model predicting emergence of western corn...

Predicting emergence of westernPredicting emergence of westernPredicting emergence of westernPredicting emergence of westernPredicting emergence of western Predicting emergence of western corn rootworm, corn rootworm, Diabrotica virgiferaDiabrotica virgifera(L C t ) d lt ith d(L C t ) d lt ith d dd

Predicting emergence of western Predicting emergence of western corn rootworm, corn rootworm, Diabrotica virgiferaDiabrotica virgifera(L C t ) d lt ith d(L C t ) d lt ith d dd(LeConte), adults with a degree(LeConte), adults with a degree--day day phenology model closely tied to phenology model closely tied to (LeConte), adults with a degree(LeConte), adults with a degree--day day phenology model closely tied to phenology model closely tied to the development of cornthe development of cornthe development of cornthe development of corn

Douglass E. Stevenson, Gerald J. Michels,Douglass E. Stevenson, Gerald J. Michels,John B. Bible, John A. Jackman, Marvin K. HarrisJohn B. Bible, John A. Jackman, Marvin K. Harris

Spread of Spread of D. virgiferaD. virgifera since 1955since 1955

195519551955195519751975197519751990199019901990

Materials & MethodsMaterials & MethodsMaterials & MethodsMaterials & Methods++ The study areaThe study area

66 Fields selectedFields selected!! Ett (N HP)Ett (N HP)!! Etter (No. HP)Etter (No. HP)!! Dalhart (No. HP)Dalhart (No. HP)!! Black (So. HP)Black (So. HP)!! Dimmitt (So. HP)Dimmitt (So. HP)

66 Med. plant dateMed. plant date!! 14 Apr (doy 104)14 Apr (doy 104)14 Apr (doy 104)14 Apr (doy 104)

66 Med VE dateMed VE date!! 23 Apr (doy 113)23 Apr (doy 113)

66 IrrigationIrrigation!! SupplementarySupplementary

Materials & MethodsMaterials & MethodsMaterials & MethodsMaterials & Methods++ Model development stepsModel development steps

66 Insect SamplingInsect Sampling!! S t ti d lS t ti d l!! Systematic random sampleSystematic random sample!! Emergence & Pherocon AMEmergence & Pherocon AM®® TrapsTraps!! 4 transects of 5 traps per field4 transects of 5 traps per field!! Checked weeklyChecked weekly

Climatic DataClimatic Data66 Climatic DataClimatic Data!! TXHPET networkTXHPET network

EtterEtterDalhartDalhartDimmittDimmitt


66 Selection of representative dataSelection of representative data!! Model development:Model development:Model development:Model development:

Etter (1999, 2000, 2001, 2002, 2005, 2006)Etter (1999, 2000, 2001, 2002, 2005, 2006)!! Validation:Validation:

Etter (1997, 1998, 2003, 2004)Etter (1997, 1998, 2003, 2004) Dalhart (1998, 1999, 2003, 2004, 2005, 2006)Dalhart (1998, 1999, 2003, 2004, 2005, 2006) Black (2006)Black (2006)

66 DegreeDegree--day computation methodday computation method!! Root orm Temp a e (no c toff)Root orm Temp a e (no c toff) Stress degreeStress degree da s (SDD)da s (SDD)!! Rootworm: Temp. ave. (no cutoff) Rootworm: Temp. ave. (no cutoff) –– Stress degreeStress degree--days (SDD)days (SDD)!! Corn: Barger (1969) method (horiz. cutoff = 86ºF)Corn: Barger (1969) method (horiz. cutoff = 86ºF)

66 Determination of base temp. & start dateDetermination of base temp. & start date!! Point prediction model with RingPoint prediction model with Ring Jackman ANOVAJackman ANOVAPoint prediction model with RingPoint prediction model with Ring--Jackman ANOVAJackman ANOVA!! Compares all prospective base temps & start datesCompares all prospective base temps & start dates

Mean & Median dates & degreeMean & Median dates & degree--day sumsday sums 64,000 prospective models64,000 prospective models

!! Selection on the basis of lowest RMSESelection on the basis of lowest RMSE


66 Selection of mathematical functionSelection of mathematical function!! 19 sigmoid functions19 sigmoid functions!! Nonlinear regression: RMSE, F statistic, Prob. > FNonlinear regression: RMSE, F statistic, Prob. > F!! OLS regression: ROLS regression: R22

!! Selection: highest R2 & F stat, lowest RMSE & Prob. > FSelection: highest R2 & F stat, lowest RMSE & Prob. > F

Determination of model coefficientsDetermination of model coefficients66 Determination of model coefficientsDetermination of model coefficients!! Nonlinear regressionNonlinear regression!! 44--parameter modified Gompertz functionparameter modified Gompertz function

