|Young, Douglas - WASHINGTON STATE UNIV|
|Kwon, Tae - KOREA RURAL ECO INSTITUTE|
|Smith, Elvin - AG & AGRI FOOD CANADA|
Submitted to: Precision Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 22, 2002
Publication Date: July 1, 2003
Repository URL: http://hdl.handle.net/10113/43136
Citation: YOUNG, D.L., KWON, T.J., SMITH, E.G., YOUNG, F.L. 2003. SITE-SPECIFIC HERBICIDE DECISION MODEL TO MAXIMIZE PROFIT IN WINTER WHEAT. PRECISION AGRICULTURE. 4:227-238. Interpretive Summary: In the Pacific Northwest (PNW), over 80% of the wheat acreage is treated with an herbicide for weed control. In contrast, 30 to 50% of the wheat acreage is treated in other areas of the United States. Winter and spring annual grasses are generally the most problematic and expensive to control in the PNW and growers normally apply the same rate of herbicide over a whole field or several fields. PALWEED: WHEAT II used in this study is a revised bio-economic weed-decision model. It is based upon field testing that leaves final decisions about the specific herbicide (within groups) used and corresponding price costs to the grower. The grower-decision trait of the model is desirable, given the weed diversity in the PNW. In our study, model recommendations were compared to farmers' and professionals' weed control recommendations. The model increased broadleaf herbicide rates compared to competing recommendations but reduced the use of the expensive grass herbicides. Based on this study, the model has the potential to substantially increase profits and is user-friendly. The model's projected profitability advantages could easily offset the estimated cost for gatherine information required for the model.
Technical Abstract: This paper describes a user-friendly computerized decision model for selecting profitable site-specific herbicide applications in winter wheat. The model is based on six years of field research in southeastern Washington State, USA. The model calibrates herbicide applications to weed densities, soil properties, and preceding management, as well as to expected input and output prices. The model increased broadleaf herbicide rates by an average of 0.65 label rates compared to the recommendations by farmers and weed science professionals, but cut the more expensive grass herbicides by an average of 0.56 label rates. The model increased average projected profitability, excluding model application costs, by 65 percent. Both the model and the cooperating farmers properly chose no grass herbicides for the study sites, but weed science experts chose up to 1.0 label rates. The estimated payoff from using the model substantially exceeded the cost of weed scouting and other information collection. Determining economically optimal sampling and management units is an important challenge for precision agricultural models like this one.