The European Union Effort Sharing Regulation (ESR) will require a 30% reduction in greenhouse gas (GHG) emissions by 2030 compared with 2005 from the sectors not included in the European Emissions Trading Scheme, including agriculture. This will require the estimation of current and future emissions from agriculture, including dairy cattle production systems. Using a farm-scale model as part of a Tier 3 method for farm to national scales provides a more holistic and informative approach than IPCC (2006) Tier 2 but requires independent quality control. Comparing the results of using models to simulate a range of scenarios that explore an appropriate range of biophysical and management situations can support this process by providing a framework for placing model results in context. To assess the variation between models and the process of understanding differences, estimates of GHG emissions from four farm-scale models (DairyWise, FarmAC, HolosNor and SFARMMOD) were calculated for eight dairy farming scenarios within a factorial design consisting of two climates (cool/dry and warm/wet)×two soil types (sandy and clayey)×two feeding systems (grass only and grass/maize). The milk yield per cow, follower:cow ratio, manure management system, nitrogen (N) fertilisation and land area were standardised for all scenarios in order to associate the differences in the results with the model structure and function. Potential yield and application of available N in fertiliser and manure were specified separately for grass and maize. Significant differences between models were found in GHG emissions at the farm-scale and for most contributory sources, although there was no difference in the ranking of source magnitudes. The farm-scale GHG emissions, averaged over the four models, was 10.6 t carbon dioxide equivalents (CO2e)/ha per year, with a range of 1.9 t CO2e/ha per year. Even though key production characteristics were specified in the scenarios, there were still significant differences between models in the annual milk production per ha and the amounts of N fertiliser and concentrate feed imported. This was because the models differed in their description of biophysical responses and feedback mechanisms, and in the extent to which management functions were internalised. We conclude that comparing the results of different farm-scale models when applied to a range of scenarios would build confidence in their use in achieving ESR targets, justifying further investment in the development of a wider range of scenarios and software tools.

The seasonal dynamics of neutral genetic diversity and the insecticide resistance mechanisms of insect pests at the farm scale are still poorly documented. Here this was addressed in the green peach aphid Myzus persicae (Sulzer) (Hemiptera: Aphididae) in Central Chile. Samples were collected from an insecticide sprayed peach (Prunus persica L.) orchard (primary host), and a sweet-pepper (Capsicum annum var. grossum L.) field (secondary host). In addition, aphids from weeds (secondary hosts) growing among these crops were also sampled. Many unique multilocus genotypes were found on peach trees, while secondary hosts were colonized mostly by the six most common genotypes, which were predominantly sensitive to insecticides. In both fields, a small but significant genetic differentiation was found between aphids on the crops vs. their weeds. Within-season comparisons showed genetic differentiation between early and late season samples from peach, as well as for weeds in the peach orchard. The knock-down resistance (kdr) mutation was detected mostly in the heterozygote state, often associated with modified acetylcholinesterase throughout the season for both crops. This mutation was found in high frequency, mainly in the peach orchard. The super-kdr mutation was found in very low frequencies in both crops. This study provides farm-scale evidence that the aphid M. persicae can be composed of slightly different genetic groups between contiguous populations of primary and secondary hosts exhibiting different dynamics of insecticide resistance through the growing season.

Concerns have been raised in Europe about the efficiency, sustainability, and environmental impact of the first genetically modified crops. The committees and regulators in charge of approving procedures have encouraged a field trial approach for safety assessment studies under current agronomic conditions. We describe the gene flow from sugar beet (Beta vulgaris L.) in a multi-year and multi-crop monitoring study on farmers' fields at two locations that has been carried out since 1995. We analyzed two sugar beet lines that have been genetically transformed for herbicide resistance. One sugar beet has resistance to glufosinate and the other to glyphosate. Large differences among lines, years and locations were observed. These differences provided a broad range of situations to estimate the risks. Sugar beet bolters produced the majority (86%) of the herbicide-resistant seeds harvested in the field. Direct pollen flow from sugar beet bolters to weed beets that were growing within the same field as well as in a neighboring field that was left fallow accounted for only 0.4% of the resistant seeds released over the years and locations. Descendants of the hybrids between the sugar beet and the weed beet produced the remaining 13.6% of resistant seeds. Herbicide-resistant seeds from the progeny of the weed beet were recorded up to 112 m away from the closest transgenic pollen donor. Indications were observed of non-randomness of the weed beet producing resistant progeny. We also analyzed pollen flow to male-sterile bait plants located within and outside of the sugar beet field. Herbicide-resistant pollen flow was recorded up to 277 m, and fitted with an inverse power regression. Using sugar beet varieties with no, or very low, sensitivity to bolting and destroying bolters are two necessary measures that could delay gene flow.