66 Synchronization with corn phenologySynchronization with corn phenology!! Corn GDD (Barger 1969)Corn GDD (Barger 1969)

Materials & MethodsMaterials & MethodsMaterials & MethodsMaterials & Methods++ Model validation stepsModel validation steps

66 Selection of independent validation dataSelection of independent validation dataEstablish 4 5Establish 4 5 day prediction interval (9day prediction interval (9 day trap ch Interval)day trap ch Interval)66 Establish 4.5Establish 4.5--day prediction interval (9day prediction interval (9--day trap ch. Interval)day trap ch. Interval)

66 Stat. Stat. –– Nonparametric method of (Kutner et al. 2004)Nonparametric method of (Kutner et al. 2004)!! Ratio MSPE / MSERatio MSPE / MSE(model)(model)

66 CalibrationCalibration!! Early / late: predictions shifted Early / late: predictions shifted ±± 10 days early & late10 days early & late!! Natrual variability (location / year): pred Superimposed on obsNatrual variability (location / year): pred Superimposed on obsNatrual variability (location / year): pred. Superimposed on obs.Natrual variability (location / year): pred. Superimposed on obs.

66 ValidationValidation!! Compute ratio & compare to calibrationsCompute ratio & compare to calibrationsCompute ratio & compare to calibrationsCompute ratio & compare to calibrations!! Compute days early & lateCompute days early & late!! Visual: Scatterplot with 4.5Visual: Scatterplot with 4.5--day prediction intervalday prediction interval

Base Temp & Start Day ResultsBase Temp & Start Day ResultsBase Temp & Start Day ResultsBase Temp & Start Day Results

Base = 50ºF, Start day = 112, Median corn VE date = 113Base = 50ºF, Start day = 112, Median corn VE date = 113

Model SelectionModel SelectionResults of NLIN & OLS RegressionsResults of NLIN & OLS Regressions

Model SelectionModel SelectionResults of NLIN & OLS RegressionsResults of NLIN & OLS RegressionsResults of NLIN & OLS RegressionsResults of NLIN & OLS RegressionsResults of NLIN & OLS RegressionsResults of NLIN & OLS RegressionsModel functionModel function RMSERMSE Med. FMed. F Prob > FProb > F RR22

Modified 4Modified 4--parameter Gompertzparameter Gompertz 6.86926.8692 339.83339.83 0.000550.00055 0.98830.9883

JohnsonJohnson--SchumacherSchumacher 25.354625.3546 89.5789.57 0.001110.00111 0.85610.8561

Modified JohnsonModified Johnson--SchumacherSchumacher 26.650626.6506 2.262.26 0.263280.26328 0.84430.8443

Gompertz (3Gompertz (3--Parameter)Parameter) 26.060826.0608 27.4427.44 0.028160.02816 0.83410.8341

LogisticLogistic 27.048227.0482 93.7093.70 0.025850.02585 0.82300.8230

JanoschekJanoschek 33 791733 7917 68 6868 68 0 035220 03522 0 79900 7990JanoschekJanoschek 33.791733.7917 68.6868.68 0.035220.03522 0.79900.7990

LogLog--LogisticLogistic 43.415443.4154 208.03208.03 0.105460.10546 0.69830.6983

Weibull (2Weibull (2--Parameter)Parameter) 40.661640.6616 293.28293.28 0.000670.00067 0.68590.6859

Von BertalanffyVon Bertalanffy 40.832140.8321 38.8738.87 0.053280.05328 0.67680.6768

RichardsRichards 78.571078.5710 1.031.03 0.555610.55561 0.67070.6707

Functions not in top 10:Functions not in top 10: MorganMorgan--MercerMercer--Florin, MichaelisFlorin, Michaelis--Menten, Jolicoeur, Menten, Jolicoeur, Modified Weibull (3Modified Weibull (3--Parameter), Schnute, MitscherlichParameter), Schnute, Mitscherlich--Spillman, Hill,Spillman, Hill,PreecePreece--Baines, ZengBaines, Zeng--Wan.Wan.

Model Coefficient ResultsModel Coefficient ResultsModel Coefficient ResultsModel Coefficient ResultsM d l ffi i tM d l ffi i t ** (( (( ((bb ** (( dd ))))))++ Model coefficients: Model coefficients: y = a * expy = a * exp((-- ((expexp((b b –– c * c * ((xx--d d ))))))

66 aa = 96.5 = 96.5 66

!! Vert. scale parameter (controls shape of curve on Vert. scale parameter (controls shape of curve on yy--axis)axis)!! upper reliability limitupper reliability limit

66 bb = 6.0= 6.0!! Horiz. scale parameter (controls shape of curve on Horiz. scale parameter (controls shape of curve on xx--axis)axis)!! determines inflection points and slope of curvedetermines inflection points and slope of curve

