Factors Influencing the Adoption ofInsect Management TechnologyJayson K. Harper, M. Edward Rister, James W. Mjelde,Bastiaan M. Drees, and Michael O. Way

Logit analyses are used in evaluating survey data concerning rice stink bug [Oebaluspugnax (Pabncius)] management by Texas rice producers. Effects of rice productionattributes on the adoption of insect sweep nets in conjunction with treatment thresholds,and the spraying of insecticides for the management of the rice stink bug, areinvestigated. The proportion of neighboring land use in pasture, the proportion of riceacreage planted to semidwarf varieties, and producers' attendance at specific field dayssignificantly affect the probability of adopting sweep nets and treatment thresholds.Adoption of sweep nets and treatment thresholds increase the probability of spraying by11.3%.

Key words: integrated pest management, rice, rice quality, rice stink bug, sweep net,technology adoption, treatment thresholds.

The adoption of technology has received fre­quent attention over the years. One avenue ofstudy has concentrated on the theory of adoptionprocesses (Griliches, Hiebert, Jarvis, Byerlee andPolanco). Other studies have focused on iden­tifying significant characteristics associated withadopters and nonadopters. Recent studies inves­tigating factors influencing technology adoptionin agriculture have dealt with microcomputers(Putler and Zilberman), reduced tillage practices(Rahm and Huffman), irrigation technologies(Caswell and Zilberman), and financial infor­mation (Garcia, Sonka, and Mazzocco). Thesetechnologies represent significant capital in­vestments and human capital costs to the adopt­er.

One area of human-capital-based technologywhich has received considerable attention in re-

The authors are, respectively, an assistant professor, Departmentof Agricultural Economics and Rural Sociology, Pennsylvania StateUniversity; associate professors, Department of Agricultural Eco­nomics. Texas A&M University; an extension entomologist. TexasAgricultural Extension Service, Bryan; and an associate professorof entomology. Texas A&M University Agricultural Research andExtension Center, Beaumont.

Technical Article No. 23012 of the Texas Agricultural Experi­ment Station.

This research was funded by the Texas Rice Research Foundation(Econo-Rice Project) and the Texas Agricultural Experiment Sta­tion (Project 6507).

The authors thank David Bessler. Oral Capps. Warren Grant.Tom Knight, Mike Mazzocco, and three anonymous reviewers ofthe Journal for their comments on earlier drafts of this paper. Typ­ing by Yvette McCoy and editing assistance by Karen Pilant andSue Durden are sincerely appreciated by the authors.

cent years is integrated pest management (IPM).An overview of economic and entomologicalactivity in this area is provided in Frisbie andAdkisson. The economic efforts therein, alongwith others (Zacharias and Grube; Masud et al.;Boggess, Cardelli, and Barfield; Smith, Wetz­stein, and Douce), have largely concentrated onrefining economic treatment thresholds in thetradition of Headley and/or evaluating the con­sequences of alternative IPM strategies. Severalstudies have investigated producer participationin group pest control (Rook and Carlson, Pin­gali and Carlson, Roe and Nygaard, Hannemanand Farnsworth, Burrows) and the impact ofproducer characteristics on the adoption of IPMtechnologies.

This study addresses the adoption of a ratherinexpensive capital technology, the insect sweepnet in conjunction with treatment thresholds. Twoobjectives encompass the study: (a) to identifyfactors influencing the adoption of the insectsweep net and treatment thresholds, and (b) toidentify factors influencing the decision to applyinsecticides to control the rice stink bug [Oeb­alus pugnax (Fabricius)].

Problem Overview

A large number of rice producersacross the TexasRice Belt have not adopted insect sweep nets to

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detect rice stink bugs (RSB), and the accom­panying use of treatment thresholds in makingspray decisions. Expert recommendations indi­cate adoption of such management technologiesare economical (Drees 1989). Many producers'perceptions of the associated economic value,however, appear inconsistent with the experts'views. No documented record of the costs andbenefits from adopting these technologies isavailable.

Adoptionrequires purchase of a sweep net (lessthan $20) along with an investment of time inthe sampling process and learning to use thetreatment thresholds. Treatment thresholds arereadily available from the Texas AgriculturalExtension Service (Drees 1983, 1989) as wellas from a number of private consultants. Be­cause the only recommended sampling tech­nique for the RSB in Texas is the insect sweepnet, knowledge of the factors influencing itsadoption along with that of treatment thresholdswould be valuable in targeting extension effortsand determining future research activities.

