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Modeling Weed Distribution for Improved Postemergence Control Decisions

Published online by Cambridge University Press:  12 June 2017

Lori J. Wiles ,
Gail G. Wilkerson ,
Harvey J. Gold  and
Harold D. Coble
Lori J. Wiles
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
Gail G. Wilkerson
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
Harvey J. Gold
Affiliation:
Dep. Statistics, North Carolina State Univ., Raleigh, NC 27695
Harold D. Coble
Affiliation:
Dep. Crop Sci., North Carolina State Univ., Raleigh, NC 27695
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Abstract

Broadleaf weeds apparently have patchy distributions within a field while POST control decisions are made assuming a regular spatial distribution. As a result, yield loss from weed competition may be overestimated, possibly leading to mistakes in choosing the optimal control treatment. Data on distribution of broadleaf weeds in 14 soybean fields were used in simulation experiments to investigate the potential for improving decision making with information about weed patchiness. The feasibility of modeling weed distribution in individual fields was also examined. Overall, the cost of assuming a regular distribution when making POST decisions was found to be low. Errors that occurred most often involved recommending more intensive control than was actually required, although in a few cases less intensive control was recommended. Error in the yield loss estimated for the uncontrolled population did not indicate the potential for a mistake in decision making for a field. Accurately modeling distribution of weeds within fields may be difficult as a result of correlations between distributions of individual species within a field and variation in distributions between fields.

Keywords

SoybeanGlycine max (L.) MerrSimulationdecision modelsweed managementweed competitionGlycine max
Type
Weed Biology and Ecology
Information
Weed Science , Volume 40 , Issue 4 , December 1992 , pp. 546 - 553
Copyright
Copyright © 1992 by the Weed Science Society of America 

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