SOCIAL DETERMINANTS OF ADOPTION OF INTEGRATED PEST MANAGEMENT (IPM) BY

QUEBEC GRAIN FARMERS

Dr. Gale E. West and Ismaëlh Ahmed Cissé, M.A., Centre de Recherche en Économie de

l’Environnement, l’Agroalimentaire, les Transports et l'Énergie (CREATE), Faculty of Agriculture and

Food, Laval University, Québec, QC

Selected Paper prepared for presentation at the 2014 AAEA/EAAE/CAES Joint Symposium: Social

Networks, Social Media and the Economics of Food, Montreal, Canada, 29-30 May 2014

Copyright 2014 by [authors]. All rights reserved. Readers may make verbatim copies of this

document for non-commercial purposes by any means, provided that this copyright notice appears

on all such copies.

SOCIAL DETERMINANTS OF ADOPTION OF INTEGRATED PEST MANAGEMENT (IPM) BY QUEBEC GRAIN FARMERS

Gale E. West, Ph.D. & Ismaëlh Ahmed Cissé, M.A., Centre de Recherche en Économie de l’Environnement, l’Agroalimentaire, les Transports et l'Énergie (CREATE), Faculty of

Agriculture and Food, Laval University, Québec, QC

1

Abstract

The purpose of this paper is to determine the socioeconomic factors that influence the

behavior of adoption of Integrated Pest Management (IPM) by Quebec grain farmers. Using an

econometric model of discrete choice, ordered logit model, the results show that majority of

Quebec grain producers are practicing IPM. Seven explanatory variables, such as amount of IPM

information received, lack of weed control knowledge, level of environmental concern,

perception that IPM is an organic production, need for monetary incentives to adopt, numbers of

years as a producer, education level appear to be the determinants of the producers' decision

process. Nevertheless, there was a gap between those who believe they are practicing IPM and

those who actually do. IPM is quite misunderstood; producers often equated it with organic

production practices. Increased information campaigns are needed to teach appropriate IPM pest

identification practices. In fact, producer organizations appear to be an ideal structure for

increasing IPM information dissemination because of the level of trust shared among producers.

Most producers worried that IPM practice might reduce yields; therefore, 75% believe that

financial assistance is needed before they would more widely adopt IPM. Level of agricultural

training plays a significant role in IPM adoption. The foundations of IPM practices should be

taught as early as possible in existing agricultural education programs.

2

Contents Introduction ............................................................................................................................................................... 3

1. IPM approach (Literature review)............................................................................................................. 3

2. Data Collection and Description ............................................................................................................... 3

2.1. Data Collection ....................................................................................................................................... 3

2.2. Description of measures ....................................................................................................................... 3

• The IPM index ........................................................................................................................................ 4

• The explanatory variables ................................................................................................................... 6

3. Ordered logit model ...................................................................................................................................... 8

4. Econometric results ....................................................................................................................................... 8

• Amount information received from differences sources (Amount Info) .................................... 9

• Lack weed control knowledge (LWCK) ............................................................................................ 10

• Concern for the environment (Enviromnt) ........................................................................................ 10

• Perception that IPM is equivalent to organic production (IPM as organic) ........................... 11

• Perception that IPM give a financial level benefits for the farm (FincBen) ......................... 11

• Number of years as a farmer (Exp) ..................................................................................................... 11

• Type of agricultural training (Educ) ................................................................................................... 11

• Marginal effects ........................................................................................................................................ 13

Conclusion ............................................................................................................................................................... 15

Bibliography ................................................................................................................................... 15

Appendix 1. Distribution of the adoption index IPM ..................................................................... 16

Appendix 2. Principal components analysis and reliability test ..................................................... 17

3

Introduction

1. IPM approach (Literature review)

2. Data Collection and Description

2.1.Data Collection

Data were collected by postal survey between February and June 2012, among a representative

sample of 1,500 Quebec producers of field crops (grain corn, small grain cereals and soybeans).

From the list of the Joint plan of the producers of commercial cultures of Quebec, Producers

Federation of Quebec commercial cultures (Fédération des producteurs de cultures commerciales

du Québec or FPCCQ) produced a stratified sample in proportion to the percentage of producers

in every region of Quebec and based on predetermined criteria volumes grain marketed. The

questionnaire was developed with the help of a committee of agricultural experts, as well as from

a review of the literature on the IPM and analysis of interviews face- to-face with small number

producers. The questions were categorized in terms of: 1) general questions about phytosanitary

and agro-environmental practices, 2) the production profile, 3) pest management, 4) opinions on

IPM and 5) the sociodemographic questions. We obtained a response rate of 26.3 %, which

correspond to 395 responses in total. Of the respondents, 287 were producing grain corn, 210

small grain cereals and 291 soybeans1.

