Which Factors Influence Farmers’ Use of Protective Measures During Pesticides Exposure?

Abstract

Farmers in developing countries use harmful pesticides while taking few or no protective measures. There is limited evidence on factors affecting their safety measures. The objective of this study was to identify the underlying factors influencing farmers’ protective behaviors (PBs) and use of personal protective equipment (PPE) during the exposure to pesticides. From April to August 2017, a descriptive study was conducted in Twiserkan County in western Iran among 474 farmers from 104 villages. A questionnaire was developed to measure demographic characteristics and factors suggested in integrated agent-centered (IAC) framework. The questionnaire was validated in terms of content validity through expert reviews and tested for reliability in a group of farmers. Data were collected by face-to-face interviews with farmers. Physiological arousal (β = .154, p < .05), intention (β = .345, p < .05), habit (β = .188, p < .05), and contextual factors (β = .101, p < .05) had a significant and positive impact on farmers engaging in pesticide PBs. Among the assessed factors, only physiological arousal (β = .122, p < .05) and habit (β = .646, p < .05) were found to have a significant and positive effect on the use of PPE, but the intention (β = –.039, p > .05) and contextual factors (β = –.009, p > .05) had no significant relation with the use of PPE. The results of this study identified determinants of farmers’ safety measures. Our results suggest that the IAC framework could serve as a guide to developing a more effective intervention for safety measures of Iranian farmers.

Keywords

farmers protective behaviors personal protective equipment pesticides developing countries

Introduction

Pesticides are considered an essential part of modern agriculture, playing a key role in crop production to control harmful insects, weeds, and pathogenic organisms (Damalas, 2009; Jallow, Awadh, Albaho, Devi, & Thomas, 2017a). The unprotected use of pesticides can cause severe threats to human health and the environment, making it a very important public health concern (Damalas & Eleftherohorinos, 2011; Saeed et al., 2017). Concerns about adverse health effects of pesticides, particularly in the developing nations, have been decreased by adopting protective behaviors (PBs) and personal protective equipment (PPE) (Hashemi, Rostami, Hashemi, & Damalas, 2012; Khan & Damalas, 2015). The Food and Agriculture Organization (FAO), the World Health Organization (WHO), and various non-government organizations focusing on low and middle-income countries have emphasized improving PBs and personal protection equipment for farmers in pesticide use (Fan et al., 2015). Studies indicate that PPE and behaviors are effective in reducing farmers’ exposure to pesticides (Damalas & Hashemi, 2010). Despite indications of efficacy, it has been shown that protective measures are not frequently used (Damalas & Hashemi, 2010).

Using PPE and engaging in safe behaviors depend on a variety of factors such as farmers’ knowledge of pesticide hazards and correct use of practices (Jallow et al., 2017b), farmers’ beliefs and perceptions about the risks of pesticide exposure (Hashemi et al., 2012), perceived and actual barriers for the adoption of safety behaviors and use of PPE by farmers (Blanco-Munoz & Lacasana, 2011; Damalas & Hashemi, 2010), and social influences (Wang, Deng, & Ma, 2017).

Identifying the various effective factors in using protective measures during pesticide exposure by farmers is necessary to provide insight into the underlying motives which drive farmers’ safe pesticide behaviors (Wang et al., 2017). These factors have seldom been investigated in a comprehensive study (Hashemi et al., 2012). There are a number of studies that have focused on the influences of farmers’ individual characteristics and governmental regulations (e.g.,Wang et al., 2017; Zhang & Li, 2016); however, studies assessing the role of a wide range of factors based on a specific framework are limited. Feola and Binder (2009, 2010b) suggested that because of complexity and the multi-level nature of the agricultural system, an integrated and dynamic approach should be applied to research in this field. Having a specific framework could improve our understanding of farmers’ behavior within the context of the agricultural system where they work (Feola & Binder, 2009). In this study, we used the integrated agent-centered (IAC) framework to understand factors and paths by which these factors may influence the adoption of the protective measures by farmers.

The IAC Framework

The IAC framework is based on studies by Feola and Binder (2010b) which combines the Giddens’s (1984) Structuration Theory (ST) and the Theory of Interpersonal Behavior (TIB) (Triandis, 1980). In the framework, each farmer’s action (use of PPE and PBs) can be explained based on personal internal factors, such as intention, habit (past behavior), physiological arousal (fear and risk perception), and external contextual factors (knowledge, perceived barriers, and facilitators). The intention itself is determined by consequent expectations (health and social outcome expectations and the expected value of consequences), social norms, and affect (Feola & Binder, 2009, 2010b).

To the best of our knowledge, the interaction between the components of the IAC framework have been applied in only a few studies (Feola & Binder, 2010a, 2010c), and there have been no studies that included Iranian farmers. Thus, the objective of the study was to identify the underlying influencing factors on farmers’ PBs and their use of PPE during the use of pesticides.

