Transcript A Crop and Soil Based Strategy for Sensor-Based Variable-Rate N Management John Shanahan, Jim Schepers, Richard Ferguson, Viacheslav Adamchuk, Dennis Francis, Mike Schlemmer,

A Crop and Soil Based Strategy for
Sensor-Based Variable-Rate N Management
John Shanahan, Jim Schepers, Richard Ferguson, Viacheslav Adamchuk,
Dennis Francis, Mike Schlemmer, Aaron Bereuter, Jeff Shanlee, Myron
Coleman, Fernando Solari, Paul Hodgen, Darrin Roberts, Luciano Shiratsuchi,
Brian Krienke
USDA-ARS & University of Nebraska, Lincoln, NE
Crop Based Strategy
Varvel et al. 2007
Index
Chl of Managed
SISufficiency
= 0.8073 + 0.002
* (N(SI)
rate) =
- 0.0000056
* (Nrate)2 Area
22
Chl 200
Reference
RApplication
0.70
Proposed NR
== 0.70
Window
20
1.0
R4
Measured SI = 0.89
15
0.9
10
150
~ 130 kg N/ha needed
to maximize yield
(180 - 50 = 130)
R2
Traditional
Preplant
N Application
100
50
0.8 5
N Uptake (kg ha-1)
Sufficiency Index
Dry Matter Accumulation (Mg ha-1)
1.1
VT
Matter N rate ~180 kg/ha
Dry Optimum
Theoretical N amount
0
N
Total
V8from equation for SI = 0.89 needed for maximum yield
0
V4
0.7
July
June
May
0.6
0
1500
1000
500
0
2000
Aug
Growing Degree Units
50
100
150
N Rate (kg N ha-1)
2500
Sept
200
250
300
< 95% = N stress
Crop Circle
ACS-210
Sensor Bands (amber & NIR)
~590 nm and ~880 nm
Distance = 36”
20”
Relationship between Sensor Readings
and SPAD Readings
Small Plot Sites In 2005 With Varying Amounts Of Applied N
• Sensor and Chl Meter Data Collected on V11, V15, R2, R4
• Sensor readings converted to:
 NDVI= (NIR - Amber)/(NIR + Amber)
 Chlorophyll index = (NIR/Amber) – 1
• Grain yield determined
Chlorophyll Meter
Vs. Sensor Readings
Taken from Solari et al. (2008)
V11 & V15 Growth Stages Combined
1.200
y = 0.4882x + 0.5002
R2 = 0.5454
1.100 NDVI
Sensor SI
1.000 CHL
0.900
0.800
0.700
y = 1.3069x - 0.323
R2 = 0.7364
0.600
0.500
0.400
0.600
0.700
0.800
0.900
Chlorophyll Meter SI
1.000
1.100
Sensor Algorithm
Sensor Algorithm
N Application (kg/ha)
SI SPAD  0.8073
250  0.002  N rate  0.0000056  N rate
200  0.563  SI
SI SPAD  0.421
CI
150
N app  370 1000.97  SI sensor
50
0
0.7
0.75
0.8
0.85
0.9
0.95
Sensor Sufficiency Index (SI)
1
1.05
Prototype
High-Clearance N Applicator
GPS receiver
Active sensors
Fertilizer Nozzles
Crop & Soil Based Strategy
Soil EC?
Soil
Color?
Topography?
SAM
or
Sensor & Map
Sensor Only
Six Study Fields in 2007 & 2008
Sandy,
Low (~1%) Organic Matter,
Spatially Variable Soils
Silt Loam,
High (~3%) Organic Matter
Low Spatial Variability
Silt Loam,
High (~3%) Organic Matter
Spatially Variable Soils
Study Treatments
Soil Spatial Data Collected
Grid Sampling
Soil Color
Soil Electrical Conductivity (ECa)
Rep 1
Rep 2
Rep 3
N application Treatments
N Application Map
Grain Yield Map
Yield Response to N Treatments
$45/A Savings
$15/A Savings
Process for
Layering Spatial Data
Correlation of Soil Variables
With Crop Responses
Field
2007 Sandy Site
2008 Sandy Site
2007 Silt Loam Site
2008 Silt Loam Site
Crop
Paramet
er
Yield
Yield
Yield
Yield
Soil
Color
X
X
ECdp
ECsh Elevrel
X
X
X
X
Management Zone Delineation
Field BR07 Zone Delineation
Field-Specific Approach
Soil Chemical Properties of MZ
Field
Sandy Soil 07
Sandy Soil 08
Silt Loam Soil 07
Silt Loam Soil 08
Zone n
1
2
1
2
1
2
1
2
7
9
10
14
11
5
15
6
Bray-P
ppm
OM
%
23
17
25
12
60
22
50
30
1.4
1.1
2.5
1.9
3.6
3.0
3.5
3.3
Yield Responses To At Planting N rates
Sandy Soil 07
-$1880
Silt Loam Soil 07
$8300
Sandy Soil 08
$3800
Silt Loam Soil 08
$1800
Crop Responses for Field Strips
Sandy Soil 07
Silt Loam Soil 07
Sandy Soil 08
Silt Loam Soil 08
Treatment Yield and N Rate by Zone
Partial Factor Productivity
as Measure of Nitrogen Use Efficiency
Average PFP
Field
45AP+VR
Zone1
Zone2
90AP+VR
Zone1
Zone2
UNL Rec
Zone1
Zone2
––––kg grain (kg N applied)-1––––
Sandy Soil 07
47
43
66
50
59
53
Sandy Soil 08
86
65
91
58
95
85
Silt Loam Soil 07
138
65
125
62
54
50
Silt Loam Soil 08
82
59
86
71
64
60
Average
88
58
92
60
68
62
Conclusions




Use of sensor algorithm alone showed some
benefit in improving N management
Use of MZ further explained crop response to N
Results show promise for integrating active
sensors and MZ
Further efforts are needed to explore how best
to integrate these two N management
approaches
2009 Work
•Develop a process for management
zone delineation based on landscape
attributes (topography, soil electrical
conductivity (EC), soil color, etc) that
can be used to apply crop inputs such N
fertilizer and seed and more efficiently.
Experimental Treatments Applied
Active Sensors Show
Potential for Improving
Nutrient Management
Can we practically
implement?
Questions?
Landscape Attributes Acquired
Soil Map & Color
Crop Image
Elevation and EC
Yield
Map