Using precision farming technology to quantify yield effects due to weed competition and herbicide application
- Publication Type
- Journal contribution (peer reviewed)
- Authors
- Gerhards, R. and Gutjahr, C. and Weis, M. and Keller, M. and Sökefeld, M. and Möhring, J. and Piepho, H.P.
- Year of publication
- 2012
- Published in
- Weed Research
- Band/Volume
- 52/1
- DOI
- 10.1111/j.1365-3180.2011.00893.x
- Page (from - to)
- 6--15
Field experiments using Precision Farming technology and Geographic Information System, following a so-called Precision Experimental Design, were conducted in maize, winter barley and winter wheat and compared with two randomized plot experiments in maize to quantify yield effects due to weed competition and weed control. Fields were divided into cells and weed densities for all weed species, soil conductivity and grain yield were measured in each cell. Untreated plots and herbicide treatments against grass-weeds or broadleaved weeds were included in all three experiments. Chenopodium album L, Polygonum ssp. and Echinochloa crus-galli L. were the dominating weed species in maize. Stellaria media (L.) Vill., Veronica hederifolia L., Matricaria chamomilla L., Alopecurus myosuroides Huds. and Galium aparine L. were the most abundant weed species in the winter barley and winter wheat fields. All species were distributed heterogeneously within the fields with densities ranging from 0 to more than 200
plants m-2. In the Precision Experimental Design, it was found that grass-weed competition and herbicide application had a significant effect on grain yield using a linear mixed model with spatial correlation structure to determine the effects of groups of weed species, soil variability and herbicide application on grain yield separately. When a conventional plot experiment was set up in the same field, no statistically significant grain yield difference between the treatments was found. The results highlight the benefits of Precision Experimental Design for studying weed-crop competition. Data can be used to calculate yield loss functions for groups of weed species and to create a decision support system for site-specific weed control.