determination of factors influencing integrated pest management adoption in coffee berry borer in...

Agriculture, Ecosystems and Environment 87 (2001) 159–177

Determination of factors influencing integrated pest managementadoption in coffee berry borer in Colombian farms

B. Chavesa,∗, J. Rileyb

a Centro Nacional de Investigaciones de Café, Chinchiná, Caldas, Colombiab IACR-Rothamsted, Harpenden, Herts AL5 2JQ, UK

Received 31 October 2000; received in revised form 24 January 2001; accepted 28 February 2001

Abstract

Integrated pest management is promoted in coffee plantations to control pests and disease in a manner less harmful tothe environment than the use of pesticides alone. The rate of adoption of these practices is variable, possibly influenced bydifferent social, economic, environmental and institutional factors. This was explored by fitting standard non-linear curvesto uptake data for each of four chosen integrated pest management (IPM) recommendations for control of coffee berryborer,Hypothenemus hampei (Ferrari), in Colombian coffee. Logistic curves were shown to be the most efficient for all fourrecommendations. Logistic regression analysis was then used to determine the impact of different factors upon the uptakeof the recommendations singly and in combination. Comparisons are made between the results of the analyses to confirmthe choice of the single or combined datasets and the reliability of the models. The results showed that different factorsaffected the adoption processes at different stages in time for the different recommendations, both when used singly and whenin combination. A link was demonstrated between level of education, wealth of the farmer and choice of recommendation,poorer farmers choosing recommendations that did not require a large financial outlay or which required a high level oftechnological skill. Suggestions for more controlled studies of adoption are presented. © 2001 Published by Elsevier ScienceB.V.

Keywords: Technology adoption; Coffee berry borer; Logistic curves; Logistic regression analyses

1. Introduction

1.1. IPM recommendations for coffee

Coffee (Coffea arabica) is the most important agri-cultural product in Colombia. More than 500,000 fam-ilies cultivate coffee on about 869,000 ha and theirincome comes from the coffee sold (Federacion Na-cional de Cafeteros de Colombia, 1997). The disease

∗ Corresponding author. Tel:+57-6850-6550;fax: +57-6850-4723.E-mail address: [email protected](B. Chaves).

and sanitation aspects of the crop are the main preoc-cupation of the farmers; they aim to maintain the qual-ity of the Colombian coffee to ensure good returns.

Coffee berry borer (CBB),Hypothenemus hampei(Ferrari) is the most important pest in the coffee world;it arrived in Colombia in 1988. Coffee berry borerbores the bean and enters the seed causing damage notonly to the weight but also to the coffee quality be-cause it itself feeds and reproduces inside. In order tomanage and control this pest and avoid possible dam-age to the ecosystem caused by indiscriminate use ofchemicals (Cadena, 1991), CENICAFE started a re-search project to develop integrated pest management(IPM) practices as soon as the insect was detected

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160 B. Chaves, J. Riley / Agriculture, Ecosystems and Environment 87 (2001) 159–177

(Bustillo et al., 1998). The IPM strategy is based onseveral recommendations focusing on biological andcultural control and also involves some rational use ofchemical control. Despite the fact that IPM researchgenerates many recommendations for many manage-ment practices, only four main ones are examinedhere:

• Sampling is a measure of the level of infestation,made by counting the total berries and the berriesbored in a branch selected at random from eachcoffee tree for 30 trees selected systematicallyin a W or X shape in a plot of about 1 ha. Thepercentage of berries bored per branch out of thetotal of berries per branch is calculated and theaverage of the 30 branches calculated. This is nota method of control and is therefore not used onits own. It is used by some farmers to determinethe best time to apply the other recommendedtreatments.

• Cultural practice or RERE is the permanent har-vesting of mature berries of coffee such that therewill always be only green berries in the coffeeplot.

• Biological control is the application of the fungusBeauveria bassiana, which kills the coffee berryborer.

• Non-biological control is the spraying of insecti-cides of low toxicity when the level of infestationis high and more than 50% of the insect is outsidethe berries or in the process of boring.

Once the recommendations are released they aretransferred to the farmers through the Extension Ser-vice of the National Federation of Coffee Growers ofColombia using different mass media channels.

