Challenges to sensor- based N-Management for Cotton E.M. Barnes 1, T. Sharp 2, J. Wilkerson 3, Randy...
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Transcript of Challenges to sensor- based N-Management for Cotton E.M. Barnes 1, T. Sharp 2, J. Wilkerson 3, Randy...
![Page 1: Challenges to sensor- based N-Management for Cotton E.M. Barnes 1, T. Sharp 2, J. Wilkerson 3, Randy Taylor 2, Stacy Worley 3 1 Cotton Incorporated, Cary.](https://reader030.fdocuments.us/reader030/viewer/2022012919/56649e9f5503460f94ba11bb/html5/thumbnails/1.jpg)
Challenges to sensor-based N-Management for
CottonE.M. Barnes1, T. Sharp2, J. Wilkerson3,
Randy Taylor2, Stacy Worley3
1Cotton Incorporated, Cary NC2Oklahoma State University, Okmulgee & Stillwater3University of Tennessee, Knoxville
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Acknowledgements
• Tom Clarke, Glenn Fitzgerald, P. PinterUSDA, ARS, Arid Land Research CenterMaricopa, AZ
• Pete Waller, University of ArizonaPaul Colaizzi, USDA, ARS – Lubbock, TXJulio Haberland – Chile Mike Kostrzewski - Arizona
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Outline
• Cotton 101
• Why cotton interest in sensors is high
• The challenges of Cotton
• One proposed solution
![Page 4: Challenges to sensor- based N-Management for Cotton E.M. Barnes 1, T. Sharp 2, J. Wilkerson 3, Randy Taylor 2, Stacy Worley 3 1 Cotton Incorporated, Cary.](https://reader030.fdocuments.us/reader030/viewer/2022012919/56649e9f5503460f94ba11bb/html5/thumbnails/4.jpg)
Cotton 101
![Page 5: Challenges to sensor- based N-Management for Cotton E.M. Barnes 1, T. Sharp 2, J. Wilkerson 3, Randy Taylor 2, Stacy Worley 3 1 Cotton Incorporated, Cary.](https://reader030.fdocuments.us/reader030/viewer/2022012919/56649e9f5503460f94ba11bb/html5/thumbnails/5.jpg)
Data from USDA, NASS
The Cotton Belt
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Cotton & Nitrogen
• Perennial plant managed as an annualIndeterminate flowering pattern
• ~50 lbs-N per lint bale (1 bale = 480 lbs)• Over-application of N:
Energy partition to vegetative vs. reproductive development
Large plants prevent efficient harvestGrowth regulators applied to control vegetative
development
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Why interest in sensors now?
• Cost of N• Producers receiving In-Time images
And now Deere imagery through Jimmy Sanders
• On-farm tests done in Alabama to use GreenSeekerTM to apply growth regulator (PIX)
• Cotton researchers joining in
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2
3
4
6
1 Lowest Biomass
7 Highest Biomass
5
5.0 gpa
6.0 gpa
7.0 gpa
8.0 gpa
4.5 gpa, 24 fl. oz Prep, 1.5 dry oz Dropp
8.0 gpa, 42.67 fl. oz Prep , 2.67 dry oz Dropp
8.0 gpa
Location: Arkansas Delta
Crop: Cotton
Field Size: 339.5 Acres
Imagery Acquired: September 7, 2004
VR Defoliation Applied: September 14, 2004
Notes:This prescription was applied
using a hydraulic aerial VR system. The consultant was able to achieve a one-time defoliation
on this field, for $15.94/A in chemical.
Variable Rate Defoliation
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Variable Rate Nitrogen Top-Dressing
2
3
4
6
1 Lowest Biomass
7 Highest Biomass
5
0 lbs./A
100 lbs./A
100 lbs./A
0 lbs./A
0 lbs./A
0 lbs./A
100 lbs./A
Location: Arkansas Delta
Crop: Cotton
Field Size: 156.53 A’s
Imagery Acquired: July 5, 2004
VR Fertilizer Applied: July 13, 2004
Notes:This prescription was applied using a variable rate equipped high clearance spreader. Unity [16% nitrogen (N)] was applied
midseason, to supplement areas in the field which had become N deficient. Classes 1 and 2 were
beyond salvaging with the additional N, while classes 6 and
7 required no additional N.
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Challenges
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Wind blows & Index Changes
+ Heliotropic; + New Growth
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Sample data set
• 1999 Growing season
• AGIIS sensor (calibration panel every minute)
• Water and Nitrogen treatments
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1999 CCCI (relative to WN)
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
160 180 200 220 240 260 280
DOY
CC
CI
/ (C
CC
I_W
N)
HwLn
LwHn
LwLn
Last N Application
Squares Green Boll Open Boll
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Yield = -m*NDVI + C ?
Limie 1999 - Central AZ
r all data = -0.27y = 4511.9x - 2326.2
R2 = 0.6579
100011001200130014001500160017001800
0.6 0.7 0.8 0.9
NDVI - DOY 236
Lin
t yi
eld
, kg
/ha WN
Wn
wN
wn
Linear (wn)
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Possible solution?
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Combining Data
• Use NDVI / Greenseeker as a “biomass” sensor
• Historic yield maps.
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Concept
High SZmax SZ YG - H
Med SZ YG - M HZ
Low YG - L HZ HZmax
Low Med HighNDVI
Yie
ld P
ote
nti
al
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Application
NDVI Yield ID Zone N Rate Pix Rate Comment on Nitrogen rateHi Hi 1 YG - H H M N rate: yield goal based
Med Med 2 YG - M M L N rate: yield goal basedLow Lo 3 YG - L L 0 N rate: yield goal basedHi Lo 4 HZ 0 H N rate: extreme HZ - no NHi Med 5 HZ L H Nrate: moderate HZ - lower one level from medium yield goal
Med Lo 6 HZ 0 M Nrate: moderate HZ - lower one level from low yield goal = 0Low Hi 7 SZmax H 0 Stress unknown - grower's preference (conservative = YG rate)Med Hi 8 SZ H 0 Stress unknown - grower's preference (conservative = YG rate)Low Med 9 SZ M 0 Stress unknown - grower's preference (conservative = YG rate)
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Theoretical Example
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Combined
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Conclusions
• Cotton can be tricky to manage
• Efforts to apply sensors for N management are increasing rapidly
• Hope to learn from work here most efficient methods to develop cotton N management strategies
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AgIIS (Agricultural Irrigation Imaging System)
Bands (nm): Green (555), Red (670), Edge (720), NIR (790)IRT
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Field during 1999 Cotton Season
October 1, 1999 AgIIS
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CCCI
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0 0.2 0.4 0.6 0.8 1
Fraction Crop Cover
ND
RE
High NLow N
CCCI =
(C-B)/(A-B) B
C
A
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1999 RVI (relative to WN)
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
160 180 200 220 240 260 280
DOY
RV
I / (
RV
I_W
N)
HwLnLwHnLwLn Last N Application
Squares Green Boll Open Boll