SUPPL. NO. 27 SOUTHWESTERN ENTOMOLOGIST DEC.2003

PECAN NUT CASEBEARER PHEROMONE MONITORINGAND DEGREE.DAY MODEL VALIDATION ACROSS THE PECAN BELT

D. E. Stevensonr, A. E. Knutsonr, W. Reel, J. A. Jackmanr, A. Deanr, J' H. Matis2,J. McVat', M. Nesbitta R. Mizell5, J. Dutcher6, W. Reid7, M. Ha[8, D' Barlowe,

M. T. Smithro, P. Mulderro, M.W. Smithro, J. G. Millartr and M. K. Harrist

ABSTRACT

Pecan nut casebearer (PNC) was monitored at thirly-seven locations in eight statesacross the pecan belt with sticky traps baited with gray butyl rubber caps feated wittt 100

micrograms (pg) E(8),2(l 1)-hexadecadienal. The pheromone proved to be an effective lureat all locations. Longevity oflure effectiveness exceeded 8 weeks. Data from taps proved aneffectivemeans fordevelopingemergencecurvesforadultmalemoths andestimatingpercentemergence and adult activity. In locations where available weather data permittedphenologicalpredictions bythe Texas PNC degree-daymodel, pheromonemonitoringprovedeffective in model vatidation" improvement of model predictions and in continued modeldevelopment.

INTRODUCTION

The pecan nut casebearer , Acrobasis nuxvorella Neunzig (PNC), is a key pest ofcommercially grown pecan, Cqrya illinoinersis (Wangengh,) K. Koch, in the United Statesand Mexico. Payne (1991) suggests that much of the $75 million spent annublly onmanagement of pecan insects in the United States went toward suppressing this pest. USDA(1992) estimatedthecostto GeorgiaPecanproducers at $25 million forthatstate alone. Harris( I 998) estimated that its cost to Texas pecan producers has increased substantially since I 989and in most years exceeds that of Georgia.

rDepartsnent of Entomolory, Texas A&M University, College Station, TX2Dopartnent of Statistics, Texas A&M University, College Station, TX3Department of Entomology, Auburn University, ALaDepartnent of Horticulture, Auburn University, ALsDepartment of Entomology & Nematology, Univenity of Floridg Tallahassee, FL6Department of Entomology, University of Georgi4 Athens, GATPecan Experiment Field, Chetopg KSsDeparunent ofEntomology, Louisiana State University, Baton Rouge, LAeDepartrnent of Entomolory, Oklahoma State Univenity, Stillwater, OKt\.lsoR-sIR, Beltsville, MDrrDepartment of Entomology, University of Califomia at fuverside, Riverside, CA

57

Experiment Station Directors (SAAESD) Multistate ResearchFund (MRF) SupportedProjectS-j93, following the discovery ofthe pecan nut casebearer sex attractant pheromone, E(9),

Z(l l)-6e*u6."adienal, by Miliar et al. (tSSO), This opened the way for many applications of

pheromone technology to the study and management of this pest.

The most widely used degree-day models in agriculture today were developed in

conjunction with pheromone tapping. Almost all of them depend on a "biofix" with direot

bioiogical observation ofthe pesior pheto.one traps to establish a start date or correction for

the midel. In contrast, the oldest and most widely used PNC phenology model establishes its

start date from frost free day (FFD) environmental cues or a "biofix" of bud break.

The PNC pheromone is becoming an effective aid in pecan pest management.Producers and entomologists are effectively integrating it into existingpecanPestmanagemerysystems (Hanis et al. t9i95). Although PI\iC phEromone appeqeq late_rn-the development of

ptcan IPM systems, with economic-thresholds, scouting methods and phenological models

att Ueing devetoped several years before and independently ofthe pheromone, curent use of

ptt tonr-onr traps for monitoring is being rapidly adopted_bf producers. Tfrg qase of use, the

ieadiry uiriUf" results and the lJn, expense of trapping all have contributed to its adoption by

pecan producers.' 'The fact that excellent scouting procedures and several reasonably successful efforts

at modeling PNC phenology existed pr{or to the development ofthe-PNC pheromone provides

an existiniconteit for thi-s new tool. There are more than 2,000 known insect pheromones

ifufuyo tigt, lrn et al. 1996), but less than 100 are used with any fgulartty in pest

*onitoting (Bedoukian 2003, fr6c€ 2003, Hercon 2003)' Eren feler fr1!^;rse in active

