71st SWCS International Annual Conference July 24-27, 2016 Louisville, KY

1. Watershed scale precision farming
and soil conservation to enhance
nutrient use efficiency and
profitability
Khakbazan, M.; Moulin A.; Wilson H.; Huang,
J.; Whyte D.; Zhang C.; Michiels P.
SWCS
2016 Louisville KY, USA

2. Study Issue
• Water quality in Lake Winnipeg has
deteriorated due, in part, to excessive non‐
point source nutrient contributions from
agricultural activities within the Red and
Assiniboine rivers drainage basin (953,000
Km2).
• Land management, cropping systems, and
nutrient management significantly affect
water quality of aquatic ecosystems.

3. Importance of precision agriculture
and soil conservation
• Runoff can remove significant quantities of
N applied as fertilizer.
• Two‐thirds of cropland in the United States
does not meet the criteria of best nitrogen
management practices.
• Nitrogen management can be modified to
improve water quality.

4. • Evaluate the economic and
environmental viability of precision
agriculture and soil conservation to
improve nutrient use efficiency and
profitability at the watershed scale.
Objective

5. Study Area
• 150 km2 SW of Winnipeg,
Manitoba, Canada

6. • Spatial
distribution of
crops in 2014
• Wheat and canola
are common crops
in STC
• About 35 farms
and 350 fields

7. Definitions
Management zones: Homogeneous
areas with respect to soils,
environment and productivity which
can be managed uniformly.

8. Definitions
Agricultural Capability (AgCap): The Canadian
System for Agricultural Capability consists of
7 classes based on soil properties
determined from soil surveys, landscape
features and climate. Classes within the
same group have similar limitations for
agricultural production. Productivity
decreases from Class 1 to Class 7.

9. Methodology
• Agronomic, yield and soil data were
compiled by field for STC from 1991‐
2014.
• These data and agricultural capability
were used to delineate management
zones in the watershed.

10. Methodology
• Zoning Based on AgCap and Average size of
fields
Zone AgCap
Total Area
(ha)
Average Area
(ha)
1 1&2 40883 18
2 3 30723 16
3 4 15677 15
4 5&6 5950 11

11. Methodology
• STC Panel Data Set Structure
• STC panel data sets included sequential blocks or
cross‐sections of data, within each of which
resides a time series (1991‐2014).
• The zonal statistics were calculated to compare
and contrast characteristics across zones and
within zones
• Limdep (NLOGIT 4.0) Econometric Software
developed by William H Greene was used
because it is more suited for panel data analysis

12. Results
• Average N applied = 98.6 kg/ha
Average over years 1997 to 2014
for crops Canola, Barley, and
Wheat
• Clear spatial variability with
regards to applied nitrogen.
Potential for N improvement.

13. Results
Currently delineated zones based on
Agricultural Capability

14. Results
1991‐2014 Wheat Canola
Zone 1 2 3 4 1 2 3&4
Number of
Fields 822 786 402 154 663 605 397
Average
number of
Fields 34 33 17 6 28 25 16

15. Results
0
10
20
30
40
50
60
70
80
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Number of Fields
Year
Count of Fields Planted Wheat per Zone
Zone1
Zone2
Zone3
Zone4

16. Results
0
10
20
30
40
50
60
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Number of Fields
Year
Count of Fields Planted Canola per Zone
Zone1
Zone2
Zone3
Zone4

17. Results
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014
Average Yield (kg/ha)
Year
Average Yield per Zone for Wheat
Zone 1
Zone 2
Zone 3
Zone 4
Linear (Zone 1)
Linear (Zone 2)
Linear (Zone 3)
Linear (Zone 4)

18. Results
0
20
40
60
80
100
120
140
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Averaged N Applied (kg/ha)
Year
Average N per Zone for Wheat
Zone 1
Zone 2
Zone 3
Zone 4
Linear (Zone 1)
Linear (Zone 2)
Linear (Zone 3)
Linear (Zone 4)

19. Results
0
500
1000
1500
2000
2500
3000
3500
Average Yield (kg/ha)
Year
Average Yield per Zone for Canola
Zone1
Zone2
Zone3
Zone4
Trendline

20. Results
0
20
40
60
80
100
120
140
160
180
Average N Applied (kg/ha)
Year
Average N per Zone for Canola
Zone1
Zone2
Zone3
Zone4
Trendline

21. Results
Significant difference for N rate and Yield based on zones.
N Rate Yield
Crop Zone Zone Probt Crop Zone Zone Probt
Canola 1 2 0.856 Canola 1 2 0.006
Canola 1 3 0.018 Canola 1 3 1.6E‐06
Canola 2 3 0.032 Canola 2 3 0.017
Wheat 1 2 0.034 Wheat 1 2 0.746
Wheat 1 3 0.957 Wheat 1 3 0.115
Wheat 1 4 0.085 Wheat 1 4 0.002
Wheat 2 3 0.076 Wheat 2 3 0.190
Wheat 2 4 0.534 Wheat 2 4 0.004
Wheat 3 4 0.100 Wheat 3 4 0.052

