organs for each IL, discovering how the final yield is linked to the fruit number, size and expansion process. The correlation and distribution of source-sink parameters for ILs were analyzed. The sink parameters were optimized to find a better combination of ILs to improve the yield using Particle Swarm Optimisation (PSO) algorithm. Optimization results indicate a potential yield increase of 35% for the control M82. This model-assisted analysis provides a promising approach to deeper insight in phenotypic data. |
关键词 | GreenLab model Yield formation Parameter estimation Tomato introgression line Optimization |
DOI | 10.1007/s10681-021-02834-8 |
关键词[WOS] | FUNCTIONAL-STRUCTURAL MODEL ; LYCOPERSICON-PENNELLII ; FRUIT SIZE ; NATURAL VARIATION ; GROWTH ; LEAF ; GREENLAB ; IDENTIFICATION ; MORPHOGENESIS ; RESPONSES |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Natural Science Foundation of China[62076239] ; Natural Science Foundation of China[31700315] ; Chinese Academy of Science (CAS)-Thailand National Science and Technology Development Agency (NSTDA) Joint Research Program[GJHZ2076] |
项目资助者 | Natural Science Foundation of China ; Chinese Academy of Science (CAS)-Thailand National Science and Technology Development Agency (NSTDA) Joint Research Program |
WOS研究方向 | Agriculture ; Plant Sciences |
WOS类目 | Agronomy ; Plant Sciences ; Horticulture |
WOS记录号 | WOS:000646556800001 |
出版者 | SPRINGER |
七大方向——子方向分类 | 人工智能+农业 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44492 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Wang, Xiujuan |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100949, Peoples R China 3.Chinese Acad Sci, Beijing Engn Res Ctr Intelligent Syst & Technol, Inst Automat, Beijing 100190, Peoples R China 4.Amadeus, 485 Route Pin Montard, F-06410 Biot, France 5.Coll Hort Henan Agr Univ, Zhengzhou 450002, Peoples R China 6.Univ Montpellier, CNRS, AMAP, CIRAD,INRA,IRD, F-34000 Montpellier, France 7.Chinese Acad Agr Sci, Agr Genomes Inst Shenzhen, Shenzhen 518124, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Kang, Mengzhen,Wang, Xiujuan,Qi, Rui,et al. Analyzing and optimizing yield formation of tomato introgression lines using plant model[J]. EUPHYTICA,2021,217(6):17. |
APA | Kang, Mengzhen,Wang, Xiujuan,Qi, Rui,Jia, Zhi-Qi,de Reffye, Philippe,&Huang, San-Wen.(2021).Analyzing and optimizing yield formation of tomato introgression lines using plant model.EUPHYTICA,217(6),17. |
MLA | Kang, Mengzhen,et al."Analyzing and optimizing yield formation of tomato introgression lines using plant model".EUPHYTICA 217.6(2021):17. |
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