The degree of uptake of the recommendationsvaries both from farmer to farmer and over time foreach farmer. Several social, economic, institutionaland environmental external factors impact upon thedecision process. They can be different from onerecommendation to another and their relevance canchange over time. A method is required to estimate therates of adoption of the different recommendations, orcombinations of them. Also, it is important to knowwhich factors influence the uptake of IPM recommen-dations, and at which times in the uptake process andwhether they are different for the different recommen-dations.

1.2. Statistical methods for adoption studies

Adoption studies of recommendations for differentcrops have been made in other parts of the world. Theimpact of IPM on pesticide use, yields, and producerprofits for orange growers in California and Floridawas examined by Fernandez-Cornejo and Jans (1996).They used probit analysis and the inverse Mills ratioto estimate parameters of the adoption process. Thesevalues were then used as a regressor in the demandand profit equations and the system was estimated foradopters and non-adopters. Farm size, product price,farmer’s education and experience, off-farm labour,use of consultants and contractual arrangements for theproduction/marketing of the product were comparedfor adopters and non-adopters. They concluded thatoff-farm work might be an important barrier to IPMadoption.

Adoption of improved maize (Zea mays) wasrapid in Mali when an attractive guaranteed pricewas offered and extension activities were reinforced(Boughton and de Faran, 1994). The rate of technol-ogy adoption was strongly affected by the stage ofdevelopment of the farming system of southern Mali.The main weaknesses of the program were: (1) aweak linkage between research and extension; (2) thelack of economic analysis underpinning technical re-search findings and extension recommendations, and(3) inadequate monitoring of technology adoption.The existence of communication channels betweenresearchers and extension agents was necessary butnot sufficient for improved effectiveness. The proce-dures and criteria with which the research programevaluated proposals and results were also critical.They concluded that effective evaluation of tech-nology requires economic analysis in the researchprogram and monitoring of technology adoption inorder to provide feedback to research.

With the aim of analysing the determinants ofadoption and diffusion of sustainable agriculturaltechnologies a study was made considering somestatistical and econometric approaches (De Souza,1997). A field survey conducted in 1994 provided thefarmer data. Continuous progress can be seen in thetheory of adoption and diffusion of technologies sincethe first application of the epidemic model (Rogersand Shoemaker, 1971). Logit/probit models arewell-established approaches and have been employed

B. Chaves, J. Riley / Agriculture, Ecosystems and Environment 87 (2001) 159–177 161

in several empirical studies of both technology andagricultural technology adoption. Using these models,de Souza concluded that the probability of a farmeradopting a sustainable technology increased if he orshe was more integrated with a farmer’s organisation,had contact with non-governmental organisations, wasaware of the negative effect of chemicals on health andthe environment, could rely on family labour and hadhis or her farm located in better soil. The probabilityof adoption declined with increased farm size. His useof survival analysis additionally suggested that any in-crease in output prices and rural wages relative to theprices of an external input would lead to a decreasein the speed of diffusion of sustainable technologies.

Results from a number of linear models demon-strated that the Edinburgh survey of decision-makingon farms was successful in developing and un-derstanding the broad strategic aspects of farmerdecision-making and behaviour. These valuableinsights were obtained from models based on judi-ciously chosen small sets of variables. The impor-tance of the use of multivariate statistical techniquesto reduce large masses of data to a tractable form andthe existence of a multidisciplinary team to constructand interpret the models were demonstrated (Austinet al., 1998a). Application of non-linear models andexpert modelling approaches to social science datawere also discussed and illustrated with examplesfrom this survey. Yet non-linear models did not giveappreciably enhanced goodness of fit compared tolinear models. The strength of non-linear models andexpert models was more likely associated with theunderstanding derived from them than from goodnessof fit (Austin et al., 1998b).

Multivariate analysis, linear and non-linear models,adoption curves, logistic regression, probit and sur-vival analysis have thus been used with varying de-grees of success to model the adoption of technolo-gies. In this paper some standard non-linear curvesare fitted and contrasted to the uptake data for eachof the four chosen recommendations for coffee berryborer control. Logistic regression analysis is then usedto determine the impact of different factors at differ-ent times upon the uptake of the recommendationssingly and in combination. Comparisons are made be-tween the analyses to confirm the choice of the sin-gle or combined datasets and the reliability of themodels.