--ug"-.;t ryrtems, and mosi exist primarily as academic curiosities (Trdcd 1997). The PNC

phero-mone shows great promise in bbth monitoring adult activity and in model development

ioO u*iaution (danthim et at. 2002). Howevir, without integrating it into existing

management strategies, it could fall into that very large group of pheromones in the latter

.utrgJry. Th" t"y to'finding a practical use ofthe PNC pheromone must be its integration into

existing pest management systems.-ijnder 5-293, severaiprotocolswere developed forabeltwide PNCpheromonetesting

and PNC male monitoring p.ogra*. The 3-293 PNC pheromone tapping project had the^

following objectives: <f l iaiiaa:te effectiveness of PNCpheromone througlgut the rango ofptt6;(Ziestablishrelaiionship,ifany,between andphenologicaleventsinPNClifehistory;ana (i j determine improvements thatcouldbemade inthe degree-daymodel thatare indicated

from pheromone traP data.

MATERIALS AND METHODS

Lead entomologists at eight land grant universities in the 3-293 project region

cooperated in this PNC-phe.ornonl trappin[ and monitoring project (Table l). Additionally

two cooperators at land grant universities outside the reeion an{ 9ne cooperator in Mexicoparticipated in this project. These key cooperators coordinated \Mith county extension ag€nts

iCBes) and cooperating producers to select orchards infested with PNC'Sites were selecte'd by cooperators in the different states and counties partioipating in

the 5-293 PNC trapping project. Thirly-seven orchards represented typical production for their

specific state and'county location aria haa histories of economic damage by PNC. Within

Texas, cooperating CEAs monitored PNC in 32 pecan orchards in their counties. The map in

Fig. 2 shows the location ofcooperating orchards, and Table I lists cooperating scientists andlocation of partioipating orchards.

Before trappin[ begun, Hanis (1995) developed a standard protocol to maintainuniform procedurei foi atiture handling, trapping, data recording ?Ild the-qocedure fo.1submitting data from trapping at the end of each season. The protocols were followed at all

sampling locations for both years of the study.

59

t 3 o,

E Coopergtingorcnoro

FIG. 2. Locations ofcooperating orchards across the pecan belt.

The synthetic PNC pheromone, E,Z(g,llfhexadecadienal, was prepared in J.G.Millar's laboratory at the university of california at Riverside. The lure was a 10.7 mm graybutyl rubber cap (stopper septum) impregnated with 100 micrograms (pg) of the syntheticPNC pheromone. The study adopted the inexpensive, disposable Pherocon IIP delta tap toprovide uniform standardized data (Southwood 1 978). The sampling unit consisted of a singleTrdc6 Pherocon llI@ laelta; sticky trap with a PNC pheromone lure. Traps were placed singlyin trees near the tip of nut bearing branches, I .5 to 2.1 meters above the grorurd, maintaininga minimum distance of 50 meters between traps.

Sampling followed a standard systematic sampling scheme for all trap placement. Asystematic sampling scheme was selected over random placement because of constraintsimposed by time, economics and to ensure that pheromone plumes from any one trap wouldnot overlap or interfere with those produced by other traps. The sampling universe consistedofpecan orchards selected by cooperators in nine states and Mexico throughout the pecan belt.

Trapping began about two weeks before the earliest expected flight of theoverwintering generation. Sampling locations were mapped within the orchards, andinsecticide treatments were recorded for trees with traps and the surrounding trees.Application data included pesticides, application rate and treatment date. Traps were checkedat least twica weekly and the number of moths caught were recorded. Moths were not removedfrom haps. At the end of each trapping season, data from all PNC trapping locations weresubmitted to the TAMU Pecan Insect Laboratory for analysis.

Entomologists at Land Grant Universities in Alabama, Florida, Georgia" Mississippi,Oklahoma and Texas cooperated in the monitoring project in field trapping during 1995.Standard protocols were followed for the PNC monitoring project. The selected orchards andcoordinated trapping with county extension agents (CEAs) and cooperating producers,followed the standard protocols for the PNC monitoring project. Twenty-seven orchardsrepresenting tJpical production for their state and county with histories of economic damageby PNC were monitored. Ten baited taps and two unbaited fiaps were utilized per orchard.

60

TABLE L States, Cooperating Scientists and Participating Pecan Orchards in 5-293 BeltwidePNC Pheromone Trapping.

