This study evaluated the effects of precision agriculture techniques like variable-rate nitrogen management on farm profitability and nutrient use efficiency at both the farm and watershed scales in Canada. Field experiments were conducted on canola crops from 2014-2016, applying different nitrogen rates based on historical yield zones. At the watershed scale, data from 2006-2017 was analyzed to assess spatial and temporal impacts. Results found that while nitrogen and zones significantly affected yields at individual sites, temporal trends had a larger influence watershed-wide. Conservation tillage also improved crop yields and revenues. Overall, precision agriculture showed potential to enhance nutrient use efficiency and farm economics.

1. Effects of precision agriculture, soil
conservation, N rate and temporal
trends at farm and watershed scale
M. Khakbazan, J. Huang, A. Moulin, P. Michiels
SWCS Meeting
2018, Albuquerque, NM

2. Study Issue
• Land management, cropping systems, and nutrient
management significantly affect nutrient use
efficiency and profitability.
• Overuse of fertilizer causes significant
environmental and economic impacts.
• With significant attention being paid to precision
agriculture in recent time, more research is needed
to assess the effectiveness of this technology

3. Importance of precision agriculture
• Research Question: Can variable rate N
management be used to improve efficiency of
fertilizer use and farm economy?

4. • Evaluate the viability of using precision
agriculture to improve N use efficiency
and profitability at the farm and
watershed scale.
Objective

5. Methodology
• Research was conducted with producer’s
field-scale equipment in Saskatchewan
and Manitoba during 2014, 2015, and
2016
• Eight fields were seeded to canola.
Field-scale study

6. A. Moulin, M. Khakbazan
Brandon Research and Development Centre
Field Scale Research in the Canadian Prairies on canola
yield as affected by variable application of N fertilizer in
relation to historical yield, terrain attributes and soil
properties.

7. Methodology
• Randomized fertilizer treatments (0, 50,
100, and 150% of soil test
recommendations for N) with 4
replicates were located in low, average,
and high producing zones based on
analyses of 3 to 5 years of yield maps.
Field-scale study

8. Watershed-scale study
South Tobacco Creek (STC)
150 km2 SW of
Winnipeg,
Manitoba, Canada
Spatial
distribution
of crops in 2016
Wheat and canola
are common
crops in STC
About 35 farms
and 350 fields

9. Methodology
• Agronomic, yield, and soil data were compiled
by field for the STC from 2006-2017.
• These data and a productivity Index (MASC)
based on a 10-year moving average were used
to delineate management zones in the
watershed.
• GIS and Limdep (NLOGIT 4.0) Econometric
Software were used to analyze the data
because Limdep is more suited for the STC
panel data analysis.

11. Methodology
• A yield function and net revenue were
estimated, taking into account zones, temporal
trends, and individual management practices.
• Zone (spatial), time (temporal), and other
conservation management effects (Xi):
• Yield =a+bN+cN2+βiXi+λ*Zone + θ*Time
• Quadratic and linear were tested to find the fit
Field and watershed scales

16. Effects of input variables, zones, and years on wheat yield
Inputs Estimate fixed effects Estimate fixed effects
Variables Coefficient t-ratio Zone Coefficient t-ratio Period Coefficient t-ratio
N 28.18 5.99 5D 58.84 3.42 2006 -501.13 -7.27
N2 -0.09 -5.20 5E -71.61 -2.81 2007 -627.56 -6.88
P -1.11 -0.65 12E -61.06 -1.13 2008 84.45 1.38
K 3.65 2.46 2009 334.99 4.67
S 7.62 3.33 2010 136.43 2.54
Res Cov 327.12 2.39 2011 -1037.14 -16.41
Constant 1472.93 4.95 2012 123.66 2.30
2013 77.93 1.05
2014 496.22 9.65
2015 378.51 5.91
2016 -116.26 -2.19
2017 263.10 3.00

17. Effects of input variables, zones, and years on wheat net
return
Inputs Estimate fixed effects Estimate fixed effects
Variables Coefficient t-ratio Zone Coefficient t-ratio Period Coefficient t-ratio
N 5.12 5.07 5D 11.75 3.19 2006 -107.42 -7.27
N2 -0.02 -5.41 5E -14.54 -2.65 2007 -138.90 -7.10
P -1.35 -3.66 12E -11.56 -1.00 2008 8.33 0.63
K 0.30 0.94 2009 55.10 3.58
S 0.76 1.54 2010 55.04 4.75
Res Cov 155.95 5.31 2011 -245.36 -18.09
Constant -37.23 -0.58 2012 -12.53 -1.09
2013 -18.48 -1.16
2014 127.66 11.58
2015 98.80 7.19
2016 -1.80 -0.16
2017 67.67 3.60