Method

Research Design and Sampling

From April to August 2017, a descriptive correlational study was conducted in Twiserkan (Toyserkan) County, located in the Hamadan province in west Iran. This county has an appropriate climate for the extensive cultivation of crops. These products include wheat, barley, garlic, almonds, peach, plum, apple, and especially walnuts as its main commodities. Farmers work on their farms during the week, and pesticide use in this region is highly varied (Agriculture Organization of Hamadan Province, 2017).

A stratified sampling procedure was followed to select the participants in 104 villages of Twiserkan County. At first, we obtained a list of eligible farmers residing in each village from corresponding health houses, and we conducted simple random sampling to select participants within each village. Farmers were included if they resided in one of these villages and conducted pesticide spraying during their jobs. The sample size was calculated based on 95% confidence level and a precision of 9%. Assuming the values derived from Gaber and Abdel-Latif (2012) study, a total sample of 474 was necessary.

Data Collection

We collected data in collaboration with health providers of health houses (known as Behvarz in Iran) from all 104 villages of Twiserkan County. At the time of the study, 54 of the 104 villages had health houses, while the remaining 50 villages traveled to these other villages to receive their care. Data were collected through face-to-face interviews with selected farmers, using a questionnaire. Interviews were conducted in a private room of the health houses and interview time was scheduled based on participants’ convenience. The questionnaire was developed based on a review of the literature (Burton, 2004; Feola & Binder, 2009, 2010b; Price & Leviston, 2014; Senger, Borges, & Machado, 2017).

We measured the PBs and use of PPE 4 weeks after measuring the IAC constructs (i.e., knowledge, perceived barriers, facilitators, social norms, social outcome expectations, health outcome expectations, expected value of consequences, affect, physiological arousal, habit, and intention) and demographic data through a self-reported questionnaire. The rationale for this approach was that while we expected that the constructs of the IAC would predict the farmers’ PBs, measuring behaviors cross-sectionally meant that we were providing measures of past or current behavior rather than future behavior. Past behavior accounts for the unique variance in predicting the future behavior (Conner & Armitage, 1998).

We collected data on key factors influencing the use of PPE and adoption of PBs during pesticides exposure by farmers based on the IAC framework. The survey consisted of questions about demographic and baseline variables and IAC framework constructs including knowledge about protective measures (five items), perceived barriers to adopt protective measures (10 items), facilitators to adopt protective measures (three items), social norms to adopt protective measures (nine items), social outcome expectations to adopt protective measures (three items), health outcome expectations to adopt protective measures (three items), expected value of consequences to adopt protective measures (six items), affect to adopt protective measures (two items), physiological arousal to adopt protective measures (four items), habit to adopt protective measures (20 items), intention to adopt protective measures (four items), PBs for reducing exposure to pesticides (12 items), and use of PPE for reducing exposure to pesticides (eight items).

Validity and Reliability

The first draft of the questionnaire was developed using items as presented in the literature and based on the experiences of researchers. Then to assess content validity, the questionnaire was reviewed by a panel of experts consisting of nine specialists in occupational health and health education and promotion. The content validity ratio (CVR) was calculated through experts’ opinion, and based on Lawshe’s (1975) table, items with the score of 0.78 or more remained in the questionnaire. Content validity index (CVI) was calculated by researchers using a 4-point scale assessed by Waltz and Bausell (1981). The score of 0.79 was considered as the least suitable CVI. The face validity of the questionnaire was examined by interviewing 10 farmers so that the questions were read for them, and we assessed the farmers ability to understand the complexity level of questions that were assessed. The dimensionality of the questionnaire was determined by performing an exploratory factor analysis (EFA). EFA was performed for half of the sample (n = 200). Factors were extracted by the most likelihood method and by the rotation method (varimax). To distinguish the best structure, the eigenvalue greater than 1 and factor loading equivalent or greater than 0.4 were applied (Sharma, 1995). Accordingly, 21 items were removed from the scale.

To approximate the test–retest reliability, the interclass correlation coefficient (ICC) was used (Nunnally & Bernstein, 1994). Test–retest reliability was evaluated by conducting a pilot study on 30 sprayer farmers using convenience sampling in the rural area of Twiserkan County. The questionnaire was re-administered to the same sprayer farmers 2 weeks after the first. The ICCs for test–retest reliability was above .87 for all scales (rating from .87 to .99). The validity and reliability stage suggested minor changes to the questionnaire before finalization.

Data Analysis

A number of statistical tests were performed to examine the reliability of the measures using IBM© SPSS© Statistics version 24 (e.g., t test and ANOVA, differences between the means were analyzed using the Tukey test) and structural equation modeling (SEM) technique with a partial least squares (Smart PLS 2.0) approach. We replaced missing values with values imputed from the observed data. PLS model study comprised of two stages. A measurement model was assessed in the first stage and involved the development of a test measurement model to validate the study model. Also, after the model was found to meet the criteria for the measurement model, the structural models were examined. Figure 1 shows the conceptual framework used in the study.

Figure 1.

Structural model in the prediction of path coefficients.