The RSB damages the rice crop in the south­ern United States by lowering the quality ofmilled grain. Damage occurs when the RSB at­tacks the developing rice kernel (Drees 1989).Feeding activity causes circular lesions on thegrain and often introduces fungi, which furtherweaken and discolor the kernel (Drees 1983).This type of damage, called peck damage("peck"), also increases the number of brokenkernels produced during milling, thereby low­ering the farmer's head yield (the percentage ofwhole-kernel rice). In addition, peck-damagedrice has a lighter bushel weight than undamagedrice (Grant, Rister, and Brorsen).

RSB-induced peck can significantly influencethe price received by rice producers and, thus,the profitability of growing rice (Brorsen, Grant,and Rister). Grant, Rister, and Brorsen suggestthat reducing peck damage by one percentagepoint would have raised the price received forTexas rough rice during the marketing years of1981/82 through 1983/84 by an average of be­tween $0.126 and $0.676 per hundredweight,depending on market location. Given that eco­nomic control measures are available, adoptionof sweep nets and treatment thresholds couldimprove the profitability of producing rice.

Preventive or routine treatments are not rec­ommended for the RSB in nonseed rice produc­tion because infestations vary greatly amongfields over time. The one-month period duringwhich the RSB causes damage occurs from

Amer. J. Agr. Econ.

heading through maturity. During this period theuse of a sweep net is recommended to monitorRSB populations, with insecticide treatmentsapplied when threshold levels are exceeded(Drees 1989).

The recommended sampling procedure is totake ten consecutive 180 degree sweeps with a15-inch-diameter insect sweep net at several 10­cations across each field (Drees 1989). Treat­ment recommendations are based on the averagenumber of adult RSB captured in the sweep net.Historically, treatment thresholds were set at fiveRSB per ten sweeps for the first two weeks after75% panicle emergence, and ten RSB per tensweeps thereafter (McIlveen, Bowling, andDrees). Current treatment recommendations(Drees 1989) call for application of insecticidewhen RSB infestation levels reach the economictreatment threshold. These flexible economicthresholds developed in 1988 allow for consid­eration of (a) cost of treatment, (b) price of rice,(c) planting date, (d) potential rice yield, and(e) stage of grain development (Harper).

Analysis Procedure

The decisions either to adopt the use of a sweepnet and treatment thresholds or to spray insec­ticides can be analyzed with binary choicemodels. Binary choice models are appropriatewhen the choice between two alternatives de­pends on the characteristics of the problem (Pin­dyck and Rubinfeld). Application of a linearprobability model to this type of problem, how­ever, suffers from a number of deficiencies(Capps and Kramer).

Difficulties of the linear probability model canbe circumvented through the use of a monotonictransformation (probit or logit specification)which guarantees that predictions lie within theunit interval (Capps and Kramer). The choice ofwhich transformation to use is largely a questionof convenience (Hanushek and Jackson). Thelogit model was selected for this analysis.

When using logit models, maximum likeli­hood is the preferred estimation technique (Cappsand Kramer). In this study, little is known aboutthe relationship between producers' attributes andeither their adoption of sweep net and treatmentthreshold technology or their spraying deci­sions. Thus, a level of significance of 20% isused, as suggested by Manderscheid for suchcases.

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Data Source

Insect Management 999

'Variable dropped from model to avoid a singular matrix.

Table 1. Definitions of Variables Used inLogit Analyses of 1986 Texas Rice Stink BugSurvey Data

Farm-level data were obtained from a mail sur­vey of Texas rice producers conducted duringthe fall of 1986 with a follow-up survey in thespring of 1987. Data were gathered on produceractivities for sampling and controlling the RSB,production information for the 1986 season, ageand educational level of the farm managers, at­tendance at field days and county extensiondemonstrations, and farm business organiza­tional characteristics. Usable responses wereobtained from 117 producers with 411 rice fieldstotalling approximately 42,000 acres. This re­sponse represented approximately 17% of the1986 Texas rice acreage and 15% of the riceproducers.

Models for Sweep Net/TreatmentThreshold Adoption and InsecticideSpraying

The information available at the time of deci­sion making is appropriate to use in analyzingproducers' decisions. Whether to adopt sweepnets and treatment thresholds is essentially awhole-farm decision. Information on age andeducation level, size and complexity of opera­tion (i.e., total rice acreage and number of ricefields), and business organizational character­istics are used to ascertain if managerial styleand underlying labor availability affect theadoption of sweep nets and treatment thresh­olds. The proportionate use of scmidwarf ricevarieties (Texas Agricultural Experiment Sta­tion) is a proxy for the rice producer's attitudetoward adoption of related technologies. Theproportion of neighboring land in different usesand a regional class dummy variable indicate thepotential effects of environmental factors onadoption. Attendance at field days and countyextension demonstrations gauges the effective­ness of educational programs intended to en­courage adoption.

