TABLE CONTENTS
How different or similar are nematode communities - International ...
How different or similar are nematode communities - International ...
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
Practical Site-specific Nematicide Delivery on Cotton Farms in the Mid-<br />
South USA<br />
Monfort, W.S. (1), T.L. Kirkpatrick (2), A.H. Khalilian (3) & J.D. Mueller (3)<br />
(1) University of Arkansas, Lonoke Extension Center, Lonoke, AR 72086, USA; (2) University of Arkansas,<br />
Southwest Research and Extension Center, Hope, AR 71801, USA; (3) Clemson University, Edisto Research<br />
and Education Center, Blackville, SC 29817, USA.<br />
Cotton growers in the southern U.S. routinely include the application of either 1,3-<br />
dichloropropene (Telone II) or aldicarb (Temik) in their farming operations for nematode<br />
management. The most economically important nematodes in cotton are Meloidogyne<br />
incognita and Rotylenchulus reniformis. Nematicide applications are made at a single rate on<br />
a whole-field basis, and decisions to apply these chemicals are usually based on assay of a<br />
single composite soil sample arbitrarily collected from each field. However, nematode<br />
distributions and associate damage are not typically uniform in most fields. Soil texture is a<br />
key factor influencing the reproductive success and damage potential of these nematode<br />
species and on the crop. An understanding of soil textural changes within individual fields<br />
could allow more efficient sampling for nematode detection and quantification and provide a<br />
platform for determining strategies for site-specific nematicide placement. In field<br />
investigations (2005 & 2007), mobile soil electrical conductivity (EC) meters (Veris<br />
Technologies) were used to estimate and map site-specific soil textural variations within a<br />
production field in Northeast Arkansas. The soil EC maps were used to identify soil textural<br />
regions (zones) that were sampled individually for nematodes. Nematode population<br />
densities from each zone were then compared with known damage threshold values, and<br />
prescription application maps for site-specific placement of 1,3-dichloropropene were<br />
developed. Site-Specific applications of 1,3-dichloropropene were compared to an untreated<br />
control and a single 3 gal/acre rate of 1,3-dichloropropene. Results in this field indicated that<br />
cotton yield was comparable in zones receiving site-specific nematicide application to that<br />
where a single rate was applied, but 37 to 42 percent less chemical was applied with the sitespecific<br />
approach. Both approaches resulted in significantly greater yield than where no<br />
nematicide was applied.<br />
Considering Field Physical Characteristics in Assessing Risk and<br />
Delineating Nematode Management Zones<br />
Davis, R.F. (1), B.V. Ortiz (2), C. Perry (2), D. Sullivan (3), B. Kemerait (4), G. Vellidis (2)<br />
& K. Rucker (5)<br />
(1) USDA-ARS, CPMRU, Tifton, GA 31793, USA; (2) Dept. of Biological and Agricultural Engineering, Univ.<br />
of Georgia, Tifton, GA 31793, USA; (3) USDA-ARS, SEWRU, Tifton, GA 31793, USA; (4) Dept. of Plant<br />
Pathology, Univ. of Georgia, Tifton, GA 31793, USA; (5) Univ. of Georgia, Cooperative Extension Service,<br />
Tifton, GA 31793, USA.<br />
Site-specific management (SSM) of nematodes requires identifying factors affecting<br />
nematode distribution, nematode population density, and nematode-induced yield losses, and<br />
then using that information to predict where nematode management will cost-effectively<br />
reduce yield loss. Using cotton (Gossypium hirsutum) as a model system, we accomplished<br />
this by 1) using multiple regression analysis to evaluate the relationship between cotton yield,<br />
soil physical and chemical properties, and southern root-knot nematode (RKN, Meloidogyne<br />
incognita) population density to identify factors most strongly affecting yield; 2) using the<br />
most important factors affecting yield to create a logistic regression model which predicts the<br />
5 th International Congress of Nematology, 2008 136