Exploiting spatial & temporal variability in the Prairies Chris Holzapfel, MSc, PAg

Exploiting spatial & temporal
variability in the Prairies
Chris Holzapfel, MSc, PAg
Indian Head Agricultural Research Foundation
[email protected]
Dec-15, 2009
MB Agronomists Conference
University of Manitoba, Winnipeg MB
1
Objectives
• Focus on characteristics of Prairie
landscape that create potential for
site-specific management
(fertilization)
–
–
–
–
Major causes of soil and yield variability
Zone management and delineation
Addressing temporal variability
Opportunities for the future
Dec-15, 2009
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2
definitions
Spatial Variability
• occurs when a quantity that is measured at
different spatial locations at the same time
exhibits values that differ across the locations
Temporal Variability
• occurs when a quantity that is measured at
different times in the same location exhibits
values that differ over time
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definitions
Precision Agriculture
• The application of technologies
and agronomic principles to
manage spatial and temporal
variability associated with all
aspects of agricultural
production for the purpose of
improving crop performance
and environmental quality
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Focus on Nutrient Management
• 4R Nutrient Stewardship (IPNI)
•
•
•
•
Right Source
Right Rate
Right Place
Right Time
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Trends in Precision Agriculture
• One of the fastest growing sectors in the
agriculture industry
Current trends in the U.S. Midwest
Whipker and Akridge. 2008. Precision Agricultural
Services: Dealership Survey Results
– Sponsored by CropLife Magazine and Purdue University
url: http://ageconsearch.umn.edu/bitstream/46427/2/08-09.pdf
• Results based on responses of 275 crop input
dealerships
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Precision Agriculture Trends
(Services offered)
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US Trends in Precision Ag
(Variable Rate Services)
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Trends in Precision Agriculture
(Market Area)
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Canadian Prairies
Alberta
Manitoba
Saskatchewan
Canadian
Shield
www.nrcan.gc.ca
Beaverlodge
i
rd
Co
lle
n
ra
Lacombe
g
re
Melfort
ion
Saskatoon
Lethbridge
Winnipeg
Swift Current
Dec-15, 2009
Brandon
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Gray
Dark Gray
Black
Dark Brown
Brown
10
Spatial Variability – Major Causes
•
•
•
•
•
Topography
Soil texture
Soil fertility
Erosion
Salinity
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Spatial Variability - Characterization
• Elevation (DEM)
• Conductivity (Veris,
EM38)
• Fertility (soil sampling)
• Soil Color (aerial
photos)
• Biomass (NDVI –
satellite imagery)
• Grain Yield
• etc.
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Example of Spatial Variability
• Yields within a <5 ac area at Indian Head (2008) for
adjacent passes in a canola field with no treatments applied
70
62
60
Yield (bus/ac)
50
46
49 49
49
53 53 55
46
40
30
20
NO TREATMENTS APPLIED
52
10
avg
51.4
stdev
4.79
cv
9.3%
0
1
2
3
4
5
6
7
8
9
10
Pass #
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Management Zones to Address
Spatial Variability
Definition:
• Management zone: a “sub-region of a field
that expresses a homogeneous combination
of yield limiting factors for which a single rate
of a specific crop input is appropriate”
Doerge, T.A., 1999. J. Prod. Agric. 12:54-61.
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Management Zones: Basic Approach
1. Data Collection / Mapping – collect
information to characterize spatial distribution
of parameter of interest
2. Clustering – divide field into zones based on
variability in parameter of interest (supervised vs
unsupervised)
3. Verification – confirm that productivity and/or
nutrient requirements differ between zones
4. Apply Zone Management – adjust inputs or
production practices to optimize production for
each zone
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Zone Delineation
Soil Sampling / Fertility
• Requires multiple, geo-referenced samples per
field for variable-rate fertilization
– grid-based vs landscape directed
• Advantages
– maps can be prepared for each nutrient and be used for
variable rate fertilization or to help explain observed yield
variability
• Disadvantages
– expensive (labor & capitol)
– high variability (especially for N)
– does not account for non-nutrient factors affecting
productivity (ie: moisture, organic matter)
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Management Zone Delineation
Soil Nutrients (K)
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Management Zone Delineation
Soil Nutrients (P2O5)
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Management Zone Delineation
Soil Nutrients (NO3-N)
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Zone Delineation
Topography / DEM
• Variable-rate fertilizer based on
differences in relative field elevation
– rates based on moisture availability
• Advantages
– zones static, relatively straight-forward approach,
only done once
• Disadvantages
– can be expensive (RTK)
– dry year vs wet year affects productivity of zones
(high vs low fertilizer in low areas?)