2.2.Description of measures

Particular emphasis is placed on the construction of an index of adoption of IPM used as the

dependent variable. As for the explanatory variables, a first set of questions was made from the

results of a principal component analysis (PCA). This allowed us to constitute potential variables

that may explain the adoption of IPM practices. In addition, the existing literature and economic

intuition helped us highlight a second group of explanatory variables. Finally, these two groups

have been sorted by stepwise-selection to select the most relevant explanatory variables for the

econometric study.

1 Some farmers cultivate three crops at once.

4

The IPM index

This index is the result of the sum of 26 IPM practices in the questionnaire (Table 1). The first 13

practices considered general IPM practices; a producer applies each of these practices when he

answers, "Yes, I do." The past 13 practices are specific to each field (grain corn, small grain

cereals and soybeans) that the producer could grow. Those who answered, "Yes, I used it" for at

least one of the three field have been practicing this technique.

Table 1. List of 26 IPM practices as part of the IPM index

General practices Specific practices by sowed culture 1 I keep a log pesticides 14 The monitored2

2 I adjust the sprayer (calibration, appropriate nozzle, etc.). 15 Knowledge of pest biology 3

3 I follow optimal seeding practices (seeding date, rate and depth). 16 I applied herbicides in strips or bands

4 I follow false (stale) seedbed practices before seeding. 17 I applied pesticides at rates lower than those

indicated on the label

5 I use green manure when intercropping or after a cereal crop 18 I applied pesticdes locally

6 I rotate chemical groups 19 I consulted the Réseau d’avertissement physosataires [Phytosanitaruy Advisory Network]

7 I consult MAPAQ’s «SAGE Pesticide » service 20 I used de biologic pest control by allowing natural predators to act

8 I maintain buffer zones 21 I developed my crop rotation plan taking reduced pesticides use into consideration

9

I manage organic fertilizers by taking into account the risk of introducing a new type of weed

22

Before taking crop protection ,measures, I sought information on the biology of the pests in my fields

10 I systematically read the pesticide label 23 I planted pest resistant cultivars.

11 I manage biodiversity in a way that attracts natural enemies 24 I did some mechanical weeding

12 I do pre-harvest and/or post-harvest spraying 25 I planted a refuge (grain corn)

13 I practice reduced tillage 26 I used biopesticides

By adding, the 26 practices selected our IPM index is between 2 and 23 practices, with an

average of 12.88 and a standard deviation of 3.97 (Appendix 1). Since the IPM adoption follows

a normal distribution, we eventually formed three different levels of adoption by combining our

index into three classes: "Low", "Medium", and "Intense" (Table 2).

2 Grouping of three questions: I monitored for 1) diseases, 2) weeds 3) insects or had someone else do it 3 Grouping of three questions: I searched for information on 1) diseases, 2) weeds 3) insects control thresholds

5

Levels are obtained so as to have a distribution around the mean, as recommended in the

literature (Jacobson, 1997; Hammond et al, 2006.). According to this distribution, only 19% of

producers have adopted intensive IPM, 21% have adopted a low level of IPM while the majority

(60%) have adopted moderately.

Table 2. IPM adoption among respondents according to the index levels of adoption

Low Medium Intensive IPM adoption score 2-9 practices 10-16 practices 17-23 practices Number of producers 71 204 66 Percentage of producers 20,82% 59,82% 19,35%

Otherwise, responses to the question "Did you practice IPM on your largest field of each crop

during the summer of 2011?" are quite varied. Depending on the culture, between 40% and 51%

of producers said they practice IPM (Table 3). Figure 1, shows that only 42% of producers who

thought they had already adopted IPM actually practiced intensively and 58% who didn’t think

adopt IPM were, in reality, trying to practice LI intensively.

