Field trials were established at three European sites (Denmark, Eastern France, South-West France) of genetically modified maize (Zea mays L.) expressing the CryIAb Bacillus thuringiensis toxin (Bt), the nearisogenic non-Bt cultivar, another conventional maize cultivar and grass. Soil from Denmark was sampled at sowing (May) and harvest (October) over two years (2002, 2003); from E France at harvest 2002, sowing and harvest 2003; and from SW France at sowing and harvest 2003. Samples were analysed for microbial community structure (2003 samples only) by community-level physiological-profiling (CLPP) and phospholipid fatty acid analysis (PLFA), and protozoa and nematodes in all samples. Individual differences within a site resulted from: greater nematode numbers under grass than maize on three occasions; different nematode populations under the conventional maize cultivars once; and two occasions when there was a reduced protozoan population under Bt maize compared to non-Bt maize. Microbial community structure within the sites only varied with grass compared to maize, with one occurrence of CLPP varying between maize cultivars (Bt versus a conventional cultivar). An overall comparison of Bt versus non-Bt maize across all three sites only revealed differences for nematodes, with a smaller population under the Bt maize. Nematode community structure was different at each site and the Bt effect was not confined to specific nematode taxa. The effect of the Bt maize was small and within the normal variation expected in these agricultural systems.
A major use of crop models is to evaluate management strategies. An important question is how accurate models are for such evaluations. Th e purpose of this study was to determine how to use a combined crop and decision model to evaluate irrigation strategies for corn (maize, Zea mays L.) and to estimate the uncertainty in the criteria used for evaluation. Th e uncertainty estimation has three steps. First, the sources of uncertainty are identifi ed. We considered uncertainty in the model parameters and model residual error. Second, the uncertainty in each source is quantifi ed. We used a Bayesian approach to obtain a posterior distribution of the model parameters and variances of residual error. Finally, the uncertainties are propagated through to the quantities of interest. In our case, this included calculations for observed quantities-these posterior predictive checks allowed us to verify that our uncertainty estimates were reliable-and predictions of the criteria used to evaluate the irrigation strategies. We considered several criteria including multiyear average yield and interannual yield variability. Th e uncertainty in average yield was quite small (standard deviation of about 0.2Mg/ha). Th is is due to the fact that much of the error in yield prediction cancels out when looking at average yield. Th ree major conclusions are that this model can be a powerful tool for evaluating irrigation strategies, that prediction of average yield can have much less uncertainty than prediction of yearly yield, and that it is essential to verify the reliability of uncertainty estimates using data.
The effects of maize expressing the Bacillus thuringiensis Cry1Ab protein (Bt maize) on decomposition processes under three different European climatic conditions were assessed in the field. Farming practices using Bt maize were compared with conventional farming practices using near-isogenic non-Bt maize lines under realistic agricultural practices. The litter-bag method was used to study litter decomposition and nitrogen mineralization dynamics of wheat straw. After 4 months incubation in the field, decomposition and mineralization were mainly influenced by climatic conditions with no negative effect of the Bt toxin on decomposition processes.
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