2. Materials and methods

2.1. The sample survey

The survey was done in 1997 and focussed on zoneswhere the insect had been established for more than 4years. The number of farmers was selected in propor-tion to the affected area in each of nine different zones.The farmers to be surveyed in each zone were selectedrandomly. The maximum sample size was determinedto be 400 from a total of 171,000 affected farmers.

The questionnaire had 116 questions relating to de-mographic, socio-economic, labour and technologicalaspects of the family farm and pest management meth-ods. Eight farmers were eliminated as they had miss-ing values in their data.

For the four recommendations studied here, thefarmers surveyed could decide to adopt them singlyor in appropriate combinations, 31.1% of the farmersapplied all recommendations and only 1.8% did notuse any (Table 1). None of the farmers used only theapplication ofB. bassiana or the sampling to measurethe level of infestation, 1.3% used spray insecticidesolely and 12.8% used just RERE.

Since sampling is a measure needed to determinethe level of infestation and to make decisions suchas apply insecticide, around 60% of farmers were us-ing this in combination with one or more of the otherrecommended treatments to control the borer. RERE,which is independent of the level of infestation, was

Table 1Percentage adoption of IPM recommendations

Sampling RERE B. bassiana Sprayinsecticides

Farms %

∗ ∗ ∗ ∗ 122 31.1∗ ∗ 52 13.3∗ 50 12.7

∗ ∗ ∗ 46 11.7∗ ∗ ∗ 30 7.6

∗ ∗ 28 7.1∗ ∗ ∗ 27 6.9

∗ ∗ 20 5.17 1.8

∗ 5 1.3∗ ∗ 2 0.5∗ ∗ ∗ 2 0.5∗ ∗ 1 0.3228 375 202 259 392


the most adopted practice. More than 95% of the farm-ers were using this alone or in combination with one,two or three other practices. About 65% of the farmerswere spraying insecticide. The least adopted recom-mendation, at 51%, was the application ofB. bassiana.Application of bothB. bassiana and insecticides de-pends on the level of infestation and as they are ex-pensive, they are used infrequently in combination.

The data in Table 1 were obtained at the time of thesurvey which corresponded to the final time. Counts ofrecommendation usage in earlier months were madeas follows.

Time zero was the time the insect arrived on thefarm. The number of farmers was counted in succes-sive months who were using each of the four IPMrecommendations regardless of whether they werecombined or not. As some farmers started using therecommendations before the insect arrived this meantthat there were some negative values on the horizontal(time) axis.

2.2. Statistical methods

In order to depict the rate of adoption through time,standard non-linear growth curves, as suggested bySeber and Wild (1989), were fitted to the data foreach recommendation individually, including the useof other recommendations at the same time. This wasnecessary because data were limited where only a sin-gle recommendation was used. Thus, there were nodata for use of sampling alone or for use ofB. bassianaalone. The use of spray insecticide alone was limitedto five farms although RERE on its own was used on50 farms. The empirical models fitted with Genstat 5(1993) to the counts for each of the four recommen-dations, ignoring the presence or absence of other rec-ommendations, were all of the exponential family andincluded the exponential, line+ exponential, criticalexponential, logistic, generalised logistic, Gompertz,and quadratic-divided-by-quadratic curves. The bestfitting models were selected.

When the logistic curve is fitted to the number offarmers who adopt one recommendation it is difficultto determine when different factors start and stop hav-ing an influence on its adoption. A non-continuousfactor — that is, a grouped variable, or a factor —can be included in the dataset that is modelled withany growth curve and its levels can be compared us-

ing analysis of parallelism (Ross, 1992). However, asother factors can affect these variables, the compari-son between curves for recommendations may not bevalid. The use of logistic regression at each time pointwith both continuous and grouped variables is a bettermethod to determine the different effects of externalfactors.

Then to build models to find the influential factorsaffecting uptake of each recommendation, a gener-alised linear model with logit link and binomial error(that is a logistic regression model) is fitted to the dataat each 12 months stage. Thus, time zero here is whenthe farmer takes up the recommendation. The data hereare for individual farmers and are zero if he/she hasnot adopted at that stage and one if they have.

