Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

17. Precision Farming Realities - Nicole Rabe & Ben Rosser

125 vues

Publié le

What works and what doesn’t! The latest on precision agriculture research and practical findings in Ontario.

  • Soyez le premier à commenter

  • Soyez le premier à aimer ceci

17. Precision Farming Realities - Nicole Rabe & Ben Rosser

  1. 1. Ben Rosser Corn Specialist, OMAFRA Nicole Rabe Land Resource Specialist, OMAFRA How Do You Evaluate Precision Ag Strategies On‐Farm? Lessons Learned from the GFO Precision Ag Project
  2. 2. Co-operator yield data submitted + collect other base data layers to fill gaps Goals: wireless data transfer & analyze data layers with transparent mathematics for teaching farmers Rx maps: implemented with validation built in & industry support Project Scope: This project was funded in part through Growing Forward 2, a federal- provincial-territorial initiative. The Agricultural Adaptation Council assists in the delivery of Growing Forward 2 in Ontario.
  3. 3. • ~50 acres committed to a full rotation (corn, soybeans, wheat) • good drainage • average to medium base levels P & K • Manure history: project would have to document & monitor for impacts • Farmer had to have VR equipment for at least 1 project operation (seed or fertilizer ) Total of 20-25 fields (constant), 3 year study (2015-2017)
  4. 4. Precision Ag in a nutshell: • Yield (y) results from natural processes described by f: • The function is made up of : – things that the farmer does control = x (e.g. seed / fertilizer type, source, rate etc) – field characteristics = c that a farmer does not control and they vary spatially (e.g. soil type, topography – slope) – vector z - the farmer does not control & this varies temporally (principally weather variables) Y=f(x,c,z) So far the case studies explored here are missing a couple of field characteristics (C) (e.g. soil chemistry, landforms) & weather (z) was not incorporated into variable rate prescriptions Conceptual formula courtesy: Dr. David Bullock, Ag Economist, Ohio State University
  5. 5. Historical Yield based Management Zones • 2008 Wheat • 2009 Corn • 2011 Wheat • 2012 Corn • 2014 Wheat • 2015 Corn • Project started with yield data acknowledging most farmers would have some sitting on a drive in office somewhere • Research Crop Portal: – includes fully and semi automated cleaning tools for yield data – transparent math to relay the message that maps aren’t pretty pictures! • Yield Potential Index (YPI): best to work with single crops over time (e.g. 3yrs corn, 3yrs wheat, 3yrs of soybeans) – pairing corn and wheat maintains consistent zone geometry – soybeans do not have same yield response distribution (likely to due to disease) http://cropportal.niagararesearch.ca/
  6. 6. Research Crop Portal- 2017 additions: - Delta cleaning tool - Elevation & Topographic analysis tools to create landform classes 4 Landforms Red = Tops of knolls Green = depressions
  7. 7. -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0-20% 0-40% 0-70% 0-100% DifferenceBetweenOverandUnderPerformingCells Percentage of Yield Points Over and Under Performing Gaps For 4 Landform Classes Knolls Upper Sideslopes Lower Sideslopes Depressions Yield performance is consistent across the full distribution of yield. Landform 3 always outperforms (in the data we have collected so far) $ $ $ $ Yield Performance per Landform Slide courtesy of: Dr. Mike Duncan, NSERC Prec Ag Research Chair, Niagara College
  8. 8. 8 Elevation: Topographic Wetness Potential7 Year - Yield Potential Index (YPI) UAV Natural Colour Image July 2016 Electrical Conductivity Proxy for Soil Texture Highest producing areas Middle Lowest producing areas Baseline Soil Chemistry Directed 1 ac grid Other spatial data layers collected on each field…
  9. 9. Validating Precision Ag Strategies
  10. 10. 2016 Strip Trial Examples Variable Rate Nitrogen VR Soybean Population Validating Precision Ag Strategies
  11. 11. 2016 “Learning Stamp” Example 11 Prescription Maps Yield Potential Index based so far… As-Applied Verification of Equipment Cleaned Yield Data
  12. 12. The dilemma of incorporating as-applied data and learning stamps or blocks… Smart Rectangles Points Data representation , block orientation, delays, offsets, and equipment footprint?
  13. 13. Size of blocks v.s. replication 180x180 ft blocks = 170-200yld points Simple Block Fully automated randomized and replicated 60ft aligned grid 5 acre blocks
  14. 14. Did the YPI based management zones show up in both 2015 and 2016? • Seed & Nitrogen Corn trials: on 5 fields zones no SD, 6 fields showed only two distinct zones, and 4 fields showed all three zones were distinct (Type 1 Error: 10%) • VR Soybean Population Trials: on 2 fields zones no SD, 4 fields showed only two distinct zones, and 3 fields showed all three zones were distinct (Type 1 Error: 10%) • Potential Reasons: –not enough historical yield data for reliable zone creation –medium zone stability not well defined in the YPI algorithm –extreme seasonal conditions (dry or wet) –good soil health/type –genetics masks crop response YPI = Yield potential Index SD = statistical difference
  15. 15. Relationships to other data layers? Elevation: Topographic Wetness Potential Yield Potential Index (YPI) Electrical Conductivity
  16. 16. Soil Sensing & Conductivity Readings Low conductivity High conductivity - Often correlated to yield - Sometimes positive - Sometimes negative
  17. 17. What is the value of the other spatial data layers in explaining yield variability? If a farmer doesn’t have good repository of historical yield data then could they start with elevation or soil sensing to develop management zones? • Table below shows 2015 snapshot of nitrogen corn strips trials & the % improvement in explaining yield variability by adding YPI, elevation or electrical conductivity (EC) to the regression model Data Layer Field 1 (Vernon) Field 2 (Ottawa) Field 3 (Hensall) Field 4 (Exeter) Field 5 (Tillsonburg) Notes: YPI 20% 12% 10% 4% 60% Yield increases as YPI increases Elevation 22% 12% 1% n/a 43% Highest yields associated with mid-regions EC (shallow) n/a 7% n/a n/a 70% As EC decreases across all N rates - yield decreases EC (deep) Related to parent material 21% 7% n/a n/a 70% As EC decreases across all N rates - yield decreases Clay loams Clay loam / silt loams Loamy sands / sand
  18. 18. Population Case Studies
  19. 19. 2015 Corn Population Case Study Kenmore, ON • Soil Survey: Bainsville very fine sandy loam (Poor) • Rotation: corn, soybeans • Topography: flat topography, gentle slope
  20. 20. Corn Population Validation: Corn Population Trial: Kenmore - Blocks - 30, 34 and 38K/ac Lack of Replication in All Zones
  21. 21. Corn Population Validation: Corn Population Trial: Kenmore - Blocks - 30, 34 and 38K/ac Average Yield by Zone (2015): Low: 203 bu/ac Medium: 205 bu/ac High: 217 bu/ac 0 50 100 150 200 Low Medium High Yield(bu/ac) YPI Yield Zone 30 34 38 Lack of Replication in All Zones Lack of Replication in All Zones Lack of replication… uncertain if these are true treatment effects 0 200 400 600 800 1000 Low Medium High PartialBudget($/ac) YPI Yield Zone 30 34 38 Seed: $300/80K Corn: $4.50/bu
  22. 22. Corn Population Validation: Corn Population Trial: Port Perry - Strip Test Strips - 28, 32 and 36K/ac
  23. 23. Corn Population Validation: Corn Population Trial: Port Perry - Strip Test Strips - 28, 32 and 36K/ac 1 Rep of High Yield Zone Response 1 Rep of Med Yield Zone Response 1 Rep of Low Yield Zone Response
  24. 24. Corn Population Validation: - Sufficient separation in rates important! - 32K, 34K, 36K vs. 25K, 30K, 35K
  25. 25. Corn Population Validation: - Sufficient separation in rates important! - 32K, 34K, 36K vs. 25K, 30K, 35K Hooker and Stewart, 2009
  26. 26. Corn Population Validation: - Sufficient separation in rates important! - 32K, 34K, 36K vs. 25K, 30K, 35K - Enough rates to make a conclusion - 25K and 35K vs. 25K, 30K, 35K
  27. 27. Corn Population Validation: - Sufficient separation in rates important! - 32K, 34K, 36K vs. 25K, 30K, 35K - Enough rates to make a conclusion - 25K and 35K vs. 25K, 30K, 35K - Consistency of rates across all zones of the field - Shouldn’t prejudge expected optimum rate in each zone
  28. 28. Soybean Population Case Studies
  29. 29. Soil: Fox sandy loam, Honeywood silt loam Rotation: corn & soybeans, some wheat history Tillage: vertical tillage / 1 fall / 1 spring pass Topography: gentle to very strong slopes Yield history: 7 years (80 acres) 22 48 8 0 OM by Texture Score 1 2 3 4 Case Study #2 (Ayr, ON) Case Study #1 (Hensall, ON)Soil: Brookston clay loam, Harriston silt loam Rotation: wheat, corn, soybeans Tillage: no-till Manure History: poultry after wheat Topography: gentle slopes Yield History: 8 yrs (80 acres) 3 37 30 10 OM by Texture Score 1 2 3 4 OM% Min: 2.2 Max: 6.1 Avg: 3.6 OM% Min: 0.6 Max: 3.3 Avg: 2.3 Variability? Management history & soil quality matter!