Table 3. Distribution of producers who believe they practice or not IPM by crop (in%)

Small grain cereals Grain corn Soybeans Yes, I practiced IPM 50,72 40,49 41,81 No , I didn’t practice IPM 49,28 59,51 58,19

Figure 1: Comparison of self-assessment of respondents in relation to their IPM adoption and

measuring the level of IPM practices adoption in the study

39% 50%

58% 61% 50%

42%

2 - 9 Practices(n=71)

10 - 16 Practices(n=204)

17 - 23 Practices(n=66)

No, I do not practice IPM Yes, I practice IPM

6

The explanatory variables

The first step for selected explanatory variables is performed by principal component analysis

(PCA) and the alpha test of reliability with SPSS 17 software. PCA has brought together several

questions with significant correlation between them and give meaning to each set through a

lexical analysis of the words used in each set of questions (Borooah, 2001). The reliability test

serves scale to retain the most significant set of questions. These sets are characterized by an

alpha coefficient > 0.60 and a correlation coefficient > 0.50 (Appendix 2). In sum, we created 12

explanatory variables, namely the indices for measuring Water Qlty, Enviromnt, Diffprat,

RiskES, Avang, RiskInfes, IPM as organic, Riskfarm, LWCK, FincBen, PlusMeth and EffSoil

(Table 4). Each of these variables is, for each respondent, the average of the answers to the

questions it brings. From the literature, we added 18 other variables. Table 4, summarizes the 30

potential variables that may explain the adoption of IPM, grouped into five themes: information,

pest management, environment / health, cost of production, perception and sociodemographics

perceptions.

Table 4. Potential variables for inclusion in the adoption model4

Variables Description of the questions used to create explanatory variables

Information PesticideChoice Dummy variable; 1= choice of pesticides is most influenced by an advisor from pesticide supplier

Amount Info. Average of amount information received from differences sources5 AgriEC Perception of information6 received from Agri-Environmental club;

Pesticide supplier Perception of information received from pesticide supplier QltyInfo Ratio of information quality; Pesticide supplier / AgriEC LWCK Perception of lack weed control knowledge

Environment / Health Water Qlty Level of concern of the quality of the water Enviromnt Level of concern with environmental issues

RRisk Ratio of risk environmental benefits; RiskES/AvangES IPM as organic Perception that IPM is equivalent to organic production

EffSoil Perception that IPM has beneficial effects on soil F.Env Perception that IPM is not intended first environmental

More pesticides Perception that IPM uses more long-term pesticides than the systematic spraying PestHPb Suspect that someone on the farm has had health problems related to pesticides LifeQlty Perception that reducing pesticide improve quality of work life on the farm

Pest Management PlusMeth Perception that IPM requires use of several methods

4 Some variable are the average of set of questions, see Appendix 2. 5 Research institutes, Pesticide supplier, Agri-environmental club, MAPAQ, CRAAQ, MDDEP 6 All Perception Information are an average considering the quantity, usefulness and trust of received information

7

RiksInfes Perception that IPM augment the risk of pest infestations PestM.AgriEC Dummy variable, 1= pest monitoring done by an another specialist not involved in pesticide sales

PestM. PSupplier PestM.Informal

Dummy variable, 1= pest monitoring done by a representative from a pesticide supplier Dummy variable, 1= pest monitoring done by a farm employee;

TolPest Level of tolerance for the presence of pests in fields; an average e including tolerance of harmful insect, weeds, disease

PbPest Usual level of pest problems in the fields; an average including insect, weeds, disease

Production cost FincBen Perception that IPM give a financial level benefits for the farm Riskfarm Perception that IPM represents a risk level for the farm

ReduCouts Perception that IPM reduces pest management costs InsCov Level of crop insurance coverage ; an average for the three crops

GCropsInc Proportion of agricultural income from grain crops

Sociodemographic perceptions Exp Number of years as a farmer

Educ Diffprat

Type of agricultural training. Ordinal variable: 1-Pratical, 2-High school, 3-college, 4-University Perception of difficulties related to the implementation of IPM

The second step of explanatory variables selection is to apply the stepwise-selection method,

retaining only the most significant variables. This method eliminate variables with "p-value"

associated with partial statistical Fisher test (F), is the largest, variable is added to the model at

each step and it could be removed later in the analysis (Cornillon and Matzner-Lober, 2007). We

chose, as a result of this last step, seven independent variables for our econometric study,

presented in Table 5.

