DSSHerbicide: Weed control in winter wheat with a decision support system in three South Baltic regions – Field experimental results

Abstract DSSHerbicide Germany and DSSHerbicide Poland are two decision support systems (DSS) developed for weed control in winter wheat in Northern regions of Germany and Poland. DSSHerbicide is based upon an existing Danish DSS, Crop Protection Online (CPO). Herbicide recommendations from DSSHerbicide are based on efficacy estimates from a dose–response model and required weed control levels defined by expert evaluations and practical experience. DSSHerbicide was parameterised for 22 commonly occurring weed species and 48 herbicides in Poland and for 23 weed species and 32 herbicides in Germany. Validation trials were conducted in Poland, Germany and Denmark primarily in private farmer's winter wheat fields. This enabled a comparison between the herbicide use with standard recommendations and with the existing Danish CPO and the German and Polish DSSHerbicide. Differences among herbicide applications were analysed based on the treatment frequency index (TFI, relation of actually applied dose to label rate) and the costs of herbicide treatments. Recommendations from DSSHerbicide resulted in lower herbicide use than standard recommendations from advisory services in Germany and Poland. TFI of autumn applications from DSSHerbicide recommendations were lower than standard recommendations. The spring application, however, did not differ among herbicide treatments. Yield and weed coverage, estimated by visual observations, were measured at harvest. The TFI of recommendations from DSSHerbicide, measured over the whole growing season, were approximately 20 and 40% lower than standard recommendations in Germany and Poland, respectively. Yields or weed coverage did not differ among sprayed treatments at harvest. All herbicide treatments improved yield and lowered weed coverage compared to controls of no herbicide application. The cost for the chosen herbicides did not differ among treatments, but this is considered a consequence of the limited herbicide choices in these new DSS prototypes. DSSHerbicide provided equally robust recommendations as the existing Danish CPO, and indicated a potential for herbicide reductions in Germany and Poland compared to the reference standard recommendations. As the present DSSHerbicide versions for Germany and Poland are prototypes, fields trials providing data for parameterisation of more weed – herbicide relationships are required to implement additional herbicides in the systems.

[1]  T. Nordblom,et al.  Economics of factor adjusted herbicide doses: a simulation analysis of best efficacy targeting strategies (BETS) , 2003 .

[2]  T. Nemecek,et al.  Is Integrated Weed Management efficient for reducing environmental impacts of cropping systems? A case study based on life cycle assessment , 2012 .

[3]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[4]  Robert E. Blackshaw,et al.  Integrated Cropping Systems for Weed Management , 2009 .

[5]  T. Hothorn,et al.  Simultaneous Inference in General Parametric Models , 2008, Biometrical journal. Biometrische Zeitschrift.

[6]  Alexander R. Martin,et al.  Corn–Velvetleaf (Abutilon Theophrasti) Interference Is Affected by Sublethal Doses of Postemergence Herbicides , 2007, Weed Science.

[7]  K. Neil Harker,et al.  Recent Weed Control, Weed Management, and Integrated Weed Management , 2013, Weed Technology.

[8]  E. Matyjaszczyk Plant protection in Poland on the eve of obligatory integrated pest management implementation. , 2013, Pest management science.

[9]  P. Rydahl,et al.  Four years validation of decision support optimising herbicide dose in cereals under Spanish conditions , 2014 .

[10]  Claudia Sattler,et al.  Assessing the intensity of pesticide use in agriculture , 2007 .

[11]  David J. Parsons,et al.  Using stochastic dynamic programming to support weed management decisions over a rotation , 2009 .

[12]  O. M. Bøjer,et al.  Decision Support System for Optimized Herbicide Dose in Spring Barley , 2014, Weed Technology.

[13]  Giuseppe Zanin,et al.  GESTINF: A decision model for post-emergence weed management in soybean (Glycine max (L) Merr) , 1997 .

[14]  P. Rydahl A web-based decision support system for integrated management of weeds in cereals and sugarbeet* , 2003 .

[15]  J. Cooper,et al.  Integrated Pest Management – Can it Contribute to Sustainable Food Production in Europe with Less Reliance on Conventional Pesticides? , 2012 .

[16]  Marta Monjardino,et al.  Multispecies resistance and integrated management: a bioeconomic model for integrated management of rigid ryegrass (Lolium rigidum) and wild radish (Raphanus raphanistrum) , 2003, Weed Science.

[17]  L. Andersson Characteristics of seeds and seedlings from weeds treated with sublethal herbicide doses , 1996 .

[18]  ANDREW C. BENNETT,et al.  HADSS™, Pocket HERB™, and WebHADSS™: Decision Aids for Field Crops1 , 2003, Weed Technology.

[19]  P. Kudsk Optimising herbicide dose: a straightforward approach to reduce the risk of side effects of herbicides , 2008 .

[20]  M. Liebman,et al.  Theoretical and practical challenges to an IPM approach to weed management , 2000, Weed Science.

[21]  R. Blackshaw,et al.  Effects of Variable Tralkoxydim Rates on Wild Oat (Avena fatua) Seed Production, Wheat (Triticum aestivum) Yield, and Economic Return1 , 2003, Weed Technology.

[22]  S. Mathiassen,et al.  Intraregional and inter‐regional variability of herbicide sensitivity in common arable weed populations , 2015 .