1. Introduction
Product fraud has been on the rise worldwide and continues to vex innovation, entrepreneurship, and economic progress in many countries [1]. It has many forms and manifests itself in the agrifood sector, spanning from economically motivated adulteration to large-scale agriculture-product smuggling [2,3]. Economically motivated adulteration is the deliberate sale of substandard ingredients or food products to make a profit [4,5]. This fraud’s common types involve supplanting one ingredient with another, color or flavor modification using forbidden substances, and an original component’s substitution or dilution with an inexpensive product [6,7]. However, smuggling is the illegal transfer of agricultural products across an international border breaching customs laws and regulations [8]. A price disparity between origin and its (prohibited) destination drives this practice, leading to crucial revenue losses to a country’s exports [9].
Pesticides are agricultural production’s strategic commodities [10]. Their utilization has rapidly expanded due to the increased adoption of intensification systems, responding to the growing population [11]. Moreover, they are fraudsters’ targets, such as authentic products [10,12]. Fraudulent pesticides include ones not approved for sale or use by the concerned national authorities, lacking a manufacturer’s name and address, or consciously mislabeled regarding their source [13,14]. Globally, pesticide adulteration comes in three essential categories. The first type, fake pesticides, comprises adding talcum powder, black honey, or colored water to expired, stagnant, or diluted pesticides that otherwise may have been circumscribed or revoked [15]. Counterfeits are also fraudulent pesticide examples. It is a complicated copy of branded and original pesticides, having counterfeit labels and infringing on intellectual property rights. Counterfeit pesticides contain cheaper, probably more toxic, active ingredients or less effective ingredients than authentic products [16]. Furthermore, illegal analogous imports, not having the original compound’s active ingredients, are traded along with legal pesticides. These pesticides may or may not be repackaged and marketed in varied distribution channels with the original authenticated trademark [17].
Although pesticides are among the most controlled products, the fraudulent pesticide trade has substantially risen worldwide [11,18,19]. Thus, the European Union Intellectual Property Office (EUIPO) [20] reported in its 2020 report that authentic pesticide sales reduced by 4.2% across the EU due to illegal pesticides. India and China are the leading fraudulent pesticide manufacturer and trader countries [21]. They account for 30% and 20% of the international illegal pesticide market, respectively. The fraudulent pesticide global trade boom is primarily attributable to their inexpensive production with greater profits [17]. Yet, producing one original pesticide costs around USD 286 million and requires over ten years [22], finalizing many tests and registration procedures. Then, proving the validity of its pesticide efficiency and biological effectiveness against the targeted pests and its relative safety to the environment, humans, nontargets, and beneficial organisms occurs before global marketing [10]. These tests are compulsory to register the pesticide with the country of origin and United States Environmental Protection Agency [22]. Concerning the net return, fraudulent pesticide global revenue exceeds USD 5.4 billion annually. Besides costs and revenues, some fraudulent pesticides possess the same efficacy or virtually the same quality as branded pesticides [17]. On top of that, the fraudulent pesticide trade is in the hands of criminal gangs through organized crime in many countries, adding to the global problem [23]. The African market has been a hot spot for fraudulent pesticides, specifically in Egypt, Tanzania, Uganda, and West Africa [11,15,18,19,24,25,26,27].
Such trade in fraudulent pesticides has adverse economic costs to governments concerning sales, revenues, and employment [12,28]. In this context, EUIPO’s 2020 report [20] emphasized some crucial economic indicators in the EU due to fraudulent pesticides such as decreased pesticide sales by EUR 1.0 billion, 3584 annually lost jobs across the pesticide sector, as well as expending a yearly government revenue of about EUR 0.1 billion in social security contributions and taxes. Therefore, fighting against fraudulent pesticides is a pressing priority on many countries’ agendas to secure a safe pesticide market and face the threats to sustainable agriculture and human health [10]. Aside from economic risks, fraudulent pesticides cause decreased crop productivity and reduced quality [13,27]. Moreover, it has adverse environmental effects originating from highly chemical toxic impurities that are forbidden or unlawfully used in fraudulent pesticides, leading to water or soil pollution and biodiversity loss [13,29]. Furthermore, fraudulent pesticides present severe health risks to farmers due to exposure during application and adversely affect consumers’ health due to untested product residues and undeclared active substances in foods [28,30].
Combating fraudulent pesticides is not an easy process, demanding collaborative tasks and continuous coordination among relevant authorities nationally [31,32]. Despite fruitful efforts instigated by enforcement authorities in various countries in seizing counterfeit or illegal pesticides’ mega quantities, further work is still requisite [17]. Ignoring the solemnity and breadth of the fraudulent pesticide problem, politicians hurt law enforcement by civil service administrations and custom officials, and fail to mobilize the needed resources for fighting against them [16]. Additionally, several departments share and divide regulatory responsibilities, and not necessarily with effective collaboration among them and between government ministries and agencies nationally or regionally, increasing the problem’s complexity [13,16]. Similarly, solving the fraudulent pesticide issue is broad-ranging and multifaceted. Put differently, multidisciplined specialists having competencies in customs, policing, and prosecution, and environment, agriculture, and chemicals should help fight against fraudulent pesticides. These competencies are accessible, but collaboration is minimal or deficient [16]. As suggested by the United Nations Interregional Crime and Justice Research Institute [14], assuming a broad approach for addressing this problem remains vital. The UNCERI’s perspective contains six primary areas: authorities and stakeholders’ engagement, supply-chain protection and defense activities, international harmonization and regulatory oversight, financial flows and incentive control, improved investigation and veto capacities, and end-user consciousness. Nonetheless, it is critical to note that applying effective formal and informal social control mechanisms in the regulatory, production, and supply-chain networks may be pointless without authorizing the farmer’s role as end-user in this issue [10,25].
Fathoming farmers’ perceptions and attitudes toward fraudulent pesticides is precarious in scheduling extension and consciousness programs and campaigns. These activities help sanction farmers to make decisions concerning buying fraudulent pesticides composed of collective actions from all stakeholders in the pesticide supply chain [12]. Farmers’ purchasing behavior has two descriptions: deceptive, where a farmer feels that the pesticide package is original and is not aware of purchasing fraudulent pesticides; and nondeceptive behavior, where a farmer buys any fraudulent pesticides perceptively and deliberately [10]. The current study addresses both farmers’ purchasing behaviors to specify farmers’ fraudulent pesticides complete picture of drivers in the market. Therefore, investigating the principal factors leading to fraudulent pesticides’ purchase plays a critical role in action plans for overseeing these pesticides’ probable scale in markets [15].
The fraudulent pesticide trade drivers include two economic perspectives in the literature: the fraudulent pesticides’ supply and demand sides [33]. The counterfeit supply and plagiarized products primarily depend on market incentives (e.g., bringing higher profits) to participate in breaches [11,32,34]. Moreover, the technological and distribution issues define whether these products’ production and distribution are technically feasible [33,34,35], and the institutional environment provides rigorous legal frameworks and harsh deterrent penalties [34,35,36]. Yet, the demand for these products relates to consumer beliefs and attitudes [27,37], socioeconomic characteristics (e.g., income, farm size, and education) [15,38], the product itself (e.g., perceived quality and price) [13,39], and the institutional environment concerning such products’ availability and ease of acquisition [11,32]. This article examines the fraudulent pesticides’ demand side from the farmers’ view.
National authorities documented a critical amount of fraudulent pesticides in the market in Egypt. The Ministry of Agriculture and Land Reclamation (MALR) published a report. It underlined an increase in the percentage of fraudulent pesticides, with 17% of all types of pesticide trade in 2020, including herbicides, fungicides, insecticides, nematicides, rodenticides, and other pesticides [40]. Limited studies were available to specify the active ingredients most often forged [41,42]. Moreover, only one empirical survey [15] performed on this topic studied farmers’ exposure to fraudulent pesticides and identified recognition levels. Although ongoing justifications, along with most previous studies in the Egyptian context, have centered on measuring the fraudulent pesticides’ quality by conducting laboratory tests [18,19,25,29,43], research remains limited. The literature fails to cover the factors affecting farmers’ fraudulent pesticide purchases. Therefore, this study aims to analyze the driving forces efficient on farmers’ fraudulent pesticide trade. Moreover, it seeks to identify farmers’ perceptions about fraudulent pesticides, explore the factors influencing them, and determine the differences in perception based on demographic characteristics.