State CooperatingScientists PartioipatingOrchards

Alabama

Florida

Georgie

Kansas

Louisiana

Mississippi

Oklahoma

Texas

John McVavMonte Nesbitt

Russell Mizell

James Dutcher

William Reid

Michael Hall

Michael T. Smith

D. BarlowR.A. GranthamPhilip MulderM.W. Smith

Marvin IC HanisAlan Knutson

Bill Ree

Dade Orchard. Fairhooe. ALMcKenzie Or6hard, Fiirhope, AL

Monticello, FL

Ponder Farm, Tifton, Tift Co,, GAH.C. Ellis, Chulq Tift Co., GAG.H. Block & L. Tedders, Byron, GA

Pecan Experiment Field, Chetopq KS

Baton Rouge, La

Stoneville Exp. Station, Stoneville, MS

Dooly Barlow, Ardmore, OKWes Elkins. Duncan, OKJ. Harrison, Ft. Gibson, OKS. Hulsig, Red River Farms, Bixby, OKIfuicht Orchard. Saoulpa- OKPecin Research-Staiiori, Sparks, OKAdams Orchard, Stillwater, OKR.A. Grantham, Guthrie, OK

Waverly Johnson, Brazoria Co., TXEllis Brown. Brownfield TXPecan Resedrch Station, Brownwood, TXAdriance Orchard, Burleson Co. TXRoyalty Pecans, Burleson Co., TXWilliam Tidwell, Comanche Co., TXGres Jones. Crosbv Co.. TXHoiard Orbhards,'El Piso Co., TXJim lvey, El Paso Co., TXJim Wcird, Simms Orohard, Ector Co., TXR. Lacv. Grecc Co.. TXStarteTart, SEguin, Guadatupe Co., TXGordon Pecan Orchard. Jefferson Co.. TXPreston Dudley, Irion Co., TXR.H Crawford, Lamar Co., TXSterling Pecan, Schulenburg, Lavaca Co., TXAlton Sager, Eagle Pass, Maverick Co., TXJorge GuZman, Quemadb, Maverick C6., TXLa Vern Vinson, Medina Co,, TXHewett Ranch, Navarro Co., TXSimms Pecan Orchard, Midland Co., TXFish Powell, Ft. Stockton, Pecos Co. TXNewkirk Orchard, San Saba, TXJohn Becnaud. San Ancelo, Tom Green Co. TXO.B. Joh-nson,-Tyler, Sftith Co., TXJoe Janak and Fied Stockbrauer, Victoria Co. TXH. Garf, Wharton Co., TXLeo V. and Dan Selz. Wise Co,. TXJeffHancoch Zavald Co., TX

'

Duing 1996, entomologists in Mississippi, Oklahoma and Texas selected twelvecooperating orchards and deployed six traps per orchard. They followed the standardprotocols, except bap number was reduced from l0 to 6, and no voucher specimens wereretained.

6 1

Bilsing's (1926) detailed study ofthe phenology and life cycle ofthe PNC establishedthe base for Ring's (1981) development of the Texas PNC degree-day model. Since Ring etal. (1983) generalized this model, it has reliably predicted PNC emergence in many locationsbut has proved somewhat imprecise in others. The use of the PNC pheromone to validateexisting PNC models is an independent test because, unlike most of the more widely useddegree-day models presently in use, the PNC model was developed before the discovery ofthe pheromone.

We obtained daily maximum (T.",) and minimum (T,') temperature summaries fromarchived data of weather stations neaxest a participating compiled by the National ClimaticData Center (NCDC) in a digital database (Earth Info 2002). The Texas PNC degree-daymodel computes degree-days by the averaging method, also known as the historical,traditional, rectangle, Weather Bureau and growing degree-day (GDD) method (Legg et al.2002),withalowerthresholdtemperatureof 38oF: DDrr: [(Tmu+Tnir)/2] -38 (Ring l98l).Degree-days were estimated for twenty-one locations in using NCDC max-min temperaturesand the averaging method. Accumulated degree-days for each location were used to predict10, 25, 50, 75 and 90%o emergence for particular dates using the event tables from Ring(1e81 ) .

We usedthe frostfree days (FFD) model generalizationprocedure ofRingetal. (1983)to determine the starting date for the model at each location. Frost free days were availablefrom the National Oceanic and Atmospheric Agency (NOAA 2003). We delayed stadingdegree-day accumulation by the model one day for every 2.72 FFD less than College Station.We added one day for every 2.72FFD greater than College Station.

Pheromone efficacy (Objective l) compared the average number of PNC mothscaptured by PNC pheromone baited traps to the number captured in the unbaited traps duringthe first and second PNC flights of 1995. The comparison used Student's t-test ofpaired dataand tested a null hypothesis (Ho) that on the average there would be no significant differencein the number ofmoths captured by baited (po) and unbaited (po) traps (FIo: pu - Fo = 0).