The logit model used in this study to analyzethis technology adoption is

(l) SWEEP = ao + a, AGE + a z EDUC+ a 3 ACREAGE + a4 NFIELD

+ asPLI + a6PL2 + a7PL3 + agPVI+ a9 WEST + alO COEXT + all FDI

+ an FD2 + al3 FD3 + a'4 FD4+ al5 PARTNER + al6 CORP,

where the variables are defined in table I.

VariableName

SWEEP

SPRAY

AGEEDUC

ACREAGENFIELDFIELDPLlPL2PL3

PIA'

PVI

PV2'

LI

L2

L3

L4'

VIV2'WEST

NITROGENPLTDATECOEXT

FDI

FD2

FD3

FD4

SOLE'

PARTNER

CORP

Description

1 if rice producer adopted use of a sweepnet and treatment thresholds, 0otherwise

1 if rice producer sprayed for the ricestink bug, 0 otherwise

age of the farm manager (years)education level of farm manager, I if

more than high school, 0 if high schoolor less

total rice acreage on the farmnumber of rice fieldsfield size (acres)proportion of neighboring land in pastureproportion of neighboring land in riceproportion of neighboring land in grain

sorghumproportion of neighboring land in other

than pasture, rice, or grain sorghumproportion of rice acreage planted to

semidwarf rice varietiesproportion of rice acreage planted to

traditional rice varietiesI if predominant neighboring land is

pasture, 0 otherwiseI if predominant neighboring land is rice,

o otherwiseI if predominant neighboring land is grain

sorghum, 0 otherwiseI if neighboring land is other than

pasture, rice, or grain sorghum, 0otherwise

I if semidwarf rice variety, 0 otherwiseI if traditional rice variety, 0 otherwiseI if farm located west of Houston, 0 if

located east of Houstonnitrogen fertilizer applied (IbIacre)planting date (Julian day)I if attends county extension

demonstrations, 0 otherwise1 if attends summer field day at Eagle

Lake, 0 otherwiseI if attends summer field day at

Beaumont, 0 otherwise1 if attends fall field day at Beaumont, 0

otherwise1 if attends field days given by chemical

companies, seed companies, etc., 0otherwise

1 if farm business is a soleproprietorship, 0 otherwise

1 if farm business is a partnership, 0otherwise

I if farm business is a corporation, 0otherwise

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The decision to spray for the RSB is an in­season decision made on an individual field ba­sis. This model resembles the SWEEP model withthe following exceptions: (a) the size of indi­vidual fields is considered instead of a produc­er's number of rice fields; (b) binary variablesare used to classify adjoining land rather thanrepresenting the proportion of neighboring landin various uses; (c) management details relatedto planting date and applied nitrogen are in­cluded for the individual fields; and (d) the pro­ducer's binary decision on the adoption of sweepnets and treatment thresholds is included as anindependent variable. The resulting logit modelis

(2) SPRAY = {3o + {31 AGE + {3z EDUC+ {33 ACREAGE + {34 FIELD

+ {35 SWEEP + {36 L1 + {37 L2 + {38 L3+ {39 VI + {31O WEST + {3u PLTDATE

+ {3IZ NITROGEN + {313 COEXT+ {314 FDI + {315 FD2 + {316 FD3

+ {317 FD4 + {318 PARTNE,R+ {319 CORP,

where the variables are defined in table 1.Descriptive statistics for the variables ob­

tained from the survey are summarized in table2. Mean values of the qualitative variables referto the proportion of 117 rice producers or 411rice fields taking on particular qualitative attri­butes in 1986. As examples, approximately 38%of the producers had adopted sweep nets andtreatment thresholds, and 73% attended countyextension demonstrations (SWEEP model).Similarly, approximately 53% of the fields weresampled using sweep nets and treatment thresh­olds and 78% were farmed by producers whoattended county extension demonstrations(SPRAY model). The continuous variables in­dicate that each farm had, on average, 3.3 ricefields of approximately 103 acres each, whichwere planted on the 88th day of 1986 (i. e., 29March).

