non-GM Maize Fields Research Articles

Zea maize reference materials for genetically modified organism detection in Mexico.

Zea maize reference materials for genetically modified organism detection in Mexico.

Sampling Strategies for Evaluating the Rate of Adventitious Transgene Presence in Non-Genetically Modified Crop Fields.

According to E.U. regulations, the maximum allowable rate of adventitious transgene presence in non-genetically modified (GM) crops is 0.9%. We compared four sampling methods for the detection of transgenic material in agricultural non-GM maize fields: random sampling, stratified sampling, random sampling + ratio reweighting, random sampling + regression reweighting. Random sampling involves simply sampling maize grains from different locations selected at random from the field concerned. The stratified and reweighting sampling methods make use of an auxiliary variable corresponding to the output of a gene-flow model (a zero-inflated Poisson model) simulating cross-pollination as a function of wind speed, wind direction, and distance to the closest GM maize field. With the stratified sampling method, an auxiliary variable is used to define several strata with contrasting transgene presence rates, and grains are then sampled at random from each stratum. With the two methods involving reweighting, grains are first sampled at random from various locations within the field, and the observations are then reweighted according to the auxiliary variable. Data collected from three maize fields were used to compare the four sampling methods, and the results were used to determine the extent to which transgene presence rate estimation was improved by the use of stratified and reweighting sampling methods. We found that transgene rate estimates were more accurate and that substantially smaller samples could be used with sampling strategies based on an auxiliary variable derived from a gene-flow model.

Design of a decision support tool for managing coexistence between genetically modified and conventional maize at farm and regional levels

Over the last 15years, several studies on coexistence have used simulation results of spatially explicit gene flow models. These models predict the adventitious presence (AP) of GM grains in non-GM fields at the landscape scale. However, result uncertainty is not quantified. Moreover, most of the models require an important amount of input data on climate, land use, and crop management practices which might not always be available. A comprehensive Bayesian statistical approach has been implemented in the case of gene flow. This approach makes it possible to inform the decision-maker on AP, whatever the amount of information available in a given situation, to provide information on the uncertainty of the predictions and to model the variability of AP within a field, which helps set up sampling strategies.The resulting decision-support tool (DST) can compute the expected AP and its probability distribution in non-GM maize fields at different times of the growing season and under different management scenarios. Integrated through a web interface, the DST is designed to be operationally helpful for managing coexistence between GM and non-GM maize crops for a wide range of stakeholders from farmers to policy makers.

Using relational decision trees to model out-crossing rates in a multi-field setting

Nearly three-quarters of the genetically modified maize (the insect resistant type MON 810, also called Bt maize) produced in the EU are cultivated in Spain, where the share of Bt maize cultivation in some regions (Catalonia) is very high (above 70%). In order to ensure coexistence with the production of conventional maize and satisfy the 0.9% EU threshold for adventitious presence of authorized genetically modified (GM) material in conventional (non-GM) maize crops, a set of preventive coexistence measures must be applied. These measures usually include the setup of large and fixed isolation distances, pollen barriers, flowering coincidence, crop rotation and other measures, which are very hard to fulfill in a multi-field setting. Basic empirical and modeling studies that explore the feasibility of coexistence between GM and non-GM crops focus on pair-based interactions between fields while multi-field studies build upon them, attempting to consider the complexity of gene flow under crop management practices.In this study, we use the methodology of relational data mining (which can take into account several coexistence measures at the same time) to predict gene flow from GM to non-GM maize fields under multi-field crop management practices at a local scale. The approach extends the pair-based assessments of out-crossing rate by considering all neighboring fields within the entire study area, along with the farming practices applied to them. The estimation of the out-crossing rates is performed by using a PostgreSQL relational database that is analyzed with the algorithm TILDE for building relational classification trees. In building the trees, TILDE explores the relations describing spatial aspects, maize flowering and crop management practices for the 400ha maize oriented production area Pla de Foixà in Catalonia, Spain, in the period 2004–2006.Our approach proposes a new methodology to predict the level of adventitious presence on a multi-field setting, where the influence of more than one GM field is considered at the same time. The structure of the obtained models can be used in the design of coexistence measures of the second generation, which should not be used individually but treated as synergetic coexistence measures, offering different alternatives to achieve a particular coexistence threshold (e.g., 0.9%, 0.45%, or 0.1%). The possibility to consider multiple measures simultaneously makes farmers more flexible in their management decisions as compared to the rigid use of isolation distance only, which is currently the most commonly recommended coexistence measure.

Spatial sensitivity of maize gene-flow to landscape pattern: a simulation approach

Pollen dispersal is a critical process defining connectivity among plant populations. In the context of genetically modified (GM) crops in conventional agricultural systems, strategies based on spatial separation are promoted to reduce functional connectivity between GM and non-GM crop fields. Field experiments as well as simulation studies have stressed the dependence of maize gene flow on distances between source and receptor fields and on their spatial configuration. However, the influence of whole landscape patterns is still poorly understood. Spatially explicit models, such as MAPOD-maize, are thus useful tools to address this question. In this paper we developed a methodological approach to investigate the sensitivity of cross-pollination rates among GM and non-GM maize in a landscape simulated with MAPOD-maize. The influence of landscape pattern on model output was studied at the landscape and field scales, including interactions with other model inputs such as cultivar characteristics and wind conditions. At the landscape scale, maize configuration (proportion of and spatial arrangement in a given field pattern) was shown to be an important factor influencing cross-pollination rate between GM and non-GM maize whereas the effect of the field pattern itself was lower. At the field scale, distance to the nearest GM maize field was confirmed as a predominant factor explaining cross-pollination rate. The metrics describing the pattern of GM maize in the area surrounding selected non-GM maize fields appeared as pertinent complementary variables. In contrast, field geometry and field pattern resulted in little additional information at this scale.