We compared the relationship of moths captured in pheromone traps (Objective 2) toPNC phenology prediction from the Texas PNC degree-day model. Data from pheromonetraps has been used in both validating and checking the precision ofvarious insect phenolorymodels(Riedl etaI. l9T6,Huberetal. 1976, Stinneretal.l9SS,Z.alometal. 1992). Weusedthe PNC pheromone traps to check the precision of the PNC model at locations where it hashistorically been accurate as well as those where it has been historically imprecise. We alsoexamined possible causes of early or late bias in model predictions.

The percentage ofthe total moths captured by a particular date during the flight periodofthe overwintering generation was compared to the percentage emergence predicted by themodel. By plotting the observed PNC activity from increasing accumulations of PNC mothsin pheromone traps we developed emergence curves for each location. The determination ofthe end of an emergence period at aparticular location was when traps caught no PNC mothsfor more than a week. By plotting emergence predicted by the model and observed activity intraps onthe same graph, we obtained aroughvisual comparison ofthe model precision at eachlocation.

Visual inspection oflinear plots ofprobit transformed percentages ofactivity observedand emergence predicted allowed us to estimate the degree of early or late bias of the modelat each location. Predicted events were then compared to observed events with the modelvalidation method of Neter et d. (1996) in which the mean square error for the model(MSE,od.r) is compared with the mean squzue prediction enor (MSPE) obtained fromregression analysis.

Accumulated degree-days, day-of-year (D.O.Y.), the observed event and date ofpredicted events were included in validation sets to determine how precisely the Texas PNC

62

model predicted emergence. Although probit and logit transforms both effeotively linearizepercentages, we preferred probit values because tley were positive, which made it easier tocalculate MSPE. Probit tansformed percentages for both predicted and observed events wereregressed on degree-days. Regression of model predicted percentages on degree-days gaveMSE od.r. We then calculated MSPE from the squared residuals derived from regressionanalysis (Neter et al, 1 996) and compared the MSPE to model predicted emergence (MSE *u)for each location. Ifthe two are not very different, it can be concluded that the model is notseriously biased (Neter et d. 1996). We decided to use the MSPEA4SE,".., ratio as acomparison because very large differences would appear as comparatively large ratios.

Because Neter et al.'s (1996) procedure is somewhat subjective, we calibrated ourprocedure with two additional regressions. First we examined the effect of inaccurate startdates, which shift the model predictions in time. We compared the eflect on MSPEA4SE,.d.1ratios ofone-day forward and backward shifts over a range often days using a constantaccumulation of 3 5 DD3 8 per day. The results confirmed expectations that the earlier and laterthe model predicted the larger the ratios would be (Fig. 3).

We selected an acceptable prediction interval for the model of 3 days on either side ofan event. We did this because ifcooperators followed the minimum protocol ofrecording hapdata twice a weelg there would be a three to four day enor margin built into the samplingmethod, Ifan event oscurred the night after a tap was checked, then the event would not berecorded until two or three days later. If the model was predicting on time it would appearslightly early. If the model appeared to be predicting slightly late, a three day limit was placedon it to confine it to the week of the event. Beyond this would make the predictions too latefor management. We found that thrce-day shifts produced MSPE/MSE.*, ratios slightlygreater than two. A plot ofthe MSPEA,ISE,'d.r ratios (Fig. 3) allowed us to effectively set thelimits to which only start date affected the precision of the model.

Early Prsdiction (-dayc) & L8b PBdi:tbn {*dayg}

FIG. 3. The effect of shifting the model in time and comparing MSPEA4SE ratios of theshifted model to the unshifted model (0 days).

Our second procedure was designed to provide a measrue of how much variationaround the model predictions occurred in field observations if the model predictedsimultaneously with the activity associated with capture of overwintering adult males. Weresetthe model to 1,350 degree-days, whichconespondedto Ring's (1981) firstobservedadultof the overwintering generation, This in-season adjusfinent (SA) of the model effectively

1 0o

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63

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superimposed model predictions over field observations. We plotted the MSPE/MSE od.lratios (Fig. 4) and found that they ranged from about 0.5 to about 4.9. This established aminimumratio forapreciselypredictingmodel. Takingthetwocalibrationsinto considerationit was possible for a model predicting more or less precisely with a range of 3 days on eitherside of aphenological event to exhibit a MSPE/Ir4SE.*.,ratio of slightly more than six. Ratiosgreater than six would indicate increasingly serious bias in the model.