Empirical Results

The summary statistics for the SWEEP [equa­tion (1)] and the SPRAY [equation (2)] modelsare presented in table 3. Likelihood ratio testsindicate that the amount of variation explainedin each model is significantly different from zero.

In table 3, two separate goodness-of-fit mea­sures are presented. The first, McFadden's RZ

,

is a commonly used goodness-of-fit measure forbinary choice models and is expressed as

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Table 2. Descriptive Statistics for VariablesUsed in Logit Analyses of the 1986 Texas RiceStink Bug Survey Data

SWEEP Model' SPRAY Model"

Std. Std.Variable Mean Dev. Mean Dev.

SWEEP 0.376 0.486 0.533 0.500SPRAY 0.747 0.452AGE 44.162 12.134 43.246 10.594EDUC 0.778 0.418 0.854 0.354ACREAGE 378.239 448.257 895.088 973.483NFlEW 3.256 3.992FIELD 103.173 70.947PLI 0.505 0.465PL2 0.149 0.312PL3 0.162 0.330PL4' 0.184 0.364PVI 0.770 0.362PV2' 0.230 0.362Ll 0.516 0.466L2 0.212 0.409L3 0.151 0.358IA' 0.121 0.280VI 0.725 0.447V2' 0.275 0.447WEST 0.769 0.423 0.818 0.387NITROGEN 179.956 32.184PLTDATE 87.652 15.522CO£)IT 0.726 0.448 0.781 0.414FDi 0.462 0.501 0.630 0.483FD2 0.291 0.456 0.343 0.475FD3 0.145 0.354 0.185 0.389FD4 0.709 0.456 0.783 0.412SOLE' 0.581 0.498 0.391 0.482PARTNER 0.376 0.486 0.543 0.499CORP 0.043 0.203 0.066 0.248

a Data from 117 rice producers.b Data from 411 rice fields.c Variable dropped from model to avoid a singular matrix.

McFadden's RZ

= 1 - (Log L(fiML»/Log Lo,

where Log Lo is the value of log-likelihoodfunction subject to the constraint that all regres­sion coefficients except the constant term arezero, and Log L (~ML) is the maximum value ofthe log-likelihood function without constraints(Capps and Kramer).

The McFadden R 2 is not comparable to the RZ

reported in OLS regressions. The McFadden RZ

values of 0.218 for the SWEEP and 0.336 forthe SPRAY models (table 3) are similar to thoseobtained in other studies. McFadden RZ's in therange of 0.2 to 0.4 are typical for logit models(Sonka, Hornbaker, and Hudson).

The second measure is correct classificationof decisionmakers as either selecting the SWEEP/DO NOT SWEEP alternative or the SPRAY/

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Table 3. Summary Statistics for the SWEEPand SPRAY Logit Models, 1986 Texas RiceStink Bug Survey

Statistic SWEEP Model SPRAYModel

Number ofIterations' 6 9

Log of likelihoodfunction -60.60 -154.48

Likelihood ratiotest" 29.44 (16 d.f.) 130.49 (19 d.f.)

McFadden's R' 0.218 0.336ObservationsCorrectly classified' 80.3% 81.5%

Sensitivity" 61.4% 91.2%Specificity' 91.8% 52.9%

a Convergence tolerance of 0.001b Test that all slope coefficients are equal to zero (both models aresignificant at the 5% level).c Based on a 50-50 classification scheme.d Proportion of fields where SWEEP or SPRAY equals I that werepredicted correctly., Proportion of fields where SWEEP or SPRAY equals 0 that werepredicted correctly.

DO NOT SPRAY alternative based on the ex­planatory variable information. Both modelsperformed well in terms of correct classifica­tion: 80.3% for the SWEEP model and the 81.5%for the SPRAY model (table 3). Measures ofsensitivity and specificity indicate the SWEEPmodel classifies better when the decision is tonot adopt sweep nets and treatment thresholds,while the SPRAY model classifies better whenthe decision is to spray.

Adoption of Sweep Nets and TreatmentThresholds

Influence of the explanatory variables on theprobability of adopting sweep nets and treat­ment thresholds is shown in table 4. Results in­dicate that of the sixteen factors analyzed, theprobability of Texas rice producers adoptingsweep nets and treatment thresholds in 1986 wassignificantly associated (at the 20% level) withsix of these factors: (a) the education level ofthe farm manager (EDUC) , (b) the proportionof neighboring land use that is in pasture (PL1),(c) the proportion of rice acreage planted tosemidwarf rice varieties (PVl) , (d) the geo­graphic location of the farm within the TexasRice Belt (WEST), (e) attendance at the EagleLake field day (FDl), and (j) attendance at theBeaumont fall field day (FD3). Other thanEDUC, none of the variables relating to man-

Insect Management 1001

agerial style and characteristics (AGE,ACREAGE, NFIELD, PARTNER, and CORP)were significant.

