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Wang Y, Zhang Y, Huang G, Wang J, Lv L, Zhao S, Lu X, Zhang M, Guo M, Zhang C, Men Q, Guo X, Zhao C. Association analysis of maize stem vascular bundle micro-characteristics with yield components based on micro-CT and identification of related genes. Sci Rep 2025; 15:13009. [PMID: 40234583 PMCID: PMC12000327 DOI: 10.1038/s41598-025-96518-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2024] [Accepted: 03/28/2025] [Indexed: 04/17/2025] Open
Abstract
The distribution pattern of vascular bundles and microstructure characteristics significantly impact crop yield. Previous studies have primarily focused on investigating the micro-phenotypic characteristics and genetic regulation of individual internode, neglecting the exploration of the relationship between different internodes. This study, for the first time, comprehensively analyzed multi-scale phenotypic information of stem cross-sections, zones, and vascular bundles in three different internodes (basal third internode, ear internode and highest internode) of 268 inbred maize lines using Micro-computed tomography scanning. Key findings revealed that basal third internode and ear internode exhibited more stable microscopic characteristics than highest internode. Inbred lines with higher numbers of vascular bundle and well-developed inner zone in ear internode exhibited better yield characteristics, particularly in the kernel number per row. Genome-wide association analysis respectively identified 15, 1 and 1 putative candidate genes in basal third internode, ear internode and highest internode. These genes encode a variety of enzymes, such as oxidases, synthetases, ligase enzyme and protein kinases. Notably, Zm00001d042490 may be an important putative candidate gene for The number of vascular bundles in the periphery zone and corn grain traits. This study provides an important theoretical basis and genetic resources for accurately identifying different internode phenotypes of maize stalks, potentially advancing the selection of high-yielding, high-quality maize varieties.
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Affiliation(s)
- Yanru Wang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ying Zhang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
| | - Guanmin Huang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Jinglu Wang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Lujia Lv
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Shuaihao Zhao
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Minggang Zhang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Minkun Guo
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Changyu Zhang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qingmei Men
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
| | - Chunjiang Zhao
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing, 100097, China.
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, 100097, China.
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Yu S, Cui L, Cui H, Liu X, Liu J, Xin Z, Yuan J, Wang D. Spray performance of flexible shield canopy opener and rotor wind integrated boom-sprayer application in soybean: effects on droplet deposition distribution. PEST MANAGEMENT SCIENCE 2024; 80:3334-3348. [PMID: 38380840 DOI: 10.1002/ps.8037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/16/2024] [Accepted: 02/19/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND Canopy density is high during mid-to-late soybean growth as a result of dense planting to improve yield, which seriously affects the control of pests and diseases. The dilemmas of difficult droplet penetration, nonuniform deposition, and droplet drift in field spraying remain challenges to the precise control of droplet distribution. This paper proposed a novel spraying application mode combined flexible shield canopy opener (FSCO) with rotor wind. The design of the key components of the new boom-spraying machine are described. The effects of the comparative spraying modes on spray deposition and droplet drift were studied in a field validation test to explore the feasibility of the novel spraying application. RESULTS The study found that droplet coverage inside the soybean canopy was significantly affected by spraying mode, rotor wind speed and opener depth. The spraying operation that used the FSCO and rotor wind integrated mode was optimal for droplet uniformity on the adaxial and abaxial surfaces of the canopy leaves, with droplet uniformity indices of 0.966 and 0.934, respectively. At a rotor wind speed of 6 m s-1 and opener depth of 15 cm, the soybean canopy droplet coverage uniformity effect achieved the highest composite score of 0.937. The spraying mode used in this study improved droplet coverage uniformity by 82.30% and droplet anti-drift performance improved by 99.73% compared to the conventional boom-spraying mode. CONCLUSION The study shows validity of the spraying mode combined FSCO with rotor wind to open dense canopy and improved droplet deposition uniformity in canopy and anti-drift performance. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Shihui Yu
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, China
| | - Lei Cui
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, China
| | - Huiyuan Cui
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, China
| | - Xuemei Liu
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, China
- Shandong Agricultural Equipment Intelligent Engineering Laboratory, Tai'an, China
| | - Jian Liu
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, China
| | - Zhenbo Xin
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, China
- Shandong Agricultural Equipment Intelligent Engineering Laboratory, Tai'an, China
| | - Jin Yuan
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, China
- Shandong Agricultural Equipment Intelligent Engineering Laboratory, Tai'an, China
| | - Dongwei Wang
- College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao, China
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