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Precision Ag: WEATHER FORECASTING

Precision Ag: WEATHER FORECASTING. Shawna Loper. Weather Info: New Input. New Tools Automated weather stations Precision forecasting systems Climatological archives Simulated production models. User of Forecast Program . Automated Weather Station.

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Precision Ag: WEATHER FORECASTING

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  1. Precision Ag: WEATHER FORECASTING Shawna Loper

  2. Weather Info: New Input • New Tools • Automated weather stations • Precision forecasting systems • Climatological archives • Simulated production models

  3. User of Forecast Program

  4. Automated Weather Station • An automatic weather station (AWS) is an automated version of the traditional weather station, either to save human labor or to enable measurements from remote areas. The system may report in near real time or save the data for later recovery. • In OK- Mesonet

  5. Precision Forecasting Systems • Input data includes: • National Weather Service (NWS) • Global Forecast System (GFS) • ETA models • The NWS MAV and MEX MOS • European Forecast Center's ECMWF • Climatology and observational data.

  6. Climatological Archives • Small farmers have watched the weather for years • Built a knowledge base of historical climate data

  7. Simulated Production Models • Studying crop response to changes in: • Genotype • Cultivar • Soil • Weather • Climatic patterns • Management practices • Vulnerability of crops to weather events

  8. Temperature • Growing Degree Days (GDD) • Crop Growth • Insects • Stress • Soil Temperature

  9. Moisture • Rain important • Intensity • Duration • Crop Diseases • Drying Time

  10. Frost or Freeze • Crop Damage

  11. Wind • Pesticide Application • Direction • Speed

  12. Irrigation • Anticipate Water Needs • Scheduling • Evaporation rates

  13. HARVIST SystemHeterogeneous Agricultural Research Via Interactive, Scalable Technology • Incorporate Data from: • Remote sensing instruments, weather ground stations, historical crop yields • Generate Accurate Predictions • Uses classification, clustering, and prediction methods • Can predict crop yield and understand interaction between two variables

  14. Conclusion • The use of precision agriculture in weather forecasting can help farmers make more accurate management decisions, as well as give them economic benefits

  15. Questions ?

  16. References E:\weather\Agricultural Weather Services.htm http://books.google.com/books?vid=ISBN0792362705&id=9S63eJ-0f8UC&pg=PA194&lpg=PA194&ots=ayirNbi7mv&dq=precision+forecasting+systems+in+agriculture&sig=xbo9ngCqX8yprzr_Xnh6U46wlLk#PPA135,M1 http://daac.gsfc.nasa.gov/intelligent_archive/VirtualFarms_v1.pdf http://books.nap.edu/openbook.php?record_id=5491&page=17 http://gis2.esri.com/library/userconf/proc00/professional/papers/PAP601/p601.htm http://journalseek.net/cgi-bin/journalseek/journalsearch.cgi?field=issn&query=1385-2256 http://www.google.com/search?q=use+of+weather+forecasting+in+precision+ag&hl=en&rls=SUNA,SUNA:2006-03,SUNA:en&start=20&sa=N http://science.nasa.gov/headlines/y2001/ast23may_1.pdf http://www.twdb.state.tx.us/publications/newsletters/e-news/062806.htm

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