Pesticide use and fatal injury among farmers in the Agricultural Health Study
ABSTRACT PURPOSE: To assess whether pesticide use practices were associated with injury mortality among 51,035 male farmers from NC and IA enrolled in the Agricultural Health Study. METHODS: We used Cox proportional hazards models adjusted for age and state to estimate fatal injury risk associated with self-reported use of 49 specific pesticides, personal protective equipment, specific types of farm machinery, and other farm factors collected 1-15 years preceding death. Cause-specific mortality was obtained through linkage to mortality registries. RESULTS: We observed 338 injury fatalities over 727,543 person-years of follow-up (1993-2008). Fatal injuries increased with days/year of pesticide application, with the highest risk among those with 60+ days of pesticide application annually [hazard ratio (HR) = 1.87; 95% confidence interval (CI) = 1.10, 3.18]. Chemical-resistant glove use was associated with decreased risk (HR = 0.73; 95% CI = 0.58, 0.93), but adjusting for glove use did not substantially change estimates for individual pesticides or pesticide use overall. Herbicides were associated with fatal injury, even after adjusting for operating farm equipment, which was independently associated with fatal injury. Ever use of five of 18 herbicides (2,4,5-T, paraquat, alachlor, metribuzin, and butylate) were associated with elevated risk. In addition, 2,4-D and cyanazine were associated with fatal injury in exposure-response analyses. There was no evidence of confounding of these results by other herbicides. CONCLUSION: The association between application of pesticides, particularly certain herbicides, and fatal injuries among farmers should be interpreted cautiously but deserves further evaluation, with particular focus on understanding timing of pesticide use and fatal injury.
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ABSTRACT: The study provides a novel model and more comprehensive estimates of the burden of occupational morbidity and mortality in food-related industries, using a farm-to-table approach. The authors analyzed 2008 to 2010 US Bureau of Labor Statistics data for private industries in the different stages of the farm-to-table model (production, processing, distribution and storage, and retail and preparation). The morbidity rate for food system industries was significantly higher than the morbidity rate for nonfood system industries (rate ratio = 1.62; 95% confidence interval = 1.30 to 2.01). Furthermore, the occupational mortality rate for food system industries was significantly higher than the national nonfood occupational mortality rate (rate ratio = 9.51; 95% confidence interval = 2.47 to 36.58). This is the first use of the farm-to-table model to assess occupational morbidity and mortality, and these findings highlighting specific workplace hazards across food system industries.141st APHA Annual Meeting and Exposition 2013; 11/2013
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ABSTRACT: Weed dynamics models are needed to design innovative weed management strategies. Here, we developed a 3D individual-based model called FlorSys predicting growth and development of annual weeds and crops as a function of daily weather and cropping practices: (1) crop emergence is driven by temperature, and emerged plants are placed onto the 3D field map, depending on sowing pattern, density, and emergence rate; plants are described as cylinders with their leaf area distributed according to height; (2) weed emergence is predicted by an existing submodel, emerged weed seedlings are placed randomly; (3) plant phenology depends on temperature; (4) a previously developed submodel predicts available light in each voxel of the canopy; after emergence, plant growth is driven by temperature; when shaded, biomass accumulation results from the difference between photosynthesis and respiration; shading causes etiolation; (5) frost reduces biomass and destroys plants, (6) at plant maturity, the newly produced seeds are added to the soil seed bank. The model was used to test different sowing scenarios in an oilseed rape/winter wheat/winter barley rotation with sixteen weed annuals, showing that (1) crop yield loss was negatively correlated to weed biomass averaged over the cropping season; (2) weed biomass was decreased by scenarios allowing early and homogenous crop canopy closure (e.g. reduced interrows, increased sowing density, associated or undersown crops), increased summer fatal weed seed germination (e.g. delayed sowing) or, to a lesser degree, cleaner fields at cash crop sowing (e.g. sowing a temporary cover crop for “catching” nitrogen); (3) the scenario effect depended on weed species (e.g. climbing species were little affected by increased crop competition), and the result thus varied with the initial weed community (e.g. communities dominated by small weed species were hindered by the faster emergence of broadcast-sown crops whereas taller species profited by the more frequent gap canopies); (4) the effect on weed biomass of sowing scenarios applied to one year was still visible up to ten years later, and the beneficial effect during the test year could be followed by detrimental effects later (e.g. the changed tillage dates accompanying catch crops reduced weed emergence in the immediately following cash crop but increased seed survival and thus infestation of the subsequent crops). This simulation showed FlorSys to predict realistic potential crop yields, and the simulated impact of crop scenarios was consistent with literature reports.European Journal of Agronomy 02/2014; 53:74–89. DOI:10.1016/j.eja.2013.09.019 · 2.70 Impact Factor
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ABSTRACT: Weeds are both harmful for agricultural production and an essential component of biodiversity in agricultural landscapes. Therefore, new cropping systems aiming at both maximising weed-related biodiversity and minimising weed harmfulness are needed. New cropping systems are now increasingly designed with weed dynamics models but these usually only consider weed densities or crop yield losses. The present paper proposed a set of indicators for assessing the impact on crop production and biodiversity of weed communities simulated with a cropping system model. Five harmfulness indicators were developed to take account of the criteria most frequently listed by farmers via an internet survey: (1) crop yield loss, (2) harvest pollution by weed seeds, stems and leaves, (3) harvesting problems due to green weed biomass blocking the combine, and (4) field infestation represented by weed biomass averaged over cropping seasons. A fifth indicator was added, i.e. (5) the increase in crop disease (i.e. take-all disease of cereals) in the presence of weeds. The biodiversity indicators were chosen in collaboration with ecologists. Two indicators reflect the weed contribution to vegetal biodiversity: (1) species richness and (2) Pielou's index for species equitability. Three other indicators were developed to assess weeds as a trophic resource for other organisms in the agro-ecosystems: (3) the number of weed seeds present on soil surface in autumn and winter to feed field birds, (4) lipid-rich seeds on soil surface in summer to feed insects such as carabids, and (5) weed flowers in spring and summer to feed domestic bees. These indicators were tested in a series of contrasted cropping systems identified in farm surveys and simulated with FLORSYS. Analyses of variance showed that the cropping system and the crop sequence presented the highest impact on indicator values. Weather scenario and pedoclimate had little effect. Antagonisms and synergies between weed-related harmfulness and biodiversity were identified with Spearman correlations. Harmfulness indicators were all positively correlated, except for additional disease risk which was at best poorly correlated with other indicators. Most weed-related biodiversity indicators were also positively correlated, except species richness which was negatively correlated with species equitability, bird resource and insect resource. Weed harmfulness generally increased with increasing weed-related biodiversity. These correlations were though weak, and others were negative, showing that increased biodiversity could occur with decreased harmfulness (e.g. trophic resource for insects vs. yield loss or field infestation). Consequently, there are cropping systems that reconcile agricultural production and biodiversity.Ecological Indicators 01/2015; 48:157–170. DOI:10.1016/j.ecolind.2014.07.028 · 3.44 Impact Factor