The design of the measurement model involved the development of a test measurement model to validate the research model. To assess the properties of the measurement model, we conducted the confirmatory factor analysis to review homogeneity (factor loading and t value; Chin, 2010; Hair, Ringle, & Sarstedt, 2011), reliability (composite reliability and Cronbach’s alpha; Hair et al., 2011), convergent validity (average variance extracted [AVE]; Henseler, Ringle, & Sinkovics, 2009), and discriminant validity (square roots of the AVEs more than their corresponding correlation coefficients; Fornell & Larcker, 1981) of the latent variable indicators.

Ethical Approval

Informed consent was obtained from the farmers after the aim of the study was described to them. This study was approved by the Ethical Committee of Hamadan University of Medical Sciences (No.: IR.UMSHA.REC.1395.509).

Results

There were no missing values except for the PB and use of PPE in five participants. Participants were predominantly male (98.5%) and married (89.4%) with an average age of 48.15 years (Table 1). About half (49.8%) of the participants were illiterate or educated at primary school level. The majority of farmers (85.6%) were the owner of farm land and 57% of them had more than 10 years of experience in farming.

Table 1.

Demographic Characteristics of the Participating Farmers (n = 474)

Characteristics	n	%
Sex
Male	467	98.5
Female	7	1.5
Age (years)
≤25	17	3.6
26-35	91	19.2
36-45	118	24.9
46-55	102	21.5
56-65	86	18.1
≥66	60	12.7
Level of education
Illiteracy	65	13.7
Elementary school	171	36.1
Middle school	80	16.8
High school	129	27.2
≥College	29	6.2
Marital status
Married	424	89.4
Single	37	7.8
Divorced or widowed	13	2.8
Annual income (10,000,000 Iranian rial)^a
No response	235	49.6
≤5	57	12.0
5.1-10	114	24.1
10.1-15	19	4.0
15.1-20	19	4.0
>20	30	6.3
Farming years
≤10	78	16.5
11-20	146	30.8
21-30	96	20.2
31-40	74	15.6
41-50	56	11.8
≥51	24	5.1
Spraying years
≤10	204	43.1
11-20	165	34.8
21-30	66	13.9
31-40	31	6.5
≥41	8	1.7
Ownership type of farm land
Owner	406	85.6
Tenant	27	5.7
Owner and tenant	25	5.3
Laborer	16	3.4

10,000,000 Iranian rial is equal to US$307 as of February 2018.

Farmers’ most frequent PBs were as follows: avoiding smoking while spraying (81.2%), avoiding drinking and eating while spraying (72.3%), careful and safe storage of pesticides (67.6%), changing clothes or showering after applying pesticides (66.7%), use of the scarf to cover mouth and nose when spraying (57.6%), avoiding contacting hands with the eyes and mouth during spraying (47.5%), storing work clothes separately after replacement (54%), and washing hands well with soap and water after spraying (57.4%) (data not shown). The use of PPE was inadequate among 46.9% of the farmers who reported that they never used masks, 43.3% never used gloves, 18.3% never used thick and long pants, 20.3% never used thick or long-sleeved shirts, 32.5% never used long rubber boots, 59.7% never used protective glasses, and 22.6% never used gloves when mixing and loading pesticides. A total of 27.3% sometimes cover their head. There were no significant differences between the two outcome variables (i.e., PBs and use of PPE) and demographic characteristics of participants (p > .05).

In this study, the factor loading measurements of ≥0.6 and t value more than ±1.96 were accepted. The Cronbach’s alpha value was .71 and above, and composite reliability score was ≥.80 for each construct. Also, the results of the AVE test prove that the scores constructs were ≥0.51. Therefore, knowledge, two items of perceived barriers, 10 items of habit, and four items of PB were removed from the initial model to improve the model fit. Finally, after the model was found to meet the criteria for the measurement model, the structural models were tested (Table 2).

Table 2.

