utilization of crop sensors to detect cotton growth and n nutrition tyson b. raper, jac j. varco,...
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UTILIZATION OF CROP SENSORS UTILIZATION OF CROP SENSORS TO DETECT COTTON GROWTH TO DETECT COTTON GROWTH
AND N NUTRITIONAND N NUTRITION
Tyson B. Raper, Jac J. Varco, Ken J. Hubbard, Tyson B. Raper, Jac J. Varco, Ken J. Hubbard,
and Brennan C. Bookerand Brennan C. Booker
Plant and Soil Science Department Plant and Soil Science Department
Mississippi State UniversityMississippi State University
INTRODUCTIONINTRODUCTION
N in cotton productionN in cotton production
– Recent increase in fertilizer costsRecent increase in fertilizer costs– Deficiency limits Deficiency limits yield and lowers qualityyield and lowers quality
– ExcessExcess N causes N causes rank growthrank growth boll rotboll rot difficulty in harvestingdifficulty in harvesting increased need for growth regulators, insecticides, and defoliants increased need for growth regulators, insecticides, and defoliants
Variable Rate NVariable Rate N– Increase Nitrogen Use Efficiency (NUE)Increase Nitrogen Use Efficiency (NUE)
– Decrease environmental pollutionDecrease environmental pollution
INTRODUCTIONINTRODUCTION
Ground-Based SensorsGround-Based Sensors– Provide real-time cotton biomass and greennessProvide real-time cotton biomass and greenness
– Fertilize response to crop reflectance Fertilize response to crop reflectance Need a more thorough understanding of Need a more thorough understanding of
relationship between canopy reflectance, cotton relationship between canopy reflectance, cotton growth, and N nutrition.growth, and N nutrition.
OBJECTIVEOBJECTIVE
Examine the effectiveness of a ground-based Examine the effectiveness of a ground-based sensor to predict cottonsensor to predict cotton– Plant growthPlant growth
– Leaf NLeaf N
METHODSMETHODS
LocationLocation– Plant Science Research Farm, Mississippi State, MSPlant Science Research Farm, Mississippi State, MS
– Randomized complete block designRandomized complete block design
– 4 Treatments x 4 Replications4 Treatments x 4 Replications 12 rows12 rows 125’ long125’ long 3 10’ alleys3 10’ alleys 38” row spacing38” row spacing 4 sub-locations4 sub-locations
Courtesy: Web Soil Survey 2009Courtesy: Web Soil Survey 2009
METHODS (CONT.)METHODS (CONT.)
TreatmentTreatment– 0, 40, 80, and 120 lb N/acre in a split-application 0, 40, 80, and 120 lb N/acre in a split-application
Planting (50%)Planting (50%) Early square (50%)Early square (50%)
CulturalCultural– No-till on beds No-till on beds
– DPL BG/RR 445 DPL BG/RR 445
– No growth regulator appliedNo growth regulator applied
– Low pest thresholds established and maintainedLow pest thresholds established and maintained
Data CollectionData Collection– ReflectanceReflectance
YARA N Sensor (YARA International ASA, Oslo, Norway)YARA N Sensor (YARA International ASA, Oslo, Norway)
METHODS (CONT.)METHODS (CONT.)
METHODS (CONT.)METHODS (CONT.)
YARA N SensorYARA N Sensor– Tractor mounted spectrometer Tractor mounted spectrometer – Wavelength Channels: 5, user selectable*Wavelength Channels: 5, user selectable*– Wavelength range: 450-900 nmWavelength range: 450-900 nm– Optical inputs: 4 reflectance, 1 irradianceOptical inputs: 4 reflectance, 1 irradiance– Acquisition interval: 1 secondAcquisition interval: 1 second– Area scanned: 50-100 m²/sArea scanned: 50-100 m²/s– Positioning Data: Trimble Pro XRPositioning Data: Trimble Pro XR– Speed: 3.5 mphSpeed: 3.5 mph– Bandwidth= Bandwidth= ±5 nm±5 nm
Source: YARA (Hydro Agri), tec5Hellma
METHODS (CONT.)METHODS (CONT.)