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Management Zone Delineation
Topography
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Management Zone Delineation
Topography
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Management Zone Delineation
Topography
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Zone Delineation
Remote Sensing
• Ground-based – Airborne – Spaceborne
– includes conductivity (EM38), aerial photography
(soil color), satellite imagery (NDVI), etc.
• Advantages
– large data volume (minimal or no interpolation)
– cost effective
– image archives
• Disadvantages
– high computational requirements
– difficult to interpret (ie: identifying causes of
observed variability in NDVI)
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Management Zone Delineation
Remote Sensing (NDVI)
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Management Zone Delineation
Remote Sensing (NDVI)
Site 1: Mean Yield per Management Zone
30
27
25.2
Yield (bu/ac)
25
22.2
21.4
20
Zone 1 (Low)
15
Zone 2 (High)
10
5
0
2001 (Peas)
2002 (Wheat)
Year (crop)
*Lafond et al. 2006. Final Report: Indian Head Precision Farming Project
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Management Zone Delineation
Grain Yield
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Unsupervised Classification
(Basnyat et al. 2005. Can J. Soil Sci. 85:319-328)
• Acquire classification data (ie: NDVI)
• Separate spatial data into zones of similar values
using the fuzzy K-means algorithm “Fuzme”
(Minasny and McBratney 2002)
• Assess uniqueness of zones using significant
differences in grain yield between zones
• Increase or decrease number of zones until
maximum number of zones with significantly
different grain yields is achieved
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Standardized Grain Yields for Zones
Delineated from NDVI
Comparing standardized mean grain yield (year 1998) differences under
zonal combination.
Management
ZoneZ
Iteration 1
(5 Zones)
Iteration 2
(4 Zones)
Iteration 3
(3 Zones)
1
-0.36aY
-0.36a
-0.36a
2
0.12b
0.12b
0.11b
3
0.18b
0.18b
0.72c
4
0.59c
0.72c
5
0.95c
ZManagement
zones delineated using Fuzme (increase zone number
indicates increasing NDVI value
YMean
yields within columns followed by different letters are statistically
different at P≤ 0.05
*Lafond et al. 2006. Final Report: Indian Head Precision Farming Project
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Standardized Grain Yields for Zones
Delineated from Various Parameters
Comparing standardized mean grain yield (year 1998) differences
under zone delineated using different variables of interest.
Zone
NDVI
OC
1z
-0.36a
-0.3a
2z
0.11b
3z
0.72c
ON
TC
ResN
ELEV
EM38
BASIN
0.03a
-0.58a
0.11a
-0.48a
-0.37a
-0.12a
-0.05a
-0.14ab
-0.04ab
-0.03a
-0.01b
0.26b
0.02a
0.12a
0.16ac
0.3c
-0.31a
0.3bc
-0.29ac
0.04a
a,b,c in the same column indicates statistically significantly different yields
zzone 1, 2, and 3 represents the progressively higher mean value of the
variable of interest
NDVI: normalized difference vegetation index, OC: soil organic carbon at 0-15 cm
depth; ON: soil organic N at 0-15 cm depth; TC: soil total carbon at 0-5 cm depth;
ResN: average of residual N at 0-120 cm depth (1998-99); ELEV: Elevation of the
field; EM38: EM measurement of the field; and BASIN: Three basin delineated using
drainage pattern
*Lafond et al. 2006. Final Report: Indian Head Precision Farming Project
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Zones Delineated from Various
Parameters
Zone
1
2
3
E LE V A T IO N
ON
Dec-15, 2009
EM
HYDRO
TC
OC
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Management Zones Based on NDVI
N
W
E
S
Zone
1
2
3
1:6418
• Zone 1 (low)
– 46 ha (37.4%)
• Zone 2 (mid)
– 69 ha (56.1%)
• Zone 3 (high)
– 8 ha (6.5%)
*Lafond et al. 2006. Final Report: Indian Head Precision Farming Project
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Zone Stability
Spatio-temporal difference in normalized grain yield
Grain Yield
Management
Zone
1998
1999
1
-0.36a (0.68)Z
-0.64a (0.98)
2
0.11b(0.21)
0.31b(0.74)
3
0.72c(0.91)
1.05c(0.48)
Z Standard
errors of mean in parenthesis
Means followed by same letter within each column are not significantly different.