2. Methodology
2.1. Study Area
Dakahlia governorate, one of the 26 governorates in Egypt, is in the country’s northeast part. Its population is 6,577,000, with 72% living in rural areas based on the 2018 census. The governorate has an area of 3500 square kilometers (about 3.5% of Egypt) [43]. In 2020, the total agricultural area stretched to 270,000 hectares, indicating 8.2% of cultivated areas in the country [44].
2.2. Sampling Procedure
The present study utilized data from a sample of 394 farmers in the area, employing a three-stage-sampling technique. In the first stage, three districts, namely Mansoura, Aga, and Meet Ghamr, were deliberately chosen from 18 districts in the governorate because they had the broadest areas of field crops, vegetables, and fruits, respectively. In the second stage, three villages were selected in each district based on the net cultivated region of each farming activity. Finally, a proportionate stratified random technique was used to choose the sample of farmers in each farming activity using the population size in the three villages in each district in the agricultural season of 2021 (n = 4628; 2471 field crops farmers, 556 vegetables farmers, and 1601 fruit farmers). The total sample size was calculated using Yamane’s sample size formula (n = 400). Thus, 213 field crop farmers and 48 vegetable farmers were randomly chosen, and 139 fruit farmers came from the selected villages. The final sample size slightly decreased to 394 after omitting six cases from the sample of field crop farmers due to inadequate responses.
2.3. Instrument and Data Collection
The survey utilized a semistructured questionnaire as a data-gathering tool. The questionnaires comprise three essential parts. The first part covered demographic variables, such as age, education, farm size, farming experience, off-farm income, and attending extension activities on pesticides in the last three years. Yet, the second part incorporated factors affecting farmers’ purchasing behavior concerning fraudulent pesticides. In this section, the respondents rated their agreement regarding fraudulent pesticide drivers in the market on a five-point Likert scale, ranging from 1 = strongly disagree to 5 = strongly agree. The fraudulent pesticide drivers’ perception index in the market contained 20 statements, which were adopted and modified depending on the study context [15,27,38,45,46,47]. Then, the third section encompassed an open-ended question about the prerequisites necessary for fighting against fraudulent pesticides in the market from their perspectives. The questionnaires’ first versions were in English and then translated into Arabic. Five plant-protection experts at Mansoura University examined the questionnaires’ content validity and suitability in the Egyptian context. Moreover, a pilot test had ten farmers in the study area before data collection. The perception scale’s reliability was examined using Cronbach’s alpha test. The alpha coefficient was 0.86, suggesting that the data had comparatively high internal consistency. Collected data came from face-to-face interviews conducted between September 2021 and December 2021.
2.4. Data Analysis
Descriptive statistics (i.e., frequencies, percentages, means, and standard deviations) were determined using Statistical Package for Social Sciences (IBM SPSS, ver. 28.0, IBM Corp., Armonk, NY, USA) software. Moreover, inferential statistics, such as factor analysis, cluster analysis, and chi-squared test of independence, were conducted. The study’s first stage included factor analysis to specify latent variables or constructs by reducing the items of drivers of purchasing fraudulent pesticides into fewer dimensions. On top of that, factor loading (λ) explored the extent of an indicator variable explaining a latent variable. Yong and Pearce [48] suggested that factor loading values of 0.5 and greater crucially affected the latent variable. Therefore, they were reflected in the analysis for factor loading. Similarly, eigenvalues statistics specified the number of factors and determined the variance explained by a factor. Using Eigenvalues > 1 was utilized to decide how many factors to retain. In the second stage, the study attempted to establish the farmer segments that drive perceptions. The k-means clustering method was performed to organize similar observations into uniform subsets in the second stage of the study using the standardized factor scores acquired from factor analysis. The discriminant analysis determined the number of clusters in the k-means clustering method. Using Wilk’s lambda value, the number of clusters with the highest significance was deemed the most suitable classification [49]. In the third stage of the study, the chi-squared test of independence examined the differences between clusters concerning demographic characteristics [50].
3. Results
3.1. Descriptive Results of the Sample’s Demographic Characteristics
Table 1 recapitulates the respondents’ socioeconomic characteristics. The findings show that the age ranged from 30 to 79 years, with a mean age of 56.18 years. Most farmers (69%) were between 46 and 65 years old. More than one-third of the farmers (36.3%) were illiterate, while 15.5% held higher education degrees. The majority of farmers (71.1%) were smallholders (1–2 hectares) and had an average farm size of 1.72 ha. However, a small percentage (10.7%) had more than 4 hectares. The results also disclosed that 53.07% had farming experience ranging between 26 and 35 years, with 30.2% between 16 and 25 years; the average was 25.81 years. More than half of the farmers (59.1%) possessed off-farm income, and 40.9% thought farming activities were their primary profession. Furthermore, 82.2% of the respondents had attended extension activities on pesticides in the last three years. Contrarily, 17.8% had not.
3.2. Farmers’ Perception of Factors Affecting Decisions to Purchase Fraudulent Pesticides
Table 2 presents the farmers’ perception rankings of factors affecting fraudulent pesticide-purchasing decisions. The mean scores were in the range of 2.74 to 4.33. Ten statements (50%) had higher mean scores (≥4), suggesting more favorable perceptions against fraudulent pesticides. The statement, “I believe that fraudulent pesticides can be dangerous for farmers and animals’ health” possessed the highest mean score of 4.33. The perceived difference in quality between original and fraudulent pesticides was discernible, as designated by the mean score of 4.24. Likewise, the evident consensus concerning the fraudulent pesticides’ negative effect on agricultural production, discontent due to fraudulent pesticides in pest control, and the relevance of using fraudulent pesticides in agriculture were also crucial contemplations for farmers (the scores were 4.21, 4.18, and 4.15, respectively). It showed a high perception of the fraudulent pesticides’ drivers in the market.
3.3. Factor Analysis
Factor analysis was conducted to explore the most crucial factor loadings explaining fraudulent pesticides’ marketability, as depicted in Table 3. Before executing the factorial analysis, the data suitability for structure detection was evaluated using the KMO and Bartlett’s test of sphericity. Results in Table 3 indicated that KMO had a value of 0.807, implying that the factor analysis model was satisfactory for this analysis. The Bartlett’s test of sphericity value (Chi-square = 5222.810, p < 0.01) infers a correlation between the variables, hinting at the suitability of the factor analysis model.
Table 3 discloses that five factors have eigenvalues greater than 1. These factors demonstrate that 72.639% of variance explains variation in all twenty variables. Factor 1 included seven items loading critically. Factor 1 was named “beliefs,” with an eigenvalue of 5.826 based on the content of the items. It elucidated 29.13% of the total variance. Four items were loaded crucially in Factor 2. This factor was termed “policy”; it had an eigenvalue of 3.833. It revealed 19.165% of the cumulative variation. Factor 3 included three items loaded substantially. This factor was coined “price”; it had an eigenvalue of 2.017. It unraveled 10.086% of the variance elucidated. The quality recognition was the fourth factor, having loadings between 0.549 and 0.658; it had an eigenvalue of 1.470. This factor justified 7.349% of the cumulative variance. Finally, Factor 5 consisted of two items, loaded with the acceptable level of 0.703 and 0.783. This factor was named “health and environmental risks”; it had an eigenvalue of 1.381. This factor untangled 6.906% of the cumulative variance.
