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| DOI: | 10.1094/ATS-2004-1025-01-DG |
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| Web URL(s): | https://dl.sciencesocieties.org/publications/ats/articles/1/1/2004-1025-01-DG
Last checked: 03/05/2014
Access conditions: Item is within a limited-access website
https://dl.sciencesocieties.org/publications/ats/pdfs/1/1/2004-1025-01-DG
Last checked: 03/05/2014
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Access conditions: Item is within a limited-access website |
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| Publication Type:
| Refereed |
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| Author(s): | Main, Christopher L.;
Robinson, Darren K.;
McElroy, J. Scott;
Mueller, Thomas C.;
Wilkerson, John B. |
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| Author Affiliation: | Main, Robinson, McElroy and Mueller: Department of Plant Sciences, University of Tennessee, Knoxville, Tennessee; and Wilkerson: Biosystems Engineering and Environmental Science, University of Tennessee, Knoxville, Tennessee |
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| Title: | A guide to predicting spatial distribution of weed emergence using geographic information sustems (GIS) |
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| Source: | Applied Turfgrass Science. October 2004, p. [1-8]. |
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| Publishing Information: | Plant Management Network |
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| # of Pages: | 9 |
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| Related Web URL: | https://dl.sciencesocieties.org/publications/ats/abstracts/1/1/2004-1025-01-DG
Last checked: 03/05/2014
Notes: Abstract only |
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| Keywords: | TIC Keywords: GIS; Weed emergence; Integrated Pest Management; Decision-making; Temperatures
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| Abstract/Contents: | "A Geographic Information System (GIS) combines layers of spatially related information to better understand relationships that vary geographically. Scientists could benefit from the use of GIS to better explain new research, expand on prior research, and potentially develop integrated pest management (IPM) programs. Objectives of this manuscript are to demonstrate GIS applications in a posteriori weed management decision-making. The model system utilized empirically derived temperatures for large crabgrass (Digitaria sanguinalis L.) germination. Numerous studies have been conducted on the germination parameters of large crabgrass; however, there have not been attempts to apply the data to a spatial context. This report compares four techniques for creation of prediction maps to determine which method would be most useful in the creation of maps that predict time of weed emergence. Inverse Distance Weighted (IDW) technique formed distinct rings of high and low temperature values around weather station locations (producing an inaccurate prediction map). The two Spline techiniques investigated had a tendency to over- and under-predict temperature along the borders of Tennessee due to the procedures' fitting of a minimum curve to the data. Interpolation by Kriging produced the most accurate prediction map due to the spatial autocorrelation introduced by this geostatisical model." |
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| Language: | English |
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| References: | 18 |
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| Note: | Figures
Graphs |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Main, C. L., D. K. Robinson, J. S. McElroy, T. C. Mueller, and J. B. Wilkerson. 2004. A guide to predicting spatial distribution of weed emergence using geographic information sustems (GIS). Appl. Turfgrass Sci. p. [1-8]. |
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| DOI: 10.1094/ATS-2004-1025-01-DG |
| Web URL(s):
https://dl.sciencesocieties.org/publications/ats/articles/1/1/2004-1025-01-DG
Last checked: 03/05/2014
Access conditions: Item is within a limited-access website
https://dl.sciencesocieties.org/publications/ats/pdfs/1/1/2004-1025-01-DG
Last checked: 03/05/2014
Requires: PDF Reader
Access conditions: Item is within a limited-access website |
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