Table 5. Summary descriptive statistics of explanatory variables

Variable Obs Mean Std. Dev. Min Max Amount Info 341 2.05 0.54 1 4

LWCK 341 2.29 0.62 1 4 Enviromnt 341 2.78 0.66 1 4

IPM as organic 341 1.94 0.64 1 4 FincBen 341 2.86 0.63 1 4

Exp 341 28.26 11.50 2 65 Educ 341 2.02 0.96 1 4

8

3. Ordered logit model

In this model, the economic results that we seek to model corresponds more to a discrete choice

among several options that follow a logical order (Green, 2005) and the estimation method is the

maximum likelihood. In this part, we take a teaching methodology of Borooah, Vani K. (2001).

Assume a linear model such as Y is a linear function of K explanatory variables, the relationship

between Y and Xk would be:

'

1

K

i ik ikk

orY X Y X ββ ε ε=

= + = +∑ (1)

The fundamental assumption of the logit model is to assume that ε follows a logistic

distribution. Thus, under a logistic distribution, the distribution function of the random variable X

is:

Prob (X≤ x) = Λ (x) = exp (x)/ [1+exp(x)] = 1/ (1+exp (-x)) (2)

We can observe the event Y for each individual, depending on the J ordered possibilities as: Prob (Y = =)0 X )( 'βXΛ ,

Prob (Y = =)1 X ),()( ''

1ββµ XX −Λ−−Λ (3)

Prob (Y = =)2 X ),()( '

1

'

2ββ µµ XX −Λ−−Λ

.

.

. Prob (Y= XJ ) = 1 - ).( '

1 βµ XJ −Λ −

That all probabilities are positive, we must have;

0 < µ1< µ 2

< …… < 1−Jµ .

4. Econometric results

Our analysis is based on 341 observations, the results in Table 6 allow us to assess the statistical

significance of the variables, the sense of the correlation between the IPM index and each of the

explanatory variables and, finally, to quantify the impact of each explanatory variable on the

adoption behavior (Odds Ratios). We find that our model is generally good with a critical

9

probability (Prob > chi2) less than 5 %. This means that one or more of the variables included in

the model have a significant effect on the adoption of IPM. In fact, all our explanatory variables

have a significant influence on the index of IPM at the 5 % level, except the variable IPM as

organic whose impact on IPM is significant at 10% level. On the other side, Educ (4) does not

have a significant impact on the IMP index. A more detailed interpretation of the results will

allow us to better understand the impact of each variable on the probability of adopting IPM.

Table 6. Ordered logit estimation of social determinants of IPM adoption by Quebec grain farmers

Iteration 0 : log likelihood= -324.61 Number of obs = 341 Iteration 1 : log likelihood= -283.62 LR chi2(12) = 72.28 Iteration 2 : log likelihood= -281.66 Prob > chi2 = 0 Iteration 3 : log likelihood= -281.66 Pseudo R2 = 0.13 Iteration 4 : log likelihood= -281.66

Ordered logistic regression Log pseudolikelihood = -281.66

Coef. Odds Ratio P>|z|

Amount Info 0.62 1.85 0.00

LWCK -0.87 0.42 0.00

Enviromnt 0.84 2.31 0.00

IPM as organic -0.40 0.68 0.06

FincBen 0.46 1.59 0.01

Exp 0.03 1.04 0.00

Educ 2 0.59 1.80 0.03

3 0.70 2.02 0.02

4 0.82 2.28 0.11

/cut1 1.93

/cut2 5.34

Amount information received from differences sources (Amount Info)

The variable, amount of information received on IPM (Amount Info) by producers through the

various sources of information included in the questionnaire has a significant and positive impact

on the adoption behavior. More a producer receives the information and more he tends to adopt

IPM. In other words, an additional unit of quantity of information, to switch from "none"

information to "a little" information, increases the possibility to adopt IPM by a factor of 1.85.

We also analyzed the amount of information, the usefulness of information and trust in the

information received from various sources about IPM. Advisors from an agri-environmental

10

advisory club rank highest for the provision of information about IPM (Figure 2). Indeed, 50% of

producers consider receiving "A Lot" or "Quit a bit" information from them. It is the same for the

reliability of the information provided (78%) and the usefulness of this information (67%).

Pesticide suppliers are in second place for the amount of information provided about IPM. 42%

of farmers report receiving "A lot" or "Quit a bit" information from them. Reverse against their

less well-rated in terms of usefulness (62%) and trust (64%) it raises.