Friedman’s test results (Table 4) disclosed that significant differences in farmers’ perception of the extracted factors (chi-square: 465.49; p < 0.01) were evident. Stated differently, the ranks of extracted factors were not equally perceivable by the farmers. Risk perception was considered the most crucial driver for avoidance decisions concerning purchasing fraudulent pesticides (mean 3.77), pursued by beliefs (mean 4.11), policy (mean 3.84), price (mean 3.79), and quality recognition (mean 3.35).
3.4. Discriminant Analysis
The farmer segments’ driver perceptions were established by implementing the k-means clustering method for fraudulent pesticides. Thus, two segments emerged among the 394 farmers surveyed. The first segment comprised 236 (59.9%) farmers and the second 158 (41.1%) farmers. Farmers were designated depending on the last cluster centers (Table 5). The consumers in the first were the “conventional farmers” as they revealed the behavior of typical consumers fundamentally. Farmers in this segment concentrated on beliefs and the product price. The farmers in the second segment were the “conscious farmers.” Farmers in the second segment had an increased level of perception and accentuated policy issues, product quality, health, and environmental consequences. The analysis of variance (ANOVA) exposed the differences in the factors by clusters, as in Table 6. The ANOVA results depicted a significant difference between the clusters at a 1% level. They included all save for the beliefs factor.
3.5. Differences between Clusters According to Their Demographic Characteristics
Table 7 denotes the differences between clusters depending on the farmer segments’ socioeconomic profile. The chi-squared test findings revealed a significant difference (p < 0.01) between the two clusters for education level, farming activities, farm size, and farming experience. However, no significant difference (p > 0.01) existed between the two clusters for age, off-farm income, and attending extension activities on pesticides.
The “conventional farmers” in the first cluster were usually younger than “conscious farmers” in the second. The ratio of farmers having a university degree in the “conscious farmers” segment (26.6%) was 18% higher than the “conventional farmers” (8.1%). Most conventional farmers (65.7%) cultivated field crops, while most “conscious farmers” (67.1%) preferred horticulture as the essential activity. The “conscious farmers” frequently had larger farm sizes than “conventional farmers”. The percentage of farmers having more than 2 ha was 37.3% in the “conscious farmers,” while the value was 23.3% in the “conventional farmers”. Concerning farming experience, the “conscious farmers” possessed more farming experience than “conventional farmers”, specifically with the farming experience category, ranging from 16 to 25 years. Both clusters showed similarities as to off-farm income. More than half of “conscious farmers” and “conventional farmers” possessed off-farm income. Similarly, the vast majority of “conscious farmers” (83.5%) and “conventional farmers” (81.4%) joined extension activities on pesticides in the last three years
3.6. Strategies for Combatting Fraudulent Pesticides from the Farmers’ Point of View
Figure 1 shows the most crucial strategies for the fight against fraudulent pesticides in the market from the farmers’ view; farmers noted 12. They had five categories, namely awareness, capacity building and training, enforcement measures, pricing, and empowerment. The findings indicate that the most frequent strategies, declared by more than 30% of farmers, are in descending order as follows: organizing training programs for improving their fraudulent pesticide recognition behavior (65.2%), establishing awareness campaigns on fraudulent pesticide types and risks (49.2%), upholding the cooperatives’ role in delivering agricultural pesticides at a decreased price (39.8%), augmenting farmers’ awareness in obtaining a proper invoice with the trademark product before purchase (39.3%), nurturing consciousness via social media platforms (32.2%), and supporting original pesticides’ selling prices (31.7%).
4. Discussion
The farmers’ perception concerning fraudulent pesticides’ environmental and health consequences was a driving force for buying. Risk perception impels cognitive dissonance and helps individuals devise alternative strategies [51]. The farmers’ positive perceptions about fraudulent pesticides’ risks affect the farmers’ purchasing intentions. This result agrees with those of previous studies [15,38] reporting that less-perceptive farmers have a higher likelihood of buying fraudulent pesticides. Beliefs also arose as a crucial driving factor in purchasing fraudulent pesticides. This result agrees well with the expectations, suggesting the interconnection between risk perception and beliefs. According to Wood and Miller [52], cognitive dissonance arises when people witness psychological stress because they have conflicting beliefs. Cognitive dissonance leads to insecure feelings, eventually resulting in heightened risk perception. This result is similar to Ashour, Gilligan, Hoel, and Karachiwalla [27], who examined the prevalence of herbicide adulteration in the Ugandan market. They discovered that 41% of farmers opine that Glyphosate herbicide is adulterated; their beliefs correlate well with this product’s actual quality at the local markets.
The findings have also revealed that quality is a driving force for the fraudulent pesticides’ marketability. Thus, low consciousness of the counterfeits’ actual quality may urge farmers to buy them. Concurrently, farmers confront challenges detecting fraudulent pesticides because they rely on inaccurate methods such as visual inspection or low efficiency on pests as the primary criterion for recognition [16,53]. Virtually, distinguishing fraudulent pesticides is even more challenging and usually requires lab testing or dogs, unless the fake is limited [19]. The current study’s conclusion verifies this finding. The farmers noted the need for capacity-building programs to recognize and use novel digital anticounterfeiting means (Figure 1). Over the past few decades, pesticide manufacturers have devised many e-tools to secure the complete traceability and precise detection of fraudulent pesticides, from barcodes to holograms, invisible pigments, inks, and infrared markers, radio frequency identification tags, and more recently, embedded nanotechnology-based solutions [54,55,56]. However, combating this issue includes improving farmers’ capacity in such techniques to employ them as stand-alone systems and realizing collective action initiatives among all stakeholders in the pesticide supply chain against adulterated pesticides [11,19,26,57,58,59]. Many success stories have advanced and encompassed collective action initiatives for fighting fraudulent pesticide use. Yao’s work [60] in West Africa is a vivid example. Yao’s strategy relies on four harmonizing steps. The first step includes raising awareness among all stakeholders on this issue’s many risks. Then, efficient advocacy activities for lobbying and affecting decision-makers should pursue. The third should encompass organizing inspection campaigns to take appropriate actions and enforce penalties against violators. Finally, establishing training and capacity-building programs toward recognizing fraudulent pesticides are necessary for all stakeholders.
Price is a demand-side factor, affecting the purchase of fraudulent pesticides. Indeed, the actual pesticides’ rising prices and sometimes price discrimination deficiency between the adulterated and pure pesticides may urge farmers to purchase fraudulent ones. Usually, farmers buying counterfeits are attracted to the comparatively lower product price. Even if the market price is fair, some believe the product’s market price is “over-priced.” Thus, specifically, small-scale farmers may not be able to pay for the original item [12,20]. Kassem, Hussein, and Ismail [15] verify these findings, suggesting that low price has been the critical determinant among Egyptian farmers intentionally buying fraudulent pesticides. Yet, it is crucial to recommend that nondeceptive counterfeiting is unlikely in agricultural input goods. Mostly, farmers purchase fraudulent pesticides for their reasonable prices, judging that the quality is acceptable. Pesticide companies should supply lower-price products to bridge the price gaps and assess production, transaction, and market costs, minimizing the others’ risk of undercutting the product cost [37,53].
