Challenges to sensor- based N-Management for Cotton E.M. Barnes 1, T. Sharp 2, J. Wilkerson 3, Randy...

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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 NC 2 Oklahoma State University, Okmulgee & Stillwater 3 University of Tennessee, Knoxville

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.

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

Page 2: 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.

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

Page 3: 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.

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.

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.

Data from USDA, NASS

The Cotton Belt

Page 6: 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.

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

Page 7: 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.

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

Page 8: 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.

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

Page 9: 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.

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.

Page 10: 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.

Challenges

Page 11: 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.

Wind blows & Index Changes

+ Heliotropic; + New Growth

Page 12: 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.

Sample data set

• 1999 Growing season

• AGIIS sensor (calibration panel every minute)

• Water and Nitrogen treatments

Page 13: 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.

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?

Page 16: 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.

Combining Data

• Use NDVI / Greenseeker as a “biomass” sensor

• Historic yield maps.

Page 17: 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.

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

Page 18: 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.

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)

Page 19: 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.

Theoretical Example

Page 20: 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.

Combined

Page 21: 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.

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

Page 22: 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.

AgIIS (Agricultural Irrigation Imaging System)

Bands (nm): Green (555), Red (670), Edge (720), NIR (790)IRT

Page 23: 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.

Field during 1999 Cotton Season

October 1, 1999 AgIIS

Page 24: 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.

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

Page 25: 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.

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