*Lafond et al. 2006. Final Report: Indian Head Precision Farming Project
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Wheat Response to N in Different
Management Zones (NDVI)
2
W H EAT
Standardized Yield
1
0
Management Options
-1
Variable Rate Zone 1
Variable Rate Zone 2
Variable Rate Zone 3
Uniform Rate
-2
-3
0
50
100
N Fertilizer (kg ha-1)
150
*Lafond et al. 2006. Final Report: Indian Head Precision Farming Project
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Canola Response to N in Different
Management Zones (NDVI)
2
C AN O LA
Standardized Yield
1
0
Management Option
-1
Variable Rate Zone 1
Variable Rate Zone 2
Variable Rate Zone 3
Uniform Rate
-2
-3
0
20
40
60
80
N Fertilizer (kg ha-1)
100
120
*Lafond et al. 2006. Final Report: Indian Head Precision Farming Project
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Temporal Variability – Major Causes
•
•
•
•
Precipitation
Temperature
Disease
Insects
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Wheat Yield Variability in Canada
(1910-1975)
Sakamato et al. 1980.
Climate and global grain
yield variability. Climatic
Change. 2:349-361
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Wheat Yield Variability in Canada
(1961-2007)
Avg
S
25.8
5.4
CV
20.8
*Food and Agriculture Organization – FAOSTAT (2009)
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Regional Grain Yield Variability
1955-2007
Source
R.E. Karamanos, R.H. McKenzie, Yantai Gan, G.P.
Lafond, C.A. Jones and S.S. Malhi. 2010.
Fertilizer management for maximum yield of
common crops in the Northern Great Plains of
North America. in S.S. Malhi et al. (eds) Recent
Trends in Soil Science and Agronomy Research in
the Northern Great Plains of North America,
Research Singpost (in press)
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Historical Wheat Yields
3.0
3.5
(d)
2.5
2.5
Wheat gain yield, Mg ha-1
Wheat grain yield, Mg ha-1
3.0
2.0
1.5
1.0
AB
0.5
0.0
MT
2.0
1.5
1.0
0.5
0.0
01 04 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06
55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05
3.0
3.0
(b)
(e)
SK
2.0
2.5
Wheat gain yield, Mg ha-1
2.5
Wheat grain yield, Mg ha-1
• Average (2-yr
moving) wheat grain
yields for western
Canada (1955-2007)
and the northern
states (1901-2007)
(Karamanos et al.
2010)
(a)
1.5
1.0
2.0
1.5
1.0
0.5
0.5
0.0
0.0
01 04 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06
07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06
3.0
4.0
(c)
MB
2.6
2.4
3.5
3.0
Wheat gain yield, Mg ha-1
2.8
Seed yield, kg ha-1
ND
2.2
2.0
1.8
1.6
1.4
1.2
MN
(f)
2.5
2.0
1.5
1.0
1.0
0.5
0.8
0.6
0.0
60
Dec-15, 2009
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
94
96
98
00
02
04
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21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90 93 96 99 02 05
40
Long-Term Wheat Yields (MB)
3.0
(c)
2.8
2.6
Seed yield, kg ha-1
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
60
Dec-15, 2009
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
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94
96
98
00
02
04
41
Historical Barley Yields
3.5
4.0
(a)
3.0
Barley gain yield, Mg ha-1
-1
Barley grain yield, Mg ha
3.0
2.5
2.0
1.5
2.5
AB
0.5
2.0
1.5
1.0
0.0
0.5
0.0
55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05
01 04 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06
3.5
4.0
(b)
2.5
3.5
3.0
Barley gain yield, Mg ha-1
-1
(e)
SK
3.0
2.0
1.5
1.0
2.0
1.5
0.5
0.0
0.0
07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06
4.0
(c)
4.0
3.5
Barley gain yield, Mg ha-1
-1
3.0
01 04 07 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97 00 03 06
4.5
MB
3.5
Barley grain yield, Mg ha
ND
2.5
1.0
0.5
2.5
2.0
1.5
1.0
MN
(f)
3.0
2.5
2.0
1.5
1.0
0.5
0.5
0.0
0.0
60
Dec-15, 2009
MT
(d)
1.0
Barley grain yield, Mg ha
• Average (2-yr
moving) barley grain
yields for western
Canada (1955-2007)
and the northern
states (1901-2007)
(Karamanos et al.