The Validity and Reliability of the IAC Framework Scales

Construct and questions	Scoring	Factor loading	Cronbach’s alpha	Composite reliability	AVE^a	T value
Contextual factors			.87	.89	0.68
Perceived barriers^a	8-40		.71	.84	0.63
No label or brochure on pesticide containers		0.719				24.04
Expensive personal protective equipment		0.729				35.93
Expensive tractor-carried sprayer machine		0.608				18.91
Not knowing how to protect yourself against pesticides		0.701				24.24
Not having place to wear protective equipment in farm		0.707				28.95
Inaccessible water to wash hands and spraying equipment		0.776				34.51
Cumbersome use of personal protective equipment		0.764				33.88
Mocking for the use of personal protective equipment		0.748				31.80
Facilitators^a	3-15		.85	.89	0.51
Having sufficient skill for spraying and mixing pesticides		0.825				35.05
Access to personal protective equipment		0.787				35.25
Look after due to previous experience of poisoning		0.753				27.23
Social norms^a	9-45		.88	.90	0.51
Emphasizing the health personnel to take the protective measures		0.712				50.52
My family’s emphasis on taking the protective measures		0.693				11.91
Emphasis of agricultural experts to take the protective measures		0.827				41.70
Carrying out individual protection measures by other farmers		0.713				21.89
Asking from agricultural experts about the pesticides		0.799				35.94
Providing recommendations by Behvarz and the doctor		0.812				37.99
Training about pesticides by seller pesticides		0.623				14.69
Asking for help from the family when reading the label		0.643				15.52
Using the experience of other farmers when reading the label		0.698				19.11
Consequence expectations			.88	.90	0.65
Health outcome expectations^a	3-15		.84	.90	0.76
Stay healthy by taking personal protective measures		0.885				34.65
Reduce medical costs by taking protective measures		0.882				48.23
Helping family health by taking protective measures		0.887				74.11
Social outcome expectations^a	3-15		.82	.89	0.74
Consult others with the farmers due to protective measures		0.875				70.22
Respecting others to the farmers due to protective measures		0.882				65.19
Have relationship with agricultural experts		0.834				43.45
Expected value of consequences^b	6-30		.86	.89	0.59
The importance of health for farmers		0.763				28.30
The importance of reducing the medical costs for farmers		0.784				30.41
The importance of family health for farmers		0.795				30.00
The importance of consulting other farmers with farmer		0.751				27.37
The importance of respecting other farmers to the farmer		0.772				38.45
The importance of establishing relationships with experts		0.776				34.92
Affect^b			.87	.93	0.88
Feeling comfortable with personal protective measures	5-10	0.941				102.04
Self-satisfaction with personal protective measures		0.941				77.48
Physiological arousal^a			.72	.80	0.52
Fear of poisoning due to the use of pesticides	4-20	0.824				18.69
Fear of cancer due to the use of pesticides		0.605				6.53
No problem due to caution when using pesticides		0.713				8.52
Not poisoning due to years of use of pesticides		0.716				8.71
Habit^c	10-50		.88	.91	0.51
Separate clothing in contact after replacement		0.622				10.95
Stopping spray during wind		0.888				7.19
Mixing the pesticides with gloves and wood		0.702				21.25
Use of thick and long trousers when spraying		0.843				54.12
Use of thick and long sleeves shirt when spraying		0.842				56.65
Use of masks when spraying		0.758				28.88
Use of long rubber gloves when spraying		0.850				59.33
Use of protective glasses when using pesticides		0.680				55.70
Use of long rubber boots when using pesticides		0.837				55.51
Covering the head with a suitable hat when spraying		0.614				17.93
Intention^c	4-20		.86	.90	0.71
Intention to carry out the protection measures during spraying		0.792				23.91
Intention to keep the pesticides in suitable place		0.840				35.44
Intention to take the protection measures during mixing		0.900				64.08
Intention to carry out the protective measures in transportation		0.847				46.37
Protective behaviors^c	8-40		.86	.89	0.51
Keep the pesticides in a safe place		0.774				27.51
Take a shower immediately after spraying		0.773				22.38
Do not smoke during spraying		0.718				18.30
Do not eat or drink when using pesticides		0.752				22.63
Cover the mouth and nose with a scarf while spraying		0.762				30.19
Hand wash with soap and water after use of pesticides		0.718				21.47
No contact hands with the eyes and mouth during spraying		0.701				20.59
Separate clothing in contact after replacement		0.672				11.24
Use of personal protective equipment^c	8-40		.90	.92	0.61
Mixing pesticides with gloves and wood		0.712				22.50
Use of thick and long trousers when spraying		0.834				50.65
Use of thick and long sleeves shirt when spraying		0.842				54.89
Use of masks when spraying		0.782				35.62
Use of long rubber gloves when spraying		0.855				73.87
Use of protective glasses when using pesticides		0.712				30.41
Use of long rubber boots when using pesticides		0.840				56.07
Covering the head with a suitable hat when spraying		0.635				20.08

Note. IAC = integrated agent-centered framework; AVE = average variance extracted.

5-point Likert-type: strongly disagree (1 point), disagree (2 point), no ideas (3 point), agree (4 point), and strongly agree (5 point).

5-point Likert-type: very little (1 point), a little (2 points), to some extent (3 points), much (4 points), and very much (5 point).

5-point Likert-type: never (1 point), rarely (2 points), sometimes (3 points), often (4 points), and always (5 point).

We tested all dominos and paths suggested by IAC. The path coefficients to predict intention, PBs, and use of PPE are presented in Figure 1. The expectations (β = 0.348, p < .05), social norms (β = 0.329, p < .05), and affect (β = 0.231, p < .05) had a positive effect on intention to adopt protective measures. The coefficient of determination (R²) for intention was 46.7% indicating that the expectations, social norms, and affect constructs could explain 46.7% of the variability in participants’ intention. According to paths suggested by IAC, physiological arousal (β = 0.154, p < .05), intention (β = 0.345, p < .05), habit (β = 0.188, p < .05), and contextual factors (β = 0.101, p < .05) had a positive impact on PBs, with an explained variance of 24.8%. In regard to the use of PPE, physiological arousal (β = 0.122, p < .05) and habit (β = 0.646, p < .05) were found to have a positive effect on use of PPE, accounting for as much as 44.2% of the variance. However, intention (β = −0.039, p > .05) and contextual factors (β = −0.009, p > .05) did not have a significant effect on the use of PPE.