Government policy also is a vital milestone in the fight against fraudulent pesticides. It serves as the contributor or the inhibitor of these pesticides’ prevalence in the market [28,61]. Thus, the farmers’ perceptions of the government policy, whether it approves or not, affect their behavior towards buying counterfeits [47]. Many laws and regulations exist to protect intellectual property rights and punish unfair practices in the pesticide market in Egypt. The Egyptian policy in combating fraudulent pesticides is in a regulatory framework activated initiated in 2013 with many control measures and rules enforced by the Agricultural Pesticides Committee (APC) of the MALR. This framework allocates the APC authorities and defines responsibilities for registering, handling, and using agricultural pesticides in Egypt depending on the Egyptian Agricultural Law No. 53/1966, the ministerial decree No. 2188 of 2011 [17]. It underlines the penalties in case the rules and criteria set by APC are violated. Moreover, the APC requires the actual pesticides’ registration in many stages, such as submitting an application by the stakeholder to the APC, inspecting pesticide samples to secure conformity with chemical and physical specifications, evaluating pesticides on the targeted pest based on the protocol, and reviewing the test results by the APC for two consecutive agricultural seasons [62]. The APC has also been cooperating with the Ministry of the Interior embodied by the Environment Police to deepen the inspection campaigns on all markets for agricultural production supplies to grasp mega quantities of counterfeit pesticides in shops and arrest the violators. Almost 7000 tons of fraudulent pesticides were seized and logged from 2016 to 2019 [40]. Moreover, the APC immediately shuts down shops selling fraudulent pesticides, takes violators to the prosecution office, and establishes committees to list all licensed and unlicensed pesticide trade stores and makes them available in all agricultural cooperatives [17].
Despite all Egyptian pesticide regulatory measures, the issue, as disputed by Haggblade, Diarra, and Traoré [11], amounts to the markets’ ineffectiveness of post-registration monitoring, product quality, traders, and effect on human health and the environment. The post-registration monitoring’s inadequate enforcement is fraudulent pesticides’ frequent presence in the markets. It is not only valid for Egypt; many African countries have the same issue due to the limited public resources and cooperation among stakeholders [18,27,30,63,64]. Therefore, efficient antifraud efforts demand the implementation of practical ways in the short and medium terms. Urgently organizing awareness campaigns by pesticide value chain’s stakeholders [60,65]; boosting the environmental monitoring of pesticide use and impacts [11,66], and stimulating the application of integrated pest management [67,68] are vital. In the medium term, expanding resources for post-registration enforcement [11,33,69], reinforcing regulatory systems by regional harmonization of pesticide testing protocols [11,33], and laboratory upgrading [11,70] are critical issues.
The findings also disclosed that the “conventional farmers” segment considered beliefs and price the primary factors affecting the fraudulent pesticides’ marketability. It may be because “conventional farmers” were less educated than “conscious farmers.” Moreover, they had smaller farm sizes and predominantly cultivated field crops; hence, they may have difficulty paying for original pesticides. However, the “conscious farmers” had a higher consciousness of pesticide regulations, fraudulent pesticides’ adverse effects, and their role in food safety; thus, they reflected policies, quality recognition, and risk perception when buying fraudulent pesticides.
The farmers’ characteristics could also be critical determinants depending on their views concerning fraudulent pesticides and designing efficient policy instruments [71]. Thus, the results designated that the “conscious farmers” segment profile crucially differed from the “conventional farmers” segment concerning their education, farming experience, farming activity, and farm size. The farmers’ education level plays a precarious role in the fight against fraudulent pesticides; it is particularly true when farmers participate in deceptive counterfeiting transactions. It relates to the availability and access to pertinent information and the farmer’s capacity to comprehend it [47]. According to Kassem, Hussein, and Ismail [15], information deficiency leads farmers to think that these products possess the same quality and are more likely to purchase them. Farmers with high farming experience can have access to trusted knowledge sources for pesticides and obtain information from various sources to secure the precision and safety of the products. Farming experience also helps attain positive attitudes towards complying with regulations and standards. The present and previous studies’ findings [15,38] align well in that the farming experience variable is vital for farmers’ perception and purchasing behavior concerning fraudulent pesticides. Likewise, in the present study, “conscious farmers” having mega farm sizes and managing horticultural systems prioritized quality, policy, health, and environmental risk issues relative to “conventional farmers.” Undoubtedly, greater landholdings and cultivating high-value cash crops usually mean higher economic losses if crops are damaged, coercing larger-scale farmers to conform to good agricultural practices and sustainability standards [72,73] and purchase high-quality inputs [74]. Accordingly, managing a greater farm size and participating in horticultural value chains is an efficient means of raising farm income [75]. Moreover, it is an encouraging factor for developing an adverse attitude towards purchasing adulterated products [47].
5. Conclusions
This article gives insights on the factors affecting the purchase of fraudulent pesticides among Egyptian farmers. It also investigates how these factors change depending on farmer segment and demographic characteristics. Factor analysis includes five perception indices: beliefs, health and environmental risks, recognition of quality, price, and policies. They collectively explain about 72% of the variation and are beneficial for identifying the farmer segments. Two farmer segments—“conventional farmers” and “conscious farmers”—are contrasted depending on fraudulent pesticides’ examined drivers. The findings reveal that the most critical motivating factors for the fraudulent pesticides’ marketability from the “conscious farmers” perspective are policy issues, recognition of the product quality, and health and environmental consequences; yet beliefs and the product price are the primary concerns for conventional farmers.
Factors influencing purchasing fraudulent pesticides developed in this study have implications in both theory and practice. The literature on counterfeit and illegal pesticides suggests increases in these pesticides’ numbers, specifically in developing countries. Most previous studies have adopted a top-down approach to explore the pesticides’ adulteration impact on sustainable development in several sectors, such as agriculture, addressing how the policies and legislation can help support and enforce this issue. Nonetheless, very few studies have methodically investigated the bottom-up approach on how the farmers identify this problem and which factors may affect their purchasing behavior. Accordingly, the fraudulent pesticide dilemma is far from complete. The present study contributes to the literature by examining the drivers for fraudulent pesticides in Dakhalia governorate, Egypt. It reposes on the extant literature in line with the essential strategic objectives of the country’s sustainable agricultural development 2030 strategy for fighting fraud in the agrifood sector [75]. Furthermore, this paper offers a relatively simple view of the determinants of prevailing fraudulent pesticides at micro and macro levels without losing their complexity. These factors provide a practical guide with a tested and reliable rating scale to assist future researchers who want to study the issue of fraudulent pesticides. Undoubtedly, investigating how the farmer segments’ driver perceptions of fraudulent pesticides enrich this issue’s theoretical aspect.
Practically, this paper provides insights into the gaps that need to be filled by policymakers during registering, handling, and using agricultural pesticides. Suggestions cited by the farmers expose a window to enhance pesticide regulatory policy implementation and address necessary future reforms across Egypt. This study supplies five beneficial policy interventions, putting these suggestions in place. First, as perceptions depend on exposure to knowledge, organizing awareness campaigns using mass media and new information tools will enable many farmers to improve their perception of the adverse risks. Then, building capacity programs should allow the stakeholders to enhance their recognition skills and use new digital means for detecting fraud. Third, urging cooperatives to partner with pesticide companies to regulate pesticides’ prices is vital. Fourth, consolidating the enforcement mechanisms to the extant laws and regulations and reinforcing surveillance by relevant authorities in fraudulent pesticide entry routes is decisive. Fifth, empowering farmers in developing and operating a hotline number for reports and complaints remains critical. Farmers should also have access to existing pesticide licenses and registration data.
However, this paper has some limitations needing acknowledgment. We have carried out the study in one governorate, and therefore we cannot generalize the findings to include other governorates within Egypt or other countries. The evaluation of fraudulent pesticide marketability depends on the farmers’ self-reports, implying that this approach relies on what the respondents believe to be accurate, biasing the results of the perception. Selecting farmers as an actor within the pesticide value chain does not depend on representing the other actors, such as input suppliers, extension agents, or policymakers, which may not allow us to explore the full picture of views regarding combating fraudulent pesticides. Finally, these results are context-specific and may not be completely applicable in other countries with varied cultural milieus. Exploring the results’ applicability in future settings is recommendable. This article has primarily focused on the fraudulent pesticides’ demand side. Therefore, investigating the supply side in future studies in terms of exploring market incentives, the technological and distributional challenges, and the institutional environment would be invaluable and offer a greater insight into the pesticide issue.