These analyses were done for:

• the data for each recommendation, ignoring all otherrecommendation applications;

• for the data for single recommendations, whereavailable, and combinations of two, three or allfour recommendations.

The fitting of survival curves would not be appropri-ate here because the uptake of combinations of recom-mendations is not constant, i.e. some farmers take upsampling and spray insecticide, and then stop spray-ing insecticide at different times. Also the influentialfactors differ from time to time and these are what wewish to explore.

The environmental and managerial effects were de-scribed in terms of grouped variables or factors (hav-ing a bank loan or not, having a group of friends ornot, having labour or not. . . ) and a number of contin-uous variables.

The minimum, mean and maximum of the contin-uous variables were:

• time in years with coffee (1; 23.5; 70);• time in months with the insect (8; 39.3; 72);• farmer’s age (17; 46.6; 84);• the level of education as the number of years (0;

4.163; 16);• number of capacitating events — e.g. community

meetings, field days, IPM courses, extension servicevisits, demonstration plots viewing — (0; 6.7; 100),size of coffee plot in ha (0.05; 6.5; 120);

• crop density as number of trees ha−1 (500; 4251;10,000), and


• time in months with a technical assistant (0; 32.0;120).

Second-order interactions of the grouped variableswere included in the analyses. Higher orders couldnot be included because of the relatively small sizesof the individual datasets, and the small number ofdegrees of freedom for error. For each year the samefactors and variables were included in the same orderand dropped if they were not significant. The analysestransform the data to the logit scale, i.e. logit(pi) =loge(pi/(1 − pi)), where

pi = exp(β0 + β1X1i + · · · + βkXki)

1 + exp(β0 + β1X1i + · · · + βkXki)

= eg(x)

1 + eg(x)(1)

P(Y = 1| g(x)) = pi is the probability that theithfarmer adopts one or a combination of recommen-dations and exp(βi) is the probability of adoptionincrease for an increase in anXji variable or theodds ratio. Distributions and residuals of the pro-posed models were checked in order to find highleverage observations and atypical points. Evaluationof overdispersion was made in cases where the meandeviance was greater than one (Collett, 1991). Wald’stests and likelihood ratio statistics were used to testthe significance of the factors.

3. Results

3.1. Estimation of the adoption curve

All of the curves provided very good fits, the per-centage of explained variation accounting for more

Table 2Estimates and standard errors of the parameters of the logistic curve fitted to the data for each recommendation

Parameters Estimation Sampling RERE B. bassiana Spray insecticide

α Coefficient −11.00 −17.00 7.52 −9.30Standard error 12.12 11.92 6.47 10.71

β Coefficient 0.070 0.096 0.082 0.082Standard error 0.010 0.007 0.010 0.009

γ Coefficient 245.5 392.2 214.7 265.6Standard error 17.27 14.04 17.98 15.05

µ Coefficient 15.08 3.75 35.67 15.23Standard error 1.73 0.08 2.02 1.34

than 97% in each case. The S.E. of unexplained vari-ation was low, between 6.62 and 16. However, thelogistic curve was chosen because it had the leastincidence of influential points for all of the recom-mendations, there were no systematic patterns in theresiduals, suggesting that the errors were independent.Estimated logistic parameters and their standard errorsare presented in Table 2 for each recommendation.

Theα parameter, the lower asymptote, was no dif-ferent from zero for all the recommendations stud-ied. The parameterβ, relating to the uptake rate, wasgreater than zero for all four recommendations. Theupper asymptoteγ indicating the maximum numberof adopters, was greater for the RERE recommenda-tion. The parameterµ is the time where the adoptionrate reaches the maximum value. The smallest value ofµ was found for the RERE recommendation; the esti-mates ofµ for the sampling and spray insecticide rec-ommendations were similar to each other. The largestµ estimate was for the application ofB. bassiana. Thefitted curves are shown in Fig. 1.