2010)
3.5
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
94
96
98
00
02
04
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42
Long-Term Barley Yields (MB)
4.0
(c)
Barley grain yield, Mg ha-1
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
60
62
Dec-15, 2009
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
94
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96
98
00
02
04
43
Historical Canola Yields
• Average (2-yr moving) canola grain yields for western
Canada (1955-2007) and the northern states (19902007) (Karamanos et al. 2010)
1.8
2.5
(a)
AB
MB
1.6
Canola grain yield, Mg ha-1
Canola grain yield, Mg ha-1
2.0
1.5
1.0
0.5
(c)
1.4
1.2
1.0
0.8
0.0
0.6
55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05
60
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
94
96
98
00
02
04
2.0
Canola grain yield, Mg ha-1
1.6
SK
(b)
2.5
(d)
2
Canola grain yield, Mg ha-1
1.8
1.4
1.2
1.0
0.8
0.6
1.5
1
0.5
0.4
Montana
North Dakota
Minnesota
0.2
0.0
44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06
Dec-15, 2009
0
1990
1992
1994
1996
1998
MB Agronomists Conference
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2000
N_ST
2002
2004
2006
2008
44
Long-Term Canola Yields (MB)
1.8
(c)
Canola grain yield, Mg ha-1
1.6
1.4
1.2
1.0
0.8
0.6
60
Dec-15, 2009
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
92
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94
96
98
00
02
04
45
Swift Current Old Rotation Study
1967-2009
*data contributed by Robert Zentner – AAFC
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Swift Current, SK
Fallow Wheat Yields
Mean
32.9
Mean
34.5
St. Dev.
10.2
St. Dev.
11.3
CV (%)
31.0
CV (%)
32.7
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Swift Current, SK
Stubble Wheat Yields
Mean
18.3
Mean
24.8
St. Dev.
7.9
St. Dev.
10.5
CV (%)
43.3
CV (%)
42.4
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Swift Current, SK
Wheat Yield Response to N & P Fertilizer
Mean
4.7
Mean
40.7
St. Dev.
9.6
St. Dev.
52.6
CV (%)
204.9
CV (%)
129.1
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Scott, SK – Rotation C
1913-2009
*data contributed by Stu Brandt – AAFC (retired)
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Scott, SK
Fallow Wheat Yields
Mean
31.6
Mean
38.2
St. Dev.
11.2
St. Dev.
13.3
CV (%)
35.6
CV (%)
34.9
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Scott, SK
Stubble Wheat Yields
Mean
21.2
Mean
33.4
St. Dev.
8.5
St. Dev.
11.3
CV (%)
40.2
CV (%)
33.7
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Scott, SK
Wheat Yield Response to N & P Fertilizer
Mean
23.0
Mean
81.6
St. Dev.
22.9
St. Dev.
73.2
CV (%)
99.6
CV (%)
89.8
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Indian Head, SK – Long-Term Rotations
1958-2007
*data contributed by Guy Lafond – AAFC
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Indian Head, SK
Fallow Wheat Yields
Mean
28.4
Mean
37.2
St. Dev.
9.3
St. Dev.
9.3
CV (%)
32.7
CV (%)
25.1
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Indian Head, SK
Stubble Wheat Yields
Mean
14.1
Mean
30.7
St. Dev.
5.5
St. Dev.
10.8
CV (%)
39.0
CV (%)
35.1
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Indian Head, SK
Wheat Yield Response to N & P Fertilizer
Mean
42.4
Mean
144.1
St. Dev.
53.4
St. Dev.
