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| Web URL(s): | http://www.swss.ws/wp-content/uploads/2020-SWSS-proceedings_Final03.pdf#page=230
Last checked: 05/03/2022
Requires: PDF Reader
Notes: Item is within a single large file |
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| Publication Type:
| Report |
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| Author(s): | Wilber, A. L.;
McCurdy, J. D.;
Richard, M.;
Czarnecki, J. M.;
Sullivan, D. G. |
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| Author Affiliation: | Wilber, McCurdy, Richard, and Czarnecki: Mississippi State University, MS; Sullivan: TurfScout, LLC, Greensboro, NC |
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| Title: | Evaluating St. Augustinegrass (Stenotaphrum secundatum) sod grow-in following preemergence herbicide application using multispectral imaging |
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| Section: | Oral abstracts
Other records with the "Oral abstracts" Section
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| Meeting Info.: | Biloxi, MS: January 27-30, 2020 |
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| Source: | Proceedings of the Southern Weed Science Society 73rd Annual Meeting. Vol. 73, 2020, p. 131. |
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| Publishing Information: | Biloxi, MS: Southern Weed Science Society |
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| # of Pages: | 1 |
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| Abstract/Contents: | "St. Augustinegrass (Stenotaphrum secundatum) sod is widely produced in the southeastern and gulf-coastal United States. Producers apply preemergence herbicides to prevent annual weed emergence. Those herbicides may negatively affect sod grow-in and harvest characteristics. Our objective is to evaluate the effects of commonly used preemergence herbicides upon St. Augustinegrass grow-in using two multispectral imaging sensors in comparison to traditional visual evaluation. Field research was conducted as a randomized complete block design at Mississippi State University during summer 2019. Within each experimental unit (2.32 m2 ), ten plugs (232 cm2 apiece) of 'MS-B-2-3-98' St. Augustinegrass were planted on 12 June. Herbicide treatments were applied on 13 June with a CO2 pressurized back-pack sprayer in a water carrier volume of 374 L ha-1. Treatments included a non-treated check, prodiamine (0.60 kg ai ha-1), pendimethalin (1.66 kg ha-1), oxadiazon (2.24 kg ha-1), S-metolachlor (2.78 kg ha-1), atrazine (2.24 kg ha-1), atrazine + S-metolachlor (2.24 + 1.74 kg ha-1), and dithiopyr (0.42 kg ha-1), as well as the treated-check, indaziflam (0.033 kg ha-1). Percent visual cover and spectral reflectance (?=670, 730, and 780 nm) were observed weekly using a Holland Scientific RapidSCAN CS-45 Handheld Crop Sensor. Spectral reflectance (?=475, 560, 668, 717, and 840 nm) using a MicaSense RedEdge-MX sensor mounted on a UAV was observed weekly to biweekly, weather dependent. Spectral reflectance data was evaluated using vegetative indices: Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and Chlorophyll IndexRed Edge (CI-RE). Data were regressed in order to determine treatment effects upon grow-in. Percentage visual cover data were log transformed and fit to a sigmoidal, variable slope regression model to determine time to 50% cover (GraphPad Prism 8.0.0, GraphPad Software, San Diego, CA). Study-long data were correlated and regressed using PROC CORR and REG (SAS 9.4 SAS Institute Inc., Cary, NC). Sensitivity equivalents (SEq) values were calculated by dividing slope by root mean square error. Prodiamine, pendimethalin, S-metolachlor, atrazine + S-metolachlor, dithiopyr and indaziflam increased estimated time to reach 50% cover compared to the nontreated. When compared over the length of the study, there were strong correlations between indices and percent visual cover, coefficient of determination (r2 ) values between 0.8586 and 0.9001. Pearson's correlation coefficients showed similar correlation values for the same index regardless of sensor platform: 0.9488 and 0.9440 for UAV and handheld RVI, respectively; 0.9266 and 0.9389 for UAV and handheld NDVI, respectively; 0.9319 and 0.9480 for UAV and handheld CI-RE, respectively. SEq values were consistently low for all indices, but used a relative comparison, UAV RVI showed highest sensitivity to visual cover, 0.098, and UAV NDVI showed lowest sensitivity to visual cover, 0.080. Although correlations between indices and percent visual cover were high, it is still unknown which device and index is best at detecting differences between treatments over time. Different indices are suited for different growth stages, so the best index for sparse vegetation/new planting may not be the most ideal for full canopy cover. Other green organisms, such as algae and weeds, may artificially increase multispectral band values that a visual evaluation would not account for. Ongoing research seeks to create a workflow for other small plot researchers, with aerial imagery and ArcGIS Pro, to acquire vegetation indices on a large scale in a time efficient manner." |
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| Language: | English |
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| References: | 0 |
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| See Also: | Updated version appears in Abstracts of the Annual Meeting of the Weed Science Society of America, 2021, p. 83, R=322700. R=322700 |
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| Note: | This item is an abstract only! |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Wilber, A. L., J. D. McCurdy, M. Richard, J. M. Czarnecki, and D. G. Sullivan. 2020. Evaluating St. Augustinegrass (Stenotaphrum secundatum) sod grow-in following preemergence herbicide application using multispectral imaging. South. Weed Sci. Soc. Proc. 73:p. 131. |
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| Web URL(s):
http://www.swss.ws/wp-content/uploads/2020-SWSS-proceedings_Final03.pdf#page=230
Last checked: 05/03/2022
Requires: PDF Reader
Notes: Item is within a single large file |
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MSU catalog number: b2207931 |
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