2009 EARLY FLOWER
WAVELENGTH, nm
400 500 600 700 800 900
RE
FL
EC
TA
NC
E, %
0
20
40
60 0 lb N/Acre40 lb N/Acre80 lb N/Acre120 lb N/AcreYARA N Sensor
METHODS (CONT.)METHODS (CONT.)
Data CollectionData Collection– Reflectance (cont.)Reflectance (cont.)
YARA N SensorYARA N Sensor– Set 76” above soil Set 76” above soil – Sense entire fieldSense entire field– Views rows 2, 3, 4, 9, 10, 11 Views rows 2, 3, 4, 9, 10, 11
– Data ProcessingData Processing Sub-plot locations Sub-plot locations
– Center of 15’ bufferCenter of 15’ buffer– 4 points selected4 points selected
METHODS (CONT.)METHODS (CONT.)
Data CollectionData Collection– Sub-Location Plant DataSub-Location Plant Data
Plant HeightPlant Height– 5 measured per sub-location5 measured per sub-location
Leaf SampleLeaf Sample– 5 recently matured per sub-5 recently matured per sub-
location location – % Leaf N% Leaf N
Whole Plant SampleWhole Plant Sample– Prior to defoliationPrior to defoliation– Yield, total N uptakeYield, total N uptake
METHODS (CONT.)METHODS (CONT.)
Physiological StagesPhysiological Stages– Pre-SquarePre-Square– Early SquareEarly Square– 22ndnd Week of Square Week of Square– 33rdrd Week of Square Week of Square– Early FlowerEarly Flower– 22ndnd Week of Flowering Week of Flowering– Peak flowerPeak flower
Sensing / Sampling Stages Sensing / Sampling Stages
RESULTSRESULTS
2009 SEASON N UPTAKE
FERTILIZER RATE, lb N/acre
0 20 40 60 80 100 120 140
TO
TA
L N
UP
TA
KE
, lb
N/a
cre
20
40
60
80
100
120
140
r ²=0.871
2008 SEASON N UPTAKE
FERTILIZER RATE, lb N/acre
0 20 40 60 80 100 120 140
TO
TA
L N
UP
TA
KE
, lb
N/a
cre
40
60
80
100
120
140
160
r ²=0.947
RESULTSRESULTS
2008 RAINFALL
DATE
Apr May Jun Jul Aug Sep Oct Nov
1/10
0 in
.
0
50
100
150
200
250
300
PlantingN ApplicationSensing
2009 RAINFALL
DATE
Apr May Jun Jul Aug Sep Oct Nov
1/10
0 in
.
0
50
100
150
200
250
300
PlantingN ApplicationsSensing
RESULTSRESULTS
2009
PLANT HEIGHT, cm
0 20 40 60 80 100 120
GN
DV
I
0.3
0.4
0.5
0.6
0.7
0.8
0.9
r ²=0.96
GNDVI vs PLANT HEIGHTGNDVI vs PLANT HEIGHT
GNDVI vs LEAF NGNDVI vs LEAF N
LEAF N, %
3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6
GN
DV
I
0.36
0.38
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
0.56
0.58
2009 EARLY SQUARE
r ²=0.67
LEAF N, %
3.6 3.8 4.0 4.2 4.4 4.6 4.8
GD
NV
I
0.40
0.42
0.44
0.46
0.48
0.50
0.52
0.54
r ²=0.22
2008 EARLY SQUARE
GNDVI vs LEAF NGNDVI vs LEAF N
LEAF N, %
2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0
GN
DV
I
0.68
0.70
0.72
0.74
0.76
0.78
0.80
r ²=0.84
2008 EARLY FLOWER 2009 EARLY FLOWER
LEAF N, %
3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6
GN
DV
I0.60
0.62
0.64
0.66
0.68
0.70
0.72
0.74
0.76
r ²=0.08
GNDVI vs LEAF NGNDVI vs LEAF N
LEAF N, %
2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2 4.4
GN
DV
I0.72
0.74
0.76
0.78
0.80
0.82
2009 PEAK FLOWER
r ²=0.87
2008 PEAK FLOWER
LEAF N, %
2.6 2.8 3.0 3.2 3.4 3.6 3.8 4.0 4.2
GN
DV
I
0.70
0.72
0.74
0.76
0.78
0.80
r ²=0.90
GNDVI vs LEAF NGNDVI vs LEAF N
2008/2009 PEAK FLOWERING
LEAF N, %
2.5 3.0 3.5 4.0 4.5
GN
DV
I
0.68
0.70
0.72
0.74
0.76
0.78
0.80
0.82
0.84
r ²=0.86
First Derivative of Reflectance SignatureFirst Derivative of Reflectance Signature2009 EARLY FLOWER
WAVELENGTH, nm
700 710 720 730
dR
/dl R
EF
LE
CT
AN
CE
0.8
0.9
1.0
1.1
0 lb N/Acre 40 lb N/Acre 80 lb N/Acre 120 lb N/Acre
RED EDGE INFLECTIONRED EDGE INFLECTION
REIP calculated on a per plot basisREIP calculated on a per plot basis– Gaussian 4 Parameter Peak EquationGaussian 4 Parameter Peak Equation
– Utilize Utilize 700700 710710 720720 740740
RED EDGE INFLECTIONRED EDGE INFLECTION
2nd WEEK OF SQUAREPLOT 3
1 July 2009f=y0+a*exp(-.5*((x-x0)/b)^2)
WAVELENGTH, nm
690 700 710 720 730 740 750
dR
/d
RE
FL
EC
TA
NC
E
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
REIP vs LEAF NREIP vs LEAF N
EARLY SQUARE25 June 2009
LEAF N, %
3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6
WA
VE
LE
NG
TH
, nm
702
704
706
708
710
r ²=0.864
2nd WEEK OF SQUARE1 July 2009
LEAF N, %
3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8
WA
VE
LE
NG
TH
, nm
706
707
708
709
710
711
712
713
r ²=0.695
3rd WEEK OF SQUARE8 July 2009
LEAF N, %
3.4 3.6 3.8 4.0 4.2 4.4 4.6 4.8 5.0
WA
VE
LE
NG
TH
, nm
708
709
710
711
712
713
714
715
r ²=0.805
PEAK FLOWER25 July 2009
LEAF N, %
3.2 3.4 3.6 3.8 4.0 4.2
WA
VE
LE
NG
TH
, nm
706
708
710
712
714
716
r ²=0.731
REIP vs LEAF NREIP vs LEAF N
EARLY SQUARE25 June 2009
LEAF N, %
3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6
WA
VE
LE
NG
TH
, nm
702
704
706
708
710
r ²=0.864
EARLY SQUARE 25 June 2009
LEAF N, %
3.2 3.4 3.6 3.8 4.0 4.2 4.4 4.6
ND
VI
0.30
0.35
0.40
0.45
0.50
0.55
0.60
r ²=0.650
REIP and NDVIREIP and NDVI
CONCLUSIONSCONCLUSIONS GNDVI relationships with leaf N and plant height improve through to GNDVI relationships with leaf N and plant height improve through to
peak flower.peak flower.
Consistency across growing seasons supports the utility of crop Consistency across growing seasons supports the utility of crop reflectance.reflectance.
GNDVI and NDVI have the potential to be effective measurements of GNDVI and NDVI have the potential to be effective measurements of plant growth in cotton.plant growth in cotton.
REIP has the potential to be an effective measurement of N status in REIP has the potential to be an effective measurement of N status in cotton.cotton.
These results support previous REIP publications These results support previous REIP publications (Buscaglia et al., 2002; Fridgen et al., 2004).(Buscaglia et al., 2002; Fridgen et al., 2004).
Questions?Questions?