Sensor-Based Approaches for Cotton Nitrogen Management
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Transcript of Sensor-Based Approaches for Cotton Nitrogen Management
Sensor-Based Approaches for Cotton Nitrogen Management
Outline
Introduction 2008 Sensor Projects Sensor-based approaches to manage nitrogen Core data collection
Cotton Belt
USDA, NASS
Cotton and Nitrogen
Perennial plant managed as annual Indeterminate 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 harvest Growth regulators applied to control vegetative
development
2008 Sensor ProjectsState Investigators Inputs Managed Based on Modulated Active Light Sensors
PGR Defoliant Nitrogen Insecticide Plant Map Irrigation
Alabama S. Norwood, A. Winstead, J. Fulton
K. Balkcom
Arizona T. Clarke, D. Hunsaker
Arkansas M. Mozaffarri
Georgia G. Vellidis, C. Perry
H. Schomberg
Louisiana R. Leonard
B. Tubana, D. Boquet, E. Clawson
Mississippi J. Varco
Missouri G. Stevens, P. Scharf, E. Vories
New Mexico T. Carrillo, J. Ellington
North Carolina G. Robinson
Oklahoma R. Taylor, T. Sharp, S. Osborne, J. Banks, B. Arnall, B. Raun
South Carolina M. Jones, P. Bauer
A. Khalilian, W. Henderson
Tennessee J. Wilkerson
O. Gwathmey
J. Larson (Economics)
Texas K. Bronson
D. Martin
A. Thomasson, R. Sui customsensor
+ other sensor
+ other sensor
+ other sensor
+ other sensor
Sensor-Based Approaches
Relate yield potential with sensor readings and a well fertilized nitrogen plot
Direct regression relationship between sensor reading compared to a reference nitrogen plot
A growth stage specific relationship between the sensor reading and N rate
Relationship between historic yield, soil type and the sensor reading
Relate yield potential with sensor readings and a well fertilized N plot
Nitrogen RecommendationVariables that determine crop N requirements
Estimation Procedures
Crop yield level Yield goal; history(Yield potential predictive equation)
Available soil N organic matter mineralization residual N atmospheric deposition legumes credit nitrate in irrigation water manure
Soil and tissue analyses
(Estimates of increase in yield due to N using early-season canopy reflectance- response index)
SBNRC AlgorithmOSU – Arnall et al.
• N Rate = (YP0 * RI – YP0) * %N / NUE
• potential cotton lint yield, kg/ha = 235.96 e 2216.2 * INSEY
• cotton lint yield, kg/ha = 177.41 e 2216.2 * INSEY
• Where:• Yield Prediction Model: YP0 = 235.96 e 2216.2 * INSEY
• Response Index: RI = 1.8579 * RINDVI – 0.932• %N = 0.09• Nitrogen Use Efficiency: NUE = 0.50
YP0 = 235.96e2216.2x
y = 177.41e2216.2x
R2 = 0.6905
0
400
800
1200
1600
0.000 0.000 0.001 0.001 0.001
Cum INSEY (NDVI / Cumulative GDD's)
Lint
Yie
ld (k
g ha
-1)
LCB06-886CumGDDLCB06-949CumGDDLCB06-1161CumGDDLCB06-1215CumGDDLCB07-1015CumGDDLCB07-1152CumGDDLCB07-1262CumGDDSWR-846CumGDDSWR-999CumGDD
SBNRC AlgorithmOSU – Arnall et al.
LSU AgCenter Implementation of OSU approach (Tubana et al.)
Initiated in 2008, 3 sites in North Louisiana Growth stage: two weeks after early bloom INSEY is defined as NDVI divided by the number of days from planting to sensing
y = 6.4676e430.14x
r2 = 0.7377
0
200
400
600
800
1000
1200
1400
0.006 0.007 0.008 0.009 0.010 0.011 0.012 0.013
INSEY (NDVI/number of days from planting to sensing)
Lint
yie
ld, l
bs/a
cre
NERS-slNERS-clMRRS-sl
y = 5.3155x - 4.2768r2 = 0.4038
0
0.5
1
1.5
2
2.5
3
0.8 0.9 1 1.1 1.2Response Index (NDVI)
Resp
onse
Inde
x (H
arve
st)
NERS-slNERS-clMRRS-sl
LSU AgCenter Implementation of OSU approach (Tubana et al.)