Conceptualization, M.A.H.; data curation and validation, M.A.H.; methodology, H.S.K.; formal analysis, H.S.K.; writing—original draft preparation, H.S.K. and M.A.H.; writing—review and editing, H.I. All authors have read and agreed to the published version of the manuscript.
Informed consent was obtained from all subjects involved in the study.
Not applicable.
Data are contained within the article.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. Strategies for combatting fraudulent pesticides from the farmers’ point of view.
Demographic characteristics of the respondents.
| Variable | Category | Frequency | Percentage |
|---|---|---|---|
| Age (Years) | <46 | 65 | 16.5 |
| 46–55 | 136 | 34.5 | |
| 56–65 | 136 | 34.5 | |
| >65 | 57 | 14.5 | |
| Min | 30 | ||
| Max | 79 | ||
| Mean | 56.18 | ||
| SD | 9.35 | ||
| Education | Illiterate | 143 | 36.3 |
| Read and write | 115 | 29.2 | |
| Primary school | 30 | 7.6 | |
| Secondary school | 45 | 11.4 | |
| University | 61 | 15.5 | |
| Farm size (Hectares) | 1–2 | 280 | 71.1 |
| >2–4 | 72 | 18.3 | |
| >4 | 42 | 10.7 | |
| Min | 0.18 | ||
| Max | 12.63 | ||
| Mean | 1.72 | ||
| SD | 2.24 | ||
| Farming experience (Years) | <16 | 83 | 21.1 |
| 16–25 | 119 | 30.2 | |
| 26–35 | 124 | 31.5 | |
| >35 | 68 | 17.3 | |
| Min | 5 | ||
| Max | 55 | ||
| Mean | 25.81 | ||
| SD | 10.56 | ||
| Off-farm income | Yes | 233 | 59.1 |
| No | 161 | 40.9 | |
| Attending extension activities on pesticides in the last three years | Yes | 324 | 82.2 |
| No | 70 | 17.8 | |
Mean ranking of farmers’ perception of fraudulent pesticides’ drivers in the market.
| Statements * | Mean | SD | Rank |
|---|---|---|---|
| Using fraudulent pesticides in agriculture is necessary. | 4.15 | 0.78 | 5 |
| Practically speaking, fraudulent pesticides are compatible with my experiences and practices. | 3.98 | 0.66 | 11 |
| I have been satisfied with using fraudulent pesticides in pest control. | 4.18 | 0.7 | 4 |
| The use of fraudulent pesticides has a crucial effect on agricultural production. | 4.21 | 0.75 | 3 |
| My relatives and neighbors opine that I should use fraudulent pesticides. | 4.01 | 0.67 | 10 |
| Most people whose opinions I value approve of me using fraudulent pesticides next season. | 4.09 | 0.67 | 6 |
| Most farmers like me will use fraudulent pesticides within the following season. | 4.08 | 0.68 | 7 |
| There is no difference in targeting pests and diseases between original and fraudulent pesticides. | 4.24 | 0.62 | 2 |
| Inability to detect fraudulent pesticides leads to their purchase. | 3.47 | 0.93 | 17 |
| I do not purchase fraudulent pesticides because they have poor quality and no guarantee. | 2.96 | 0.84 | 19 |
| Dealers and retailers deceive consumers into buying fraudulent pesticides via promotion campaigns. | 2.74 | 0.93 | 20 |
| I think buying fraudulent pesticides is obtaining the brand at a lower cost. | 3.92 | 0.83 | 13 |
| I buy fraudulent pesticides because the price for legitimate products rises. | 4.06 | 0.71 | 8 |
| There is no price discrimination between fraudulent and genuine pesticides. | 3.37 | 0.84 | 18 |
| Inadequacies of laws and regulations drive fraudulent pesticides in the markets. | 3.95 | 0.93 | 12 |
| Trade liberalization has increased the influx of fraudulent pesticides in the country. | 3.89 | 0.95 | 14 |
| Policies, laws, and regulations are all set but lack effective enforcement mechanisms to refute the market’s fraudulent pesticides. | 3.88 | 0.87 | 15 |
| Lack of adequate intellectual property rights protection mechanisms drives trading of fraudulent pesticides. | 3.63 | 0.79 | 16 |
| I believe fraudulent pesticides can be dangerous for farmers’ and animals’ health. | 4.33 | 0.61 | 1 |
| I think using fraudulent pesticides causes environmental pollution and damage. | 4.03 | 0.64 | 9 |
* Negative statements were recorded.
Factor analysis of marketability of fraudulent pesticide variables using varimax rotation.
| Perception Statements | Rotated Components | ||||
|---|---|---|---|---|---|
| Beliefs | Polices | Price | Quality Recognition | Health and Environmental Risks | |
| Using fraudulent pesticides in agriculture is necessary. | 0.743 | 0.200 | 0.309 | 0.052 | −0.121 |
| Practically speaking, fraudulent pesticides are compatible with my experiences and practices. | 0.801 | 0.045 | −0.154 | −0.189 | 0.103 |
| I have been satisfied with using fraudulent pesticides in pest control. | 0.780 | 0.207 | 0.120 | 0.179 | −0.056 |
| The use of fraudulent pesticides has a crucial effect on agricultural production. | 0.696 | 0.222 | 0.106 | 0.220 | 0.283 |
| My relatives and neighbors opine that I should use fraudulent pesticides. | 0.840 | −0.099 | 0.024 | −0.037 | −0.029 |
| Most people whose opinions I value approve of me using fraudulent pesticides next season. | 0.805 | 0.052 | 0.113 | 0.319 | 0.028 |
| Most farmers like me will use fraudulent pesticides within the following season. | 0.863 | 0.063 | 0.141 | 0.208 | −0.045 |
| There is no difference in targeting pests and diseases between original and fraudulent pesticides. | 0.250 | −0.123 | 0.213 | 0.658 | −0.005 |
| Inability to detect fraudulent pesticides leads to their purchase. | 0.219 | 0.033 | 0.105 | 0.638 | 0.357 |
| I do not purchase fraudulent pesticides because they have poor quality and no guarantee. | 0.135 | −0.473 | 0.024 | −0.549 | 0.387 |
| Dealers and retailers deceive consumers into buying fraudulent pesticides via promotion campaigns. | 0.121 | −0.105 | 0.885 | 0.007 | 0.108 |
| I think buying fraudulent pesticides is obtaining the brand at a lower cost. | 0.300 | −0.172 | 0.745 | 0.177 | 0.090 |
| I buy fraudulent pesticides because the price for legitimate products rises. | 0.010 | 0.079 | 0.863 | 0.036 | −0.201 |
| There is no price discrimination between fraudulent and genuine pesticides. | −0.069 | −0.391 | 0.405 | −0.534 | −0.075 |
| Inadequacies of laws and regulations drive fraudulent pesticides in the markets. | 0.141 | 0.879 | 0.043 | 0.131 | 0.251 |
| Trade liberalization has increased the influx of fraudulent pesticides in the country. | 0.150 | 0.867 | −0.051 | 0.158 | 0.253 |
| Policies, laws, and regulations are all set but lack effective enforcement mechanisms to refute the market’s fraudulent pesticides. | 0.206 | 0.731 | −0.114 | 0.068 | 0.386 |
| Lack of adequate intellectual property rights protection mechanisms drives trading of fraudulent pesticides. | 0.046 | 0.831 | −0.101 | −0.170 | −0.064 |
| I believe fraudulent pesticides can be dangerous for farmers’ and animals’ health. | 0.014 | 0.287 | −0.047 | 0.390 | 0.703 |
| I think using fraudulent pesticides cause environmental pollution and damage. | −0.090 | 0.281 | −0.006 | −0.070 | 0.783 |
| eigenvalue | 5.826 | 3.833 | 2.017 | 1.470 | 1.381 |
| The percentage of total variance explained by each factor | 29.132 | 19.165 | 10.086 | 7.349 | 6.906 |
| The cumulative percentage of the variance explained by all factors | 72.639 | ||||
Kaiser–Meyer–Olkin measure (KMO) of sampling adequacy = 0.807; Bartlett’s test of sphericity: chi-square (df) = 5222.810 (190).