113.8
CV (%)
126.1
CV (%)
79.0
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Addressing Temporal Variability in
Nutrient Requirements
• Temporal variability in grain yields / fertilizer
requirements a challenge largely overlooked
by precision agriculture efforts
• Spring soil moisture estimates can be
inadequate due to importance of in-season
precipitation
• Splitting N applications (ie: topdressing a
portion in late spring / early summer) adds
flexibility and allows growing conditions to be
reassessed later in the growing season
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Active, Optical Sensors for FineTuning N Rates in Real Time
• Active optical sensor that
measures canopy reflectance
of emitted red & NIR light
• Utilizes measurements of highN reference crops to estimate
response to topdressed N
• Integratable w/ application
equipment to direct VR
applications of agrochemicals
(ie: liquid fertilizer)
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Integrated Sensing & Application
Individual Sensors
• In the RT200TM VRA system, post-emergent N rates are
based on the mean NDVI of 6 boom-mounted sensors
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Spring Wheat 2009
Dec-15, 2009
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N Topdress VRA Example
Spring Wheat Yield Potential Estimates
4500
INPUTS
• NDVI_yp0 = 0.65
• NDVI_NR = 0.71
• NDVI_max = 0.79
• GDD0 = 476
Yield Potential (kg/ha)
4000
3500
3000
YP_0
2500
YP_N
YP_MAX
2000
1500
1000
500
0
0.025
0.2
0.375
0.55
0.725
0.9
Post-Emergent N Recommendation
1
20
17
US gpa UAN
Estimated YP
• YP0 – 43 bus ac-1
• YPN – 53 bus ac-1
• YPMAX – 59 bus ac-
14
11
UAN rate
8
5
2
-1
0.025
Dec-15, 2009
0.2
0.375
0.55
0.725
NDVI
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0.9
62
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63
Dec-15, 2009
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64
Mean yield = 64 bus/ac
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Real-time Sensing
Performance, Pros and Cons
N Inputs (based on 27 field-scale trials - IHARF)
•
•
•
FP nitrogen > GS nitrogen 74% of the time (17% lower)
FP nitrogen ≤ GS nitrogen 26% of the time (9% higher)
N inputs reduced by 10% overall with GreenSeeker™
Yield (based on 27 field-scale trials - IHARF)
•
•
•
FP yield = GS yield 86% of the time
FP yield < GS yield 7% of the time
FP yield > GS yield 7% of the time
Pros
•
•
•
user friendly / easy to implement
added flexibility (weather, economic considerations)
accounts for temporal variability
Cons
•
•
•
economic gains must cover cost of post-emergent application
limitations of topdressing N apply (ie: needs rain)
cannot predict environmental conditions after sensing / topdressing
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Integration of Management Zones
and Real-time Sensing
N
• Integration of zone
management and realtime sensing is
possible and may
become important in
foreseeable future
• Enhanced ability to
exploit both spatial and
temporal variability in
soils and crops
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W
E
S
Zone
1
2
3
1:6418
67
RT Commander Pro Job Setup
Import of Rx Zone
Modification Maps
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RT Commander Pro Job Setup
Choice of rate
modification
strategy
Dec-15, 2009
Rx Zone Map
layer selection
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User-Defined Rate Modification
Map
Rx Multiplier Values
by management zone
1.50
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1.0
.5
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Field NDVI Map
Highest Vigor
Lowest Vigor
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Medium Vigor
71
Comparison of Rx Maps
Standard NDVI-Based Rx
Zone Modified Rx
1.50x
Average Rx Rate 9.4 GPA
Dec-15, 2009
1.0x
.5x
Average Rx Rate 10.5 GPA
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Summary & Conclusions
• Moisture predominant driver of both spatial and
temporal variability
• Variability in Prairies sufficiently high to justify precise
management of crop inputs (ie: fertilizer)
• Zone management embraced as a basis for variable
rate fertilizer but does not adequately account for
temporal variability
• Real-time sensing and topdressing improves ability to
address temporal variability but has important
limitations
• Integration of zone management and real-time
sensing is worthy of further consideration
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Acknowledgements
•
•
•
•
•
•
•
Bob Zentner
Guy Lafond
Stu Brandt
Brian McConkey
Alan Moulin
Prakash Basnyat
Yann Pelcat
Dec-15, 2009
•
•
•
•
•
AAFC MII program
AAFC ETAA program
CARDS
IHARF
Numerous Private
Industry Partners
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