cleaning yield monitor data for improved accuracy and insights
TRANSCRIPT
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Cleaning Yield Monitor Data for Improved Accuracy and InsightsChad Godsey, PhD
Precision Ag Insight Webinar
Sept. 30, 2015
Sponsored by XSInc
Webcast Format
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Minimized Controls
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Speakers
PresenterDr. Chad GodseyOwner, Godsey Precision Ag
HostBen GistBusiness Development Analyst, XS Inc
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Overview
Importance of Clean Yield Data
Initial Interpretation of Yield Data
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The Importance of “Clean” Yield Data
Make decisions with your yield data using these five steps.
Importance magnified for trials
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Three Steps of Data Collection
Quality data produces quality decisions
1. Calibration
2. Data Management
3. Data Cleaning
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Registration Question
Which best describes how you/your clients clean yield monitor data?
• Do not clean
• ARS Yield Editor tool
• Other Farm Management Software (e.g. SMS, AgStudio, SST Summit etc.)
• A combination of several of these
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Results
Do not clean33%
ARS Yield Editor tool9%
Other Farm Management Software (e.g. SMS,
AgStudio, SST Summit etc.)39%
A combination of several of these
19%
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Yield File “Cleaning”
• Critical step as mentioned previously.
• Especially, for more advanced analysis and on-farm trials.
• A lot of software will just delete points less than or greater than a specified value, while some do nothing without being told.
• Know what your software does automatically or what the defaults are set for.
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Cleaning and filtering Techniques
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Cleaning Flow
• FOViewer (MapShots)
• http://www.mapshots.com/fodm/
• Yield Editor (USDA-ARS)
• http://www.ars.usda.gov/services/software/download.htm?softwareid=370.
FOViewerYield
EditorData MgtSoftware
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Cleaning Summary
1. Protect your valuable information
a. Check your data after the first day of harvesting
b. Back-up every other day
2. Tips for simple, yield cleaning
1. Use FOViewer and Yield Editor
2. Set it and forget it: use standard filter settings for basic cleaning
3. Spend time on detailed cleaning when important
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Registration Question
Which best describes how you/your clients use yield monitor data?
• Look at yield values as I harvest
• Print and examine
• Evaluate average yield against another factor (e.g. yield by soil type, treatment etc.)
• Evaluate using advanced statistics methods (e.g. regressions, spatial analysis etc.)
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Results
Look at yield values as I harvest
11%
Print and examine11%
Evaluate average yield against another factor (e.g. yield by soil type,
treatment, etc.)56%
Evaluate yield using advanced statistics
methods (e.g. regressions, spatial
analysis etc.)22%
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In-depth Look at Cleaned vs Uncleaned Data Management Zones UAN Trial Compost Trial
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Summary Stats
Type
Yield Variability
(bu/ac)Avg Yield (bu/ac)
Clean 191 264
Unclean 219 250
Difference -28 14
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Rankings in AgVeritas
Clean Unclean
Management Zones Management Zones
Elevation Elevation
Spatial Effects Soil Types
UAN Spatial Effects
Compost UAN
Population plots Compost
Kicked out Kicked out
Soil Types Population plots
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Management Zone Yields
Type Avg Yield MGMT Zone (bu/ac)
Zone 40-67 68-84 85-95 96-105 106-121
Clean 159 194 233 267 290
Unclean 125 173 226 258 278
Difference 35 21 7 9 13
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Management Zone Yields
Type Avg Yield MGMT Zone (bu/ac)
Zone 40-67 68-84 85-95 96-105 106-121
Clean 159 194 233 267 290
Unclean 125 173 226 258 278
Difference 35 21 7 9 13
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UAN Comparison
Type Avg Yield UAN (bu/ac)
Rate 0 7 14 21 28
Clean 260 291 288 297 292
Unclean 245 279 278 283 277
Difference 15 12 10 14 15
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Compost Evaluation
Type Avg Yield Compost (bu/ac)
Compost Applied Unapplied Difference
Clean 284 259 26
Unclean 268 245 24
Difference 16 14 2
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Screenshots for Compost Profit Maps
Unclean: Compost profit map
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Screenshots for Compost Profit Maps
Clean: Compost profit map
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Summary
Clean, Clean, Clean!!
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Thank You
Chad Godsey, PhD
Owner, Godsey Precision Ag
Phone: 970-630-7732
Email: [email protected]
Web: godseyag.com
Twitter: @godseyag
Stick around for an AgVeritas demonstration.
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AgVeritas – Overview
• Web-based yield analysis tool
• Uses spatial statistics to analyze yield
• Analyzes all the factors that impact yield simultaneously• Planting choice / rate
• Treatment choice / rate
• Nutrient levels, soil types, elevation
• And many more
• Determines which factors impacted yield more and less
• Calculates if and where a tested management practice is profitable
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An example: Seeding rate trialManagement Zones
Seeding rate trial
https://agveritas.xsinc.com/AnalysisResults/Summary/1071
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Thank You
Chad Godsey, PhD
Owner, Godsey Precision Ag
Phone: 970-630-7732
Email: [email protected]
Web: godseyag.com
Twitter: @godseyag
Post event survey: Your feedback is greatly appreciated!