How Do You Evaluate Precision Ag Strategies On Farm? Lessons Learned from the GFO Precision Ag Project

Transcription

1 How Do You Evaluate Precision Ag Strategies On Farm? Lessons Learned from the GFO Precision Ag Project Ben Rosser Corn Specialist, OMAFRA Nicole Rabe Land Resource Specialist, OMAFRA

2 Project Scope: 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 This project was funded in part through Growing Forward 2, a federalprovincial-territorial initiative. The Agricultural Adaptation Council assists in the delivery of Growing Forward 2 in Ontario.

3 Total of fields (constant), 3 year study ( ) ~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 )

4 Precision Ag in a nutshell: Y=f(x,c,z) 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) 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 2008 Wheat 2009 Corn 2011 Wheat 2012 Corn Historical Yield based Management Zones 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)

6 additions: - Delta cleaning tool - Elevation & Topographic analysis tools to create landform classes Research Crop Portal 4 Landforms Red = Tops of knolls Green = depressions

7 Difference Between Over and Under Performing Cells 0-20% 0-40% 0-70% 0-100% Yield Performance per Landform Over and Under Performing Gaps For 4 Landform Classes $ $ $ $ Yield performance is consistent across the full distribution of yield. Landform 3 always outperforms (in the data we have collected so far) Knolls Upper Sideslopes Lower Sideslopes Depressions Percentage of Yield Points Slide courtesy of: Dr. Mike Duncan, NSERC Prec Ag Research Chair, Niagara College

8 Other spatial data layers collected on each field 7 Year - Yield Potential Index (YPI) Elevation: Topographic Wetness Potential Highest producing areas Middle Lowest producing areas Electrical Conductivity UAV Natural Colour Image July 2016 Proxy for Soil Texture Baseline Soil Chemistry Directed 1 ac grid 8

9 Validating Precision Ag Strategies

10 Validating Precision Ag Strategies VR Soybean Population 2016 Strip Trial Examples Variable Rate Nitrogen

11 2016 Learning Stamp Example Prescription Maps Yield Potential Index based so far As-Applied Verification of Equipment Cleaned Yield Data 11

12 The dilemma of incorporating as-applied data and learning stamps or blocks Points Data representation, block orientation, delays, offsets, and equipment footprint? Smart Rectangles

13 Size of blocks v.s. replication Simple Block Fully automated randomized and replicated 60ft aligned grid 5 acre blocks 180x180 ft blocks = yld points

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 Corn Population Validation: Corn Population Trial: Port Perry - Strip Test Strips - 28, 32 and 36K/ac

16 Corn Population Validation: 1 Rep of High Yield Zone Response Corn Population Trial: Port Perry 1 Rep of Med Yield Zone Response - Strip Test Strips - 28, 32 and 36K/ac 1 Rep of Low Yield Zone Response

17 Corn Population Validation: Corn Population Trial: Port Perry - Strip Test Strips - 28, 32 and 36K/ac

18 Corn Population Validation: Corn Population Trial: Guelph - Rate Blocks - 28, 32 and 36K/ac

19 Corn Population Validation: Corn Population Trial: Guelph - Rate Blocks - 28, 32 and 36K/ac

20 Corn Population Validation: Soil Conductivity Readings - Often correlated to yield - Sometimes positive - Sometimes negative Low conductivity High conductivity

21 Corn Population Validation:

22 Corn Population Validation: - Sufficient separation in rates important! - 32K, 34K, 36K vs. 25K, 30K, 35K

23 Corn Population Validation: - Sufficient separation in rates important! - 32K, 34K, 36K vs. 25K, 30K, 35K Hooker and Stewart, 2009

24 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

25 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

26 Corn Nitrogen Validation: Low Yield High Yield

27 Corn Nitrogen Validation: Rep 1 Rep 2 Rep 3

28 Corn Nitrogen Validation: Rep 1 Rep 2 Rep 3

29 Corn Nitrogen Validation: Rep 1 Rep 2 Rep 3

30 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

31 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) EC (deep) Related to parent material n/a 7% n/a n/a 70% As EC decreases across all N rates - yield decreases 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

32 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:

33 Acknowledgements Ian McDonald (Crop Innovation Specialist) Ken Janovicek (UofG Research Assistant) Thank-you! More information on the project: