development of on-the-go nitrogen application algorithms for ......2017/03/24 · n wap6 wap7 wap8...
TRANSCRIPT
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Development of On-the-go Nitrogen Application Algorithms for Cotton Production
Based on Active Reflectance Sensors
Terry Griffin Ph D CCATerry Griffin, Ph.D., CCAAssistant Professor – EconomicsDept of Agricultural Economics & AgribusinessDept. of Agricultural Economics & Agribusiness
Cotton Inc. Crop Management Seminar
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Cotton NDVI Studies
• Collaborators from 9 statesFunding from Cotton Inc– Funding from Cotton Inc.
– Applied nitrogen studies
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Caveats
• No economic analysis conducted– Statistical analysis of bio-physiological dataStatistical analysis of bio physiological data
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Choosing Unit of “Time”
• Heat unit accumulation preferred to ‘time’…– Algorithm development requires ‘categorized’ timeAlgorithm development requires categorized time– Rather than GDD60, using WAP– DAP resulted in too many ‘bins’ for given amount of data
Histogram of wap
0014
00
Histogram of ndvi_dap14
00Histogram of GDD60
700
Freq
uenc
y
600
800
1000
120
Freq
uenc
y
600
800
1000
1200
Freq
uenc
y
300
400
500
600
5 10 15
020
040
0
ndvi_dap
0 50 100 150
020
040
0
GDD60
500 1000 1500 2000 2500
010
020
0
wap
DAP WAPGDD60
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Precision Ag: The Tale of Two TechnologiesInformation-intensive & Embodied-knowledge
I f ti i t i E b di d k l dInformation-intensive• Field level data to
Embodied-knowledge• Information purchased
make decisions• Requires additional
in the form of an input• Requires minimal
data and skill• IPM
additional data/skill• Round-up Ready or Btp y
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Two Faces of Precision AgricultureTwo Faces of Precision Agriculture
Information intensive Embodied knowledgeInformation-intensive• Yield monitors
Embodied-knowledge• Automated guidance
• Traditional variable rate applications
• On-the-go sensors applying variable rates
• *Data • *Automated
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Motivation and Issue
• Analyses have been conducted for single datasetsFailed to model m ltiple climates s stems etc• Failed to model multiple climates, systems, etc.
• Global response estimated from all datasetsObj ti l h li i l i h• Objective: Develop on-the-go N application algorithm
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Motivation and Issue• Analyses have been conducted for single datasets• Failed to model multiple climates, systems, etc.• Global response estimated from pooled dataset• Derive on-the-go nitrogen application algorithms
Obj ti l lti t t i t d t t ti t• Objective: analyze multi-state experiment data to estimate response between active sensor reflectance and cotton yield for on-the-go nitrogen management– Develop on-the-go N application algorithm
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Data and Methods• Pooled model
– Multiple site-years; 9 states, >=11 PI, >=13 studiesp y– All data (sites and years) in single dataset – Datasets normalized and controlled for heterogeneity– Conditioned by year, location
• Correlation among?– yield, nitrogen rate, NDVI values and timing
• Regression analysis of pooled dataset
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Challenges
• Cotton is a perennial• N rate sufficient to cause yield penalty• N rate sufficient to cause yield penalty• Identifying appropriate timing for NDVI
– DAP GDD60– DAP, GDD60
• No economic analysis conducted– Statistical analysis of bio-physiological dataStatistical analysis of bio physiological data
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Challenges
• Cotton is a perennial• N rate sufficient to cause yield penalty• N rate sufficient to cause yield penalty• Identifying appropriate timing for NDVI
– DAP GDD60– DAP, GDD60
• Testing functional forms of relationships• Testing indexes for algorithm• Testing indexes for algorithm
– Variety, location, and GDD60 specific– Global algorithm with local intercept/slope shiftersGlobal algorithm with local intercept/slope shifters
• No economic analysis conducted– Statistical analysis of bio-physiological datay p y g
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Sensor-Base N Status
• “Yield Potential” approach available for growers not mapping soilsfor growers not mapping soils
• OSU approached “worked” everywhere evaluated (SC TN LAeverywhere evaluated (SC, TN, LA, OK); however:– How early can readings be taken? In y g
some cases response not clear until 4-weeks after early bloomDebate if soil specific reference strips– Debate if soil specific reference strips needed
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Visual/NDVI use of Ramp Strips
Th G S k d t ll ti ifi d h t i ll i The Green Seeker data collection verified what was seen visually in the ramp strips in SC. 30% N savings over blanket application with no yield reduction US modified OSU approach – 2007 to 2009
0.8
0.9
150 lbs/acre
0 5
0.6
0.7
DVI
Optimum Nitrogen use is visually seen as around 80 lbs/acre 0.3
0.4
0.5N
0 lbs/acre
around 80 lbs/acre
0.2
0 20 40 60 80 100 120 140 160
N R t (lb / )0 lbs/acre N Rate (lbs/acre)
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Other Indexes and Relationships
y = 128 12x 94 249
250
-1
Varco et alMissouri (Scharf et al.)
y = 128.12x - 94.249R2 = 0.59
100
150
200
N R
ate,
lb N
acr
e-
0
50
0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75
Opt
imal
N
Greenseeker Red/NIR Relative to High N P lots
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NDVI ~ N by HUA N t f l f d l i l ith d t i l ti
0.90
1.00
300 GDD60
Not as useful for developing algorithms… need categorical time
0 60
0.70
0.80 300 GDD60
600 GDD60
900 GDD60
0.40
0.50
0.60
ND
VI 1200 GDD60
1500 GDD60
0 10
0.20
0.30 1800 GDD60
2100 GDD60
2400 GDD60
-
0.10
0 30 60 90 120 150 180A li d Nit (lb / )
2400 GDD60
2700 GDD60
Applied Nitrogen (lbs/ac)
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NDVI response to N application by time of measurement
0 80.91.0
0 9-1 0
0.50.60.70.8
ND
VI
0.9 1.00.8-0.90.7-0.80 6-0 7
120160
0 10.20.30.4N 0.6 0.7
0.5-0.60.4-0.50 3-0 4
0
4080
0.00.1 0.3 0.4
0.2-0.30.1-0.20 0-0 10 0.0 0.1
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NDVI and Applied Nitrogen
• Determine the shape of the functional form– LinearLinear– Quadratic– Other?
0.8
– No relationship?
• Can NDVI proxy 0.4
0.6
ndvi
for applied N?0.
2
0 50 100 150
0.0
N
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Correlation: N and NDVI by TimePearson Correlation Coefficient applies only to linear relationship
Bolded numbers are significant at 90% level
N WAP6 WAP7 WAP8 WAP9 <=WAP10
Bolded numbers are significant at 90% levelWAP8 and WAP9 become significant at 89 and 88% levels
N 1 0.49 0.73 0.66 0.65 0.83
WAP6 1 0.95 0.98 0.98 0.89
WAP7 1 0.99 0.99 0.99
WAP8 1 1.00 0.97
WAP9 1 0.96
WAP<=10 1
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NDVI and Nitrogen by Time
0 80
0.90
1.00
0.60
0.70
0.80
I
NDVI_WAP6NDVI_WAP7NDVI WAP8
0 30
0.40
0.50
ND
VI NDVI_WAP8
NDVI_WAP9NDVI_WAP<=10
R squared values <0 05
0.10
0.20
0.30 R-squared values <0.05
0.6
0.8
-0 50 100 150 200
Applied N (lbs/ac) 0.0
0.2
0.4nd
vi
Applied N (lbs/ac)0 50 100 150
N
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Algorithms
Source: Randy Taylor, Oklahoma State University
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Histogram of ndviHistogram of NDVI Values
800
600
Algorithm can be “hard bounded”by 0.2 and 0.8
Freq
uenc
y
400
200
0.0 0.2 0.4 0.6 0.8
0
Source: Randy Taylor, Oklahoma State University
ndvi
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100
100
100
Yield potential as function of NDVI by WAP70
8090
dat$
rely
ld 6080
dat$
rely
ld 6080
dat$
rely
ld
5060
020
40
020
40
0.3 0.4 0.5 0.6 0.7
dat$ndvi
0.2 0.3 0.4 0.5 0.6 0.7 0.8
dat$ndvi
0.2 0.3 0.4 0.5 0.6 0.7 0.8
dat$ndvi
100
100
100
WAP=5 WAP=6 WAP=7
4060
80
dat$
rely
ld
4060
80
dat$
rely
ld
4060
80
dat$
rely
ld0.2 0.3 0.4 0.5 0.6 0.7 0.8
020
0.2 0.4 0.6 0.8
020
0.0 0.2 0.4 0.6 0.8
020
4dat$ndvi dat$ndvi
dat$ndvi
WAP=8 WAP=9 WAP=10
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Yield as Function of NDVI by WAP100
80
90
100
60
70
80
entia
l
YLD WAP5
40
50
Yie
ld P
ote YLD_WAP5
YLD_WAP6YLD_WAP7YLD WAP8
Upper bound for application can be set at NDVI=0.6
10
20
30 YLD_WAP8YLD_WAP9
YLD_WAP5 YLD_WAP6 YLD_WAP7 YLD_WAP8 YLD_WAP9NDVI
0
10
0.2 0.3 0.4 0.5 0.6 0.7 0.8
@maxY 1.2 0.6 0.6 1.5 0.8R2 0.4 0.15 0.22 0.32 0.38
NDVI
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Yield Penalty as Function of NDVI: WAP=7
60
40
50
y
30
40
eld
pena
lty
10
20Yi
00.2 0.3 0.4 0.5 0.6
NDVI
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Classic Cotton Lint Response to Applied N
• In theory, quadratic without plateau2 5
2.0
2.5
les/
ac)
1 0
1.5
nt y
ield
(bal
0.5
1.0
Cot
ton
lin
0.00 20 40 60 80 100 120 140
Applied N (lbs per acre)Applied N (lbs per acre)
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Data is ‘messy’ and ‘all over the place’
Cotton yield by N rate: 7 site-years3000
2000
2500
lbs/
ac)
1500
2000
lint y
ield
(l
500
1000
Cot
ton
l
00 20 40 60 80 100 120 140 160
Applied Nitrogen (lbs N per Ac)Applied Nitrogen (lbs N per Ac)
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Yield Potential by N Rate
90
100
60
70
80
tial OK
40
50
60
Yie
ld P
oten AL
TNLA1
Yield max occurs at 110-120 lbs N
10
20
30
Y MSGALA2
-
10
0 50 100 150 200Applied N (lbs/ac)
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Yield Penalty by N Rate
30
35
20
25
alty OK
15
20
Yie
ld p
ena
TNLA1MS
5
10 GALA2
-0 50 100 150 200
Applied N (lbs/ac)Applied N (lbs/ac)
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Chart Title
0.80 0.90 1.00
tial
0.50 0.60 0.70
Yie
ld P
oten
t
wap6wap7wap8
0.20 0.30 0.40
ND
VI o
r Y wap8wap9wap10yld
-0.10
0 50 100 150 200
yld
Applied N (lbs/ac)
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Chart Title
0.80 0.90 1.00
tial
0.50 0.60 0.70
Yie
ld P
oten
t
wap6wap7wap8
0.20 0.30 0.40
ND
VI o
r Y wap8wap9wap10yld
-0.10
50 70 90 110 130 150
yld
Applied N (lbs/ac)
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Practical Implications
• Lower bound = 0.2Upper bo nd 0 6• Upper bound = 0.6
• Difficult to predict total on-the-go applied productN d t t k t d t t fi ld– Need to take extra product to field
• Some applicators not capable of abruptly varying rates
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Limitations
• Cotton is a perennial grown as summer annual– Cotton is a treeCotton is a tree
• Need more data!– Locations, years, varietiesoc o s, ye s, v e es– DAP/GDD60 of measurements– Variables: canopy height, time of day
• Profit maximization has not been evaluated• Heat unit accumulation metrics
– Use of GDD60 needs to be revisited
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Acknowledgements
• Funding from Cotton Inc. • Collaborators in 9 states
State PI
Al b Ki B lk• Collaborators in 9 states Alabama Kip Balkcom
Arizona Ed Barnes
Arizona Pedro Andrade-Sanchez
Arkansas Tom Barber
Georgia George Velidis
Lousiana Brenda Tubana
Mississippi Jac Varco
Mississippi YufengMississippi Yufeng
Oklahoma Randy Taylor
South Carolina Phil Bauer
Tennessee John Wilkerson
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Terry GriffinTerry Griffin Assistant Professor - Economics501 671 2182501.671.2182 [email protected]
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Correlation: Yield and GDD60
300 600 900 1200 1500 1800 2100 2400 2700
Pearson Correlation Coefficient applies only to linear relationship
YLD300 GDD60
600 GDD60
900 GDD60
1200 GDD60
1500 GDD60
1800 GDD60
2100 GDD60
2400 GDD60
2700 GDD60
YLD 1 -0.32 -0.25 -0.15 -0.02 0.16 0.37 0.60 0.80 0.92
300 GDD60 1 1 00 0 98 0 95 0 89 0 76 0 57 0 32 0 07300 GDD60 1 1.00 0.98 0.95 0.89 0.76 0.57 0.32 0.07
600 GDD60 1 1.00 0.97 0.92 0.81 0.63 0.39 0.15
900 GDD60 1 0.99 0.95 0.86 0.70 0.48 0.25
1200 GDD60 1 0.99 0.92 0.79 0.59 0.37
1500 GDD60 1 0.98 0.88 0.72 0.53
1800 GDD60 1 0 97 0 86 0 701800 GDD60 1 0.97 0.86 0.70
2100 GDD60 1 0.96 0.86
2400 GDD60 1 0.97
2700 GDD60 1