The education relationship (EDUC) was neg­ative. That is, those farmers with more than ahigh school education had a 22.5% lower prob­ability of adopting the use of sweep nets andtreatment thresholds than those with a high schooleducation or less. A possible explanation for thisbehavior is that the higher educated producersperceive a greater return to their managementand labor time elsewhere in their operation, and/or the physical aspects of using a sweep net areunexciting or menial to such producers.

The probability of adopting sweep nets andtreatment thresholds was lowered by 0.299% foreach 1% increase in the proportion of neigh­boring land in pasture (PLl). Rice producers whohave a large proportion of their acreage border­ing pasture may believe that (a) it is useless tosample RSB populations because of the diffi­culty in adequately monitoring migrations fromthe pasture, and/or (b) preventive routine con­trol measures are required when growing rice ina field bounded by pasture because this providesa ready source of reinfestation.

The probability of adopting sweep nets andtreatment thresholds was increased by 0.394%for each 1% increase in the proportionof a farm'srice acreage planted to semidwarfvarieties (PVl).Because the effect of the proportion of riceacreage in semidwarf rice varieties is positive,perhaps more innovative, technologically awaremanagers who have adopted these higher yield­ing varieties and other yield-increasing technol­ogies (Ito, Grant, and Rister) are also adoptingsweep nets and treatment thresholds, while theless innovative producers are lagging behind.

Not surprisingly, those producers farming inthe western region (WEST) of the Texas RiceBelt exhibited a higher probability of adoptingsweep nets and treatment thresholds; the cal­culated change in probability was 31. 1%. Ob­served RSB infestation levels are higher on thewest side as a result of more favorable alter­native host vegetation throughout the generalarea. Further, harvested rice yields are higheron the west side, resulting in a potentially greaterrisk of economic crop damage from the RSB.

Attendance at the Eagle Lake summer fieldday (FD1) and the Beaumont fall field day (FD3)were significantly associated with increases inthe probability of adopting sweep nets and treat­ment thresholds by 30.0% and 25.4%, respec­tively. This suggests: (a) these field days are ap­propriate times to present educational programs

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Table 4. Maximum Likelihood Estimates for the Sweep Net and Treatment Threshold Model,1986 Texas Rice Stink Bug Survey

Variable Estimate Change in Probability' Chi-Square Statistic" Test Significance'

CONSTANT -2.4765 1.65 0.1991*dAGE -0.0053 -0.0012 0.05 0.8218EDUC -1.0014 -0.2246 2.43 0.1193*ACREAGE -0.0002 -0.4913 E - 04 0.03 0.8708NFIEW 0.1751 0.0039 1.47 0.2261PLI -1.3341 -0.2993 3.37 0.0664*PL2 -0.7351 -0.1649 0.57 0.4490PL3 0.1978 0.0044 0.05 0.8238PVl 1.7558 0.3939 4.38 0.0363*WEST 1.3870 0.3111 2.15 0.1426*COEXT -0.2127 -0.0048 0.14 0.7119FDl 1.3366 0.2998 5.47 0.0194*FD2 0.4560 0.1023 0.34 0.5589FD3 1.1299 0.2535 1.69 0.1932*FD4 0.1741 0.0039 0.08 0.7747PARTNER -0.4967 -0.1114 0.81 0.3695CORP -0.5646 -0.1267 0.14 0.7305

a Computed at the sample means.b MLE chi-square statistic (Wald statistic) for testing the hypothesis that a parameter is zero (Judge et aI., p. 215).c Critical level at which the null hypothesis that a, = 0 is just rejected. A figure less than 0.20, for example, implies rejection of the nullhypothesis at the 20% level of significance.d Asterisk indicates significant at the 20% level.

on the expected benefits and costs of sweep netsand treatment thresholds, and/or (b) farmers at­tending these field days have concerns about RSBmanagement.

