1. Environmental Variables and Observed Field Differences in Aphid Population Change Across Geographic Locations
John Gordy, Michael Brewer
Texas A&M University
Sorghum – Sugarcane Aphid
Research Exchange Meeting
Dallas, TX
January 3 – 4, 2017

2. Locations and fellow investigators
Name
Affiliation
Trial Locations
Trial Years
David Kerns
Texas A&M University
Winnsboro, LA
2014, 2016
Nick Seiter
University of Arkansas
Monticello, AR
2015
David Buntin
University of Georgia
Griffin, GA
John Gordy
Michael Brewer
Corpus Christi, Rosenberg, Gainesville, TX
2014, 2015, 2016
2015, 2016
2016

3. Introduction Considerations in grower decision to control aphids:
Yield-damage relationship (EIL), but
How quickly can populations expand?
Dry vs. Wet Conditions
Temperature
Other

4. Materials & Methods
Data from non-insecticide treated plots from 2014, 2015, and 2016 threshold plots
Precipitation and Temperature data during period of population growth
Log-linear regression of aphid population across time

5. Simple y (sca/leaf) – x (days) Regression
Location-Year n* n+
Slope
Slope 95% CI
Intercept
Intercept 95% CI
F; d.f. P R2
DT (days)
CC-2014 4 16
0.088
0.010, 0.165
4.65
3.810, 5.483
5.9; 1,15
0.0291
0.2967
7.9
LA-2014 5 40
0.176
0.142,0.210
1.78
1.236, 2.333
111.4; 1,37
<0.0001
0.7506
3.9
CC-2015 8 64
0.102
0.078, 0.124
0.89
0.284, 1.487
86.4; 1,62
0.5822
6.8
UGC-2015 12
0.092
0.040, 0.143
5.18
4.676, 5.674
15.6; 1,10
0.0027
0.6095
7.6
GA 2015 3
0.086
-0.012, 0.185
3.33
2.696, 3.969
3.8; 1,10
0.0798
0.2754
8.0
AR 2015 7 56
0.082
0.026, 0.137
3.01
2.174, 3.848
8.64; 1,54
0.0048
0.13
8.5
CC-2016
0.097
0.049, 0.144
1.79
0.947, 2.633
17.18; 1,35
0.0002
0.3292
7.2
NTX-2016
0.128
0.108, 0.147
-0.93
-1.491,
174.1; 1,62
0.7374
5.4
LA-2016 20
0.170
0.098, 0.242
1.01
-0.001, 2.019
24.7; 1,18
0.5785
4.1
UGC 2016 24
0.317
0.293, 0.341
1.83
1.640, 2.013
761.7; 1,22
0.9719
2.2
n* number of dates used in regression
n+ total data points used in regression

6. Beginning Growth Stage
Location-Year
Hybrid
Beginning Growth Stage
Ending Growth Stage
Doubling Time
CC-2014
Tx430 V8
bloom
7.9
LA-2014
boot
milk
3.9
CC-2015 V4
heading
6.8
UGC-2015
DKS 53-67
7.6
GA-2015
SS800A
late vegetative
8.0
AR-2015
P83P99
hard dough
8.5
CC-2016
7.2
NTX-2016
5.4
LA-2016
DKS 38-88
4.1
UGC 2016 V3 V6
2.2

7. Doubling Time – DD / Precipitation Regression
Model n
PPD
95% CI
MADD
Intercept
F; d.f. P R2
DT=PPD 9
-14.9
-21.8, -8.01
 ---
---
7.67
6.52, 8.82
26.17; 1,7
0.0014
0.789
DT=MADD
0.178
-0.60, 0.96
-0.31
-24.5, 25.1
0.29; 1,7
0.6060
0.040
DT=PPD+
-19.25
-24.7, -13.8
-0.41
-0.69, -0.12
21.04
11.5, 30.6
39.19; 2,6
0.0004
0.929
DT = Doubling Time
PPD = Mean Precipitation per Day
MADD = Mean Accumulated Degree Days per Day

8. Doubling Time – DD / Precipitation Regression
Population Doubling Time (days)
Mean Precipitation Per Day (inches)
Mean DD50 Accumulated Per Day

9. Key Learnings
Population doubling time ranged from 2.2 to 8.5 days, with a mean of 6.2 days across 10 location years.
Precipitation had a greater effect on population growth than cumulative degree days, explaining about 79% of the variability in DT across locations (univariate model).
A bivariate model adding temperature to precipitation explained about 93% of DT variability
Although increased precipitation was associated with greater aphid population growth, yield may be stable when there is good soil moisture
Take home: Sampling frequency and spray decisions need to be based on quick doubling time of less than a week until we can better gauge doubling time for any particular situation.

10. Next Steps Integrate doubling time into economic threshold calculation
Other variables such as when precipitation occurs, hybrid background, natural enemy activity may be relevant

11. Discussion