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SmartIrrigation Apps: Ideas in Progress

SmartIrrigation Apps: Ideas in Progress. K.W. Migliaccio, G. Vellidis, C. Fraisse , K.T. Morgan, J. Andreis, D. Rowland, L. Zotarelli. www.smartirrigationapps.org SECC Meeting November 2013. Goals. Dynamic thinking Professional products Build on strengths – expertise

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SmartIrrigation Apps: Ideas in Progress

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  1. SmartIrrigation Apps: Ideas in Progress K.W. Migliaccio, G. Vellidis, C. Fraisse, K.T. Morgan, J. Andreis, D. Rowland, L. Zotarelli www.smartirrigationapps.org SECC Meeting November 2013

  2. Goals • Dynamic thinking • Professional products • Build on strengths – expertise • Become a source of apps for southeast – not just irrigation • Long term sustainability

  3. Motivating factors: irrigation apps Need • Irrigation is a large user of groundwater and surface water (60% worldwide of fresh water – USGS) • Demand for fresh water resources expected to increase Knowledge and information • Commodity specific irrigation research • Availability of weather data • Drought adaptation strategies • Climate change and variability Implementation opportunity • Computing power – dynamic possibilities • Availability and widespread use of technology

  4. Irrigation scheduling • Provide the most accurate, site-specific, real-time and forecast information • Minimum user input • Ready to use output provided • Notify user as needed, no requirements of user to consistently remember to check • Engaging, not static

  5. Four Phases • Phase I: Develop with stakeholder groups citrus, cotton, strawberry, urban turf irrigation apps • ET based irrigation schedule • Phase II: Use apps with demonstration sites • Assess different rainfall (R) sources to site measured data • Add drought strategy component • Possibly add R to irrigation schedule • Phase III: Test apps with replicated field trials and add peanut app • Phase IV: Add avocado, tomato, and lettuce app

  6. Phase I: 2011-2015 • NIWQ grant • Develop 4 new irrigation apps • Citrus, Strawberry, and Urban Turf have been reviewed by stakeholder groups and are available at app stores • Cotton app is in development with potential release in 2014 • Each app is unique to the commodity

  7. Basis of irrigation schedule • Weather data from FAWN and GAEMN • Citrus, strawberry, and urban turf • Uses average FAO PM ET with Kc from previous 5 days, updates every 15 days

  8. Citrus app • Irrigation system: micro sprinkler • Tree row distances, emitter characteristics, soil type, irrigation depth, trigger depth • Irrigation delays for rainfall amounts (days) • Irrigation schedule (minutes) every so many days • User can select the day of week to receive irrigation notifications

  9. Citrus screenshot

  10. Strawberry app • Irrigation system: drip • Between-row, planting date, harvest date, irrigation rate, efficiency • Irrigation schedule (minutes/hrs) and degree days accumulated for everyday irrigation

  11. Urban turf app • Irrigation system: sprinkler heads • Soil type, root depth • Micro sprinkler, spray, multi-stream spray, gear driven rotors, impacts • Days of week to irrigate • Irrigation schedule in minutes considering number of irrigation events per week • Notifications used to adjust irrigation schedules due to rainfall

  12. Evaluation of previous 5 day approach

  13. Cotton app • Irrigation application rate • Plant phenology and crop coefficient (Kc) change with accumulated heat units (GDDs) • User can override GDD-driven phenology • Does not recommend irrigation amounts • Advises user of available soil water and stress threshold

  14. Cotton app • Uses real-time rain data from FAWN and GAEMN • A daily water balance approach: allow for R to be changed and I to be input

  15. Forecast data • National Weather Service data: temperature, relative humidity, wind speed, probability of rain • Current conditions • Forecast by hour for next 11 hrs • Forecast by day for next 5 days

  16. Average water savings 36% Test forecast for turf app

  17. Phase II: Demonstration sites, rainfall, drought strategies 2013-2016 • CIG funded • Demonstration sites • Turf: Homeowners, HOA • Citrus: Florida • Cotton: Georgia • Strawberry: Florida

  18. Rainfall data • FAWN & GAEMN • NWS 4 km grid data • Commercial providers (RainWave, IBM) • Site measurement using tipping buckets (Onset rain gauge tipping bucket)

  19. Questions • Does one source of data provide more accurate rainfall data consistently than another considering amount and event occurrence? • Does the irrigation schedule generated including the most accurate rainfall data vary significantly from that which only considers ET? • What is feasible considering data processing and integration, costs, benefits?

  20. Homestead sites

  21. Rainwave (3 sites)

  22. Drought adaptation/strategies • Turf app • User selected wet season irrigation deficit • Irrigate to 80% of field capacity • Peanut and cotton apps • Drought adaptation • Early stress of plants (using deficit irrigation)

  23. Phase III: Replicated field studies 2013-2016 • NIWQ • Example: Urban turf app • 4 treatments: ¾ in twice a wk, app based irrigation, ET controller 1, and ET controller 2 • Replicated 4 times • St. Augustine

  24. Study site

  25. Additional measurements • Soil water sensors • Rain gauge / tipping bucket • Turf quality assessments

  26. Phase IV: Additional apps 2013-2015 • FDACS Block Grant • Funding starts in November 2013 • Similar approach • Add avocado, cabbage, and tomato apps

  27. Ideas in progress • Analysis and integration of real-time rainfall • Integration of user selected drought strategies • Forecast ET data • Compare to using 5-day average for developing irrigation schedule • Not yet available, Spring 2014

  28. Acknowledgements • USDA NIFA NIWQ • USDA NIFA CIG • FDACS BLOCK Grant • FAWN and GAEMN • NWS • SECC More information Website: http://smartirrigationapps.org/ Email: klwhite@ufl.edu Twitter @TRECwater

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