Respondents reported receiving much less information on IPM from MAPAQ, CRAAQ,

MDDEP, researchers and the UPA and other agricultural unions. The usefulness and trust in

information from the MDDEP and the UPA and other agricultural unions were rated much lower

than other sources of information.

Lack weed control knowledge (LWCK)

Lack weed control knowledge (LWCK) has a significant and negative impact on the adoption of

IPM. The increase of one degree of LWCK makes the averse producers IPM and decreases by

42% the probability of adopting IPM. To illustrate, a producer who “tends to disagree” with the

fact that “he doesn’t have the necessary knowledge to identify weeds”, has 42% chance of not

adopting IPM rather than that “tends to agree” with the same opinion. The fact that relatively few

respondents practiced intensively IPM (19% according to our IPM index) can be explained by the

lack of knowledge to identify weeds and consequently the real practices of IPM.

In addition, only 35.3% of producers surveyed responded that an expert has previously advised

them to adopt IPM. Indeed, among the producers who claim to use IPM on at least one of their

largest fields of crops, 75.6% also reported having been advised by an agri-environmental

advisory club.

Concern for the environment (Enviromnt)

The level of concern over the loss of biodiversity, climate change and greenhouse gases

(Enviromnt) has a significant impact on the IPM adoption behavior. Worry about the

environmental challenges present as a factor facilitating the adoption of IPM by Quebec grain

farmers. An increase of one unit of concern for the environment (spend “Not very concern” to

“Somewhat concerned”, for example) increases 2.31 times the probability of adopting IPM.

11

Perception that IPM is equivalent to organic production (IPM as organic)

The perception that IPM is a transition to organic agriculture is not as statistically significant as

the previous variables in our model. Indeed, the coefficient of the variable IPM as organic is only

significant at the 10% level. However, considering this level, the perception of the IPM does not

favor the adoption of IPM practices for producers. For an increase of one unit in belief that IPM

is organic, the probability of adopting IPM decreases by 68%.

Over the producer believes that IPM excludes the use of pesticides, only uses mechanical weed

control methods and equivalent to organic agriculture, the less it tends to opt for practical IPM.

Perception that IPM give a financial level benefits for the farm (FincBen)

Production costs are represented in our model by the variable FincBen. The perception of

financial level benefits associated with the adoption of IPM appears significantly and appears as

an incentive to the adoption of IPM. If producers see an added value and the provided financial

support by government program by practice IPM products, each unit increase of FincBen

increase of 1.59 times the probability of adopting IPM.

Number of years as a farmer (Exp)

Experience in agricultural areas is as an important component in the behavior of adoption of IPM

as the impact of the variable Exp is significant and positive. The years of experience as a farmer

encourage the adoption of IPM for the Quebec grain farmers. Indeed, for an additional year in the

job, the chance to practice IPM is multiplied by 1.04 times.

Type of agricultural training (Educ)

Regarding the type of agricultural training, represented by the variable Educ, we take as reference

the producers with practical training. Only coefficients producer’s representatives who have

received high school formation (Educ2) and those kind of technical college formation (Educ3)

are significant at the 5% level. In addition to these levels of training, the possibility of adopting

IPM is twice as large as that of producers received only a practical training.

12

Figure 2. Perception of information on LI from seven different sources (%)

Agri-environmental club Pesticide supplier

MAPAQ CRAAQ

17

33 26 25 22

45

19 14

33

45

14 8

0

10

20

30

40

50

60

Beaucoup Assez Un peu Aucune

Quantité Utilité Confiance

11

31 33

25

11

51

21 17

10

54

24

12

Beaucoup Assez Un peu Aucune

Quantité Utilité Confiance

3

28

46

23

8

51

25

16 14

54

21

11

0

10

20

30

40

50

60

Beaucoup Assez Un peu Aucune

Quantité Utilité Confiance

3

26

46

26

9

53

23

15 14

58

21

8

Beaucoup Assez Un peu Aucune

Quantité Utilité Confiance

13

MDDEP Research institutes

UPA & Other farmers' unions

Marginal effects

We have, from the results in Table 6, to highlight the impact of each variable on the adoption

behavior of producers. However, remember that the goal of our study is not simply to know the

socio-economic factors that influence the behavior of producers about IPM. It also aims to

determine the degree to which the producer would adopt IPM function of the explanatory

variables. To do this, Table 7 quantifies the probability that a producer belongs to a category of

practice IPM (Low, Medium or Intense) based on variables in the model.