MSPE/MSE ooc Callbration For LocationDD38 setat 1360 atOnsetof Flight Day En I 0

arf !{a Our Bu. l{hr T$ gltp n{v 6ru crc ?lf To$ fhx i.n lt.{ Mld Frt

FIG. 4. Effect on MSPEA4SE,"..1 by resetting PNC model to 1350 DD38 to superimposeobserved activity over model predicted emergence at different locations.

After calibrating our procedure, we conducted regressions offield observations andmodel predictions on degree-days accumulated from start dates established for each locationby the FFD correction procedure ofRing et al. (1983). Although comparisons ofdifferencesbetween predicted and observed events were conducted for each event in the model, thedifference between predicted and observed peak activity was an adequate indicator ofcalendardate and degree-day precision ofthe model. Ifmodel validation showed that no serious biasexisted in predicted emergence within a prediction range of three days on either side ofobservedevents, thenthe model wasprecise enoughforpestmanagementPurposes. However,where ratios of MSPE/MSE,..., exceeded six and predictions were outside the acceptableinterval, then the model was considered biased and not precise enough for pest managementpurposes at that location.

Although MSPE/MSE,.d". ratios provided an index of bias, they do not show whetherthe model is predicting too early or too late, This was determined directly by subtracting theD,O.Y. of a predicted event from the D.O.Y. ofthe observed event. Negative results indicatedearliness ofactual observed emergence compared to the prediction, while positive resultsindicated lateness.

RESULTS AND DISCUSSION

Cooperators in different states reported similar findings. In all cases the PNCpheromone baited traps captured PNC male moths. The flight period of the overwinteringgeneration averaged about 25 days, being no shorter than2l nor longer than 38 days. Mothflights peaked on average about 7 to l0 days after the first moths appeared in traps (Table 2)'

64

lTABLE 2. PNC Flight Period: D.O,Y.s of First Moth, Flight Peak and Last Moth.

Location

D.O.Y. Flight Began and EndedFirst FlishtMoth Peik

LastMoth

FlishtPeriod

Brazori4 fiB-rownfield, TXBrownwood, TXBurleson, fiComanche, TXCrosby, TXEctor, TXGregg, TXIrion, TXMedina. TXMidland. fiPecos Co., TXQuemado, TXRoyalty, TXSan Saba, TXTom Green, TXTyler, TXVictoria, TXWharton, TXTifton, GAChula, GAArdmore. OKDuncan. OK

126150125l l 8121134l l 9tzl133tt7l l 8r251211 1 9t20132l l 9l l 4120122t22140l 3 l

135158136t2513515714 l129139124140137125l2sl 3 l135129t24r27129128t42154

t49t7l150145152t64l 5 l144l 6 l138150163150t42t42163144135l 5 l150152t57170

232125273 l303223282 l32382923223 l252 l3 l2830t 739

A running total of moths captured in pheromone traps permitted the calculation of ap€rcentage of the grand total for each location and the plotting of curves comparable toemergence curves (Fig. 5). Locations at approximately the same elevation and latitude, andwith similar temperature data, showed emergence occuning at about the same time. Forexample, the orchards at F4irhope, Alabam4 near the Gulf of Mexico recorded emergence atabout the same time as orchards at Monticello, Florida, also located near the Gulf.

Further inland and further north, without the Gulf to moderate climate, orchards atByron, Chula and Tifton, Georgi4 all recorded emergence events 5 to l0 days later thanlocations further south and nearer the Gulf. Similarly, the orchard at Stoneville, Mississippi,located slightly further north and firdher inland than those in Georgia recorded emergenceevents about five days later. Orchards in Oklahoma, furthest north of any in this study,recorded the latest emergence, about I 0 to I 5 days later than orchards further south in Texas,Georgia and Mississippi. Oklahoma also recorded a more prolonged emergence period, whilein orchards further south the emergenoe period was shorter and emergence curves from trapdata were steeper.

65

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- : ; - l l : - - : ' ' * t ^ : . " t '

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FIG' 5. PNC adult male activity representative of 44 locations observed during emergence ofthe overwintering adult generation in 1995 and 1996 at locations across the pecan belt.

PNC pheromone effectively attracts PNC male moths. student's t-tests (g=0.05) ofthe difference between the average number ofmoths captured per baited trap and the averagenumberperunbaited trap showed inall cases thatbaitedhaps caught significantlymoremothsthan unbaited traps. student's r-tests all showed significance at the a=0.01 level ofsignificance. ln losations where PNC existed at higher densities, the differences between ftapswere greater than in locations with low densities. In areas of comparatively low mothdensities, variability between traps often consisted of one or two PNC moths captured versusno moths captured. In locations with comparatively higher densities, all traps consistentlycaptured PNC male moths.