In this study, the goodness of fit value was obtained as 0.589 indicating an excellent overall fit of the model to the data (Tenenhaus, Vinzi, Chatelin, & Lauro, 2005). Predictive validity (Q²) value for intention, PB and use of PPE was 0.330, 0.125, and 0.263, respectively (Table 3). These values show that the model had the power to predict the changes in intention, PBs, and the use of PPE constructs.

Table 3.

The Result of the Structural Model for Examining Paths in IAC Framework of Contextual Factors and Personal Factors

Relationships hypothesis	T value	R ^2a	Predictive Validity (Q²)^b	Size of the effect (f²)^c
Consequence expectations to intention	7.070	.467	0.330	0.13
Social norms to intention	6.700	.467	0.330	0.15
Affect to intention	5.106	.467	0.330	0.07
Physiological arousal to protective behaviors	3.682	.248	0.125	0.04
Contextual factors to protective behaviors	2.178	.248	0.125	0.02
Intention to protective behaviors	5.978	.248	0.125	0.13
Habit to protective behaviors	4.808	.248	0.125	0.05
Physiological arousal to use of personal protective equipment	2.448	.442	0.263	0.02
Contextual factors to use of personal protective equipment	0.227	.442	0.263	0
Intention to use of personal protective equipment	0.983	.442	0.263	0
Habit to use of personal protective equipment	14.945	.442	0.263	0.41
GOF = √average R² × average communalities = √0.560 × 0.620 = 0.589

Note. IAC = integrated agent-centered framework; GOF = goodness of fit.

R² = .19, .33, and .67 as small, medium, and large criterion value for coefficients determination, respectively (Cohen, 1988).

Q² = 0.02, 0.15, and 0.35 as small, median, and large predictive power, respectively (Hair et al., 2011).

f² = 0.02, 0.15, and 0.35 as small, median, and large size of the effect, respectively (Hair et al., 2011).

Discussion

This study was developed to enrich our understanding of the effective factors on protective measures among the farmers. Our study is among the few theoretical framework studies investigating farmers’ PBs in developing countries. Our findings showed that farmers adopt PB and PPE poorly, which is consistent with the findings of Fan et al. (2015) and Okoffo, Mensah, and Fosu-Mensah (2016) while proper protective measures and use of personal equipment could decrease the probability of poisoning by 44.3% (Dasgupta, Meisner, Wheeler, Xuyen, & Lam, 2007).

The results show that a significant amount of variance in intention, PBs, and use of PPE was explained by the independent construct model. There is a lack of evidence available toward the role of some surveyed structures in other agricultural health studies. Our study indicated that the consequence expectations had a positive effect on intention. Similar to some other studies (Maddux, Sherer, & Rogers, 1982), the results demonstrated that an increase in outcome expectancies improved the intention to do the behavior. Also, we found that social norms had an impact on intention, which are consistent with the results proposed in previous studies (Le Dang, Li, Nuberg, & Bruwer, 2014; Wang et al., 2017). In addition, the relationship between effect and intention was strongly supported. Regarding effect, the majority of the respondents associated the positive feelings with the protective measures (Feola & Binder, 2010c).

Another finding gained in the current study showed that the intention was associated with PBs during the exposure to pesticides. The intention is “instructions that people give themselves to behave in certain ways” (Triandis, 1980). According to the planned behavior theory, the positive intention is associated with positive behavior because the intention is a predictor of behavior (Ajzen, 1991). Our analyses indicated that an increase in consequence expectations, social norms, and effects was associated with a higher intention to engage in the PB. The obtained results were similar to the findings of the previous study by Sawant et al., 2016.

Contextual factors had a positive effect on PBs. This finding suggests that the farmers with previous knowledge, skill, and protective experience during exposure to pesticides had higher PB. These factors can facilitate PBs which is consistent with a study conducted in China (Xiao et al., 2014). Similar to another study (Walton, Leprevost, Linnan, Sanchez-Birkhead, & Mooney, 2017), farm workers perceived work experience as facilitating PBs. Based on previous studies, some of these most common factors included the resource (or lack thereof; Elmore & Arcury, 2001; Snipes et al., 2009), income (or lack thereof; Wang et al., 2017), dexterity (or lack thereof; Hersi et al., 2015), and (dis)comfort (Elmore & Arcury, 2001; Snipes et al., 2009).

Our findings supported that physiological arousal is effective on the PBs and the use of PPE. Farmers understand the PBs and PPE as a helping factor to avoid getting sick and staying healthy. Also, farmers are well aware of the risks associated with the exposure to pesticides, and they recognize the concern for severe consequences in the psychological, family, social, and relational spheres and in the work environment. Farmers who have experienced health problems from pesticide use showed the heightened concern about the health effects of pesticides and were more likely to use protective equipment (Hashemi et al., 2012; Schenker, Orenstein, & Samuels, 2002).