Ranking extracted factors of the drivers for fraudulent pesticides.
| Factor | Friedman Mean Rank | Mean | SD | Rank |
|---|---|---|---|---|
| Beliefs | 3.64 | 4.10 | 0.57 | 2 |
| Policy | 2.81 | 3.84 | 0.78 | 4 |
| Price | 3.09 | 3.79 | 0.68 | 3 |
| Quality recognition | 1.70 | 3.35 | 0.42 | 5 |
| Health and Environmental risks | 3.77 | 4.18 | 0.55 | 1 |
Chi-square: 465.49; p < 0.01.
Final cluster centers.
| Factor | Cluster | |
|---|---|---|
| Cluster 1 |
Cluster 2 |
|
| Beliefs | 0.12092 | 0.06112 |
| Policy | −0.56491 | 0.84380 |
| Price | 0.28881 | −0.43138 |
| Quality recognition | −0.33980 | 0.50755 |
| Health and Environmental risks | −0.49080 | 0.73310 |
K-means cluster analysis: ANOVA results.
| Factor | Cluster | Error | F | p-Value | ||
|---|---|---|---|---|---|---|
| Mean Square | df | Mean Square | df | |||
| Beliefs | 0.985 | 1 | 1.000 | 392 | 0.985 | 0.321 |
| Policy | 187.809 | 1 | 0.523 | 392 | 358.794 ** | 0.00 |
| Price | 49.087 | 1 | 0.877 | 392 | 55.950 ** | 0.00 |
| Quality recognition | 67.952 | 1 | 0.829 | 392 | 81.948 ** | 0.00 |
| Health and Environmental risks | 141.765 | 1 | 0.641 | 392 | 221.194 ** | 0.00 |
** denotes statistical significance at the 1% level.
Differences between farmer segments according to their demographic characteristics.
| Variable | Category | Cluster1 (%) | Cluster2 (%) | χ2 | p-Value |
|---|---|---|---|---|---|
| Age (Years) | <46 | 19.5 | 12 | 5.32 | 0.15 |
| 46–55 | 33.5 | 36.1 | |||
| 56–65 | 34.7 | 34.2 | |||
| >65 | 12.3 | 17.7 | |||
| Education | Illiterate | 43.6 | 25.3 | 36.37 ** | 0.00 |
| Read and write | 25.8 | 34.2 | |||
| Primary school | 9.7 | 4.4 | |||
| Secondary school | 12.7 | 9.5 | |||
| University | 8.1 | 26.6 | |||
| Main activity | Field crops | 65.7 | 32.9 | 49.76 ** | 0.00 |
| Vegetables | 5.1 | 22.8 | |||
| Fruits | 29.2 | 44.3 | |||
| Farm size (Hectares) | 1–2 | 76.7 | 62.7 | 11.26 ** | 0.004 |
| >2–4 | 13.1 | 25.9 | |||
| >4 | 10.2 | 11.4 | |||
| Farming experience (Years) | <16 | 27.1 | 12.0 | 16.43 ** | 0.00 |
| 16–25 | 24.6 | 38.6 | |||
| 26–35 | 31.4 | 31.6 | |||
| >35 | 16.9 | 17.7 | |||
| Off-farm income | Yes | 59.3 | 58.9 | 0.008 | 0.927 |
| No | 40.7 | 41.1 | |||
| Attending extension activities on pesticides at the last three years | Yes | 81.4 | 83.5 | 0.310 | 0.578 |
| No | 18.6 | 16.5 |
** denotes statistical significance at the 1% level.
References
1. Aung, M.M.; Chang, Y.S. Traceability in a food supply chain: Safety and quality perspectives. Food Control; 2014; 39, pp. 172-184. [DOI: https://dx.doi.org/10.1016/j.foodcont.2013.11.007]
2. Soon, J.M.; Abdul Wahab, I.R. A Bayesian Approach to Predict Food Fraud Type and Point of Adulteration. Foods; 2022; 11, 328. [DOI: https://dx.doi.org/10.3390/foods11030328] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35159479]
3. Jurica, K.; Brčić Karačonji, I.; Lasić, D.; Bursać Kovačević, D.; Putnik, P. Unauthorized Food Manipulation as a Criminal Offense: Food Authenticity, Legal Frameworks, Analytical Tools and Cases. Foods; 2021; 10, 2570. [DOI: https://dx.doi.org/10.3390/foods10112570] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34828851]
4. Johnson, R. Food Fraud and “Economically Motivated Adulteration” of Food and Food Ingredients; Congressional Research Service: Washington, DC, USA, 2018.
5. Levi, R.; Singhvi, S.; Zheng, Y. Economically motivated adulteration in farming supply chains. Manag. Sci.; 2020; 66, pp. 209-226. [DOI: https://dx.doi.org/10.1287/mnsc.2018.3215]
6. TRACIT. Mapping the Impact of Illicit Trade on the un Sustainable Development Goals; The Transnational Alliance to Combat Illicit Trade: New York, NY, USA, 2019.
7. Brooks, C.; Parr, L.; Smith, J.M.; Buchanan, D.; Snioch, D.; Hebishy, E. A review of food fraud and food authenticity across the food supply chain, with an examination of the impact of the COVID-19 pandemic and Brexit on food industry. Food Control; 2021; 130, 108171. [DOI: https://dx.doi.org/10.1016/j.foodcont.2021.108171]
8. Soon, J.M.; Manning, L. Food smuggling and trafficking: The key factors of influence. Trends Food Sci. Technol.; 2018; 81, pp. 132-138. [DOI: https://dx.doi.org/10.1016/j.tifs.2018.09.007]
9. Meagher, K. Smuggling ideologies: From criminalization to hybrid governance in African clandestine economies. Afr. Aff.; 2014; 113, pp. 497-517. [DOI: https://dx.doi.org/10.1093/afraf/adu057]
10. Sarkar, S.; Gil, J.D.B.; Keeley, J.; Jansen, K. The Use of Pesticides in Developing Countries and Their Impact on Health and the Right to Food; European Union: Brussels, Belgium, 2021.
11. Haggblade, S.; Diarra, A.; Traoré, A. Regulating agricultural intensification: Lessons from West Africa’s rapidly growing pesticide markets. Dev. Policy Rev.; 2022; 40, e12545. [DOI: https://dx.doi.org/10.1111/dpr.12545]
12. Sambrook, C.I. Counterfeit Pesticides: A Mixed Methods Study of a Transnational Organised Crime Threat to the UK Agricultural Industry. Ph.D. Thesis; Harper Adams University: Shropshire, England, 2016.
13. CropLife International. Pesticide Retailer Course: Trainer Manual; CropLife International: Brussels, Belgium, 2017.
14. UNICRI. Illicit Pesticides, Organized Crime and Supply Chain Integrity; United Nations Interregional Crime and Justice Research Institute: Torino, Italy, 2016.
15. Kassem, H.S.; Hussein, M.A.; Ismail, H. Toward Fraudulent Pesticides in Rural Areas: Do Farmers’ Recognition and Purchasing Behaviors Matter?. Agronomy; 2021; 11, 1882. [DOI: https://dx.doi.org/10.3390/agronomy11091882]
16. Fishel, F.M. The Global Increase in Counterfeit Pesticides; Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida: Gainesville, FL, USA, 2009.
17. Bayoumi, A.E. Counterfeit Pesticides. ACS Chem. Health Saf.; 2021; 28, pp. 232-237. [DOI: https://dx.doi.org/10.1021/acs.chas.1c00010]
18. Haggblade, S.; Keita, N.; Traoré, A.; Traoré, P.; Diarra, A.; Thériault, V. A Market Survey of Fraudulent Pesticides Sold in Mali; Feed the Future Research Report 157 Michigan State University: East Lansing, MI, USA, 2019.
