Assessing herbicide symptoms by using a logarithmic field sprayer

Research output: Contribution to journalJournal articleResearchpeer-review

Beatriz Ribeiro da Cunha, Christian Andreasen, Jesper Rasmussen, Jon Nielsen, Christian Ritz, Jens Carl Streibig

Background: In field experiments, assessment of herbicide selectivity and efficacy is rarely taking advantage of dose-response regressions. The objective is to demonstrate that logarithmic sprayers, which automatically make a logarithmic dilution of a herbicide rate, can extract biologically relevant parameters describing the efficacy of herbicides in crops, compare localities, and time of assessment.

Results: In a conventional and an organic field, canola, white mustard, and no crop plots were sprayed with diflufenican and beflubutamid. A mixed effect log-logistic dose-response regression, with autoregressive correlation structure, estimated ED50 and ED90, for visual and Excess Green Index symptoms at various Days After Treatment (DAT). For visual assessment, ED50 differed within no crop between locations for beflubutamid at 12 DAT and 26 DAT. For diflufenican, the ED50 was different within crops at the two fields at 12DAT, but not at 26 DAT. The Excess Green Indices at ED50 were not different among herbicides, locations and corps; ED90 differed for white mustard and canola for beflubutamid but not for diflufenican.

Conclusion: Suitable nonlinear regression models are now available for fitting dose-response data from a logarithmic sprayer in field experiments. The derived parameters (e.g., ED50 ) can compare selectivity and efficacy at numerous cropping systems. This article is protected by copyright. All rights reserved.

Original languageEnglish
JournalPest Management Science
Volume75
Issue number4
Pages (from-to)1166-1171
ISSN1526-498X
DOIs
Publication statusPublished - Apr 2019

    Research areas

  • The Faculty of Science - Dose-response, UAV images, Chemical weed control, Mixed models, Autocorrelation
  • autocorrelation, chemical weed control, dose–response, mixed models, UAV images

ID: 204306874