The Spray Decision

The probability of spraying for the RSB wassignificantly associated (at the 20% level) withthirteen of the nineteen variables included in themodel (table 5). The probability of spraying in­creases as age (AGE), field size (FIELD), andnitrogen application per acre (NITROGEN) in­crease. The probability of spraying also in­creases (by 11.3%) when sweep nets and treat­ment thresholds are adopted (SWEEP), theproducer has more than a high school education(EDUC), neighboring land is in pasture or grainsorghum (Ll and L3), the field is located westof Houston (WESn, and the farmer of the fieldattends the Eagle Lake (FDl), Beaumont sum­mer (FD2), and/or commercially offered fielddays (FD4). The probability of spraying for theRSB is significantly decreased if the farmer ofthe field attends either county extension dem­onstrations (COEXn or the Beaumont fall fieldday (FD3).

As noted by Grant, Rister, and Brorsen, theevidence of RSB damage in marketed rough ricein Texas is substantial. Their results likely stem

from producers not detecting economicallydamaging levels and/or lack of timely spraying.The sign and significance of the variable SWEEPindicates that the adoption of sweep nets andtreatment thresholds increases the probability ofspraying for the RSB. Nonadopters have a lowerprobability of spraying. This indicates rice pro­ducers who adopt sweep nets and treatmentthresholds can more accurately monitor RSBpressure within their fields, and/or visual ob­servation underestimates RSB pressure.

Comparison to Prior AgriculturalTechnology Adoption Studies

A review of the variables identified as statisti­cally significant in studies of technology adop­tion by Pulter and Zilberman, Rahm and Huff­man, Caswell and Zilberman, Rook and Carlson,and Garcia, Sonka, and Mazzocco is revealing.In this study of the adoption of sweep nets andtreatment thresholds, farm size (total acreage),for example, was not found to significantly in­fluence adoption. This indicates that the deci­sion to use these relatively inexpensive tech­nologies is linear in nature. Farm size, however,was important in all the other studies exceptCaswell and Zilberman, where it was not in­cluded as an explanatory variable.

Higher capital outlays and more intense man-

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Table 5. Maximum Likelihood Estimates for the Spray Model, 1986 Texas Rice Stink BugSurvey

Variable Estimate Change in Probability' Chi-Square Statistic" Test Significance'

CONSTANT -8.5924 17.47 0.0001 *dSWEEP 1.1848 0.1133 10.09 0.0015*AGE 0.0358 0.0034 4.84 0.0278*EDUC 0.6311 0.0034 1.79 0.1810*ACREAGE 0.0001 0.1329E - 04 0.29 0.5892FIELD 0.0045 0.4272 E - 03 3.39 0.0656*LI 0.7049 0.0067 3.29 0.0695*L2 0.2002 0.0019 0.18 0.6726L3 0.8655 0.0083 3.47 0.0625*VI 0.0602 0.0058 0.02 0.8968WEST 2.6877 0.2570 17.34 0.0001 *NITROGEN 0.0191 0.0018 8.42 0.0037*PLTDATE 0.0026 0.2495 E - 03 0.06 0.8067COE.XT -1.3815 -0.1321 8.36 0.0038*FDi 1.0082 0.0096 7.56 0.0060*FD2 2.3415 0.2239 9.46 0.0021*FD3 -1.6932 -0.1619 5.51 0.0189*FD4 0.6076 0.0058 2.19 0.1391*PARTNER 0.3268 0.0031 0.75 0.3852CORP 7.2301 0.6912 0.13 0.7136

a Computed at the sample means.b MLE chi-square statistic (Wald statistic) for testing the hypothesis that a parameter is zero (Judge et al., p. 215).c Critical level at which the null hypothesis that (3, = 0 is just rejected. A figure less than 0.20, for example, implies rejection of the nullhypothesis at the 20% level of significance.d Asterisk indicates significant at the 20% level.

agement input associated with microcomputers(Putler and Zilberman), reduced tillage practices(Rahm and Huffman), and financial information(Garcia, Sonka, and Mazzocco) characterize de­cisions associated with economies of size. In thepresent study, field size was important in thedecision to spray, with the probability of spray­ing increasing for larger fields, Similarly, Rookand Carlson identified a tendency for farmerswith larger farms to participate in group pestcontrol.

A broad class of environmental variables ap­pears relevant in explaining technology adop­tion behavior. In the present study, for example,neighboring land uses significantly affect boththe sweep net adoption and insecticide usage de­cisions, In Rahm and Huffman's reduced tillagestudy, crop rotation and soil type were signifi­cant. Similarly, source of irrigation water wassignificant in Caswell and Zilberman' s irriga­tion study. In Rook and Carlson, a relatively highopportunity cost for producers' time (repre­sented by a large acreage of a time-competingcrop) was associated with increased participa­tion in group pest control.

