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case_images: Socio-demographic characteristics per country and farmer classification. Working hours per week as average over the week before first and second visit.
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case_images: Ugandan farmers (N = 302) were 48.0 years old (SD 13.6) with 8.0 years of school (SD 3.8, Figure 2). Figure 2), (ii) a larger proportion of singles and widows among organic farmers (32.5% and 25.0%) and non-applicators (25.0% and 35.7%), in contrast with a larger proportion of married among mixed (69.0%) and conventional farmers (74.4%; Figure 3), (iii) a relatively low number (60.0%) of organic farmers reporting to farm crops as their main occupation (Figure 3), and (iv) a correspondingly lower amount of working-hours per week on the farm for organics (mean 20.9, SD 10.6 h/wk) vs. conventional (mean 35.3, SD 16.9 h/wk; Figure 2). Furthermore, conventional male farmers worked on average 36.9 h/wk (SD 17.5), while their organic male counterparts only worked 16.7 h/wk (SD 10.0; P < .001).
case_images: Socio-demographic characteristics per country and farmer classification. Nationality indicates nationality at birth, and occupation. Other indicates any profession besides crop farmer (see also Table 1).
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case_images: They were mostly married (65.6%) and Ugandan nationals (99%, Figure 3). Figure 2), (ii) a larger proportion of singles and widows among organic farmers (32.5% and 25.0%) and non-applicators (25.0% and 35.7%), in contrast with a larger proportion of married among mixed (69.0%) and conventional farmers (74.4%; Figure 3), (iii) a relatively low number (60.0%) of organic farmers reporting to farm crops as their main occupation (Figure 3), and (iv) a correspondingly lower amount of working-hours per week on the farm for organics (mean 20.9, SD 10.6 h/wk) vs. conventional (mean 35.3, SD 16.9 h/wk; Figure 2). Furthermore, conventional male farmers worked on average 36.9 h/wk (SD 17.5), while their organic male counterparts only worked 16.7 h/wk (SD 10.0; P < .001).
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case_images: The framework links aspects of smallholder farmers' livelihood around pesticide use. The numbers indicate the research questions. The framework is derived from our own thought process.
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case_images: In Costa Rica, the crops grown were considerably different between conventional and mixed farmers (ie, potatoes, carrots, and coriander) and organic farmers (ie, tomatoes, bell peppers, and lettuce, Supplementary Figure SF1). Bananas were most popular among mixed farmers (53.6%) compared to all other farmer groups (ie, organic, mixed, and conventional; 27.5%; Supplementary Figure SF1).
case_images: Socio-demographic characteristics per country and farmer classification. Working hours per week as average over the week before first and second visit.
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case_images: Ugandan farmers (N = 302) were 48.0 years old (SD 13.6) with 8.0 years of school (SD 3.8, Figure 2). Figure 2), (ii) a larger proportion of singles and widows among organic farmers (32.5% and 25.0%) and non-applicators (25.0% and 35.7%), in contrast with a larger proportion of married among mixed (69.0%) and conventional farmers (74.4%; Figure 3), (iii) a relatively low number (60.0%) of organic farmers reporting to farm crops as their main occupation (Figure 3), and (iv) a correspondingly lower amount of working-hours per week on the farm for organics (mean 20.9, SD 10.6 h/wk) vs. conventional (mean 35.3, SD 16.9 h/wk; Figure 2). Furthermore, conventional male farmers worked on average 36.9 h/wk (SD 17.5), while their organic male counterparts only worked 16.7 h/wk (SD 10.0; P < .001).
case_images: Socio-demographic characteristics per country and farmer classification. Nationality indicates nationality at birth, and occupation. Other indicates any profession besides crop farmer (see also Table 1).
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case_images: They were mostly married (65.6%) and Ugandan nationals (99%, Figure 3). Figure 2), (ii) a larger proportion of singles and widows among organic farmers (32.5% and 25.0%) and non-applicators (25.0% and 35.7%), in contrast with a larger proportion of married among mixed (69.0%) and conventional farmers (74.4%; Figure 3), (iii) a relatively low number (60.0%) of organic farmers reporting to farm crops as their main occupation (Figure 3), and (iv) a correspondingly lower amount of working-hours per week on the farm for organics (mean 20.9, SD 10.6 h/wk) vs. conventional (mean 35.3, SD 16.9 h/wk; Figure 2). Furthermore, conventional male farmers worked on average 36.9 h/wk (SD 17.5), while their organic male counterparts only worked 16.7 h/wk (SD 10.0; P < .001).
case_images: (a) and (b) Forest plot with coefficients for multivariate robust regression of logarithmic household income and its predictors. (c) and (d) Forest plot with Odds Ratio for multivariate logistic regression of received pesticide training and its predictors. Univariate results can be found in Supplementary Figure SF2.
