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International audience; A substantial honeybee decline is being observed worldwide. Agricultural intensification and loss of wild floral resources rank among the main factors contributing to this decline. Landscape enhancement of floral resources has been proposed as an agro-environmental scheme intended to provide honeybees with compensatory food sources in intensive agrosystems. Floral scheme efficiency has rarely been evaluated with respect to landscape context. In this study, we developed and validated a modeling tool to delineate the landscape areas likely to be associated with higher efficiency of floral enhancement schemes. In particular, the proximity of some landscape elements used by honeybees, either as foraging habitat or as visual landmark for orientation, may partly determine floral scheme efficiency. We investigated this issue using resource selection functions (RSFs), i.e. models that aim to predict the occurrence of foraging honeybees at floral patches as a function of the presence of keystone landscape elements in their proximity. However, deciding which landscape elements are effectively in the proximity or not is mostly a matter of subjectivity. The novelty of our approach resides in its use of a distance-weighting function to explicitly account for the spatial location of surrounding landscape elements. In that respect, a distance function should be scaled on movement patterns of foraging organisms. Herein, we inferred movement patterns from the autocorrelative properties of honeybee foraging activity. This modeling approach was developed on Phacelia (Phacelia tanacetifolia) field margin strips, a typical “honeybeefriendly” floral scheme. A foraging survey conducted on 170 Phacelia plots (2 m × 2 m) from 17 Phacelia strips, all positioned within the foraging range of an experimental apiary, revealed that (i) the floral scheme efficiency is positively influenced by the presence of linear landscape elements such as hedgerows and forest edges, but negatively affected by the presence of alternative floral resources, and that (ii) weighting the relative importance of those landscape elements by incorporating a distance function into models considerably improved their predictive power. This modeling tool has the potential to help land managers optimizing their financial investment by avoiding low-efficiency landscape areas, or favoring high-efficiency ones, at the time of planning floral enhancement schemes

In the frame of the European TOPPS project (Train the Operator to prevent Pollution from Point Sources), 200 on farm audits and 300 tele interviews were performed in the Yser catchment area. The objective was to determine the critical points for point source pollution within the spraying process and to inform advisors, intermediaries and farmers on practical measures and achievable solutions to reduce the contamination of the surface water by Plant Protection Products (PPP) due to point source pollution. For the on farm auditing, the Aquasite tool (Arvalis-France) was used. This audit was performed on 100 farms in the Flemish Yser catchment and on 100 farms at the French side. This audit reveals the weak points in infrastructure and technology on the farm in relation to the spraying process. Next, 150 tele interviews were held in the respective catchment areas. These interviews assess the awareness and behaviour of the farmers on point source pollution. The strength of these studies is in giving a view on the real situation on the farms with respect to spraying. The critical points and risks for point source pollution were similar for both regions. Especially the filling and mixing of the sprayer, internal and external cleaning of the sprayer and the management of the waste fraction need specific training, demonstration and advice. However, there is a large difference in the risk perception of point source pollution between farmers on both sides of the border. The transgressing approach of the Yser catchment allows to make a comparison between both regions and allows to assess in which way the legislation had part in explaining the differences between the regions as the agriculture in both regions is similar. Also, the results stress the importance of trainings and sensibilisation at a regional scale.