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

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1 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, 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.

2 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

3 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. 1

4 Contents Introduction IPM approach (Literature review) Data Collection and Description Data Collection Description of measures... 3 The IPM index... 4 The explanatory variables Ordered logit model Econometric results... 8 Amount information received from differences sources (Amount Info)... 9 Lack weed control knowledge (LWCK) Concern for the environment (Enviromnt) Perception that IPM is equivalent to organic production (IPM as organic) Perception that IPM give a financial level benefits for the farm (FincBen) Number of years as a farmer (Exp) Type of agricultural training (Educ) Marginal effects Conclusion Bibliography Appendix 1. Distribution of the adoption index IPM Appendix 2. Principal components analysis and reliability test

5 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 soybeans 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. 3

6 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 monitored 2 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 I applied pesticides at rates lower than those 17 seeding. indicated on the label I use green manure when intercropping or after a 5 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 I manage organic fertilizers by taking into Before taking crop protection,measures, I sought 9 account the risk of introducing a new type of information on the biology of the pests in my 22 weed 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 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 4

7 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 practices practices Number of producers 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% 61% 50% 50% 58% 42% 2-9 Practices (n=71) No, I do not practice IPM Practices (n=204) Practices (n=66) Yes, I practice IPM 5

8 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 model 4 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 sources 5 AgriEC Perception of information 6 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 6

9 RiksInfes PestM.AgriEC PestM. PSupplier PestM.Informal TolPest PbPest FincBen Riskfarm ReduCouts InsCov GCropsInc Exp Educ Diffprat Perception that IPM augment the risk of pest infestations Dummy variable, 1= pest monitoring done by an another specialist not involved in pesticide sales Dummy variable, 1= pest monitoring done by a representative from a pesticide supplier Dummy variable, 1= pest monitoring done by a farm employee; Level of tolerance for the presence of pests in fields; an average e including tolerance of harmful insect, weeds, disease Usual level of pest problems in the fields; an average including insect, weeds, disease Production cost Perception that IPM give a financial level benefits for the farm Perception that IPM represents a risk level for the farm Perception that IPM reduces pest management costs Level of crop insurance coverage ; an average for the three crops Proportion of agricultural income from grain crops Sociodemographic perceptions Number of years as a farmer 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 LWCK Enviromnt IPM as organic FincBen Exp Educ

10 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 X k would be: K ' Y i k X or ik i Y X β k = 1 β 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: That all probabilities are positive, we must have; Prob (Y = 0 X ) = Λ ( X ' β ), ' ' Prob (Y = 1 X ) = Λ ( µ X β ) Λ( X β ), (3) 1 ' ' Prob (Y = 2 X ) = Λ ( µ X β ) Λ( µ X β ), ' Prob (Y= J X ) = 1 - ( µ X ). 0 < µ < 1 µ < < µ 2 J 1. Λ J 1 β 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 8

11 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= Number of obs = 341 Iteration 1 : log likelihood= LR chi2(12) = Iteration 2 : log likelihood= Prob > chi2 = 0 Iteration 3 : log likelihood= Pseudo R2 = 0.13 Iteration 4 : log likelihood= Ordered logistic regression Log pseudolikelihood = Coef. Odds Ratio P> z Amount Info LWCK Enviromnt IPM as organic FincBen Exp Educ /cut /cut 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 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 9

12 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. 10

13 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. 11

14 Figure 2. Perception of information on LI from seven different sources (%) Agri-environmental club Pesticide supplier Beaucoup Assez Un peu Aucune Quantité Utilité Confiance Beaucoup Assez Un peu Aucune Quantité Utilité Confiance MAPAQ CRAAQ Beaucoup Assez Un peu Aucune Quantité Utilité Confiance Beaucoup Assez Un peu Aucune Quantité Utilité Confiance 12

15 MDDEP Beaucoup Assez Un peu Aucune Quantité Utilité Confiance Research institutes Beaucoup Assez Un peu Aucune Quantité Utilité Confiance UPA & Other farmers' unions Beaucoup Assez Un peu Aucune Quantité Utilité Confiance 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. 13

16 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. 14

17 Conclusion Bibliography Borooah, Vani K. (2001). LOGIT and PROBIT: Ordered and Multinomial Models. Thousand 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 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): Greene, William. (2005). Économétrie, 5 ième 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): Jacobson, Barry J. (1997). Role of plant pathology in integrated pest management. Annual Review of Phytopathology 35: Lichtenberg, Erik et David Zilberman. (1986). The econometrics of damage control: Why specification matters. American Journal of Agricultural Economics 68 (2): MAPAQ (Ministère de l agriculture, des pêcheries et de l alimentation). (2011). Stratégie phytosanitaire québécoise en agriculture, Québec, Québec : Gouvernement du Québec. 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. 15

18 Appendix 1. Distribution of the adoption index IPM Number of practices Number of Percentage producers 2 1,3,3 3 2,6,9 4 3,9 1, ,5 3, ,1 5, ,6 7, ,8 11, ,1 20, ,2 27, ,3 37, ,6 47, ,0 57, ,2 66, ,1 75, ,6 80, ,0 87, ,8 91, ,2 94, ,1 96,8 21 3,9 97, ,1 99,7 23 1,3 100,0 Cumulative percentage Standard N Minimum Maximum Mean deviation ,89 3,97 16

19 Appendix 2. Principal components analysis and reliability test Concerns Index Name Label (questions) Reliability test Care for water Water Qlty Care for environment Enviromnt Erosion Pesticides in water Fertilizers in water Climate-greenhouse gases Loss of biodiversity Alpha of reliability.800 Correlation.550 "Agree- Disagree" questions Index Name Label (questions) Reliability test Perception of difficulties related to the implementation of IPM Diffprat Health and environmental risk RiskES Health and environmental advantages AvangES Perception that IPM augment the risk of pest infestations RiskInfes Perception that IPM is turning to organic production IPM as organic IPM to increasing field operations 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 Worry that pesticide seriously contaminates drinking water 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 IPM brings environmental advantages IPM reduces pesticides residues in the environment IPM brings benefits for my health IPM increases insect infestations IPM increase weed infestation IPM contaminates neighbouring fields IPM excludes the use of pesticides IPM uses only mechanical weed control methods IPM is equivalent to organic agriculture IPM équivaut agri-bio Alpha of reliability.868 Alpha of reliability.808 Alpha of reliability.739 Alpha of reliability.845 Alpha of reliability

20 Perception that IPM represents a risk level for the farm Riskfarm Perception of lack weed control knowledge LWCK Perception that IPM give a financial level benefits for the farm FincBen Perception that IPM requires use of several methods PlusMeth Perception that IPM has beneficial effects on soil EffSoil No agri-environmental practices if that will reduce my yield 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 workrelated stress I want my fields produce higher yields than the average yield in the area I don t have the necessary knowledge about how weed compete with crops I have no experience in the practice of IPM I don t have the necessary knowledge to identify weeds 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 IPM uses several methods to control pest IPM requires that pest be monitored before selecting a pest control method IPM reduces soil compaction IPM reduces soil erosion Alpha of reliability.743 Alpha of reliability.715 Correlation.520 Correlation.490 Correlation