22. Results ‐ Wheat
• Zone (spatial) and time
(temporal) effects:
Yield = a*Nrate + b + λ*Zone +
θ*Time
• Quadratic was also tested
but linear fitted better than
quadratic
Year Zone Parameter Estimate StdErr Probt t-ratio
a 13.58 0.98 0.00
b 1781.35 87.06 0.00
1 49.90 16.42 3.04
2 9.36 16.57 0.56
3 ‐38.02 26.94 ‐1.41
4 ‐262.01 51.34 ‐5.10
1991 ‐156.46 57.72 ‐2.71
1992 260.87 54.80 4.76
1993 ‐934.07 51.05 ‐18.30
1994 ‐616.78 58.74 ‐10.50
1995 ‐636.32 56.65 ‐11.23
1996 ‐241.86 51.09 ‐4.73
1997 ‐382.85 65.80 ‐5.82
1998 ‐42.05 67.41 ‐0.62
1999 ‐195.30 57.31 ‐3.41
2000 370.13 61.01 6.07
2001 ‐352.30 65.45 ‐5.38
2002 ‐152.59 69.18 ‐2.21
2003 682.41 69.77 9.78
2004 418.08 67.95 6.15
2005 ‐669.94 74.11 ‐9.04
2006 ‐45.48 62.25 ‐0.73
2007 290.20 82.21 3.53
2008 707.09 67.73 10.44
2009 722.54 74.74 9.67
2010 564.41 61.29 9.21
2011 ‐577.62 66.85 ‐8.64
2012 527.33 59.31 8.89
2013 359.31 70.77 5.08
2014 865.49 57.95 14.93

23. Results ‐ Canola
• Zone (spatial) and time
(temporal) effects:
Yield = a*Nrate + b + λ*Zone +
θ*Time
• Quadratic was also tested
but linear fitted better than
quadratic
Year Zone Parameter Estimate StdErr Probt t-ratio
a 5.37 0.50 0.00
b 1483.82 54.44 0.00
1 70.94 11.38 6.24
2 -19.96 12.57 -1.59
3&4 -97.12 17.65 -5.50
1991 -241.83 66.22 -3.65
1992 114.91 68.03 1.69
1993 -757.32 58.62 -12.92
1994 -280.52 38.55 -7.28
1995 -561.58 48.13 -11.67
1996 46.41 90.03 0.52
1997 -341.02 48.64 -7.01
1998 183.55 44.07 4.16
1999 166.74 49.31 3.38
2000 104.07 49.68 2.09
2001 -109.31 72.31 -1.51
2002 -272.95 53.65 -5.09
2003 83.81 42.08 1.99
2004 222.57 47.33 4.70
2005 -734.96 40.90 -17.97
2006 -226.00 56.11 -4.03
2007 -347.38 39.30 -8.84
2008 623.07 51.38 12.13
2009 647.63 34.68 18.67
2010 596.48 37.76 15.80
2011 -177.82 38.60 -4.61
2012 -207.70 38.46 -5.40
2013 102.45 34.21 2.99
2014 532.30 41.55 12.81

24. Economic results
• More productive land showed 11‐18% more yield, higher
energy use efficiency per unit of input, and $70 to $75 per
ha more net return than marginal land within the STC with
the same N rate applied.
• Excessive moisture (2005&2011) or drought (2006) caused
more than 24% wheat yield loss or about $100/ha net loss.
• On average, extreme events have caused more than 20%
wheat yield loss or $80/ha net loss.
• Excessive moisture (2005&2011) or drought (2006) caused
on average 25% canola yield loss or more than $170/ha net
loss.
• On average, extreme events have caused more than 24%
canola yield loss or $161/ha net loss.

25. Conclusions
• Separating the study area into homogeneously
manageable zones is the first step to measuring
the effectiveness of precision agriculture and soil
conservation methods.
• Both spatial (zone) and temporal (time) variability
had significant effects on crop productivity, but
temporal variability had the greater effect.
• More crop and energy use efficiency can be
achieved by applying different N management in
less productive zones or cropping annual crops on
more productive fields.

26. Next Steps
• Continue study to include more crops
• Continue study based on soil productivity zones
• Continue phosphorus based zones
• Incorporate soil conservation into precision
agriculture.

27. AcknowledgementsAcknowledgements
• Agriculture and Agri‐Food Canada (AAFC)
Growing Forward for providing financial support
for this project
• Deerwood Soil and Water Management
Association for collecting data
• Manitoba Agriculture Service Corporation for
providing crop yield and N fertilizer data
• We also greatly acknowledge technical
contributions from the Lake Winnipeg Watershed
Project team.

28. GSPRECIP SWEPrecip GDD
1994 236.4347189 169.9593875 1392.072021
1995 279.4991374 317.3219062 1394.14563
1996 293.8162948 270.2023092 1494.2448
1997 260.8090143 238.339574 1495.154408
1998 187.9340707 240.0727252 1535.361094
1999 350.6474706 241.7305852 1444.82038
2000 334.5226352 118.802164 1410.177748
2001 254.9413959 261.0389698 1545.865782
2002 312.6993641 137.873931 1561.934158
2003 242.5669168 140.5690536 1603.193704
2004 294.6076741 210.4858774 1234.980258
2005 393.1291593 207.0100104 1524.013874
2006 163.3365515 168.9043485 1642.011542
2007 349.8368992 223.0162388 1555.458764
2008 318.9027064 206.9959846 1405.622878
2009 302.6351424 265.8715177 1371.604642