scholarly journals Increased Activity of 5-Enolpyruvylshikimate-3-phosphate Synthase (EPSPS) Enzyme Describe the Natural Tolerance of Vulpia myuros to Glyphosate in Comparison with Apera spica-venti

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 725
Author(s):  
Muhammad Javaid Akhter ◽  
Solvejg Kopp Mathiassen ◽  
Zelalem Eshetu Bekalu ◽  
Henrik Brinch-Pedersen ◽  
Per Kudsk

Rattail fescue (Vulpia myuros (L.) C.C. Gmel.) is a self-pollinating winter annual grassy weed of winter annual crops. The problems with V. myuros are mostly associated with no-till cropping systems where glyphosate application before sowing or emergence of the crop is the most important control measure. Ineffective V. myuros control has been reported following glyphosate applications. Experiments were performed to study the effectiveness of glyphosate on V. myuros, and determine the causes of the lower performance of glyphosate on V. myuros compared to other grass weeds. Estimated GR50 values demonstrated that V. myuros was less susceptible to glyphosate than Apera spica-venti regardless of the growth stage. Within each species, glyphosate efficacy at different growth stages was closely related to spray retention. However, the low susceptibility to glyphosate in V. myuros was not caused by lower retention as previously suggested. A significantly lower shikimic acid accumulation in V. myuros compared to A. spica-venti was associated with a higher activity of the EPSPS enzyme in V. myuros. Nevertheless, the relative responses in EPSPS activity to different glyphosate concentrations were similar in the two grass species, which indicate that EPSPS from V. myuros is as susceptible to glyphosate as EPSPS from A. spica-venti suggesting no alternation in the binding site of EPSPS. The results from the current study indicate that V. myuros is less susceptible to glyphosate compared to A. spica-venti, and the low susceptibility of V. myuros is caused by an increased EPSPS enzyme activity.

2016 ◽  
Vol 4 (3) ◽  
pp. 179 ◽  
Author(s):  
Jacob Gusha ◽  
Tonderai Chambwe ◽  
Prisca H. Mugabe ◽  
Tinyiko Halimani ◽  
Simbarashe Katsande ◽  
...  

1997 ◽  
Vol 99 (1) ◽  
pp. 185-189
Author(s):  
Wen-Shaw Chen ◽  
Kuang-Liang Huang ◽  
Hsiao-Ching Yu

2013 ◽  
Vol 39 (5) ◽  
pp. 919 ◽  
Author(s):  
Bo MING ◽  
Jin-Cheng ZHU ◽  
Hong-Bin TAO ◽  
Li-Na XU ◽  
Bu-Qing GUO ◽  
...  

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.


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