Spatial simulation model to analyse pollen dispersal and coexistence scenarios between GM and GM-free crops

Developing of appropriate coexistence plan between GM and GM free crops at field and regional level requires knowing the pollen dispersion patterns in relation to cultivated areas, climatic conditions and crop management. Besides that, in many
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    59 Breckling, B., Reuter, H. & Verhoeven, R. (2008) Implications of GM-Crop Cultivation at Large Spatial Scales. Theorie in der …kologie 14. Frankfurt, Peter Lang. Spatial simulation model to analyse pollen dispersal and coexistence scenarios between GM and GM-free crops Simone Gorelli a  , Alessandro Santucci a  , Elena Balducci b  , Marco Mazzoncini b  &  Riccardo Russu c  (  a  Pisa University, Department of Civil Engineering, Pisa, Italy; b  Pisa University, Dep. of Agronomy and Agro-ecosystem Management, Pisa, Italy; c Tuscany Region Ð  Agriculture Development Agency, Florence, Italy. Ð sgorelli@agr.unipi.it). Abstract: Developing of appropriate coexistence plan between GM and GM free crops at field and regional level requires knowing the pollen dispersion patterns in relation to cultivated areas, climatic conditions and crop management. Besides that, in many regions it is also important to know the effect of orography and natural or artificial  barriers presence on gene flow. Because many models donÕt take into account these aspects, a new model based on land morphology (DEM), characteristics of plots (e.g. form, size), characteristics of GM and GM-free crops, presence of natural and anthropo-genic obstacles between fields (e.g. hedges, buildings) and climatic characteristics has  been studied, realized and applied to a case study in Tuscany (Italy). The results obtained from a preliminary application of the new model indicate that it is able to simulate the effect of orography and barriers on air fluxes. In the next future, the model will be validated and calibrated comparing its output with field experiment results.  Keywords:  pollen dispersal simulation model, orography, natural and artificial barriers, coexistence plan. Introduction The first studies concerning pollen dispersal simulation models to analyze the coexist-ence between GM and non-GM crops date back to 1996, when Lavigne and collabor-ators carried out a pollen dispersal model specific for oilseed rape, continuously updated during time (Lavigne et al. 1996, 1998, 2002). Other studies have been realized on oilseed rape later by Colbach and collaborators taking also into account the effect of cropping systems on gene flow (Colbach et al. 2001, 2001a). These kind of models are useful to simulate different contamination scenarios within a region in relation to arable land utilization, GM and non-GM crops presence, their distribution throughout the region, cropping systems and climatic conditions. The output of these models may contribute in defining coexistence measures (Bock et al. 2002). For maize, Klein and collaborators developed a pollen dispersal model applicable to maize crop (Klein et al. 2003). This model has been improved later using research carried out in France generating the MAPOD model that has been continuously implemented and tested both in France and Italy producing good results (Messean 2005; Messean et al. 2003, 2006;  Simone Gorelli et al. 60 INRA 2007; Balducci et al. 2007; Mazzoncini et al. 2007). Despite that, these models do not take into account some aspects like the land morphology (DEM) and the  presence of natural and anthropogenic obstacles between fields (e.g. hedges, buildings) that can play an important role in pollen dispersal within some environmental contexts such many areas of Central Italy. The landscape in these regions is characterized by hilly and flat areas with different cultivations, forests, lines of trees and hedgerows. Beside that the arable lands are shared out and scattered all over the territory producing a lot of small homogeneous crop units. In this framework, it is very important to analyse the gene contamination risks in relation with the altimetry of different cultivation plots and the presence of obstacles in order to plan adequate coexistence measures and appropriate monitoring plans. The authors describe a new spatial simulation model, based on the integration of Territorial Information System (TIS) with the Calmet model, able to evaluate also the effect of the orography and barriersÕ presence on crops gene flux. In this paper, the structure of the model is described and a preliminary application of the model at territorial scale is discussed disregarding the crop gene fluxes but taking into account only the capability of the model of simulating different air fluxes in relation to mountain  presence, valley breezes and barriersÕ presence. Methods The spatial simulation model built up takes into account all territoryÕs information available file in an appropriate Territorial Information System (TIS), thus data set recognition is the first step of our study. The data set is catalogued within the TIS in two different groups: !    