19. Haggblade, S.; Diarra, A.; Jiang, W.; Assima, A.; Keita, N.; Traoré, A.; Traoré, M. Quality Comparison of Fraudulent and Registered Pesticides in Mali; Policy Research Brief 76 Michigan State University: East Lansing, MI, USA, 2019; pp. 1-3.
20. EUIPO. 2020 Status Report on Ipr Infringement: Why IP Rights are Important, IPR Infringement, and the Fight against Counterfeiting and Piracy; European Union Intellectual Property Office: Alicante, Spain, 2020.
21. Carter, B.; Durrant, C. Counterfeit and Illegal Pesticides in Food Supply Chains: What Should Businesses Be Doing to Minimise the Risk?. Available online: http://www.adas.uk/News/counterfeitand-illegal-pesticides-in-food-supply-chainswhat-should-businesses-be-doing-to-minimise-the-risk (accessed on 9 November 2021).
22. McDougall, P. The Cost of New Agrochemical Product Discovery, Development and Registration in 1995, 2000, 2005–8, and 2010–2014: R&D Expenditure in 2014 and Expectations for 2019: A Consultancy Study for CropLife International; CropLife International: Brussels, Belgium, 2016; pp. 1-41.
23. Sambrook, C.I. Counterfeit plant protection products: A mixed-methods study of an emerging crime threat to the agricultural industry. Eur. Police Sci. Res. Bull.; 2016; 14, 44.
24. Guyer, R. The problem of Counterfeit and Illegal Pesticides in Africa Middle East; CropLife Africa Middle East. 2017; Available online: https://tpsalliance.org/pdf/topics/The%20problem%20of%20Illegal%20and%20counterfeit%20Pesticides%20in%20Africa%20Middle%20East.pdf (accessed on 9 November 2021).
25. Haggblade, S.; Diarra, A.; Jiang, W.; Assima, A.; Keita, N.; Traore, A.; Traore, M. Fraudulent pesticides in West Africa: A quality assessment of glyphosate products in Mali. Int. J. Pest Manag.; 2021; 67, pp. 32-45. [DOI: https://dx.doi.org/10.1080/09670874.2019.1668076]
26. Ashour, M.; Billings, L.; Gilligan, D.O.; Karachiwalla, N. Evaluation of the Impact of E-Verification on Counterfeit Agricultural Inputs and Technology Adoption in Uganda; International Food Policy Research Institute (IFPRI): Washington, DC, USA, 2015.
27. Ashour, M.; Gilligan, D.O.; Hoel, J.B.; Karachiwalla, N.I. Do beliefs about herbicide quality correspond with actual quality in local markets? Evidence from Uganda. J. Dev. Stud.; 2019; 55, pp. 1285-1306. [DOI: https://dx.doi.org/10.1080/00220388.2018.1464143]
28. De Boef, W.; Hasson, O.; Pierson, B.; Kim, D.; Mennel, J.; Engle, C.; Prabhala, P.; Bryce, J.; Nemeth, N.; Jethani, A. Counterfeiting in African agriculture inputs—Challenges & solutions: Comprehensive findings. Gates Open Res; 2019; 3, 250.
29. Zikankuba, V.L.; Mwanyika, G.; Ntwenya, J.E.; James, A. Pesticide regulations and their malpractice implications on food and environment safety. Cogent Food Agric.; 2019; 5, 1601544. [DOI: https://dx.doi.org/10.1080/23311932.2019.1601544]
30. Murphy, M.; Sanderson, W.; Birch, M.; Liang, F.; Sanyang, E.; Canteh, M.; Cook, T.; Murphy, S. Type and toxicity of pesticides sold for community vector control use in the Gambia. Epidemiol. Res. Int.; 2012; 2012, pp. 1-6. [DOI: https://dx.doi.org/10.1155/2012/387603]
31. Karingu, A.W.; Kenyatta, J.; Ngugi, P. Determinants of the infiltration of counterfeit agro-based products in Kenya: A case of suppliers in Nairobi. Int. J. Hum. Resour. Procure.; 2013; 1, pp. 28-36.
32. Salca-Rotaru, C.M. Effects of Non-Compliance with Intellectual Property Rights regarding Pesticides. Bull. Transilv. Univ. Braşov. Ser. VII Soc. Sci.; 2021; 14, pp. 443-452. [DOI: https://dx.doi.org/10.31926/but.ssl.2021.14.63.2.29]
33. OECD. Recommendation of the Council on Countering the Illegal Trade of Pesticides; OECD/LEGAL/0446 OECD: Paris, France, 2021.
34. WHO. Capacity Strenthening for Soung Management of Pesticides; World Health Organization: Geneva, Switzerland, 2011.
35. Italian Ministry of Economic Development. The Protection of Intellectual Property Rights in the Euromediterranean Area: Focus on the the Agro-Food Sector; Italian Ministry of Economic Development: Rome, Italy, 2018.
36. Malkov, M.; Prischepa, S.; Kutonova, T. Counteraction to Counterfeit and Contraband Pesticides: Methodology; Organization for Security and Co-operation in Europe (OSCE) with cooperation of the Environment and Security Initiative (ENVSEC): Vienna, Austria, 2015.
37. Gharib, M.H. Perception of Seed Fraud and Farmer Behavior: Value of Maize Seed Certification in Kenya. Ph.D. Thesis; University of Delaware: Newark, Delaware, 2018.
38. Kassem, H.S.; Alotaibi, B.A. Do farmers perceive risks of fraudulent pesticides? Evidence from Saudi Arabia. PLoS ONE; 2020; 15, e0239298. [DOI: https://dx.doi.org/10.1371/journal.pone.0239298]
39. OECD. Best Practice Guidance to Identify Illegal Trade of Pesticides; ENV/JM/MONO(2018)35 Organisation for Economic Co-Operation and Development: Paris, France, 2018.
40. MALR. Counterfeit Pesticide Survey in Egypt; Ministry of Agriculture and Land Reclamation (MALR): Cairo, Egypt, 2021.
41. Khattab, G.M.; Abdelghany, W.; Abdelmegeed, M.I.; Attalah, I. Monitoring of counterfeit abamectin pesticide products in Egypt. Arab Univ. J. Agric. Sci.; 2020; 28, pp. 601-615. [DOI: https://dx.doi.org/10.21608/ajs.2020.21756.1146]
42. Abdelhameed, D.T.; Mashtoly, T.A.; El-Zemaity, M.S. Completion of label information and physico-chemical properties of commercial pesticide samples traded in Egyptian markets. Arab Univ. J. Agric. Sci.; 2018; 26, pp. 313-325. [DOI: https://dx.doi.org/10.21608/ajs.2018.14012]
43. CAPMAS. Statistical Report of Egypt; Central Agency for Public Mobilization and Statistics: Cairo, Egypt, 2020.
44. MALR. Statistical Yearbook 2020; Ministry of Agriculture and Land Reclamation (MALR): Cairo, Egypt, 2020.
45. Quoquab, F.; Pahlevan, S.; Mohammad, J.; Thurasamy, R. Factors affecting consumers’ intention to purchase counterfeit product. Asia Pac. J. Mark. Logist.; 2017; 29, pp. 837-853. [DOI: https://dx.doi.org/10.1108/APJML-09-2016-0169]
46. Viot, C.; Le Roux, A.; Kremer, F. Attitude towards the purchase of counterfeits: Antecedents and effect on intention to purchase. Rech. Appl. En Mark.; 2014; 29, pp. 3-31. [DOI: https://dx.doi.org/10.1177/2051570714533474]
47. Lwesya, F. Factors influencing the marketability of counterfeit and pirated products in Dar-Es-Salaam Region, Tanzania-A factorial analysis. Saudi J. Humanit. Soc. Sci.; 2017; 2, pp. 95-105.