The habit was positively associated with PBs and use of PPE during exposure to pesticides. The term habit is broadly used to predict and to explain the behavior. Also, a part of the behavior and use of equipment is due to the habits (Lensink et al., 2000). The influence of habits on current behavior is a topic that has involved considerable attention in general (Lensink, Boissy, & Veissier, 2000). A number of studies have confirmed that habit contributes significantly to the prediction of future behavior over and above other constructs (De Vries, Eggers, Lechner, van Osch, & van Stralen, 2014; Verplanken & Melkevik, 2008).

Contextual factors (barriers and facilitators) had no positive impact on the use of the PPE. We considered several factors for measuring the farmers’ barriers including price of equipment and accessibility of protective equipment and the mean score obtained by participants was 30.92 (out of 50). This result indicated that many farmers found it difficult to use the PPE. On the contrary, the mean score obtained from the facilitators was 39.03 (out of 50). As IAC defines the contextual factors as the facilitating conditions, it seems that in this study, these two factors counteract the effects of each other.

The relationship between intention and use of PPE was not strongly supported. According to other studies, intention to use PPE was low (Hasanah, Setiawati, & Apriani, 2016). The majority of the farmers intended to use the equipment during exposure to pesticides. Despite the intention, most farmers did not manage pesticides and did not adhere to the use of PPE. Cost, general lethargy, and the discomfort caused by wearing the protective equipment were the main reasons given for not using such devices (Indra, Bellamy, & Shyamsundar, 2007).

The results of this study should be considered in light of its limitations. First, the results of the research might be affected by self-report bias. Second, this study was conducted in a limited geographic area. However, there are many similarities in farming characteristics between Hamadan province and other areas of Iran. For example, the educational level of farmers and type of agriculture (i.e., horticulture, permaculture, and agriculture) are comparable in Hamadan province with other provinces of Iran (Statistical Center of Iran, 2014). Measuring the variables in two waves may have influenced subjects’ responses to questions in the second wave.

Conclusion

Agriculture has been ranked as one of the most hazardous industries. It is evident that there is a relationship between pesticide exposures and farmers’ health. This study helps to identify the underlying factors influencing pesticide PBs and use of PPE in farmers. The results of this study indicated that the use of these models within agricultural settings is fairly limited. The findings of this study could help community/occupational health care providers to better understand the underlying factors of farmers’ behaviors and to develop intervention programs to modify the health-PBs of farmers.

Footnotes

Author’s Note

This project has been approved by the Research and Technology Deputy of Hamadan University of Medical Sciences.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Hamadan University of Medical Sciences (reference number: 9511126847).

Author Biographies

Maryam Afshari is a PhD graduate in health education and promotion at the Hamadan University of Medical Sciences. Her master’s and PhD theses focused on safety promotion.

Jalal Poorolajal is a professor of epidemiology at Department of Epidemiology & Biostatistics, School of Public Health in Hamadan University of Medical Sciences, Iran.

Forouzan Rezapur-Shahkolai is an associate professor at the Hamadan University of Medical Sciences, Iran. She has supervised several research projects in the field of health education and promotion with specialization in safety promotion and injury prevention.

Mohammad Javad Assari is an assistant professor of occupational health in the Department of Occupational Health and Occupational Health and Safety Research Center at the Hamadan University of Medical Sciences, Iran.

Akram Karimi-Shahanjarini is an associate professor of health education in the Department of Public Health at the Hamadan University of Medical Sciences, Iran. Her research interest is primarily in the area of the determinants of health behaviors and developing and evaluating intervention approaches. She is also interested in qualitative reviews and knowledge synthesis methods.

References

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. doi:10.1016/0749-5978(91)90020-T

Blanco-Munoz

Lacasana

(2011). Practices in pesticide handling and the use of personal protective equipment in Mexican agricultural workers. Journal of Agromedicine, 16, 117-126. doi:10.1080/1059924X.2011.555282

Burton

R. J. F.

(2004). Reconceptualising the “behavioural approach” in agricultural studies: A socio-psychological perspective. Journal of Rural Studies, 20, 359-371. doi:10.1016/j.jrurstud.2003.12.001

Chin

W. W.

(2010). How to write up and report PLS analyses. In Esposito Vinzi

Chin

Henseler

Wang

(eds.), Handbook of partial least squares. Berlin, Heidelberg: Springer.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Conner

Armitage

C. J.

(1998). Extending the theory of planned behavior: A review and avenues for further research. Journal of Applied Social Psychology, 28, 1429-1464. doi:10.1111/j.1559-1816.1998.tb01685.x

Damalas

C. A.

(2009). Understanding benefits and risks of pesticide use. Scientific Research and Essays, 4, 945-949.

Damalas

C. A.

Eleftherohorinos

I. G.

(2011). Pesticide exposure, safety issues, and risk assessment indicators. International Journal of Environmental Research and Public Health, 8, 1402-1419. doi:10.3390/ijerph8051402

Damalas

C. A.

Hashemi

S. M.

(2010). Pesticide risk perception and use of personal protective equipment among young and old cotton growers in northern Greece. Agrociencia, 44, 363-371.

10.