Real-time data:  characterized by an high frequency of updating (e.g. every hour, every day) such as: pluviometric data (mm of rainfall); thermometric data (T max, T min, T med); wind data (direction and intensity); humidity data (relative humidity). !   Off-time data:  characterized by a low frequency of updating (e.g. ones a year) Ð Digital Elevation Model (DEM); Digital Terrain Model (DTM); vectorial data of the  barriersÕ presence (windbreak, hedges, etc.); cultivated plots (GM and non-GM  plots, crops variety, flowering date, etc.). The second step consists of a pollen spreading analysis carried out with a meteoro-logical diagnostic model named Calmet (Scire et al. 2000). The main algorithms used  by the model are: !    Diagnostic module for the reconstruction of the wind field: it includes parametric algorithms to evaluate local orography effects (e.g. mountain presence, valley  breeze, kinematic effects), insertion of surface observations and their extrapolation in altitude. In detail, the analysis of kinematic effects of terrain is based on the approach of Liu and Yocke (1980) and is carried out independently for each hour. !   M icrometeorological module (Holtslag & van Ulden 1983): starting from standard meteorological parameters (wind, clouds high and presence, temperature, pressure,  Spatial simulation model to analyse pollen dispersal 61 humidity) and soil information (orography, land use, roughness), the module assesses the net radiation and the superficial energetic balance and starting from these data it describes the turbulences. In practice, using the Calmet model it is possible to characterize the wind field and the turbulences generate by local orography. Finally, all the data processed by Calmet (wind field) are imported as input file in a GIS system, that works with this file coupling with the other territorial data include in the TIS, cited in the first step. This coupling generates the output of the model: the pollen spreading map and the plots contamination map. The first one shows the average wind field and the pollen spreading in the area. The latter indicates the pollen flux from plot to plot according to specific wind fields obtained by taking into account the presence of natural and anthropic  barriers and other parameters such as: the differences in flowering time between GM and non-GM crops, the inclusion of buffer zone and rows of non-GM crops. The data  processed by Calmet and the TIS data set are elaborated by geo-processing that utilized an algorithm ÒifÉthenÓ. In figure 1 the logic scheme of the model is shown. Fig. 1. Logic scheme of spatial simulation model.  Simone Gorelli et al. 62 The model has been applied to a representative area for maize cultivation in Tuscany Region (Italy), the Chiana Valley (in the ArezzoÕs province) that represents 26 % of the entire surface cultivated with maize in Tuscany. Maize has been chosen as case study  because it is the more potentially cultivable GM-crop in Italy. More specifically, the  preliminary applications of the model have been performed using the input data set relative to the area of Marciano della Chiana community, where the ARSIA (Agricul-ture Development Agency) experimentation centre is located. For this simulation we used the 10 years meteorological data set (from 1996 to 2006), cultivated maize plot data referred to 2006 and natural and anthropic barriers data obtained by photo-inter- pretation of aerial-photos referred to 2005. Fig. 2. Particular of the plot contamination map of Marciano della Chiana (windÕs effect both left and right, and barrierÕs effect on the right). Fig. 3. Zoom of the plots contamination map of Marciano della Chiana (Arezzo) with presence of barrier (scale: 1:10.000).  Spatial simulation model to analyse pollen dispersal 63 Results The plot contamination map of Marciano della Chiana shows wind and barrier effects (Fig. 2, 3). The figures underline the effects of barriers and of a hedgerow with medium-high porosity and medium height (about 10 m) on hampering pollen flux coming from the GM-plot. However, it is necessary to take into account that the Òhedgerow effectÓ is inversely proportional to the porosity and the height of the barrier and to the wind speed. Fig. 4: Three-dimensional representation of the plot contamination map with presence of barrier. Fig. 5: Vertical profile of the potentially contaminated area with presence of barrier and with consideration of the altitude differences between plots. The three-dimensional representation of the plots contamination map (Fig. 4) highlights the importance of the vertical profile (Fig. 5) in relation to the potentially contaminated areas with consideration of the altitude differences between plots. The results show that  barriers or/and different altitudes between GM and GM-free plots can determine the  presence of Òshade conesÓ or areas where the GM pollen deposition is low or poor. These aspects are of fundamental importance in relation to the specific environmental
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