This study also consider the impact of PGR (applied at early bloom) on the yield relationship.
y = 5.2758e444.66x
r2 = 0.7464
y = 6.5961e432.91x
r2 = 0.7634
0
200
400
600
800
1000
1200
1400
0.0070 0.0080 0.0090 0.0100 0.0110 0.0120 0.0130
INSEY (NDVI/number of days from planting to sensing)
Lint
yie
ld, l
bs/a
cre
Without PGR With PGR
LSU AgCenter Implementation of OSU approach (Tubana et al.)
Clemson Modification of OSU Approach (Khalilian et al.)
Soil specific yield by INSEY relationships (soil electrical conductivity categories)
Coastal plain soils of South Carolina INSEY was calculated as days from emergence to date of sensing.
HighEC: Medium Low
R2 = 0.9818
R2 = 0.9694
R2 = 0.9798
1700
2000
2300
2600
2900
3200
3500
0.008 0.01 0.012 0.014 0.016
INSEY
Seed
Cott
on Y
ield
INSEYSe
ed C
otton
Yie
ld y = 413.46e 104.98x
R2 = 0.9103
0
1000
2000
3000
4000
0 0.005 0.01 0.015 0.02 0.025
Direct regression relationship between sensor reading compared to a reference nitrogen plot
Missouri (Scharf et al.)
Best-band-ratio predictor of economically optimal nitrogen rate
y = 128.12x - 94.249R2 = 0.59
0
50
100
150
200
250
0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75
Greenseeker Red/NIR Relative to High N Plots
Opt
imal
N R
ate,
lb N
acr
e-1
Missouri (Scharf et al.)
Calculating N rate based on NDVI and refNDVI: Crop Circle 210: N rate (lb/ac) = 573 - [549 x (NDVI/refNDVI)] GreenSeeker: N rate (lb/ac) = 760 - [732 x (NDVI/refNDVI)] Cropscan: N rate (lb/ac) = 691 - [673 x (NDVI/refNDVI)]
Ceilings on total N rate are use: 200 lbs N/ac for heavy soils (clay, clay loam) 150 for other soils
Farmers decide on lowest and highest N rates for the system (within the ceiling)
A growth stage specific relationship between sensor readings and N rate
Mississippi (Varco et al.)
Relationship between GNDVI or NDVI at a specific stage of growth and fertilizer N rate
Leaf N approach (graph): Utilize N rates to that produce leaf N values at various physiological stages
Mississippi (Varco et al.)
Example of established relationship at the third week of squaring (experimental data):
Fertilizer N rate equivalence (FNRE) = -999.562 + 1584.984(GNDVI or NDVI on-the-go value)
Variable N rate = FNRE (experimental data) – target rate Ceilings on total N rate (cutoff values)
Relationship between historic yield, soil type, and sensor readings
Tennessee (Wilkerson et al.)
Bracket the response of real-time nitrogen prescription algorithms Incorporate a map of historic information: identify areas within a field
as historically low or high yielding and increment/decrement the N prescription accordingly
Additional agronomic data to determine the size of increment or decrement in response to yield potential is needed.
Historic Yield Potential
NDVI Low Average HighHigh R-- R- RAverage R- R R+Low R R+ R++Where: R is the average recommended rate, R+ and R++ are positively incremented, and R- and R-- are decremented from the recommended rate.
Texas (Sui et al.)
Canopy chlorophyll content index (CCCI) – relative estimate of the chlorophyll content in a crop canopy
CCCI accounts for variation in canopy density and less likely to give false indications of low chlorophyll content (vs. simple ratio or NDVI)
Well-suited for the three-band sensor (Crop Circle)
Core Data Collection
Develop a simplified yield potential nitrogen rate estimate – under discussion.
Producer usable approach - 2010
Core Data Collection Growth stages for plant data collection:
Early squaring: 3 to 7 squares Early bloom: 2 to 3 blooms Mid bloom Peak bloom
Nitrogen rates: 0, 90, and 120 lb N/ac
Core Data Collection Soils data set (Mehlich-3 P & K, soil NO3)
Preplant samples Post-harvest samples
Core Data Collection Within season plant information:
Sensor readings
Core Data Collection Within season plant information:
SPAD readings and plant height Tissue samples for leaf N
Hand harvest – 13.3 ft of row Machine – 40 feet of 2 rows Determine seed cotton and lint weight Seed N content
Yield Data Collection
Other Record Keeping
Varieties Planting and harvest dates Nitrogen rates, timing, application methods and N
sources PIX rates and dates (if applicable) Irrigation amounts (indicate in furrow, pivot, drip) Rainfall
Take Home Notes
One or more of these approaches should be ready for extended on-farm evaluation in 2010.
Will this end with one concept: analysis of core data: correction procedures (thermal time, days from planting
to sensing etc.) components (yield potential, response index, simple ratio)
Questions!!!