Department of Agronomy
Estimating Seeding Dates for Winter
Canola in Iowa
Rafael Martinez-Feria
Graduate Student in Sustainable Agriculture,
and Crop Production and Physiology
GIS Certificate Candidate
CRP 595
March 30th, 2015
The efforts here presented are possible
thanks to the involvement of multiple parties.
Funding:
North Central SARE
Leopold Center for Sustainable Agriculture
Vernon C. Miller Scholarship in Agronomy
Advise:
Dr. Mary Wiedenhoeft
Dr. Tom Kaspar
Midwest Cover Crop Council
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Department of Agronomy
Presentation Outline
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Background
Methods
•  Conventional Method
•  Proposed Method
•  Spatial Interpolation
Results
Conclusion
Seeding date atlas for winter canola in Iowa
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Unsustainable erosion and nutrient pollution
of water bodies is a major drawback of
conventional crop rotations.
(NRCS, 2013)
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Growing winter crops grown after harvest of
summer helps conserve soil and water.
(Lime Creek Watershed Improvement Association, 2012)
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Living covers prevent soil erosion and
nutrient pollution.
(NRCS, 2013)
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Winter canola could be used to provide
soil cover during the winter in Iowa.
Sorenson Farm plots, Nov 2012
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Winter canola can potentially produce a
marketable oilseed crop.
Sorenson Farm plots, Jul 2013
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Midwest farmers report planting winter crops
in the fall is challenging.
(North Central SARE Survey, 2013)
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Planting winter crops winter crops in the fall
is a race against time.
Average Fall Air Temperature, Ames IA
90
80
Temperature (F)
70
60
50
Mean
40
Max
30
Min
20
10
0
230
250
270
290
310
330
350
Day of year
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There are tools available to help farmers time their
cover crop plantings.
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Research Question
How late can we plant winter
canola to achieve sufficient growth
and winter survival in Iowa?
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Winter canola needs to form between at least five
to maximize potential winter survival.
7-8 leaves
11-9 leaves
5-6 leaves
3-4 leaves
(Lääniste et al., 2013)
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Convention: plant six weeks before the first
<-4.5 ºC frost (Boyles et al., 2012).
•
Conditions vary
widely among
locations and
years.
•
Is this estimate
relevant for
Iowa?
This is a healthy six-leaf rosette
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Proposal: We could also estimate seeding dates
using canola’s thermal-time requirement for
developing at least five leaves.
Emergence
Site SOR 2012 BRU 2013 Seeding Date 31-­‐Aug 17-­‐Sep 1-­‐Oct 3-­‐Sep 13-­‐Sep 1-­‐Oct Median = Leaf development
GDD to Emergence 223 181 129 345 156 156 168.5 15
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What is thermal time?
-  Accumulation of temperature over time
-  Growing Degree Days (GDD)
if Tmin< TBASE, then Tmin=TBASE;
if Tmax< TBASE, then Tmax=TBASE;
if Tmax >TMAX, then Tmax=TMAX;
if Tmin > TMAX, then Tmin=TMAX.
TBASE = 4.5ºC
TMAX = 30ºC
(Mcmaster and Wallace, 1997)
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Using field data, we estimate that canola needs
about 515 GDD to develop five leaves.
Emergence
Site SOR 2012 BRU 2013 Seeding Date 31-­‐Aug 17-­‐Sep 1-­‐Oct 3-­‐Sep 13-­‐Sep 1-­‐Oct Median = Leaf development
GDD to Emergence 223 181 129 345 156 156 168.5 TTRoverwintering = 169 + 346 = 515 GDD
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Calculating Latest Reliable Seeding
Date (LRSD)
Conventional method
•  Plant 42 days before the
first frost
Proposed method
•  Plant 515 GDD before the
first frost
!"#$ = ! !!!.!℃! − !!
!"#$ = ! !!!.!℃! − !42!
!
∋ ! −!
(!!!!"#$% ) == ! !!! !
! !!.!℃ !!
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Spatial Question
Will these two methods produce
different estimates for Iowa?
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Weather data* form 1972-2011 at 110
stations was used for this analysis.
-  Daily observations
of GDDs
-  Date of first
-4.5ºC frost
*Iowa Environmental
Mesonet
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Results from calculation
1. With the 40 year range,
we obtain distributions of
LRSD at every location.
2. From these distributions,
we calculate the 10th, 50th
and 90th percentiles (aka
probability levels).
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Interpretation of the probability levels
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0.1 Probability level:
90% of the times there is enough time
•
0.5 Probability level:
50% of the times there is enough time
•
0.9 Probability level:
10% of the times there is enough time
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Yes, they lead us to different conclusions.
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The conventional method tends to underestimate the
thermal time available for growth in Iowa.
