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Predictable Yields Using Remote and Field Monitoring
The document discusses the integration of remote sensing and field monitoring techniques to optimize irrigation management and improve crop yield, particularly for potatoes. A case study outlines the challenges faced in predicting yield and managing water stress, revealing limited correlation between satellite data and actual yield. Future efforts aim to expand measurement capabilities and refine data collection methods to enhance the predictive accuracy of irrigation practices.
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Predictable Yields Using Remote and Field Monitoring
- 2. 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
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- 4. MANAGING IRRIGATION WHEN TOTURN 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?
- 5. CASE STUDY–YEAR 1 VARIABLERATE IRRIGATION PRESCRIPTION Create a systematic approach to developing VRI prescription maps that improve water and energy use in an economical way
- 6. PROBLEM More complicated thanexpected • 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)
- 7. RESEARCH SITE Site Center pivotvariable 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.
- 8. SATELLITE Source Physical AnalogSensing 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.
- 9. QUICK FIELD DEPLOYMENT Sensor auto-recognition Bluetoothsmartphone setup Integrated solar panel GPS location data Cloud-enabled via cellular network
- 10. INSTRUMENTATION ZL6TEROS 21 TEROS12ATMOS 41 IRT
- 11. SITE INFORMATION YEAR 1 FieldSites (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
- 12. SENSOR INSTALLATION Auger 4ft. 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.
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- 16. 2018 RESULTS:SINGLE FIELD SOILWATER CONTENT (18 in.)
- 17. 2018 RESULTS:SINGLE FIELD MATRICPOTENTIAL (18 in.) Matric Potential (kPa) -5 to -100 Plant optimal -100 to -1500 Stress < -1500 Permanent wilt
- 18. SITE STRESS EFFECTS ONYIELD 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
- 19. SATELLITE PREDICTION OF YIELDFROM WATER POTENTIAL WEAK BUT SIGNIFICANT CORRELATION Yield(Mg/ha) Matric Potential Delta
- 20. YEAR 2 CAN WEEXPAND 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
- 21. 2019 SITE SELECTION Insitu 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
- 22. SOIL VOLUMETRIC WATERCONTENT Vine kill
- 23. IN SITU SOILMATRIC POTENTIAL Vine kill Matric Potential (kPa) -5 to -100 Plant optimal -100 to -1500 Stress < -1500 Permanent wilt
- 24. TARGET RANGE Vine kill MatricPotential (kPa) -5 to -100 Plant optimal -100 to -1500 Stress < -1500 Permanent wilt
- 25. 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
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- 27. OVERALLYIELD CONNECTIONS TO SATELLITE Notmuch correlation between yield and satellite derived NDWI Yield data from seven fields shows no clear patterns
- 28. GROWER FEEDBACK Looked atdata 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
- 29. SUMMARY Picking single measurementsite 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.
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