Field-scale Nitrogen Application Using Crop Reflectance Sensors

Report
FIELD-SCALE N APPLICATION
USING CROP REFLECTANCE
SENSORS
Ken Sudduth and Newell Kitchen
USDA-ARS
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Questions addressed in this presentation




Why the reflectance sensor approach?
How to implement it?
What are some results from Missouri research?
What are additional considerations?
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Why the reflectance sensor approach?




Timing
Temporal variability
Spatial variability
Automation
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Application can be synchronized to time of
maximum crop need
V7-V10
30%
Adapted from Schepers et al., NE, U.S.A.
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Temporal variability in climate – crop – soil
interaction
% of Years With Greater Than 14"
Rainfall During April-June
0-9
10 - 19
20 - 29
30 - 39
40 - 49
50 - 59
60 - 70
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Spatial variability in optimum N rate
Oran00 Rep1 Block6
Oran00 Rep3 Block26
16
16
12
12
8
8
32% of fields had within-field
variation in EONR ≥ 100 lbs
N/acre.
Yield (Mg ha-1)
Yield (Mg ha-1)
Nopt
4
Nopt
4
0
0
0
100
N rate (kg ha-1)
200
300
0
100
N rate (kg ha-1)
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
200
300
Automating plant-based N sensing
Chlorophyll meter
Passive (sunlight)
crop sensors
Active light source
crop sensors
Remote sensing
Implementing N sensing with active crop
canopy reflectance sensors




Sensors
Real-time sensing and control system
Algorithm
Application hardware
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Active reflectance sensors
By using an internal light source,
these sensors eliminate problems
with sun angle and cloud
variations
 GreenSeeker by NTech
Industries (now Trimble)
 Crop Circle by Holland
Scientific (now marketed by
Ag Leader)
LED Light Source
Detector
Source Colimation
Detector
Colimation
Detector Optics
Source Optics
32"
24"
GreenSeeker
Crop Circle
ACS-210
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Sensor outputs



Raw reflectance data – visible and NIR
Ratio data – Visible/NIR
Vegetation index data, e.g. NDVI:
NDVI = (NIR – visible)/(NIR + visible)
Non-N-limiting reference area


Reflectance from a non-N-limiting reference strip or area is used
to standardize the reflectance from the application area
Requires N application to part of the field prior to sidedress
Real-time sensing and control
Prior to Application
Collect
Reference
Data
Create whole-field
reference map
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Real-time sensing and control
Prior to Application
4289950
4289900
Collect
Reference
Data
4289850
4289800
4289750
Create whole-field
reference map
4289700
4289650
4289600
4289550
4289500
4289450
4289400
553600
553700
Real-time sensing and control
Prior to Application
Collect
Reference
Data
Create whole-field
reference map
Get Reference
Value at Current
Point
Get Current
Position by
GPS
Sensor 1
Sensor 2
Sensor 3
Select and/or Combine Sensor Outputs
Spatial or
time-base
filtering
Sensor 4
Real-time sensing and control
Prior to Application
Collect
Reference
Data
Create whole-field
reference map
Get Reference
Value at Current
Point
Get Current
Position by
GPS
Sensor 1
Sensor 2
Sensor 3
Sensor 4
Select and/or Combine Sensor Outputs
Spatial or
time-base
filtering
N Recommendation
Algorithm
So what about that
algorithm?
Smoothing,
Deadband,
Hysteresis
Valve Control
Output
Application System
Algorithms, algorithms, and more
algorithms…….

Research groups around the country have developed
algorithms :
Missouri
 Oklahoma
 Nebraska
 Virginia
 etc….



There is ongoing work to test these algorithms under a
variety of conditions
Can we get to a common algorithm?
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Missouri algorithm developed from previous
plot research
Equations for calculating N rates (lbs N/acre) from active canopy sensors
Corn Growth Stage
Sensor Type
V6-V7 (1 to 1.5-ft tall corn)
V8-V10 (2 to 4-ft tall corn)
Crop Circle
(330 x ratiotarget / ratioreference) - 270
(250 x ratiotarget / ratioreference) - 200
GreenSeeker
(220 x ratiotarget / ratioreference) - 170
(170 x ratiotarget / ratioreference) - 120

Notes:

Maximum N rate should not exceed 220 lbs N/acre.

For V6-V7 corn, the value of ratioreference should not exceed 0.37 for Crop Circle
and 0.30 for GreeenSeeker. Set this as a ceiling.

For V8-V10 corn, the value of ratioreference should not exceed 0.25 for Crop Circle
and 0.18 for GreeenSeeker. Set this as a ceiling.
240
Missouri
algorithm
graphically
Nrate, lbs N/acre
200
160
120
Crop Circle V6-V7
GreenSeeker V6-V7
Crop Circle V8-V10
GreenSeeker V8-V10
80
40
0.8
1.2
1.6
Ratiotarget/Ratioreference
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
2
2.4
Sensors
+
System
+
Algorithm
=
Confusion?
Integrated systems are available
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Dry
N Application
Hardware
Fluid
Anhydrous Ammonia
Dry
N Application
Hardware
240
Fluid
200
Nrate, lbs N/acre
However…
Not all application hardware can
accurately provide the ~ 4:1 range in
rates needed
160
120
Crop Circle V6-V7
GreenSeeker V6-V7
Crop Circle V8-V10
GreenSeeker V8-V10
80
40
0.8
Anhydrous Ammonia
1.2
1.6
Ratiotarget/Ratioreference
2
2.4
Commercial options are available
Fields and situations most suited for sensorbased variable rate N application







Fields with extreme variability in soil type
Fields experiencing a wet spring or early summer (loss of
applied N) and where additional N fertilizer is needed
Fields that have received recent manure applications
Fields receiving uneven N fertilization because of
application equipment failure
Fields coming out of pasture, hay, or CRP management
Fields of corn-after-corn, particularly when the field has
previously been cropped in a different rotation
Fields following a droughty growing season
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO
Risks, concerns, and considerations







Technical aptitude/ability
Suitability of N application hardware
Narrow window for application without highclearance equipment
Balance between meeting early-season N need and
crop stress detection
Suitability of a single reference for a large, variable
field
Algorithm?
How many, and which type of sensor?
Translating Missouri USDA-ARS Research and Technology into Practice
A training session provided by USDA-ARS-CSWQRU, 10-11 October 2012, Columbia, MO

similar documents