  30. 30. Variable why? Management history & soil matter Case Study #1 (Ayr, ON) 2015 Soybean population block trial: • High yield zones: average 5bu (120,000 sds/ac) and 25bu (210,000 sds/ac) higher than medium & low zones • BUT most profitable was 120,000 sds/ac (gained $25/ac) • Low yield zones: very light textured soil, yield increased by 11bu/ac ($110/ac) for 190,000sds/ac rate v.s. 120,000 sds/ac (Caution: due to lack of replication – less confidence in statistical differences) Rx Soybean Theory: High yield corn zones get low population due to disease pressure in wetter years. Low yield zones get higher population. (Type 1 Error 10%) Zone Yield (bu/ac) Return ($) High 53.3 $626a Med 28.3 $555b Low 48.1 $288c Does the prescription theory work across regions and years?
  31. 31. 2016 Soybeans: • Low Yield Zone: prescription assumption of increasing seeding rate was incorrect • Profit decreased linearly at a rate of $0.97/ac per 1000 seeds/ac (i.e. $97/ac loss from 100 to 200 thousand seeds/ac) Case Study #2 (Hensall, ON) Zone Yield Return ($) High 65.2 $794a Med 60.5 $730b Low 58.6 $705b (Type 1 Error 10%) - Soybean Price: $13.50/bu - Soybean seed Cost $0.57/1000 seeds
  32. 32. Variable Corn Nitrogen Case Study
  33. 33. 2015 Corn Variable Rate N - Case Study Chesterville, ON • All 3 mng’t zones were present in 2015 (Type 1 Error 5%) • Soil: North Gower (Poor)/ Morrisburg (Well) / Wolford (Imperfect) - clay loams, • Tillage: no-till • Manure: none • Topography: nearly level • Yield History: 3 yrs (2 yrs soybeans, 1 yr corn) • Acres: 84 Zone Avg. Yield (bu/ac) Return ($) High 184.8a $ 767.98a Medium 179.5b $ 744.05b Low 169.4c $ 698.85c
  34. 34. Corn Nitrogen Validation: Low Yield High Yield 5 rates Replicated twice Every yield point matched with corresponding layers (i.e. EC, elevation, YPI etc)
  35. 35. Relationship to other data layers • YPI Zones Grouped: YPI of less than 2 (low) YPI between 2 and 2.7 (Medium) and YPI over 2.7 (high) increased the variability explained by the statistical model from 63% to 75%. • Elevation: increased corn yield variability explained by the statistical model from 63% to 75% • Soil Sensing: EC (both shallow and deep) increased corn yield variability explained from 63% to 70%. – Corn grain yields tended to decrease as electrical conductivity (shallow and deep) decreased – larger decreases occurring where N rates were 67 or 97 lb-N/ac and electrical conductivity (shallow and deep) values were less than 21 (ms/m) Veris – Electrical Conductivity
  36. 36. Corn Nitrogen Results • Corn yields at this trial for all N rates generally increased as YPI increased, and yields decreased as elevation increased (especially over 248 ft) and electrical conductivity decreased (especially below 21 units(ms/m)). • In all cases the corn yield responses to YPI, elevation and electrical conductivity were greatest with the lowest N rate (37 lb-N/ac). • Delta Yield Recommendations: – Based on actual regression curves this site required 26 to 55 lb-N/ac over the base rate of 37 lb-N/ac as YPI decreased from 3.2 to below 1.5 100 120 140 160 180 200 67 97 127 157 187 Yield(bu/ac) Nitrogen (lbs/ac) Yield x Zone x N Rate Low Med High Yields zones statistically distinct, but small differences in optimum N rates by zone
  37. 37. Common Grower Comments With Validation - Zero nitrogen rate prescriptions - Validation blocks are lined up with equipment passes - Rate transitions - Be familiar with prescription setup and loading - Equipment setup for wide range of rates, or adjust speed
  38. 38. Future Work 2018 • Include baseline soil chemistry (directed 1 ac grid) – best interpolation method? • Add topographic derivatives: potential wetness index, landform classes etc. • In-season imagery: include 2017 UAV imagery into the analysis as additional layer of information to explain yield variability • Determine best statistical approach to comparing field trial areas to growers normal practice within a growing season • Relationship to soil health parameters – subset of 10 fields NDVI Red Edge NDVI Green NDVI Acknowledge UAV Partner:
  39. 39. Acknowledgements Ian McDonald (Crop Innovation Specialist) Ken Janovicek (UofG – Research Assistant) Thank-you! nicole.rabe@ontario.ca ben.rosser@ontario.ca More information on the project: http://gfo.ca/Research/Understanding-Precision-Ag

×