Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/8/2008
Publication Date: 8/8/2008
Citation: Forcella, F., Gramig, G.G., Spokas, K.A., Bredeson, C. 2008. Critical plant life-history trait linkage to weather, management, and clients [abstract]. The Ecological Society of America. Paper No. P9091.
Technical Abstract: We developed software that contains an assortment of self-contained and linked models whose ultimate purpose is to predict the timing and extent of weed seedling emergence in crop-based agroecosystems. These predictions aid farmers, crop advisors, extension educators, and agrichemical industry personnel in determining the need and proper time to initiate weed control. The predictions are equally useful for clients interested in organic weed management as for those inclined towards chemically-based weed control. The basis of the software is a soil microclimate simulation tool, STM, that estimates hourly and daily values of soil temperature and soil water potential for each cm of soil depth from the surface to 18 cm deep based upon user-supplied daily air temperature and rainfall data. STM is connected to SeedChaser, a matrix model that estimates depth distributions of weed seeds on a cm by cm basis according to a sequence of any of several tillage and planting operations selected by the user. Specific submodels for each weed species calculate daily seed germination and seedling growth as functions of soil hydrothermal time and soil thermal time, respectively, at all depths where seeds or growing points of seedlings reside. Species-specific burial depth tolerances are used to adjust proportions of successfully emerged seedlings. Calculations are updated daily, and seedling emergence is reported as the daily cumulative percentage of the maximum expected value. Six species were included in the original software (Abutilon theophrasti, Avena fatua, Chenopodium album, Datura stramonium, Senecio vulgaris, and Setaria faberi), representing small- and large-seeded species, early- and late-germinating species, and grass and broadleaf species. Additional species can be added easily by users to the software. The software was completed only in mid 2008, but some of the component models have been available since 2006. A wide diversity of (unexpected) clients are using these models, ranging from molecular biologists at the University of Chicago (adaptation of Arabadopsis populations to light quality); Percival Scientific, Inc. (regulation of growth chamber controllers to mimic user-selected local microclimates); and the Solar Division of British Petroleum (worldwide placement of solar panels). More expectedly, the software also is being used by weed scientists and weed ecologists in Europe and North America.