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

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

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

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

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, -0.364 174.1; 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

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

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

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

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.

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

Discussion