Linear regression analysis ofthe validation set and predicted emergence from theTexas PNC model gave three general validation results. These included locations where themodel was valid within a predictive interval of three days, locations the model predictedearlier than 3 days and locations the model predicted later than 3 days.

Comparison of mean square prediction error (MSPE) with MSE,od.r for predictionsshowed differences resulting from a cascade of errors resulting primarily, we suggest, froman inaccurate start date (Fig. 6). Inaccurate start dates resulted in over or under estimation ofdegree-days on a particular calendar day and the phenological events associated with them.Starting too early resulted in estimating phenological events too early. Starting too lateresulted in late predictions.

66

o'€cg

Txs

ul{tttilIr.oI

Texas PNC Model ValdiationPredicted Emergence vs Observed Activity

Gry EmQuc Tf fut mdchu B|z P.cLoestlon

FIG. 6. PNC model validation results from different locations in the 5-293 study, showing noserious bias in 14 of 2l locations (MSPEA4SE,*a < 6) with Tshowing bias primarily due toinaccurate start day.

It was noted that FFDs are a 3O-yearmoving average, and inthe pasttwentyyears FFDvalues for several locations have changed enough in NoAA (1983, 1988, 2003) tables tosignificantly change the start dates determined by Ring et al. (1 983) to initially generalize themodel. Use of tables after 1983 introduced different FDD and different start days than usedin the original model generalization. Frost-free days varied from the 1970 tables by as littleas two and as much as 20, which shifted start dates by one or two days to more than eightdays. The FFDs and start days determined from NOAA (1988, 2003) tables were closer tothose stated by Ring et al. (1983) inthe model generalizationthanNOAA (1983, 1986) rables.

Although a change of one or two days usually did not introduce serious bias into themodel, a start date varying by more than four days began to introduce increasing amounts ofbias into the model. An example is the stated D.o.Y. start date of 73 for Brownwood, Texasby Ring et al. (1983) based on 263 FFD. However, subsequent use ofNoAA (1983) tablesshowed 243 FFD and a D.O.Y. start date of 80, which biased model predictions by more thanseven days. Historically, the pecan producing area ofSan Saba, Brownwood and comanchecounty, Texas has predicted about a week late when using a start date based on 243 FFD.Similarly, NOAA (1988,2003) tables showed a FFD of 255 and a D.o,y. start date of 76,which only changed the original start date by three days and placed the 1995 predictionswithin the acceptable range.

locations where the model showed the greatest bias fiom inaccurate start dates werethose where FFD established start dates that produced degree-day estimates more than a weekoutside the acceptable range. Although the accuracy ofthe FFD database is not in question,its use to establish start days for the model may introduce inaccuracies. Examining theihangesin the moving average for FFD in the NoAA tables over the past twenty years, it becomesaPparent that perhaps a more stable environmental signal than FFD might be developed toestablish a start date for the model in different locations.

The model was valid in 16 of 23 orchards examined where observed activity inpheromone traps fell within 3 days of predictions by the Texas pNC model (Table 3). iwoorchards in Burleson County (Adriance and Royalty) in the Brazos Valley ofTexas supported

67

the model very closely. This was expected because the model was developed using Bilsing's(1926) dar4 which came exclusively from this area where the model has consistently andaccurately predicted biological events like overwintering generation moth emergence and firstsurnmer generation nut entry for more than two decades. Validation studies and subsequentuse ofthe model have consistently shown it to be very precise in Burleson and other nearbycounties in the Brazos Valley. Fig. 7 (A) shows observed and predicted emergence alongsidea graph ofthe probit analysis. Predicted and observed events vary by less than one day.

TABLE 3. Validation of Texas PNC Degree-day Model: Observed vs Predicted Event andMSPEA,[SE,..,, Ratio for Overall Bias (MSPE/lvfSE < 6: No Serious Bias).