Farm organizational characteristics (NFIELD,PARTNER, and CORP) were not significant inthe present study. However, business organi-

zational characteristics were found to be signif­icant in two of the studies; type of farming op­eration in Putler and Zilberman and the use ofa bookkeeping system in Garcia, Sonka, andMazzocco.

Education has a significant but negative in­fluence on technology adoption in the presentstudy. This contrasts with the positive relation­ships found by Rahm and Huffman and by Put­ler and Zilberman. Garcia, Sonka, and Maz­zocco found that level of education had no effecton adoption of financial management technol­ogy. Some elements of extension educationalprograms had a positive influence on the adop­tion of RSB management technology. Only thereduced tillage study by Rahm and Huffman hadsimilar findings, although their direct extensionvariable was insignificant; however, continuingeducation and media services were significant.

The effect that the adoption of other forms oftechnology had on the technology being studiedwas not considered by the other studies, Thesignificance of the variable reflecting the adop­tion of other production technology (semidwarfrice varieties) in both the SWEEP and SPRAYmodels thus provides additional insight into theadoption process.

The general nature of these findings suggests

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a broad class of variables may significantly ex­plain adoption behavior across various types oftechnologies, although the nature of the tech­nology and its associated financial and humancapital requirements are important. These re­sults indicate a need for more in-depth analysisof producers' perceptions of the net economicconsequences of adopting or not adopting themanagement technology.

Conclusions

The procedures reported in this paper are ap­plicable to a wide range of management datacollected by research and extension economistsand other agricultural professionals. The result­ing information can be useful in targeting edu­cational programs and defining research objec­tives. Extension specialists could analyze surveydata to identify those characteristics of produc­ers most likely to influence the adoption of eco­nomical technologies. Further, such analyses maybe useful in isolating factors requiring more in­depth study regarding their impact on the adop­tion of a particular technology.

In this study, it is evident that extension ac­tivities at field days can significantly affect theadoption of new technologies like insect sweepnets and treatment thresholds. From a researchstandpoint, more information is needed for pro­ducers on the cause-and-effect relationships ofrice stink bug-related damage and suggestedeconomic thresholds. The dissemination of ex­tension service guidelines (Drees 1989) basedon integrated multidisciplinary efforts (Harper)is the direct result of these concerns.

[Received May 1989; final revision receivedNovember 1989.J

References

Boggess, W. G., D. J. Cardelli, and C. S. Barfield. "ABioeconomic Simulation Approach to Multi-SpeciesInsect Management." S. J. Agr. Eeon., 17(1985):43­55.

Brorsen, B. W., W. R. Grant, and M. E. Rister. "A He­donic Price Model for Rough Rice Bid/AcceptanceMarkets." Amer. J. Agr. Eeon. 66(1984): 156-63.

Burrows, T. M. "Pesticide Demand and Integrated PestManagement: A Limited Dependent Variable Analy­sis." Amer. J. Agr. Eeon. 65(1983):806-10.

Byerlee, D., and E. H. de Polanco. "Farmers' StepwiseAdoption of Technological Packages: Evidence from

Amer. J. Agr. Econ.

the Mexican Altiplano." Amer. J. Agr. Eeon.68(1986):519-27.

Capps, 0., Jr., and R. A. Kramer. "Analysis of Food StampParticipation Using Qualitative Choice Models." Amer.J. Agr. Eeon. 67(1985):49-59.

Caswell, M. F., and D. Zilberman. "The Choice of Irri­gation Technology in California." Amer. J. Agr. Eeon.67(1985):224-33.

Drees, B. M. "Insect Management-Rice Stink Bug." 1989Rice Production Guidelines. Texas Agr. Extens. ServoD-1253, Jan. 1989.

--. Rice Insect Management. Texas Agr. Extens. ServoB-I445, Feb. 1983.

Frisbie, R. E., and P. L. Adkisson, eds. CIPM-IntegratedPast Management on Major Agricultural Systems. TexasAgr. Exp. Sta. MP-1616, 1985.

Garcia, P., S. T. Sonka, and M. A. Mazzocco. "A Mul­tivariate Logit Analysis of Farmers' Use of FinancialInformation." Amer. J. Agr. Econ. 65(1983):136-41.

Grant, W. R., M. E. Rister, and B. W. Brorsen. Rice QualityFactors: Implications for Management Decisions. TexasAgr. Exp, Sta. B-1541, July 1986.