18. Effects of input variables, zones, and years on canola
yield
Inputs Estimate fixed effects Estimate fixed effects
Variables Coefficient t-ratio Zone Coefficient t-ratio Period Coefficient t-ratio
N 8.31 3.59 5D 23.98 2.38 2006 -392.36 -7.89
N2 -0.01 -1.87 5E -1.23 -0.10 2007 -504.28 -14.62
P 0.55 0.54 12E -127.13 -3.91 2008 350.20 8.62
K 0.59 0.81 2009 467.28 15.29
S -0.97 -0.87 2010 354.45 10.54
Res Cov 263.88 3.68 2011 -462.49 -14.51
Constant 1398.25 8.16 2012 -384.63 -11.87
2013 -72.69 -2.50
2014 307.84 8.56
2015 155.76 4.91
2016 -195.89 -5.86
2017 290.64 7.61

19. Effects of input variables, zones, and years on canola net
return
Inputs Estimate fixed effects Estimate fixed effects
Variables Coefficient t-ratio Zone Coefficient t-ratio Period Coefficient t-ratio
N 2.17 2.13 5D 8.62 1.95 2006 -203.92 -9.36
N2 0.00 -1.32 5E -0.35 -0.07 2007 -246.71 -16.33
P -1.15 -2.59 12E -46.10 -3.23 2008 117.24 6.59
K -0.33 -1.05 2009 169.76 12.67
S -1.36 -2.82 2010 202.91 13.77
Res Cov 162.00 5.15 2011 -232.51 -16.65
Constant 157.82 2.10 2012 -211.93 -14.92
2013 -75.66 -5.94
2014 185.76 11.79
2015 118.47 8.53
2016 -33.25 -2.27
2017 177.35 10.60

20. Effects of input variables, zones, and years on barley
yield
Inputs Estimate fixed effects Estimate fixed effects
Variables Coefficient t-ratio Zone Coefficient t-ratio Period Coefficient t-ratio
N 28.26 5.75 5D -36.05 -0.86 2006 -157.98 -1.63
N2 -0.07 -4.02 5E 132.14 2.89 2007 30.26 0.26
P -1.30 -0.37 12E -255.08 -2.80 2008 364.45 1.97
K -5.77 -1.59 2009 589.04 4.18
S 16.02 1.94 2010 -169.45 -1.32
Res Cov 1553.86 4.94 2011 -1818.49 -12.72
Constant 1324.45 3.74 2012 -606.61 -4.65
2013 9.93 0.07
2014 269.85 2.22
2015 681.92 5.48
2016 -581.67 -3.85
2017 1818.84 11.04

21. Effects of input variables, zones, and years on barley net
return
Inputs Estimate fixed effects Estimate fixed effects
Variables Coefficient t-ratio Zone Coefficient t-ratio Period Coefficient t-ratio
N 3.11 4.43 5D -2.28 -0.38 2006 -24.21 -1.75
N2 -0.01 -3.92 5E 16.78 2.57 2007 4.12 0.25
P -1.27 -2.57 12E -38.95 -2.99 2008 32.67 1.24
K -1.55 -2.98 2009 77.74 3.86
S 1.04 0.88 2010 -3.63 -0.20
Res Cov 362.18 8.04 2011 -286.88 -14.03
Constant -174.69 -3.45 2012 -117.43 -6.30
2013 -33.38 -1.55
2014 64.22 3.70
2015 124.71 7.00
2016 -64.83 -3.00
2017 288.38 12.23

22. Results at field scale
• Although the production function generally had the
right shape, the results were mixed in terms of
effects of zones and N rates at each individual site.
• Overall, when all data combined, N and zones were
significant.
• The high zones, on average, generated $67 more and
the low zones $61 less NR as compared to the
average zones.
• Location had significant effects on yield and NR
ranging from -$490 to 530.

23. Results at watershed scale
• Both spatial (zone) and temporal (time) variability
had effects on crop productivity, but temporal trends
had the greater effect.
• Additionally, conservation tillage had positive effects
on crop yield and economics.
• Conservation tillage could increase NR of wheat by
$23-$46 and NR of canola by $36-$49 ha-1.
• When the same rate of N was applied, more
productive lands showed higher yield and nearly $40
ha-1 more net revenue than less productive lands
within the STC.

24. Economic results
• Excessive moisture (i.e., 2011), drought (i.e., 2006),
or severe weather (i.e., 2007) in the past 12 years
have caused, on average, about 6% and 8% ha-1 yr-1
yield loss for wheat and canola, respectively.
• The average net loss was about $44 and $76 ha-1
yr-1 for wheat and canola, respectively, or $60 ha-1
yr-1 for a wheat-canola cropping system.
• The effect of temporal trends highlights the
importance of other management practices like
“tile drainage” in Manitoba.

25. Acknowledgements
• Agriculture and Agri-Food Canada (AAFC) Growing
Forward for providing financial support
• Deerwood Soil and Water Management Association for
collecting data
• Manitoba Agriculture Service Corporation for providing
crop yield and N fertilizer data
• Canola Council of Canada for providing financial support
• Northeast Agricultural Research Foundation for the data
from Melfort, Saskatchewan
• We also greatly acknowledge technical contributions
from the Lake Winnipeg Watershed Project team.