48. Yong, A.G.; Pearce, S. A beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutor. Quant. Methods Psychol.; 2013; 9, pp. 79-94. [DOI: https://dx.doi.org/10.20982/tqmp.09.2.p079]
49. Hennig, C.; Meila, M.; Murtagh, F.; Rocci, R. Handbook of Cluster Analysis; CRC Press: Boca Raton, FL, USA, 2015.
50. Denis, D.J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics; John Wiley & Sons: Hoboken, NJ, USA, 2018.
51. Pueschel, J.; Chamaret, C.; Parguel, B. Coping with copies: The influence of risk perceptions in luxury counterfeit consumption in GCC countries. J. Bus. Res.; 2017; 77, pp. 184-194. [DOI: https://dx.doi.org/10.1016/j.jbusres.2016.11.008]
52. Wood, E.; Miller, S.K. Cognitive dissonance and disaster risk communication. J. Emerg. Manag. Disaster Commun.; 2021; 2, pp. 39-56. [DOI: https://dx.doi.org/10.1142/S2689980920500062]
53. Zimba, M.; Zimudzi, C. Pesticide management practices among rural market gardening farmers near Harare, Zimbabwe. S. Afr. J. Sci.; 2016; 112, pp. 1-5. [DOI: https://dx.doi.org/10.17159/sajs.2016/20150443]
54. Frezal, C.; Garsous, G. New Digital Technologies to Tackle Trade in Illegal Pesticides; OECD: Paris, France, 2020.
55. IPEC. Supporting Innovation, Creativity & Enterprise: Charting a Path Ahead (Joint Strategic Plan on Intellectual Property Enforcement FY 2017–2019); Office of the U.S. Intellectual Property Enforcement Coordinator: Washington, DC, USA, 2020.
56. Shao, D.; Edward, S. Combating fake agro-inputs products in Tanzania using mobile phones. Int. J. Comput. Appl.; 2014; 97, pp. 21-25. [DOI: https://dx.doi.org/10.5120/17099-7681]
57. Food Chain Evaluation Consortium. Ad-hoc study on the trade of illegal and counterfeit pesticides in the EU. Eur. Comm. DG Health Food Saf.; 2015; Available online: https://ec.europa.eu/food/system/files/2016-10/pesticides_ppp_illegal-ppps-study.pdf (accessed on 14 December 2021).
58. Soon, J.M.; Manning, L. Developing anti-counterfeiting measures: The role of smart packaging. Food Res. Int.; 2019; 123, pp. 135-143. [DOI: https://dx.doi.org/10.1016/j.foodres.2019.04.049]
59. Yao, B. Industry efforts to combat counterfeit and other illegal pesticides. Proceedings of the Disruptive Innovations and Rural Development; Washington, DC, USA, 14 June 2018.
60. Miszczyk, M.; Płonka, M.; Stobiecki, T.; Kronenbach-Dylong, D.; Waleczek, K.; Weber, R. Official control of plant protection products in Poland: Detection of illegal products. Environ. Sci. Pollut. Res.; 2018; 25, pp. 31906-31916. [DOI: https://dx.doi.org/10.1007/s11356-018-1739-2] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29616478]
61. APC. Guidelines for the Registration of Pesticides in Egyptian Pesticide Industry; Agricultural Pesticides Committee (APC), Ministry of Agriculture and Land reclamation Cairo: Cairo, Egypt, 2018.
62. MIR Plus. Evaluation de la qualité des pesticides commercialisés dans huit pays de l’espace CEDEAO; ECOWAS and UEMOA: Abuja and Abidjan, Nigeria, 2012.
63. Rodenburg, J.; Johnson, J.-M.; Dieng, I.; Senthilkumar, K.; Vandamme, E.; Akakpo, C.; Allarangaye, M.D.; Baggie, I.; Bakare, S.O.; Bam, R.K. Status quo of chemical weed control in rice in sub-Saharan Africa. Food Secur.; 2019; 11, pp. 69-92. [DOI: https://dx.doi.org/10.1007/s12571-018-0878-0]
64. Yao, B.O. Box 5.2: Industry efforts to combat fraudulent pesticides in West Africa. AGRA, Africa Agriculture Status Report. Feeding Africa’s Cities: Opportunities, Challenges, and Policies for Linking African Farmers with Growing Urban Food Markets; The Alliance for a Green Revolution in Africa (AGRA): Nairobi, Kenya, 2020; 110.
65. Vryzas, Z.; Ramwell, C.; Sans, C. Pesticide prioritization approaches and limitations in environmental monitoring studies: From Europe to Latin America and the Caribbean. Environ. Int.; 2020; 143, 105917. [DOI: https://dx.doi.org/10.1016/j.envint.2020.105917] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32619916]
66. Jepson, P.C.; Murray, K.; Bach, O.; Bonilla, M.A.; Neumeister, L. Selection of pesticides to reduce human and environmental health risks: A global guideline and minimum pesticides list. Lancet Planet. Health; 2020; 4, pp. e56-e63. [DOI: https://dx.doi.org/10.1016/S2542-5196(19)30266-9]
67. Dara, S.K. The new integrated pest management paradigm for the modern age. J. Integr. Pest Manag.; 2019; 10, 12. [DOI: https://dx.doi.org/10.1093/jipm/pmz010]
68. Vijver, M.G.; Hunting, E.R.; Nederstigt, T.A.; Tamis, W.L.; van den Brink, P.J.; van Bodegom, P.M. Postregistration monitoring of pesticides is urgently required to protect ecosystems. Environ. Toxicol. Chem.; 2017; 36, pp. 860-865. [DOI: https://dx.doi.org/10.1002/etc.3721]
69. DuBois, J. Trade Africa—Côte d’Ivoire and Senegal SPS Laboratory Improvement Action Plan; USDA Foreign Agriculture Service: Washington, DC, USA, 2019.
70. Piñeiro, V.; Arias, J.; Dürr, J.; Elverdin, P.; Ibáñez, A.M.; Kinengyere, A.; Opazo, C.M.; Owoo, N.; Page, J.R.; Prager, S.D. A scoping review on incentives for adoption of sustainable agricultural practices and their outcomes. Nat. Sustain.; 2020; 3, pp. 809-820. [DOI: https://dx.doi.org/10.1038/s41893-020-00617-y]
71. Kassem, H.S.; Alotaibi, B.A.; Aldosari, F.O.; Herab, A.; Ghozy, R. Factors influencing smallholder orange farmers for compliance with GobalGAP standards. Saudi J. Biol. Sci.; 2021; 28, pp. 1365-1373. [DOI: https://dx.doi.org/10.1016/j.sjbs.2020.11.070]
72. Krause, H.; Lippe, R.S.; Grote, U. Adoption and income effects of public GAP standards: Evidence from the horticultural sector in Thailand. Horticulturae; 2016; 2, 18. [DOI: https://dx.doi.org/10.3390/horticulturae2040018]
73. Komarek, A.M.; De Pinto, A.; Smith, V.H. A review of types of risks in agriculture: What we know and what we need to know. Agric. Syst.; 2020; 178, 102738. [DOI: https://dx.doi.org/10.1016/j.agsy.2019.102738]
74. Suprehatin, S. Characteristics of Farmer Adopters of High Value Horticultural Crops in Indonesia. J. Manaj. Agribisnis; 2019; 16, pp. 181-190. [DOI: https://dx.doi.org/10.17358/jma.16.2.181]
75. MALR. Sustainable Agricultural Development Strategy 2030; Ministry of Agriculture and Land Reclamation: Cairo, Egypt, 2016.
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; Hussein, Mohamed A 2 ; Ismail, Hamed 3
1 Department of Agricultural Extension and Rural Society, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia
2 Extension Programs Research Department, Agricultural Extension and Rural Development Institute, Agricultural Research Center, Giza 12619, Egypt;
3 Department of Food Science, Ontario Agricultural College, University of Guelph, Guelph, ON N1G-2W1, Canada;