Hélène Raynal

Occupational exposure to pesticides and risk of hematopoietic cancers: meta-analysis of case-control studies

ObjectiveIn this study we conducted a meta-analysis of 13 case–control studies that examined the occurrence of hematopoietic cancers in pesticide related occupations in order to undertake a qualitative and quantitative evaluation of a possible relationship.MethodsPubmed databases were searched for case–control studies published between 1990 and 2005 investigating the relation between hematopoietic cancers and occupational exposure to pesticides. Fixed and random effect meta-analysis models were used depending on the presence of heterogeneity between studies.ResultsThe overall meta-odds ratio obtained after pooling 44 ORs from 13 studies was 1.3 (95% CI: 1.3–1.5). We realized stratified analysis on three different types of hematopoietic cancers (non-Hodgkin lymphoma (NHL), leukemia and multiple myeloma). A significant increased risk of NHL was found (OR = 1.35; 95% CI = 1.2–1.5). Moreover, increased risks of Leukemia (OR = 1.35; 95% CI = 0.9–2) and multiple myeloma (OR = 1.16; 95% CI = 0.99–1.36) were also detected but these results were not statistically significant. Significant heterogeneity existed among the different studies and a publication bias was detected. Therefore, a meta-regression was carried out. Our results showed that a long period of exposure (more than 10 years) provided an increase in the risk of all hematopoietic cancers and for NHL by fractions of 2.18 (95% CI = 1.43–3.35) and 1.65 (95% CI = 1.08–2.51), respectively. Conclusions: The overall meta-odds ratio suggests that there is a significantly positive association between occupational exposure to pesticides and all hematopoietic cancers as well as NHL. A major limitation of our meta-analysis is the lack of sufficient data about exposure information and other risk factors for hematopoietic cancer (genetic predisposition, ethnic origin, immunodepression…). In addition, data concerning specific subtypes of hematopoietic cancers are often confusing. Thus, future epidemiological studies should undertake a major effort to assess the identity and the level of pesticides exposure and should control for the most likely potential confounders.

Exposure to pesticides and risk of childhood cancer: a meta-analysis of recent epidemiological studies.

Objectives The authors performed a meta-analysis of case–control and cohort studies to clarify the possible relationship between exposure to pesticides and childhood cancers. Methods Two cohort and 38 case–control studies were selected for the first meta-analysis. After evaluating homogeneity among studies using the Cochran Q test, the authors calculated a pooled meta-OR stratified on each cancer site. The authors then constructed a list of variables believed to play an important role in explaining the relation between parental exposure to pesticide and childhood cancer, and performed a series of meta-analyses. The authors also performed a distinct meta-analysis for three cohort studies with RR data. Results Meta-analysis of the three cohort studies did not show any positive links between parental pesticide exposure and childhood cancer incidence. However, the meta-analysis of the 40 studies with OR values showed that the risk of lymphoma and leukaemia increased significantly in exposed children when their mother was exposed during the prenatal period (OR=1.53; 95% CI 1.22 to 1.91 and OR=1.48; 95% CI 1.26 to 1.75). The risk of brain cancer was correlated with paternal exposure either before or after birth (OR=1.49; 95% CI 1.23 to 1.79 and OR=1.66; 95% CI 1.11 to 2.49). The OR of leukaemia and lymphoma was higher when the mother was exposed to pesticides (through household use or professional exposure). Conversely, the incidence of brain cancer was influenced by the fathers exposure (occupational activity or use of household or garden pesticides). Conclusion Despite some limitations in this study, the incidence of childhood cancer does appear to be associated with parental exposure during the prenatal period.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems

Due to significant changes in agro-ecological contexts, farmers need new solutions to produce goods. Modelling complements field experiments in the design of new farming systems. French researchers involved in such design issues developed a specific modelling platform to help model, simulate and evaluate cropping systems. After testing several existing environments, the RECORD platform was developed under the VLE environment, allowing the design of atomic and coupled models. It integrates different time steps and spatial scales and proposes some standard formalisms used to model agro-ecosystems (e.g. difference equations, differential equations, state charts...). A graphic user interface was designed to simplify coding tasks. A variety of research projects already use this platform. Examples are given showing the ability to recode simple models, encapsulate more complex models, link with GIS and databases, and use the R statistical package to run models and analyse simulation outputs. The option to use web interfaces enables application by non-scientist end-users. As the models follow a given standard, they can be placed in a repository and used by other researchers. Linking RECORD to other international platforms is now a compelling issue.