Griliches, Z. "Hybrid Corn: An Exploration in the Eco­nomics of Technological Change." Econometrica25(1957):231-52.

Hanneman, W. M., and R. L. Farnsworth. "The Role ofRisk Preferences and Perceptions in the Adoption ofIntegrated Pest Management." Dep. Agr. and Resour.Econ. Work. Pap. University of California, Berkeley,1981.

Hanushek, E. A., and J. E. Jackson. Statistical Methodsfor Social Scientists. New York: Academic Press, 1977.

Harper, J. K. "Developing Economic Thresholds for RiceStink Bug Management in Texas Using Dynamic Pro­gramming." Ph.D. thesis, Texas A&M University,1988.

Headley, J. C. "Defining the Economic Threshold." PestControl Strategies for the Future. Washington DC:National Academy of Sciences, 1972.

Hiebert, D. "Risk, Learning, and the Adoption of FertilizerResponse Seed Varieties." Amer. J. Agr. Eeon.56(1974):764-68.

Ito, S., W. R. Grant, and M. E. Rister. "Impacts of Tech­nology Adoption on the U.S. Rice Economy-The Caseof High-Yielding Semidwarfs." Paper presented atAAEA annual meeting, Reno NV, 27-30 July 1986.

Jarvis, L. S. "Predicting the Diffusion of Improved Pas­tures in Uruguay." Amer. J. Agr. Eeon. 63(1981):495­502.

Judge, G. G., W. E. Griffiths, R. C. Hill, H. Lutkepohl,and T. C. Lee. The Theory and Practice of Econo­metrics, 2nd ed. New York: John Wiley & Sons, 1985.

McIlveen, G., C. C. Bowling, and B. M. Drees. "1981Rice Insect Control." Entomology Notes (Texas Agr.Extens. Serv.) vol. 22, spec. issue no. 1,17 Feb. 1981.

Mandersheid, L. V. "Significance Levels-0.05, 0.01, or?" J. Farm Eeon. 47(1965):1381-85.

Masud, S. M., R. D. Lacewell, J. R. Stoll, J. K. Walker,J. F. Leser, and C. Seller. "Impact of a More IntensiveInsect Pest Infestation Level on Cotton Production:Texas High Plains." S. J. Agr. Eeon. 17(1985):117­25.

at University of N

orth Carolina at G

reensboro on Novem

ber 9, 2014http://ajae.oxfordjournals.org/

Dow

nloaded from

Harper, RISler, Mjelde, Drees, and Way

Pindyck, R. S., and D. L. Rubinfeld. Econometric Modelsand Economic Forecasts, 2nd cd. New York: Me­Graw-Hili Book Co., 1981.

Pingali, P. L., and G. A. Carlson. "Human Capital, Ad­justments in Subjective Probabilities, and the Demandfor Pest Controls." ArneI'. J. Agr. Econ. 67(1985):853-61.

Putler, D. S., and D, Zilberman. "Computer Use in Ag­riculture: Evidence from Tulare County, California."Amer. J. Agr. Econ. 70(1988):790-802.

Rahm, M. R., and W. E. Huffman. "The Adoption of Re­duced Tillage: The Role of Human Capital and OtherVariables." ArneI'. J. Agr. Econ. 66(1984):405-13.

Roe, T., and D. Nygaard. Wheat Allocative Error and Risk:Northern Tunisia. Economic Development Center Bull.No.5, University of Minnesota, 1980.

Insect Management 1005

Rook, S. P., and G. A. Carlson. "Participation in PestManagement Groups." ArneI'.J. Agr. Econ. 67(1985):563-66.

Smith, G. S., M. E. Wetzstein, and G. K. Douce. "Eval­uation of Various Pest Management Characteristics."S. 1. Agr. Econ. 19(1987):93-101.

Sonka, S. T., R. H. Hornbaker, and M. A. Hudson. "Man­agerial Performance and Income Variability for a Sam­ple of Illinois Cash Grain Producers." N. Cent. J. Agr.Econ, 11(1989):39-47.

The Semidwarfs-A New Era in Rice Production. TexasAgr. Exp. Sta. B-1462, March 1984.

Zacharias, T. P., and A. H. Grube. "Integrated Pest Man­agement Strategies for Approximately Optimal Controlof Com Rootworm and Soybean Cyst Nematode."Amer. J. Agr. Econ. 68(1986):704-15.

at University of N

orth Carolina at G

reensboro on Novem

ber 9, 2014http://ajae.oxfordjournals.org/

Dow

nloaded from