A test of parallelism was done in order to test theequivalence of these curves (Ross, 1990). The linearparametersα andγ had lower and upper asymptotesthat were different for the different IPM recommenda-tions as did the non-linear parametersβ andµ. Thus,the curves were not parallel and differed with regardto the non-linear parameterµ.

3.2. Results of logistic regression analyses forindividual recommendations

Tables 3–6 show the coefficients and standard errorsof the significant factors and interactions identifiedfor the four individual recommendations. The factors


Fig. 1. Estimated logistic curves for the four-IPM recommendations.

were not the same for each recommendation nor foreach time point. For the adoption of the samplingrecommendation, the maximum set of factors was:the level of education, participation in capacitatingevents, size of the coffee plot, time with the insect,time with a technical assistant, having a bank loan,crop density, age of the farmer and quantity of labour(Table 3). Some interactions with capacitating eventswere evident in the first 3 years only.

For the RERE recommendation the time with cof-fee berry borer, time with technical assistance, coffeeplot size, and the age of the farmer were importantinfluences in several years (Table 4). Participation incapacitating events appeared important only in year 4and the final year. Some interactions with age wereevident, but largely only in the early years.

The level of education, which affected all the years,time with the insect, affect of other labour in the farmand size of coffee plot explained the adoption ofB.bassiana applications in the early years. (Table 5).Other factors like a bank loan, belonging to a group offriends, having other crops, capacitating events, suffi-ciency and scarcity of labour, size of coffee plot andshade trees in the coffee plot affected the adoption of

the fungus during the last 3 years. Some two factorinteractions were important, but not consistently so.

Level of education, time with the insect, size of thecoffee plot, crop density and having other crops werethe most important factors to explain the variability ofthe spray insecticide adoption (Table 6). Farmer’s ageand having a group of friends showed effects in only1 or 2 years.

In summary, adoption of different IPM recommen-dations for coffee berry borer control was influencedby different factors and at different times. Level ofeducation was important in all recommendations ex-cept for RERE. Time with the insect and size ofcoffee plot were important for all the recommenda-tions and in most of their years. Time with a tech-nical assistant was important only for sampling andRERE and to varying degrees. Participation incapacitating events was important for the samplingrecommendation, less so for RERE andB. bassianaapplication. Crop density was important only forsampling and for insecticide spraying. Having abank loan was only important for sampling andB. bassiana application, as were scarcity andsufficiency of labour. Farmers’ age was important

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at varying times for all recommendations except forB. bassiana application.

3.3. Results of logistic regression analyses fordifferent combinations of recommendations

Analyses of the data for the single RERE rec-ommendation and for combinations of two, three orall four of the recommendations are shown in Ta-bles 7–10. Only those sets of data having over 25farms initially in the years were used (see Table 1).

Table 7 shows that the use of RERE alone was in-fluenced by the level of education, capacitating events,access to a loan bank, time with coffee, time with theinsect, size of the coffee plot and crop density. Onlysome factors identified in this analysis — capacitatingevents, time with the insect and size of plot — werecommon to the analyses of the data for RERE withother recommendations, and their incidences did notalways correspond. The signs of the regression coeffi-cients, except the constant term in some years, in Ta-ble 7 were all negative. This reflects the fact that thisgroup of farmers was characterised by its low levelof education (mean of 2.5 years), little participationin capacitating events (4.13 events), no access to abank loan, low density (3695 trees ha−1), small farms(3.39 ha), little time with coffee (18.65 months) andwith the insect (33.16 months).

The use of sampling and RERE together was largelyinfluenced by the age of the farmers, coffee plot size,the existence of other crops on the farm, and time withthe insect (Table 8a). Crop density had a small effect inyear 1 and other incomes in year 2. These farmers hadsmall coffee plots (3 ha). Their mean age was 39.47years, the mean time with the insect was 32.34 monthsand the crop density was 3761 trees ha−1. Most of theadopters had no other crop on the farm.

The level of education and access to a bank loanwere key influences related to the use of RERE withspray insecticides. Time with the insect, crop densityand age of the farmers were influential in the earlyyears (Table 8b). This group of farmers had the fol-lowing average characteristics: age of the farmers (50years), the level of education (2.83 years), crop density(3831 trees ha−1), time with the insect (37.72 months)and size of the coffee plot (4.29 ha). Coffee plot sizewas larger for this group than that of the farmers usingonly RERE or sampling and RERE.