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case_images: Being more educated and working longer shifts were associated with a higher household income, whereas being single, separated, or divorced was associated with a lower household income (Figure 4). Being more educated, not being a crop farmer as a main occupation, and using mixed farming practices were associated with a higher household income (Figure 4). Studying the determinants of household income, we found associations with farming practice, farm size, education, occupation, working hours and civil status in either or both countries, with effect sizes being larger in Uganda than those in Costa Rica (Figure 4). In the multivariate logistic regression model, the main predictors of whether a farmer had ever received pesticide training were being an organic farmer and being more educated (Figure 4). In the regression model, the best predictors were the application of alternative pest management practices, being more educated, and having more years of experience handling/applying pesticides (Figure 4).
case_images: Socio-economic factors of individual farmers and their farms by country and farming practice.
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case_images: Conventional and mixed farms were closer to rivers compared to their organic and non-applying counterparts (Figure 5). Non-applicators had more years working in agriculture and handling/applying pesticides than farmers from other groups (Figure 5). In both study settings, mixed farms were the largest in size, while organic farms were the smallest and the furthest from water sources (Figure 5).
case_images: (a) Pesticide entry sites into the body. Ears was not an answer option but explicitly mentioned in Uganda by 10.9% of participants. (b) Access and use of PPE.
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case_images: In both countries, most farmers acknowledged that pesticides could affect their health (CR 97.7%; UG 90.4%) and identified dermal, inhalation, and ingestion as the main routes of pesticide exposure to the body (Figure 6a). In Costa Rica, some farmers had access to masks with (32.4%) and without carbon filter (44.1%), glasses (39.3%), gloves (47.8%), rubber aprons (32.4%), and water proof pants (80.2%), but not all of them used them (eg, only 8.5% and 10.9% reported using masks with carbon filter or glasses always or often when applying pesticides, respectively; Figure 6b). In Uganda, farmers' access to PPE was much more limited (eg, only 2.7% and 4.3% had access to masks with carbon filter and glasses, respectively [Figure 6b]).
case_images: Hygiene practices in relation to pesticide application: Bathing and changing clothes after pesticide application and handling, responsible person washing clothes used during pesticide application and handling.
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case_images: More organic farmers (79.3%) reported washing their own clothes compared to mixed (59.0%) and conventional farmers (48.3%, P < .001) (Figure 7).
case_images: Disposal of residual water from pesticide application equipment cleaning and disposal of empty pesticide containers.
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case_images: In both countries, across all farming practices, water used for cleaning pesticide application equipment was mainly disposed in the drain (CR 54.7%), directly in the garden (UG: 46.7%), or elsewhere on the farm directly onto soil (CR: 30.2%; UG: 37.5%) (Figure 8). Empty pesticide containers were either recycled (CR: 77.4%; UG: 32.0%), buried (UG: 42.5%), or burnt (CR: 22.6%; Figure 8). In Uganda, some conventional (13.4%) and mixed farmers (12.0%) left containers behind in their fields (Figure 8).
case_images: Proportion of participants using the listed active ingredients over the last 12 months. In parentheses first the World Health Organization recommended classification of pesticides by hazard (Ia, extremely hazardous; Ib, highly hazardous; II, moderately hazardous; III, slightly hazardous; U, unlikely to present acute hazard in normal use), followed by the Pesticide Action Network grouping for highly hazardous pesticides (1, acute toxicity; 2, long term effects; 3, environmental toxicity; 4, conventions).
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case_images: In Costa Rica, the most commonly applied pesticide active ingredients during the twelve months prior to the study visit were the fungicide chlorothalonil, the herbicides paraquat and glyphosate, and the pyrethroid insecticide cypermethrin (Figure 9). Besides these 3 chemicals, profenofos, and 2,4-D, all other pesticides were used by <11% of participants (Figure 9). Six active ingredients were used in both countries (Figure 9), 3 of which also ranked among the top 5 used pesticides in the country: mancozeb, glyphosate, and cypermethrin.
case_images: Y Axis: Share of participants applying the active ingredient; X Axis: Average exposure duration per active ingredient during the week prior to the 2 study visits; Bubble size: Average area of pesticides applied, in hectares; Color: WHO Toxicity classification.
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case_images: Together with mancozeb and propamocarb, they were also applied for longer periods of time (in h/wk) compared to other pesticides (Figure 10). During the week prior to the each visit, the most frequently applied pesticides were mancozeb and cypermethrin, but the ones applied for the longest periods of time were the herbicides 2,4-D and glyphosate, and the insecticide lambda-cyhalothrin (Figure 10).