Dasgupta

Meisner

C. M.

Wheeler

Xuyen

Lam

N. T.

(2007). Pesticide poisoning of farm workers: Implications of blood test results from Vietnam. International Journal of Hygiene and Environmental Health, 210, 121-132. doi:10.1016/j.ijheh.2006.08.006

11.

De Vries

Eggers

S. M.

Lechner

van Osch

van Stralen

M. M

. (2014). Predicting fruit consumption: The role of habits, previous behavior and mediation effects. BMC Public Health, 14, Article 730. doi:10.1186/1471-2458-14-730

12.

Elmore

R. C.

Arcury

T. A.

(2001). Pesticide exposure beliefs among Latino farmworkers in North Carolina‘s Christmas tree industry. American Journal of Industrial Medicine, 40, 153-160. doi:10.1002/ajim.1083

13.

Fan

Niu

Yang

Qin

Bento

C. P.

Ritsema

C. J.

Geissen

(2015). Factors affecting farmers’ behaviour in pesticide use: Insights from a field study in northern China. Science of the Total Environment, 537, 360-368. doi:10.1016/j.scitotenv.2015.07.150

14.

Feola

Binder

C. R.

(2009, June 4). The integrative agent-centred (IAC) framework as a conceptual tool to investigate transition processes in local agricultural systems. Paper presented at the First European Conference on Sustainability Transitions: Dynamics and Governance of Transitions to Sustainability, Amsterdam, The Netherlands.

15.

Feola

Binder

C. R.

(2010a). Identifying and investigating pesticide application types to promote a more sustainable pesticide use. The case of smallholders in Boyacá, Colombia. Crop Protection, 29, 612-622. doi:10.1016/j.cropro.2010.01.008

16.

Feola

Binder

C. R.

(2010b). Towards an improved understanding of farmers’ behaviour: The Integrative Agent-Centred (IAC) framework. Ecological Economics, 69, 2323-2333. doi:10.1016/j.ecolecon.2010.07.023

17.

Feola

Binder

C. R.

(2010c). Why don‘t pesticide applicators protect themselves? International Journal of Occupational and Environmental Health, 16, 11-23.

18.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50. doi:10.2307/3151312

19.

Gaber

Abdel-Latif

S. H.

(2012). Effect of education and health locus of control on safe use of pesticides: A cross sectional random study. Journal of Occupational Medicine and Toxicology, 7, Article 3. doi:10.1186/1745-6673-7-3

20.

Giddens

(1984). The constitution of society: Outline of the theory of structuration. Cambridge, UK: Polity Press.

21.

Hair

J. F.

Ringle

C. M.

Sarstedt

(2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19, 139-152. doi:10.2753/MTP1069-6679190202

22.

Hasanah

Setiawati

E. P.

Apriani

(2016). Knowledge and intention to use personal protective equipment among health care workers to prevent Tuberculosis. Althea Medical Journal, 3, 120-125. doi:10.15850/amj.v3n1.713

23.

Hashemi

S. M.

Rostami

Hashemi

M. K.

Damalas

C. A.

(2012). Pesticide use and risk perceptions among farmers in southwest Iran. Human and Ecological Risk Assessment: An International Journal, 18, 456-470. doi:10.1080/10807039.2012.652472

24.

Henseler

Ringle

C. M.

Sinkovics

R. R.

(2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing (vol. 20, pp. 277-319). Bingly, UK: Emerald Group Publishing Limited.

25.

Hersi

Stevens

Quach

Hamel

Thavorn

Garritty

. . . Moher

(2015). Effectiveness of personal protective equipment for healthcare workers caring for patients with filovirus disease: A rapid review. PLoS ONE, 10, e0140290. doi:10.1371/journal.pone.0140290

26.

Indra

Bellamy

Shyamsundar

(2007). Facing hazards at work: Agricultural workers and pesticide exposure in Kuttanad, Kerala. South Asian Network for Development and Environmental Economics, 19, 1-4.

27.

Jallow

M. F. A.

Awadh

D. G.

Albaho

M. S.

Devi

V. Y.

Thomas

B. M.

(2017a). Pesticide knowledge and safety practices among farm workers in Kuwait: Results of a survey. International Journal of Environmental Research and Public Health, 14, Article 340. doi:10.3390/ijerph14040340

28.

Jallow

M. F. A.

Awadh

D. G.

Albaho

M. S.

Devi

V. Y.

Thomas

B. M.

(2017b). Pesticide risk behaviors and factors influencing pesticide use among farmers in Kuwait. Science of the Total Environment, 574, 490-498. doi:10.1016/j.scitotenv.2016.09.085

29.

Khan

Damalas

C. A.

(2015). Factors preventing the adoption of alternatives to chemical pest control among Pakistani cotton farmers. International Journal of Pest Management, 61, 9-16. doi:10.1080/09670874.2014.984257

30.

Lawshe

C. H.

(1975). A quantitative approach to content validity. Personnel Psychology, 28, 563-575. doi:10.1111/j.1744-6570.1975.tb01393.x

31.

