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Predictable Yields Using Remote and Field Monitoring

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What happens when you take satellite products and add soil water potential data?

New data sources offer tools for growers to optimize production in the field. But the task of implementing them is often difficult. Research work is underway and offers a guide on how data from soil and space can work together to make the job of irrigation scheduling easier.

In this presentation, METER’s Dr. Colin Campbell explains the formula for prescribing irrigation events that will get you the yields you want.

Published in: Technology
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Predictable Yields Using Remote and Field Monitoring

  1. 1. PREDICTABLE YIELDS USING REMOTE & FIELD MONITORING Colin S. Campbell1,2, Bryan G. Hopkins3, Neil C. Hansen3, Ryan G. Smith4, Florian Detsch1, Tim Appelhans1, and Ryan Christiansen5 1METER Group, Inc., 2Washington State University, 3Brigham Young University, 4Missouri S&T University, 5BKR Farms
  2. 2. SOUTHERN IDAHO DRY FARM
  3. 3. MANAGING IRRIGATION WHEN TO TURN WATER ON & OFF What we need • Knowledge of if and how much water is available to plants • Information at the same scale as the ability to control the water (# of valves) What we have • Measurements of water in the field • Drone data • Satellite products Next steps • Combine them to extract key information from each, drive overall knowledge Challenge • How are these scales connected and does combining them provide meaningful results?
  4. 4. CASE STUDY–YEAR 1 VARIABLE RATE IRRIGATION PRESCRIPTION Create a systematic approach to developing VRI prescription maps that improve water and energy use in an economical way
  5. 5. PROBLEM More complicated than expected • Significant variation in soil water, ET, stress, crop water productivity Predicted moisture stress did not correlate with yield (from yield mapping system) Yield(Mgha-1) 2016 Average Crop Stress (Ks)
  6. 6. RESEARCH SITE Site Center pivot variable rate irrigation 2018–Seed potatoes 2019–Irrigated wheat 3-year rotation Grower Innovative grower with potato yield monitor and variable rate irrigation system (VRI) Telemetry Data logger 906 = ‘Site 6’ or ‘6’ Data logger 907 = ‘Site 7’ or ‘7’ etc.
  7. 7. SATELLITE Source Physical Analog Sensing Parameter Technical Details NDVI (Landsat 8) Canopy greenness Reflected red and near infrared light Repeat measurements every 12 days. Cloudy scenes removed. Thermal image (Landsat 8) Canopy temperature Emitted surface infrared radiation ~11 um wavelength. Repeat measurements every 12 days. Cloudy scenes removed Radar backscatter (Sentinel-1) Plant and soil moisture Radar backscatter 5.6 cm wavelength. Repeat measurements every 8 days Normalized Difference Water Index (Sentinel-2) Plant moisture Reflected infrared light 5-day repeats, 10 – 60 m resolution. Cloudy scenes removed.
  8. 8. QUICK FIELD DEPLOYMENT Sensor auto-recognition Bluetooth smartphone setup Integrated solar panel GPS location data Cloud-enabled via cellular network
  9. 9. INSTRUMENTATION ZL6TEROS 21 TEROS 12ATMOS 41 IRT
  10. 10. SITE INFORMATION YEAR 1 Field Sites (6 total) Measurements Depths or Heights Trade Name Water content, temp, EC 6,18, 30 in. TEROS 12 Matric potential, temp 6, 18 in. TEROS 21 Canopy temp 6 ft. (3 sites) Infrared Thermometer (IRT) All-in-one weather station 6 ft. (3 sites) ATMOS 41 (Solar, wind, T, RH, pressure, precipitation) Cloud telemetry N/A ZL6
  11. 11. SENSOR INSTALLATION Auger 4 ft. deep x 4 in.wide installation hole (3 minutes;low effort and site disturbance) Durable epoxy and steel needles Tool inserts sensors exactly perpendicular to soil wall Installed at 6, 18, and 30 in.
  12. 12. MOISTURE SENSOR INSTALLATION
  13. 13. MOISTURE SENSOR INSTALLATION
  14. 14. DATA STORAGE AND VISUALIZATION
  15. 15. 2018 RESULTS:SINGLE FIELD SOIL WATER CONTENT (18 in.)
  16. 16. 2018 RESULTS:SINGLE FIELD MATRIC POTENTIAL (18 in.) Matric Potential (kPa) -5 to -100 Plant optimal -100 to -1500 Stress < -1500 Permanent wilt
  17. 17. SITE STRESS EFFECTS ON YIELD 1 Mg/ha = 7.96 cwt/ac Site Days in Stress Yield (Mg/ha) 9 42 31.47 12 53 32.77 10 44 37.44 11 0 39.67 6 0 40.08 7 16 40.33
  18. 18. SATELLITE PREDICTION OF YIELD FROM WATER POTENTIAL WEAK BUT SIGNIFICANT CORRELATION Yield(Mg/ha) Matric Potential Delta
  19. 19. YEAR 2 CAN WE EXPAND EFFORT? 7 fields growing several varieties of potatoes • Cemetery, Max’s Pivot, Barbara’s West Mini, Home Place NW, Home Place SW, Reed’s Middle, and Anderson Pivot Grower-purchased systems • VWC @ 6 and 12 in., matric potential @ 12 in., rain gauge, cloud-connected data logger Limited funds dictated only a single measurement system per field
  20. 20. 2019 SITE SELECTION In situ measurement sites selected by satellite estimation of seasonal wetness across each field Locations for: 1. Average moisture across season 2. Driest moisture location across season
  21. 21. SOIL VOLUMETRIC WATER CONTENT Vine kill
  22. 22. IN SITU SOIL MATRIC POTENTIAL Vine kill Matric Potential (kPa) -5 to -100 Plant optimal -100 to -1500 Stress < -1500 Permanent wilt
  23. 23. TARGET RANGE Vine kill Matric Potential (kPa) -5 to -100 Plant optimal -100 to -1500 Stress < -1500 Permanent wilt
  24. 24. CROP STRESS DAYS BELOW -100 kPa 2018 Potato Field (Variable Rate Irrigation) Site Days in Stress Yield (cwt/ac) 9 42 251 12 53 261 10 44 298 11 0 316 6 0 319 7 16 321 Field Name Days Below -100 kPa Yield at Sensor (cwt/ac) Average Yield at Field (cwt/ac) Difference (cwt/ac) Standard Deviation of Yield (cwt/ac) Cemetery 18 327 282 45.3 58.1 Barbara's West Mini 14 302 318 -16.7 84.4 Max’s Pivot 17 321 290 33.4 78.8 Home Place NW 0 255 248 6.4 55.7 Home Place SW 0 329 270 59.7 58.9 Anderson Pivot 6 Reed’s Middle 21
  25. 25. NDWI CORRELATION WITH POTATO YIELD
  26. 26. OVERALLYIELD CONNECTIONS TO SATELLITE Not much correlation between yield and satellite derived NDWI Yield data from seven fields shows no clear patterns
  27. 27. GROWER FEEDBACK Looked at data on ZENTRA Cloud every day! Much lower water use • Typical year: 20 to 25 in of water • This year: 17.5 to 19 in. of water • Note: cooler year – need verification from ET analysis Potato ‘average’ sites–right on average • Dug potatoes at typically dry spot and typically wet spot along with measured location • Always close to average of the field, like yield map finding
  28. 28. SUMMARY Picking single measurement site with satellite data worked well Grower irrigating to in situ water potential produced great potato yields and seemed to use less water Scaling up using a single season’s satellite data was not successful. More data needed.
  29. 29. QUESTIONS?

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