3

17

36

44

4

33 36

28

7

41

30

22

0

10

20

30

40

50

60

Beaucoup Assez Un peu Aucune

Quantité Utilité Confiance

2

20

40 38

8

47

23 22 18

54

19

10

Beaucoup Assez Un peu Aucune

Quantité Utilité Confiance

1

15

42 41

3

37 33 27

6

45

28 21

0

10

20

30

40

50

60

Beaucoup Assez Un peu Aucune

Quantité Utilité Confiance

14

Table7. Marginal effects of explanatory variables on IPM adoption Low Medium Intensive Coef. P>|Z| Coef. P>|Z| Coef. P>|Z| Amount Info -0,08 0,00 0,006 0,63 0,08 0,01 LWCK 0,12 0,00 -0,008 0,64 -0,10 0,00 Enviromnt -0,11 0,00 0,008 0,63 0,10 0,00 IPM as organic 0,05 0,06 -0,004 0,65 -0,05 0,06 FincBen -0,06 0,01 0,004 0,68 0,06 0,01 Exp -0,005 0,00 0,000 0,63 0,00 0,00 Educ

2 -0,09 0,03 0,018 0,22 0,07 0,05 3 -0,10 0,01 0,015 0,39 0,08 0,03 4 -0,11 0,05 0,008 0,80 0,10 0,20

According to the marginal effects Amount Info for each unit increase of information on the IPM,

increases the probability to adopted IPM intensively almost 8% and 8% decreases the probability

of having a low level adoption. In fact, when the sense of lack weed control knowledge (LWCK)

increments by one unit, the probability of falling into the category of a low level of adoption

augment almost 12% and the probability of practice already IPM intensively down 10%.

The marginal effects of the belief that the adoption of the IPM is a turning to organic farming

(IPM as organic) are not statistically significant at 5% level, but they have logical signs (ie, this

belief decreases the probability of intensive adoption of IPM and increases the probability of a

low level of adoption). Environmental concerns also influence the level of adoption of the IPM.

A unit increase in concern for the environment (Enviromnt) increase the probability of intensive

practice in IPM almost 11% and decreases the probability of a low level of 11%.

Each increase of one unit of anticipation that the adoption of IPM could bring financial level

benefits (FincBen) to the farm also increases the probability of having already adopted weakly

IPM almost 6%. Although, the marginal effects of years of experience as an agricultural producer

(Exp) are statistically significant, they are extremely minimal. The effects of level of education of

the respondent (Educ) are more interesting. Make college or university level decreases by about

10% probability that the respondent is engaged very lowly in the adoption of IPM practices.

15

Conclusion

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Oaks, Calif.: Sage Publication Series Quantitative Applications in the Social Sciences.

Boutin, Denis. (2004). Réconcilier le soutien à l’agriculture et la protection de l’environnement : Tendances et perspective. Conférence présentée dans le cadre du 67e Congrès de l’Ordre des agronomes du Québec « Vers une politique agricole visionnaire ». Sherbrooke, Québec : 11 juin 2004. http://www.mddefp.gouv.qc.ca/milieu_agri/agricole/publi/tendance-perspect.pdf

Cornillon, Pierre-André et Éric Matzner-Løber. (2007). Régression : Théorie et applications. Paris, France : Springer-Verlag.

Debailleul, Guy. (2004). Analyse comparative des réglementations environnementales concernant les productions animales et position relative du Québec. Québec, Québec : Université Laval, Rapport rédigé pour le Ministère de l’Environnement du Québec.

Finnoff, David, Jason F. Shogren, Brian Leung et David Lodge. (2007). Take a risk: Preferring prevention over control of biological invaders. Ecological Economics 62(2):216–222.

Greene, William. (2005). Économétrie, 5ième édition. Upper Saddle River, New Jersey: Prentice Hall.

Hammond, Clarissa M., Edward C. Luschei, Chris M. Boerboom et Pete J. Nowak. (2006). Adoption of integrated pest management tactics by Wisconsin farmers. Weed Technology 20(3):756-767.

Jacobson, Barry J. (1997). Role of plant pathology in integrated pest management. Annual Review of Phytopathology 35:373-391.

Lichtenberg, Erik et David Zilberman. (1986). The econometrics of damage control: Why specification matters. American Journal of Agricultural Economics 68 (2):261-273.

