Five countries' standards and procedures for testing pesticide residues.

"[Report to the] ranking minority member, Subcommittee on Agricultural Research and General Legislation, Committee on Agriculture, Nutrition, and Forestry, United States Senate"--P. [1].

Can we cook away veterinary drug residues in food?

When most people think of food safety they think of food poisoning and bacteria. They also, one hopes, generally follow the well-understood public advice on bacterial risks and store their food properly and cook it thoroughly. But what about chemical risks in food? Do many consumers ask the question “if drug residues are in my food, does cooking make it safe?” Or do they assume that following the good advice on bacterial risks also affords some protection against the health risks of chemical contaminants? In this short report we highlight some difficulties in assessing the stability of veterinary drug residues during cooking and summarise our cooking studies on anthelmintics, nitroimidazoles and nitrofuran residues in various foods. safefood Knowledge Networks http://safefood.ning.com/

Dual layer solid phase extraction based on molecular imprinting technology: Seeking a route to enhance selectivity for trace analysis of pesticide residues in olive oil.

Aiming to introduce a multiresidue analysis for the trace detection of pesticide residues belonging to organophosphorus and triazine classes from olive oil samples, a new sample preparation methodology comprising the use of a dual layer of “tailor-made” molecularly imprinted polymers (MIPs) SPE for the simultaneous extraction of both pesticides in a single procedure has been attempted. This work has focused on the implementation of a dual MIP-layer SPE procedure (DL-MISPE) encompassing the use of two MIP layers as specific sorbents. In order to achieve higher recovery rates, the amount of MIP layers has been optimized as well as the influence of MIP packaging order. The optimized DL-MISPE approach has been used in the preconcentration of spiked organic olive oil samples with concentrations of dimethoate and terbuthylazine similar to the maximum residue limits and further quantification by HPLC. High recovery rates for dimethoate (95%) and terbuthylazine (94%) have been achieved with good accuracy and precision. Overall, this work constitutes the first attempt on the development of a dual pesticide residue methodology for the trace analysis of pesticide residues based on molecular imprinting technology. Thus, DL-MISPE constitutes a reliable, robust, and sensitive sample preparation methodology that enables preconcentration of the target pesticides in complex olive oil samples, even at levels similar to the maximum residue limits enforced by the legislation.

An innovative rapid method for analysis of 10 organophosphorus pesticide residues in wheat by HS-SPME-GC-FPD/MSD

Pesticide Food Safety Act of 1988 [microform] : hearings before the Subcommittee on Department Operations, Research, and Foreign Agriculture of the Committee on Agriculture, House of Representatives, One Hundredth Congress, second session, on H.R. 4937, July 28 and September 7, 1988.

"Serial no. 100-99."

EDUCATION, TRAINING AND AWARENESS OF LAWS AS DETERMINANTS OF COMPLIANCE WITH PLANT PROTECTION LAW: THE CASE OF PESTICIDE USE PRACTICES IN TANZANIA

Pesticide residues in food and environment pose serious health risks to human beings. Plant protection laws, among other things, regulate misuse of agricultural pesticides. Compliance with such laws consequently reduces risks of pesticide residues in food and the environment. Studies were conducted to assess the compliance with plant protection laws among tomato farmers in Mvomero District, Morogoro Region, Tanzania. Compliance was assessed by examining pesticide use practices that are regulated by the Tanzanian Plant Protection Act (PPA) of 1997. A total of 91 tomato farmers were interviewed using a structured questionnaire. Purposive sampling was used in selecting at least 30 respondent farmers from each of the three villages of Msufini, Mlali and Doma in Mvomero District, Morogoro Region. Simple Random Sampling was used to obtain respondents from the sampling frame. Individual and social factors were examined on how they could affect pesticide use practices regulated by the law. Descriptive statistics, mainly frequency, were used to analyze the data while associations between variables were determined using Chi-Square and logistic regression model. The results showed that respondents were generally aware of the existence of laws on agriculture, environment and consumer health, although none of them could name a specific Act. The results revealed further that 94.5% of the farmers read instructions on the pesticides label. However, only 21% used the correct doses of pesticides, 40.7% stored pesticides in special stores, 68.1% used protective gear, while 94.5% always read instructions on the label before using a pesticide product. Training influenced the application rate of pesticide (p < 0.001) while awareness of agricultural laws significantly influenced farmers’ tendency to read information on the labels (p < 0.001). The results showed further that education significantly influenced the use of protective gears by farmers (p = 0.042). Education also significantly affected the manner in which farmers stored pesticide-applying equipment (p = 0.024). Furthermore, farmers’ awareness of environmental laws significantly (p = 0.03) affected farmers’ disposal of empty pesticide containers. Results of this study suggest the need for express provisions on safe use and handling of pesticides and related offences in the Act, and that compliance should be achieved through education rather than coercion. Results also suggest establishment of pesticide disposal mechanisms and structures to reduce unsafe disposal of pesticide containers. It is recommended that farmers should be educated and trained on proper use of pesticides. Farmers’ awareness on laws affecting food, environment and agriculture should be improved.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

Endrin in forest streams after aerial seeding with endrin-coated Douglas-fir seed /

Mode of access: Internet.

The regulation of pesticides in the United States /

Cover title.

EPA pesticide regulatory program study [microform] : hearing before the Subcommittee on Department Operations, Research, and Foreign Agriculture of the Committee on Agriculture, House of Representatives, Ninety-seventh Congress, second session, December 17, 1982.

"Serial no. 97-NNNN."

Disposal of dilute pesticide solutions /

Mode of access: Internet.

QuEChERS: um método moderno de preparo de amostra para determinação multirresíduo de pesticidas em alimentos por métodos cromatográficos acoplados à espectrometria de massas

This review attempts to provide an updated overview of the Quick, Easy, Cheap, Effective, Ruged and Safe (QuEChERS) multiresidue extraction method, that involves initial extraction in acetonitrile, an extraction/partition step after the addition of salt, and a cleanup step utilizing dispersive solid phase extraction. QuEChERS method is nowadays the most applied extraction method for the determination of pesticide residues in food samples, providing acceptable recoveries for acidic, neutral and basic pesticides. Several applications for various food matrices (fruits, vegetables, cereals and others) in combination with chromatographic mass spectrometry analysis were presented.

Application of radiochemistry techniques in food processing research : radioisotopic tracer techniques in evaluation and improvement of industry practices for removal of pesticide residues from foods : annual report no. 4 /

"D-1548."

Application of radiochemistry techniques in food processing research : radioisotopic tracer techniques in evaluation and improvement of industry practices for removal of pesticide residues from foods : annual report /

"TID-4500."