Feasibility of isolation perimeters for genetically modified maize

Using geographic information system datasets and Monte Carlo simulations, this study investigates to what extent the intra-regional variability in maize share and field distribution affects the feasibility of implementing isolation perimeters between genetically modified (GM) and non-GM maize fields. More specifically, five scenarios differing in shares and spatial distributions of GM maize were tested for various isolation perimeters in six agricultural areas in Flanders (Belgium). Proportions of non-GM maize fields and farmers having at least one non-GM maize field occurring within isolation perimeters were calculated to assess how spatial co-existence measures would affect the freedom of choice of neighbouring farmers to grow non-GM maize on their fields. Irrespective of the scenario tested, our results demonstrated that the proportions of non-GM maize fields and their corresponding farmers falling within the isolation perimeters are approximately two to eight times higher in areas with the highest maize share than in areas with the lowest maize share. The higher the share of GM maize and the wider the imposed isolation perimeter, the lower the intra-regional differences became. Hence, those findings confirm that farmers will not be equally affected by isolation perimeters, indicating the importance of considering intra-regional differences in the choice of appropriate spatial co-existence measures. Since uniform and wide isolation perimeters tend to be difficult to implement in practice and hardly reflect the diversity of the agricultural landscape, relying on flexible or combining various co-existence measures is worthwhile considering. As an alternative to single co-existence measures for limiting the GM input from cross-fertilisations between neighbouring maize fields, the appropriateness of other on-farm co-existence measures is discussed for Flemish agricultural conditions. Proposing the most appropriate co-existence measures on a case-by-case basis may be one step forward in reaching proportionate, fair and consistent co-existence at the regional and landscape level.

A method to search for optimal field allocations of transgenic maize in the context of co-existence

Spatially isolating genetically modified (GM) maize fields from non-GM maize fields is a robust on-farm measure to keep the adventitious presence of GM material in the harvest of neighboring fields due to cross-fertilizations below the European labeling threshold of 0.9%. However, the implementation of mandatory and rigid isolation perimeters can affect the farmers' freedom of choice to grow GM maize on their fields if neighboring farmers do not concur with their respective cropping intentions and crop plans. To minimize the presence of non-GM maize within isolation perimeters implemented around GM maize fields, a method was developed for optimally allocating GM maize to a particular set of fields. Using a Geographic Information System dataset and Monte Carlo analyses, three scenarios were tested in a maize cultivation area with a low maize share in Flanders (Belgium). It was assumed that some farmers would act in collaboration by sharing the allocation of all their arable land for the cultivation of GM maize. From the large number of possible allocations of GM maize to any field of the shared pool of arable land, the best field combinations were selected. Compared to a random allocation of GM maize, the best field combinations made it possible to reduce spatial co-existence problems, since at least two times less non-GM maize fields and their corresponding farmers occurred within the implemented isolation perimeters. In the selected field sets, the mean field size was always larger than the mean field size of the common pool of arable land. These preliminary data confirm that the optimal allocation of GM maize over the landscape might theoretically be a valuable option to facilitate the implementation of rigid isolation perimeters imposed by law.

Implementing isolation perimeters around genetically modified maize fields

Due to the growing cultivation area of genetically modified (GM) maize and the rising number of GM maize varieties commercially available to European farmers, the co-existence between GM and non-GM maize is becoming a burning issue in some European regions. Hence, Member States are imposing or discussing specific co-existence measures to keep the adventitious presence of GM material in non-GM produces below the established labelling threshold. As maize is a cross-pollinated crop that uses wind for the dispersal of its pollen, on-farm co-existence measures may rely on the spatial isolation of GM and non-GM maize fields. In this study, we developed an approach that combines geographic information system (GIS) datasets with Monte Carlo simulations to assess the feasibility of implementing isolation perimeters around GM maize fields, since its practical implementation is rarely addressed in the co-existence debate. More specifically, five scenarios differing in shares and spatial distributions of GM maize were tested for various isolation perimeters in two agricultural areas in Flanders (Belgium). The GIS analyses emphasised the small size of maize fields and their scattered distribution throughout the cropped area. The feasibility of implementing isolation perimeters was largely affected by the (GM) maize share, the spatial distribution of GM maize, and the width of isolation perimeters. The higher the (GM) maize share and the wider the isolation perimeter, the higher the proportions of farmers with non-GM maize fields occurring within the implemented isolation perimeter. Compared with randomly distributed GM maize fields, the clustering of GM maize fields on a larger scale and at the farm level increased the feasibility of implementing isolation perimeters. The approach developed proved to be a valuable tool to quantify the feasibility of implementing isolation perimeters under real agricultural conditions.