Δmean = 2.4
Δmean =10.8
Δmean =17.5
Days
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Spatial Question
Which method produces better
LRSD estimates?
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Spatial Autocorrelation
"Everything is related to everything
else, but near things are more
related than distant things."
-  W. Tobler (1970)
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Voronoi maps reveal the how stationary are the LRSD
estimates.
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Ordinary Kriging produces prediction and error surfaces
out sample points.
Constant but unknown
! ! = !! + !!(!)!
(Johnson, 2001)
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The Z values are weighted linear combinations of the
values of neighboring points.
!
! !! = !
!! !! !! !
!!!
Can we know
how good our
predictions of Z
are?
!
!! ! = 1
<- make sure that
predictions are unbiased
!
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Variance
Yes, by fitting a semivariance function…
Semivariance measures how (dis)similar are two points
in relation to distance.
Semivariogram
Covariogram
Chen et al., (2012)
Distance
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Then we substitute the empirical semivariogram by a
known function.
Empirical
Gaussian
1
! ℎ =!
!
2!(ℎ)
!(!)
![! !! − !!(!! + ℎ)]!
!!!
! ℎ = ! !! + ! 1 − !"# −!
!!! !
!!
;
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Removing spatial trends ensures that the mean is
constant and data are normal.
Conventional method
LRSD
Global Polynomial Interpolation
residuals
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Removing spatial trends ensures that the mean is
constant and data are normal.
Proposed method
LRSD
Global Polynomial Interpolation
residuals
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OK interpolations were fitted to the LRSDs calculated by
both methods.
LRSD calculation method
Probability
Interpolation Method
Trend
Order of trend
Trend type
Trend Removal
Transformation
Searching Neighborhood
Neighbors to include
Include at least
!
Sector type
Semiaxis (m)
Semivariogram
Number of lags
Lag size (m)
Nugget
Model Type
Range (m)
Anisotropy
Partial sill
0.1
Conventional
0.5
0.9
0.1
Proposed
0.5
0.9
OK
OK
OK
OK
OK
OK
1st
Global
Yes
None
2nd
Global
Yes
None
2nd
Global
Yes
None
2nd
Global
Yes
None
2nd
Global
Yes
None
2nd
Global
Yes
None
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10
15
10
15
10
Four and 45°
Four and 45°
110,744
15
10
Four and
45°
70,633
79,531
67,445
15
10
Four and
45°
67,445
15
10
Four and
45°
67,445
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13,843
26.64
Gaussian
110,744
No
7.09
12
9,565
17.17
Gaussian
70,633
No
6.40
12
9,062
22.06
Gaussian
79,531
No
5.17
8
14,000
9.04
Gaussian
67,445
No
2.67
12
8,431
4.95
Gaussian
67,445
No
2.65
12
8,431
2.80
Gaussian
67,445
No
2.38
Four
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Errors were the lowest with the proposed method at the
0.5 and 0.9 probability levels.
LRSD
calculation
method
Probability
Conventional
0.1
0.5
0.9
0.1
Proposed
0.5
0.9
Prediction Errors
RMSE
MSE
RMSSE
ASE
5.71
-0.014
1.016
5.60
4.80
-0.004
1.006
4.76
5.21
-0.002
1.007
5.17
3.39
-0.001
1.000
3.40
2.67
-0.002
1.000
2.67
2.13
-0.001
0.991
2.14
Predicted vs Measured
Regression Equation
Slope
Intercept
0.30
168.9
0.43
150.6
0.42
160.0
0.59
97.8
0.73
68.2
0.79
51.9
!
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The proposed method was better suited for OK
interpolation.
Conventional
prediction
std error
prediction
Proposed
std error
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Conclusions on Methods
Conventional Method
•  Underestimates the
actual thermal-time
required for development
•  Estimates have poor
spatial structure
•  OK prediction errors are
relatively larger
Proposed Method
•  Based on time and
temperature
•  Estimates are relatively
more stationary
•  OK prediction errors are
relatively lower
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Research Question
How late can we plant winter
canola to achieve sufficient growth
and winter survival in Iowa?
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To create a seeding date atlas, the lower 95% confidence
interval was calculated using the proposed 0.5 probability
rasters.
prediction
std error
!"#$!"#!$ = !!! − 1.96 ∗ !"
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Weaknesses
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Winter kill can still happen despite of good
growth achieved in the fall
Will the next 40 years be on average similar to
1972 – 2011?
Growth data used is to estimate TTR is scant
(one cultivar, two years)
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Next steps
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Compute county average values so they can be
used in the Cover Crop Decision Tool
Increase the confidence in the TTR by
incorporating more field and cultivar data
Expand this analysis to other areas of the Midwest and to other crops
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Thank you!
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Questions?
Rafael Martinez-Feria
rmartine@iastate.edu
1021 Agronomy Hall
Ames, IA 50011
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