Support Model

Predicted Observed Predicted ObservedPeak Peak Peak Peak MSPWMSE

Location Jul. Date Jul. Date Day En. DD38 DD'IJ DD3B Err. ' --Rttio -

Burleson,fi 124Royalty, TX 124Ardmore, OK 141SanSabqfi 130Tyler TX, 129Victoria, Tx 125Medina, TX 125Crosby,fi 158Brownwood, 138Gregg, TX l3lComanche, TX 138TomGreen.TX 138

Wharton, TX 125Brownfield, TX 156DuncanOK, 152Irion TX, r38

Quemado, TX l16Brazori4 TX 122

-t 1672.5 1715.5 43.0-l 1640.0 1676.5 -36.5

0 1631.0 1489.0 142.0I 1664.0 1621.0 43.0I 1646.0 1603.5 42.5| 1672.5 1714.5 42.02 1648.5 1575.0 71.52 1644.0 1571.0 72.53 1700.0 1584.0 l 15.53 1653.5 1526.5 127.0

Early Model

1271 5 8154139r25125142l J l

t29124t24t57136129135135

t25t35

-2 t645.5 1729.5 -84.0 1.45-2 1661.5 1745.5 -84.0 2.43-2 1833.5 1904.0 -70.5 1.45

1653.5 1680.5 -27.0 2.&t&9.5 1692.5 43.0 1.84t649.5 1692.5 -43.0 1,35

5.344.321.231.77L48t.735.294.693.322.09

Predicted Observed Predicted ObservedPeak Peak Peak Peak MSPEA,ISE

Location Jul. Date Jul. Date Day Err. DD38 DD38 DD38 En. Ratio

Location

Predicted ObservedPeak Peak

Jul. Date Jul. Date

-9 1637.5 1998.0 -360.5 6. l l-13 1650.5 2t78.5 -528.0 n.26

Late Model

Predicted ObservedPeak Peak MSPE/IV{SE

Day En. DD38 DD38 DD38 En. Ratio

Midland, TX 144EctorCo.,TX 145Tifton, GA 134Pecos Co., TX 143

4 1654.5 1490.5 164.04 1674.0 1502.0 172.05 1650.0 1477.0 173.56 1646.5 1427.0 2t9.5

140l4 lr29t37

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Chula. GA 139 128 11 1676.0 1281.0 394.5 10.66

Fourteen other locations in Texas and Oklatroma" from 100 to 450 miles from theBrazos Valley also had trap data that fell within the acceptable range. Irion County (Fig. 7B),for example, is located about 45 miles southwest of San Angelo (283 miles from Burleson

68

Co.), but observed PNC emergence within 2 days of that predicted by the model. Texaslocations near Brownfield (400 mi.), Medina (180 mi,), Victoria (135 mi.) and Wharton (100mi.) and Oklahoma locations (300 mi) all recorded emergence datathat were not significantlydifferent from model predictions.

In Brazoria, the PNC model predicted 10, 25, 50, 75 and 90olo emergenceapproximately 9 days before the same percentages of moths captured for the flight periodappeared in traps. At Quemado the model predicted 10, 15, 50, 75 and90%o emergence 13days before the same percentages of moths captured were recorded in traps (Table 3).Regression analysis also showed (Fig. 7C) that PNC emergence predicted by the model wassubstantially the same as observed activity but wasjust shifted to the right. This suggests thatthe model was started on the inconect date and that determining the start date for theselocations ur;irry2J2 FFD conection does not provide a proper generalization for the modelin these locations. This problem with the model would cause wasted scouting effort. The chartin Fig. 4C also shows that shifting the start date an appropriate number of days to the rightwould provide an adequate model conection for this location, but as yet, we are not assuredthat changing the start date this way would improve the ability to make prediction in futureyears.

Five locations, Ector, Midland and Pecos Counties in Texas, and Chula and TiftorlGeorgia (Table 3), recorded PNC activity in pheromone traps that was more than 3 days laterthan model predictions for those locations. The problem oflate prediction could result in thebeginning of scouting and conftol measures too late to prevent economic injury from the pest.Midland and Ector County orchards are in comparatively close proximity in the Permian Basinof Texas and experience very similar climatic factors. Both locations observed events fourdays later than predicted. The orchard in Pecos County, Texas, is in Fort Stockton, about 130miles southwest from Midland, and lies in the arid, somewhat hotter Trans-Pecos regionadjacent to the Permian Basin. This orchard also observed the effect of starting the model toolate and observed events in PNC pheromone traps about six days after model predictions.

The orchards at Chula (Fig. 7 D) and Tifton, Georgi4 are in very close geographicproximity, Iess than ten miles apart in Tift County, Georgia. These orchards observed events5 and I I days later than PNC model predictions.

In all tluee regions, the Permian Basin, the Trans-Pecos and Georgiq the Texas PNCmodel has historically been imprecise, usually predicting about 7 to l0 days later thanobserved activity.