Crop density and not belonging to a group offriends influenced the combination sampling, REREand insecticides (Table 9a). These farmers had a highaverage crop density (4768 trees ha−1) thus increas-ing the probability of using a combination of recom-mendations. Farmers with an average age of 52.65years and with other crops in their farm tended to usethe combination RERE,B. bassiana, and insecticides(Table 9b). Farmers who adopted the combinationsampling, RERE,B. bassiana had bank loans, smallaverage coffee plot sizes (2.79 ha) and high averageparticipation (13.01 events) in capacitating events(Table 9c). An interaction between having or not hav-ing a bank loan and attendance at capacitating eventswas detected in all years thus indicating that exis-tence of funds may influence ability to attend suchfunctions and thus encourage continued adoption ofthis combination of recommendations.

Farmers who used all four recommendations(Table 10) had the greatest average level of educa-tion (5.81 years), bigger average coffee plot sizes(11.41 ha), more than 30 months of technical assis-tance (36.54 months), average participation in capac-itating events (8.3) and high crop density (4620 treesha−1). This implied this group of farmers to be thewealthiest. A small negative interaction between edu-cation and coffee plot size was apparent in years 2–6.

4. Discussion

4.1. Implications of the results

A number of major points can be concluded fromthis study. The logistic curves provided the best fits forall the datasets, confirming the value of these modelsand which have been used in other uptake and adoptionsituations. The curves demonstrated clear differencesbetween the maximum number of adopters for eachrecommendation and the different rates of growth. Therate of adoption for RERE was larger than for the otherrecommendations and thus its maximum was achievedearlier, and the maximum number of adopters waslarger. The sampling and the spray insecticide recom-mendations showed similar adoption curves. The ap-plication ofB. bassiana demonstrated a slower rate ofadoption, with the possibility of not having achievedits maximum number of adopters.


Rogers and Shoemaker (1971) classified farmersas innovators, early innovators, early majority, latemajority and laggards. This classification could beinappropriate here, because some farmers delayedusing a recommendation until he or she needed itand this could have been long after the release of theinnovation. On the other hand, farmers used a recom-mendation without any necessity and abandoned theinnovation after using it for a short time. Thus, it is im-portant to know if the innovation continues being usedby farmers for a long time (Duque and Chaves, 1997).

Although empirical non-linear models of growthassume that the population is homogeneous, that is tosay in this case that every farmer had the same opportu-nity to adopt a recommendation, there are factors thatdetermine when a farmer adopts a recommendation.Therefore, the farmer population cannot be consideredhomogeneous and these models themselves cannotexplain properly the rate of adoption. These modelshave been applied to biological processes where sizeof an individual is measured and it is supposed thatthe growth rate is the product of functions of thecurrent size and the remaining size. Nevertheless, em-pirical growth curves can give interesting informationabout the speed of adoption, when the adoption ratereaches a maximum and when it tends to stabilise.

The logistic regression models for the individualrecommendations ignoring other recommendationsshowed different influential factors, as would be ex-pected. Higher levels of education and availability offunds implied greater access to labour, larger farmsize, ability to buy insecticides, to make the fungusspray and to take part in capacitating events. Thesewere reflected in the data for sampling,B. bassianaand insecticide spray. But, it should be noted that sizeof farm, although implying greater wealth, may havereduced the adequacy of labour availability becauseof need for greater numbers of labourers.

The models fitted to the individual recommenda-tions ignoring existence of the other recommendationsmay have been biased by the other recommendationsand this approach is not recommended. The logisticmodels for the single and combined recommendationswill be more valuable because the data represent whathappens in reality: more farmers adopted combina-tions of recommendations although these data werenot detailed enough to show whether they adoptedthem and dropped them at different times. It is in-

