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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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Presentation Transcript
weather info new input
Weather Info: New Input
  • New Tools
    • Automated weather stations
    • Precision forecasting systems
    • Climatological archives
    • Simulated production models
automated weather station
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
precision forecasting systems
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.
climatological archives
Climatological Archives
  • Small farmers have watched the weather for years
  • Built a knowledge base of historical climate data
simulated production models
Simulated Production Models
  • Studying crop response to changes in:
    • Genotype
    • Cultivar
    • Soil
    • Weather
    • Climatic patterns
    • Management practices
  • Vulnerability of crops to weather events
temperature
Temperature
  • Growing Degree Days (GDD)
    • Crop Growth
    • Insects
    • Stress
    • Soil Temperature
moisture
Moisture
  • Rain important
    • Intensity
    • Duration
  • Crop Diseases
  • Drying Time
frost or freeze
Frost or Freeze
  • Crop Damage
slide11
Wind
  • Pesticide Application
    • Direction
    • Speed
irrigation
Irrigation
  • Anticipate Water Needs
  • Scheduling
  • Evaporation rates
harvist system heterogeneous agricultural research via interactive scalable technology
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
conclusion
Conclusion
  • The use of precision agriculture in weather forecasting can help farmers make more accurate management decisions, as well as give them economic benefits
references
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