The observations in pheromone traps and subsequent analysis show that an incorrectstart date has shifted predictions by the model to the right. The MSPE calibration for locationGig. a) and the regression analysis (Fig. 7) show that starting the model 4 days earlier atMidland and Ector Counties, about 7 days earlier in Pecos County, Texas, and between 7 tol0 days earlier in Tift Counfy, Georgia, would provide an eflective model correction for theselocations. However, it is not evident that simply pushing the start date back or use of a 1350degree-day reset of the model at the onset of the adult male flight would necessarily improvemodel precision in future years.

Problems with discrepancies between model predictions and observed events may existfor four reasons: (l) inaccurate trap dat4 (2) inaccurate temperature data, (3) incorrect startdate and (4) flaws inherent in the model (base temperature, degree-day calculation method orthe response function - the mathematical relationship between accumulated degree-dayspredicted events). Remedies exist for inaccurate trap data and weather data. Reexaminingfrapping reports for violations of tapping protocols or enors in recording the number mothscaptured in pheromone traps can reveal reasons for differences between predicted andobservedeventsandthepossibleneedfornewstudies. Reexaminationofweatherarchivescanprovide accurate weatler data for a specific location. Establishing a correct start date can

69

eliminate thisproblem, althoughhowto do itwithexistingmethods maybemoreproblematic.Howgvet, if the problem is inherent in the model, no amount of adjustnent to the start datecan fix it' The model must be reevaluated in terms of the independent variables, degree-daycomputation method and the response function. This may requlre several years of ac--curatelyrecorded ftap data and weather data.

- - The PNC trap data being accumulated from numerous sources will provide a databasefor this evaluation. A more reliable method of establishing a start date for the model isrequired. There are several reasons, not the least ofwhich is the dynamic nature ofFFDcomputations. The 30-year moving average used to determine fFD foi diflerent locations hasshown pronounced changes since the first use ofthe 1970 tables by Ring et al. (19g3).

The problems of different sources showing different dates only compound thisproblem' The use ofthese tables over the years has historically produced serious precisionproblems in several locations, such as the Edwards Plateau of Teias, the High plains and theTrans-Pecos region. The problem ofusing either bud break or FFD to establiih a start date forthe model is much like shooting at a moving target from a moving vehicle. The consistencyof the pattern of the PNC adult male phenology, beginning earlieit in the warmer southerlyregions and progressively later as colder and more northerly locations are censused, indicateenvironmental cues are driving this phenology.

We will consider other approaches to better generalize start date determination for thePNC model (Grantham et al. 2002, Sparks 1995), These will include photoperiod, coldtemperafure accumulation, and investigations in areas where native and improved pecanphenologies are distinctly different (native break bud as much as 4 weeks earlier thanimproved in Saltillo, Mexico and natives in Chatopa" Kansas). The effect ofthe latter on PNC-pecan interaction may provide clues to beffer understand cues PNC actually uses to breakdiapause,

In conclusion, synthetic pecan nut casebearer pheromone E,Z(9,1 l)-hexadecadienalis an effective attractant for male PNC moths throughout the pecan growing regions of theUnited States. Lures remain effective for PNC trapping for at least l5 weeks (Knutson et al.1998) and effectively show the presence ofPNC adult males in pecan orchards and nativepecan groves. PNC pheromone is an effective instrument for monitoring adult male activitythroughout the season both in time and duration of flight . In locations where the PNC modelcunently does not provide accurate predictions ofadult activity, pheromone traps may be thebest way to determine these events. Pheromone trapping is also a potential source ofdata tovalidate and calibrate PNC models (Grantham et aL 2002) and shows promise for theimprovement of existing PNC models or development of new ones. Validation studies oftheTexas PNC degree-day model with pheromone trap data effectively showed locations wherethe model faithfully emulated adult male emergence and where only minor adjusfinents in startdate effectively adjusted the model to an acceptable level ofprecision. Pheromone tap dataalso detected locations where the model could notbe demonshatedto bevalidandwheremorework in model revision may be neaessary. It was not clear in this study that a "biofix" withPNC pheromone traps would improve precision of the Texas PNC model. Pheromone hapsdid indicate the use frost free days and bud break, the most widely used environmental cuesto establish a start date, were inadequate for several locations. The start dates in Georgia andthe Southeast, South Texas, the Permian Basin and in the Trans-Pecos all indicated a seriousbias in the model.

70

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ACKNOWLEDGMENTS

We gratefully acknowledge the assistance and participation of the Texas PecanGrowers Association, Westem Pecan Growers Association, Southeastern Pecan GrowersAssociation, oklahoma Pecan Growers Association, the partial support from cREES project#0 I 90 I I 8 and Hatch project # 0 1 84599, and many pecan producers, without whom this studywould not have been possible.

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