Le Dang

Nuberg

Bruwer

. (2014). Understanding farmers’ adaptation intention to climate change: A structural equation modelling study in the Mekong Delta, Vietnam. Environmental Science & Policy, 41, 11-22. doi:10.1016/j.envsci.2014.04.002

32.

Lensink

Boissy

Veissier

(2000). The relationship between farmers’ attitude and behaviour towards calves, and productivity of veal units. Annales de Zootechnie, 49, 313-327. doi:10.1051/animres:2000122

33.

Maddux

J. E.

Sherer

Rogers

R. W.

(1982). Self-efficacy expectancy and outcome expectancy: Their relationship and their effects on behavioral intentions. Cognitive Therapy and Research, 6, 207-211.

34.

Ministry of Agriculture - Jahad. (2017). Twiserkan. Retrieved from https://www.maj.ir [in Persian].

35.

Nunnally

Bernstein

(1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.

36.

Okoffo

E. D.

Mensah

Fosu-Mensah

B. Y.

(2016). Pesticides exposure and the use of personal protective equipment by cocoa farmers in Ghana. Environmental Systems Research, 5, Article 17. doi:10.1186/s40068-016-0068-z

37.

Price

J. C.

Leviston

(2014). Predicting pro-environmental agricultural practices: The social, psychological and contextual influences on land management. Journal of Rural Studies, 34, 65-78. doi:10.1016/j.jrurstud.2013.10.001

38.

Saeed

M. F.

Shaheen

Ahmad

Zakir

Nadeem

Chishti

A. A.

. . .Damalas

C. A.

(2017). Pesticide exposure in the local community of Vehari district in Pakistan: An assessment of knowledge and residues in human blood. Science of the Total Environment, 587, 137-144. doi:10.1016/j.scitotenv.2017.02.086

39.

Sawant

R. V.

Goyal

R. K.

Rajan

S. S.

Patel

H. K.

Essien

E. J.

Sansgiry

S. S.

(2016). Factors associated with intention to engage in self-protective behavior: The case of over-the-counter acetaminophen products. Research in Social and Administrative Pharmacy, 12, 327-335. doi:10.1016/j.sapharm.2015.06.005

40.

Schenker

M. B.

Orenstein

M. R.

Samuels

S. J.

(2002). Use of protective equipment among California farmers. American Journal of Industrial Medicine, 42, 455-464. doi:10.1002/ajim.10134

41.

Senger

Borges

J. A. R.

Machado

J. A. D.

(2017). Using the theory of planned behavior to understand the intention of small farmers in diversifying their agricultural production. Journal of Rural Studies, 49, 32-40. doi:10.1016/j.jrurstud.2016.10.006

42.

Sharma

(1995). Applied multivariate techniques. New York, NY: John Wiley & Sons, Inc.

43.

Snipes

S. A.

Thompson

O’Connor

Shell-Duncan

King

Herrera

A. P.

Navarro

(2009). “Pesticides protect the fruit, but not the people”: Using community-based ethnography to understand farmworker pesticide-exposure risks. American Journal of Public Health, 99, S616-S621.

44.

Statistical Center of Iran. (2014). Detailed results agricultural census 2014. Retrieved from https://www.amar.org.ir [In Persian].

45.

Tenenhaus

Vinzi

V. E.

Chatelin

Y. M.

Lauro

(2005). PLS path modeling. Computational Statistics & Data Analysis, 48, 159-205. doi:10.1016/j.csda.2004.03.005

46.

Triandis

(1980). Values, attitudes, and interpersonal behavior. Nebraska Symposium on Motivation, 27, 195-259.

47.

Verplanken

Melkevik

(2008). Predicting habit: The case of physical exercise. Psychology of Sport and Exercise, 9, 15-26. doi:10.1016/j.psychsport.2007.01.002

48.

Walton

A. L.

Leprevost

C. E.

Linnan

Sanchez-Birkhead

Mooney

(2017). Benefits, facilitators, barriers, and strategies to improve pesticide protective behaviors: Insights from farmworkers in North Carolina tobacco fields. International Journal of Environmental Research and Public Health, 14, Article 677. doi:10.3390/ijerph14070677

49.

Waltz

C. F.

Bausell

B. R.

(1981). Nursing research: Design statistics and computer analysis. Philadelphia, PA: F.A. Davis Company.

50.

Wang

Deng

(2017). Relationships between safe pesticide practice and perceived benefits and subjective norm, and the moderation role of information acquisition: Evidence from 971 farmers in China. International Journal of Environmental Research and Public Health, 14, Article 962. doi:10.3390/ijerph14090962

51.

Xiao

Chen

Gao

Yan

Okafor

C. N.

(2014). Protection motivation theory in predicting intention to engage in protective behaviors against schistosomiasis among middle school students in rural China. PLoS Neglected Tropical Diseases, 8, e3246. doi:10.1371/journal.pntd.0003246

52.

Zhang

(2016). The impact of traditional culture on farmers’ moral hazard behavior in crop production: Evidence from China. Sustainability, 8, Article 643. doi:10.3390/su8070643