MAPAQ (Ministère de l’agriculture, des pêcheries et de l’alimentation). (2011). Stratégie phytosanitaire québécoise en agriculture, 2011-2021. Québec, Québec : Gouvernement du Québec. http://www.mapaq.gouv.qc.ca/fr/Publications/Strategie_phytosanitaire.pdf

MENV (Ministère de l’Environnement). (2003). Synthèse des informations environnementales disponibles en matière agricole au Québec. Direction des politiques du secteur agricole, ministère de l’Environnement, Québec, Envirodoq ENV/2003/0025, 143 pages. http://www.mddefp.gouv.qc.ca/milieu_agri/agricole/synthese-info/synthese-info-enviro-agricole.pdf

16

Appendix 1. Distribution of the adoption index IPM

Number of practices

Number of producers

Percentage

Cumulative percentage

2 1 ,3 ,3 3 2 ,6 ,9 4 3 ,9 1,8 5 5 1,5 3,2 6 7 2,1 5,3 7 9 2,6 7,9 8 13 3,8 11,7 9 31 9,1 20,8 10 21 6,2 27,0 11 35 10,3 37,2 12 36 10,6 47,8 13 34 10,0 57,8 14 28 8,2 66,0 15 31 9,1 75,1 16 19 5,6 80,6 17 24 7,0 87,7 18 13 3,8 91,5 19 11 3,2 94,7 20 7 2,1 96,8 21 3 ,9 97,7 22 7 2,1 99,7 23 1 ,3 100,0

N Minimum Maximum Mean Standard deviation

341 2 23 12,89 3,97

17

Appendix 2. Principal components analysis and reliability test

Concerns

Index Name Label (questions) Reliability test

Care for water Water Qlty

Erosion Alpha of reliability .800 Pesticides in water

Fertilizers in water

Care for environment Enviromnt

Climate-greenhouse gases Loss of biodiversity

Correlation .550

"Agree- Disagree" questions

Index Name Label (questions) Reliability test

Perception of difficulties related to the

implementation of IPM Diffprat

IPM to increasing field operations

Alpha of reliability .868

IPM take too much time IPM make work more complicated Negative experience with IPM The fields will not look as clean The conditions in my area dot not permit to apply IPM IPM is ineffective IPM increases the risk of reduced yields Prefer using other method than IPM IPM involves losing income for a period of time I hesitate to change my habits

Health and environmental risk

RiskES

Worry that pesticide seriously contaminates drinking water

Alpha of reliability .808

Pesticides are very hazardous to consumer health Even when used as recommended, pesticides are harmful to environment Even when used as recommended, pesticides are harmful to my health

Health and environmental advantages AvangES

IPM brings environmental advantages Alpha of reliability

.739 IPM reduces pesticides residues in the environment

IPM brings benefits for my health Perception that IPM

augment the risk of pest infestations RiskInfes

IPM increases insect infestations Alpha of reliability

.845 IPM increase weed infestation

IPM contaminates neighbouring fields Perception that IPM is

turning to organic production

IPM as organic

IPM excludes the use of pesticides Alpha of reliability .811 IPM uses only mechanical weed control methods

IPM is equivalent to organic agriculture IPM équivaut agri-bio

18

Perception that IPM represents a risk

level for the farm Riskfarm

No agri-environmental practices if that will reduce my yield

Alpha of reliability .743

I don’t want to sacrifice my farm’s profitability to conserve water and other natural resources No agri-environmental practices if that increase my workload Pesticides are necessary to maintain my farm’s productivity No agri-environmental practices if that increase my work-related stress I want my fields produce higher yields than the average yield in the area

Perception of lack weed control knowledge

LWCK

I don’t have the necessary knowledge about how weed compete with crops Alpha of

reliability .715

I have no experience in the practice of IPM I don’t have the necessary knowledge to identify weeds

Perception that IPM give a financial level benefits for

the farm FincBen

If crops grown using IPM had an added value, I would adopt IPM If there was a government program that provides financial support for adoption of IPM , I would adopt it

Correlation .520

Perception that IPM requires use of several methods

PlusMeth

IPM uses several methods to control pest IPM requires that pest be monitored before selecting a pest control method

Correlation .490

Perception that IPM has beneficial effects on soil

EffSoil

IPM reduces soil compaction IPM reduces soil erosion

Correlation .593