teresting to note that the influential variables in theseanalyses were fewer than for the first set of anal-yses, thus emphasising the fact that the first set ofanalyses were possibly identifying different influen-tial factors for the different recommendations. Thiswas confirmed through the comparison of the analy-ses of the sole RERE data with the RERE data ig-noring other recommendations: there was little coin-cidence in the influential factors during the range ofyears studied. The incidence of education level in thesole RERE data showed that this group of farmers hada lower level of education and went to fewer capaci-tating events throughout the whole monitoring period.Yet education level did not appear in the analysis forthe RERE data ignoring other recommendations. Alsoin the analysis of the RERE and sampling combinationdata, time with the insect, plot size, farmers’ ages andnon-existence of other crops on the farms were im-portant largely throughout the whole monitoring pe-riod, quite different from the prevalence of the factorsidentified in the sole RERE analyses. However, levelof education, time with the insect and availability of abank loan were important when spray insecticide wascombined with RERE; the first two factors were ap-parent in the analyses for spray insecticide ignoringother recommendations.

When three recommendations were adopted, thefactors identified by the analyses were few and weredifferent for each. For the sampling, RERE and sprayinsecticide combination, the high crop density mayhave encouraged the early use of the spray. The useof B. bassiana with insecticides and RERE was in-fluenced by farmers’ ages and growth of other crops.This conservative combination may have been to pro-tect the other crops that they grow. Yet this was notreflected in the combination of sampling, RERE andB. bassiana by farmers who had bank loans and ac-tively participated in capacitating events, despite hav-ing smaller coffee plots.

The adoption of all four recommendations was as-sociated with farmers who had higher average levelsof education and coffee plot sizes throughout the mon-itoring period and higher crop densities, attendance atcapacitating events and availability of technical assis-tance for large parts of the period. Labour availabilityat the start of the period was good. This implies thatricher farmers were able to adopt a greater number ofrecommendations and learn more about management


through attending more capacitating events. Differentcombinations of the recommendations were used tocope with the level of infestation but they were in-fluenced also by factors such as the level of educa-tion, economic factors, size of the plot. Farmers havingsmall coffee plots and low levels of education tendedto use only one or two recommendations. Also factorsdetected at the start of the study were likely to be deci-sive factors for the choice of the recommendation(s).Later ones may have come into play as deciding fac-tors relative to other influences.

4.2. Estimation of adoption and stability

The analyses showed that the number of farmersadopting some combinations of recommendationswithin the first 3 years became stable because theinclusion of factors and variables stabilised and theircoefficients became constant for successive years.These were the sampling and RERE recommenda-tions, spray insecticide and RERE recommendations,sampling, RERE and spray insecticide recommen-dations, RERE,B. bassiana and spray insecticiderecommendations, and the sampling, RERE andB.bassiana recommendations. When all four recom-mendations were combined, stability of adoption wasachieved in 4 years.

Although the analysis was made for a period of 6years, variations in continuous variables were not ev-ident at each time point analysed. For example: age,level of education, size of coffee plot, time with tech-nical assistance, access to a bank loan or participationin capacitating events can change each year. Since thevariation in the variables is equal for all the farm-ers — a constant: such as for time growing coffee— the estimation of the parameters does not change,only the intercept or constant term in the equation. Ifthe change in one variable from one year to anotherequalsr, the change in the constant term is equal tortimes the estimated coefficient of the original variable.Non-constant changes year-by-year will happen espe-cially in the capacitating events and access to bank

loan variables. Nevertheless, if the variations in fac-tors year-by-year can be expressed as a proportion ofthe actual value of the factor or independent variable,the reduction of the residual deviance due to the factordoes not change, neither do the Chi-square andt-testvalues. The change in the new estimated coefficient isequal to the original estimate times the inverse of theproportion used.

An example shows how the probability of adoptioncan be calculated from the coefficients. Thus, for year0 of the four recommendation combination (Table 10),the probability of adoption is

pi = exp(−3.073+ 0.155X1i + 0.018X2i + 0.023X3i + 0.235X4i − 0.803X5i )

1 + exp(−3.073+ 0.155X1i + 0.018X2i + 0.023X3i + 0.235X4i − 0.803X5i )(2)

whereX1 is the level of education,X2 the technicalassistant time,X3 the size of coffee plot,X4 the suffi-cient labour andX5 is the labour scarce. For instance,for each year more of education, the probability ofadoption is increased by a factor of exp(0.155) =1.16.