The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)...

MDE in Practice for Computational Science

The complex problems that computational science addresses are more and more benefiting from the progress of computing facilities (simulators, librairies, accessible languages,. . .). Nevertheless , the actual solutions call for several improvements. Among those, we address in this paper the needs for leveraging on knowledge and expertise by focusing on Domain-Specific Mod-eling Languages application. In this vision paper we illustrate, through concrete experiments, how the last DSML research help getting closer the problem and implementation spaces.

A new plug-in under RECORD to link biophysical and decision models for crop management

Developing sustainable crop systems is a major challenge. Presently, management practices are simulated using either biophysical models or simple farmer decision models. As a result, there is a lack of generic models integrating both biophysical parameters and farmer decision parameters. Here, we developed an original graphical plug-in to sketch and implement decision-making models and to link them with biophysical models. For that, we used the RECORD platform, standing for REnovation and COORDination of agro-ecosystem modeling. Different pop-up windows allow to create the model using a decision formalism then to implement the model under the RECORD platform. The sequence of technical operations is formally modeled as a direct multi-graph without retroaction. The plug-in allows defining activities, relation between activities, and decision rules to trigger the different activities. The resulting model is independent of any biophysical model and can then be linked with different crop models. An example is given on an innovative cropping systems part of the MicMac-Design project. The decision-making model is then linked with the STICS crop model.

The PERPHECLIM ACCAF Project – perennial fruit crops and forest phenology evolution facing climatic changes

Phenology is a bio-indicator of climate evolution. Measurements of phenological stages on perennial species provide actually significant illustrations and assessments of the impact of climate change. Phenology is also one of the main key characteristics of the capacity of adaptation of perennial species, generating questions about its consequences on plant growth and development or on fruit quality. Predicting phenology evolution and adaptive capacities of perennial species needs to override three main methodological limitations: 1) existing observations and associated databases are scattered and sometimes incomplete, rendering difficult implementation of multi-site study of genotype-environment interaction analyses 2) there are not common protocols to observe phenological stages 3) access to generic phenological models platforms is still very limited. In this context, the PERPHECLIM project, which is funded by the Adapting Agriculture and Forestry to Climate Change Meta-Program ( ACCAF) from INRA ( French National Institute of Agronomic Research), aims to develop the necessary infrastructure at INRA level ( observatories, information system, modeling tools) to enable partners to study the phenology of various perennial species ( grapevine, fruit trees and forest trees). Currently, the PERPHECLIM project involves 28 research units in France, mainly from INRA institutes. Five activities have been developed: define protocols and observation forms to observe phenology for various species of interest for the project : organize observation training, develop generic modeling solutions to simulate phenology ( Phenological Modelling Platform software and modelling platform solutions), support the building of research projects at national and international levels, develop environment/genotype observation networks for fruit-tree species, and develop an information system to manage data and documentation concerning phenology. Finally, the PERPHECLIM project aims to build strong collaborations with public ( Observatoire des Saisons) and private ( technical institutes) sector partners in order to allow a more direct transfer of knowledge.

A new graphical modeling plugin based on Forrester diagrams for the RECORD platform

Using graphic user interfaces facilitates modeling work.Different types of diagrams exist to represent complex dynamic systems.The plugin allows translating Forrester diagram to DEVS ordinary differential equation. RECORD is a modeling and simulation platform with VLE as the main software that combines different forms of equations to represent agrosystem functioning. Although RECORD has a graphic user interface based on box-and-arrow diagrams to develop models, members of modeling communities who prefer Forrester diagrams may become disoriented when using RECORD. To help them, we developed a plugin that displays models as Forrester diagrams and translates them into DEVS formalism.