66 cc = 0.00404= 0.00404!! shape parameter (controls assymetry of curve)shape parameter (controls assymetry of curve)!! small values indicate short early tail of the curvesmall values indicate short early tail of the curve

66 dd = 4.0= 4.0!! Shift parameterShift parameter!! Determines starting point along Determines starting point along xx--axisaxis

ModelModelModelModely = 96 5y = 96 5 ×× expexp(( -- ((expexp((6 06 0 -- 0 004040 00404 ××((xx -- 4 04 0 ))))))y = 96.5 y = 96.5 ×× expexp(( -- ((expexp((6.0 6.0 -- 0.00404 0.00404 ××((x x -- 4.0 4.0 ))))))

x = cumulative heat (DD50ºF)x = cumulative heat (DD50ºF)y = cumulative proportional emergence (%)y = cumulative proportional emergence (%)

ValidationValidationEarly/Late CalibrationEarly/Late Calibration

ValidationValidationEarly/Late CalibrationEarly/Late CalibrationEarly/Late CalibrationEarly/Late CalibrationEarly/Late CalibrationEarly/Late Calibration

Valid predictions within Valid predictions within ±± 4.5 days will have MSPE/MSE4.5 days will have MSPE/MSE(model)(model) ratios < 5.1819ratios < 5.1819

ValidationValidationCalibration for Natural VariabilityCalibration for Natural Variability

ValidationValidationCalibration for Natural VariabilityCalibration for Natural VariabilityCalibration for Natural VariabilityCalibration for Natural VariabilityCalibration for Natural VariabilityCalibration for Natural Variability

Unbiased predictions will have MSPE/MSEUnbiased predictions will have MSPE/MSE(model)(model) ratios < 5.0941ratios < 5.0941

Model ValidationModel ValidationMSPE/MSE Ratios for Locations & YearsMSPE/MSE Ratios for Locations & Years

Model ValidationModel ValidationMSPE/MSE Ratios for Locations & YearsMSPE/MSE Ratios for Locations & YearsMSPE/MSE Ratios for Locations & Years MSPE/MSE Ratios for Locations & Years MSPE/MSE Ratios for Locations & Years MSPE/MSE Ratios for Locations & Years

All location / year MSPE/MSEAll location / year MSPE/MSE(model)(model) ratios are within acceptable ratios are within acceptable range for prediction interval range for prediction interval

Model ValidationModel ValidationPrediction Error in DaysPrediction Error in Days

Model ValidationModel ValidationPrediction Error in DaysPrediction Error in DaysPrediction Error in DaysPrediction Error in DaysPrediction Error in DaysPrediction Error in Days

All location / year predictions are within prediction interval (All location / year predictions are within prediction interval (±± 4.5 days) 4.5 days)

Model ValidationModel ValidationScatterplot: Observed & PredictedScatterplot: Observed & PredictedModel ValidationModel Validation

Scatterplot: Observed & PredictedScatterplot: Observed & Predictedpp((±± 4.54.5--day Prediction Interval)day Prediction Interval)

pp((±± 4.54.5--day Prediction Interval)day Prediction Interval)

116 Obs hidden by prediction line

Start = corn VE date, DegreeStart = corn VE date, Degree--days = (DD50ºF days = (DD50ºF –– SDD) SDD)

116 Obs hidden by prediction line

ConclusionsConclusionsConclusionsConclusions++ Model predictions give more time for decisions to corn producersModel predictions give more time for decisions to corn producers++ Model predictions give more time for decisions to corn producers Model predictions give more time for decisions to corn producers

in the Texas High Plains than scouting or trapping alone.in the Texas High Plains than scouting or trapping alone.

++ Synchronization with corn permits integration of pestSynchronization with corn permits integration of pest++ Synchronization with corn permits integration of pest Synchronization with corn permits integration of pest management with crop management decisions.management with crop management decisions.

++ The model is applicable to adjacent regions of the So GreatThe model is applicable to adjacent regions of the So Great++ The model is applicable to adjacent regions of the So. Great The model is applicable to adjacent regions of the So. Great Plains.Plains.

++ Use in areas outside Texas High Plains will require fieldUse in areas outside Texas High Plains will require field++ Use in areas outside Texas High Plains will require field Use in areas outside Texas High Plains will require field validation.validation.

++ Shows the fungibility of this approach to developing prediction Shows the fungibility of this approach to developing prediction g y pp p g pg y pp p g pmodels for other insectsmodels for other insects

++ Phenology models developed in this way can provide very close Phenology models developed in this way can provide very close gy y ygy y yestimates of physiological base temperatures and predictions of estimates of physiological base temperatures and predictions of phenological events of interest in the life cycles of insects.phenological events of interest in the life cycles of insects.

model predicting emergence of western corn...

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