4.3. Considerations for new monitoring schemes

RERE is a very easy practice, a high level of educa-tion is not necessary to understand it. This control maybe the most efficient; it provides control at all stagesof the infestation, and ensures some return from thecoffee sold and thus can pay part of the labour nec-essary for collecting the berries. However, when thesize of the plot is big it is difficult to do cultural con-trol because of the quantity and permanency of labourneeded.

In contrast, application ofB. bassiana requires otherinputs in order to apply it. The preparation of the fun-gus demands the knowledge of some aseptic manage-ment and time-consuming use and calibration of as-perses that can interfere with other work on the farm.Level of education, capacitating events, quantity oflabour, resting time to attend to other crops and cof-fee plot size are factors that limit use of the fungus.This recommendation is efficient if the insect is out-side the berries, thus farmers need to know the correcttime to apply it in order to kill the greatest number ofborers.

The sampling recommendation demands knowl-edge of arithmetic operations, so levels of education,


capacitating events, technical assistance are rele-vant factors. In order to realise the sampling it isnecessary to have enough labour but this is influ-enced by the size of the coffee farm, consequentlyfarmers have to spend money on this recommenda-tion and access to a bank loan may be considerednecessary.

For the application of bothB. bassiana and insec-ticides their efficacy relies on the number of insectsoutside the berries or in the penetration process. How-ever, their adoption mainly depends on the level ofeducation, coffee plot size, time with the insect, exis-tence of other crops and crop density. In order to applyinsecticide efficiently, it is necessary to make sure thelevel of infestation and the number of insects outsidethe berries justify the application; the spray machinemust be calibrated and the sprayer must know how toapply the correct doses. These require a good under-standing not only of spraying techniques but also ofthe sampling procedure. Level of education is thus avery important factor.

Unfortunately, these models could only be fittedlong after the recommendations were released. In fu-ture it may be possible to plan before the recommen-dations are released for an appropriate monitoring pro-cess with extension and research activities to try toreach the target curve. This plan requires the establish-ment of a fixed sample to obtain information period-ically about the adoption rates, identification of newfactors that may affect the adoption and the stabilityof the uptake.

These analyses have shown the necessity to moni-tor the uptake of technologies in order to assess notonly the adoption rate but also external managementand environmental factors to address strategies forboth transfer and research. However, monitoring in-cludes the registering of factors that can change in timesuch as internal and external prices, rural wages andother economic indices such as rates of devaluationand costs of controls upon the adoption decision. Al-though these variables depend on market fluctuationsit is important to know their impacts upon the adoptionprocess. They do not vary among the farmers duringa period but change from period to period for all ofthem.

Finally, the success of the recommendations notonly depends on the farmers, but also relies on theirquality, feasibility and likely acceptability (Cottrell,

1997), characteristics that must also be assessed be-fore the recommendations are released.

5. Conclusions

Uptake rates were examined for four IPM recom-mendations to control coffee berry borer in Colombia.Non-linear, logistic, curves were fitted to each of thefour sets of farmer adoption counts in time. Differ-ences were shown between the uptake rates and thetimes to achieve the maximum adoption.

Logistic regression was then used to determine theinfluence of external management and environmentalfactors upon the adoption process at 12 monthly inter-vals for each of the four recommendations (ignoringtheir possible combination) and for each of the fourrecommendations sole or in combination with one, twoor three of the other recommendations. These anal-yses showed different external influences at differenttimes and for different IPM recommendations or com-binations thereof. The overriding influence of level ofeducation and wealth was clear for those recommen-dations that required knowledge of technology, pur-chase of expensive inputs and support of labour. Sug-gestions for more controlled studies of adoption werepresented.

Acknowledgements

Thanks are due to the Federacion Nacional deCafeteros de Colombia, IACR-Rothamsted, CentroNacional de Investigaciones de Café and ComitesDepartamentales de Cafeteros. Thanks are also to H.Duque who supplied the data and is a co-worker in thisproject. The authors also thank A.D. Todd for his pro-gramming advice to obtain the results. The work re-ported here was done when B. Chaves held a Rotham-sted International Fellowship. IACR-Rothamsted issupported by the UK Biotechnology and BiologicalSciences Research Council.

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