Project description:Many measures have been proposed to mitigate gaseous emissions and other nutrient losses from agroecosystems, which can have large detrimental effects for the quality of soils, water and air, and contribute to eutrophication and global warming. Due to complexities in farm management, biological interactions and emission measurements, most experiments focus on analysis of short-term effects of isolated mitigation practices. Here we present a model that allows simulating long-term effects at the whole-farm level of combined measures related to grassland management, animal housing and manure handling after excretion, during storage and after field application. The model describes the dynamics of pools of organic carbon and nitrogen (N), and of inorganic N, as affected by farm management in grassland-based dairy systems. We assessed the long-term effects of delayed grass mowing, housing type (cubicle and sloping floor barns, resulting in production of slurry and solid cattle manure, respectively), manure additives, contrasting manure storage methods and irrigation after application of covered manure. Simulations demonstrated that individually applied practices often result in compensatory loss pathways. For instance, methods to reduce ammonia emissions during storage like roofing or covering of manure led to larger losses through ammonia volatilization, nitrate leaching or denitrification after application, unless extra measures like irrigation were used. A strategy of combined management practices of delayed mowing and fertilization with solid cattle manure that is treated with zeolite, stored under an impermeable sheet and irrigated after application was effective to increase soil carbon stocks, increase feed self-sufficiency and reduce losses by ammonia volatilization and soil N losses. Although long-term datasets (>25 years) of farm nutrient dynamics and loss flows are not available to validate the model, the model is firmly based on knowledge of processes and measured effects of individual practices, and allows the integrated exploration of effective emission mitigation strategies.

Project description:To assess their potential to control poultry red mites (Dermanyssus gallinae), we tested selected predaceous mites (Androlaelaps casalis and Stratiolaelaps scimitus) that occur naturally in wild bird nests or sometimes spontaneously invade poultry houses. This was done under laboratory conditions in cages, each with 2-3 laying hens, initially 300 poultry red mites and later the release of 1,000 predators. These small-scale tests were designed to prevent mite escape from the cages and they were carried out in three replicates at each of three temperature regimes: 26, 30 (constant day and night) and 33-25 °C (day-night cycle). After 6 weeks total population sizes of poultry red mites and predatory mites were assessed. For the temperature regimes of 26 and 33/25 °C S. scimitus reduced the poultry red mite population relative to the control experiments by a factor 3 and 30, respectively, and A. casalis by a factor of 18 and 55, respectively. At 30 °C the predators had less effect on red mites, with a reduction of 1.3-fold for S. scimitus and 5.6-fold for A. casalis. This possibly reflected hen manure condition or an effect of other invertebrates in the hen feed. Poultry red mite control was not negatively affected by temperatures as high as 33 °C and was always better in trials with A. casalis than in those with S. scimitus. In none of the experiments predators managed to eradicate the population of poultry red mites. This may be due to a prey refuge effect since most predatory mites were found in and around the manure tray at the bottom of the cage, whereas most poultry red mites were found higher up in the cage (i.e. on the walls, the cover, the perch, the nest box and the food box). The efficacy of applying predatory mites in the poultry industry may be promoted by reducing this refuge effect, boosting predatory mite populations using alternative prey and prolonged predator release devices. Biocontrol success, however, will strongly depend on how the poultry is housed in practice (free range, cage or aviary systems) and on which chemicals are applied to disinfect poultry houses and to control other pests.

Project description:BACKGROUND: Predicting the spatial distribution of pathogens with an environmental stage is challenging because of the difficulty to detect them in environmental samples. Among these pathogens, the parasite Toxoplasma gondii is the causative agent of the zoonosis toxoplasmosis, which is responsible for public health issues. Oocysts of T. gondii are excreted by infected cats in the environment, where they may survive and remain infectious for intermediate hosts, specifically rodents, during months to years. The landscape structure that determines the density and distribution of cats may thus impact the spatial distribution of T. gondii. In this study, we investigated the influences of rural settings on the spatial distribution of oocysts in the soil. METHOD: We developed a spatially explicit agent based model to study how landscape structures impact on the spatial distribution of T. gondii prevalence in its rodent intermediate host as well as contamination in the environment. The rural landscape was characterized by the location of farm buildings, which provide shelters and resources for the cats. Specifically, we considered two configurations of farm buildings, i.e. inside and outside a village. Simulations of the first setting, with farm buildings inside the village, were validated using data from previous field studies. Then, simulation results of the two settings were compared to investigate the influences of the farm locations. RESULTS: Model predictions showed a steeper relationship between distance to the nearest farm and infection levels when farm buildings, and thus cats, were concentrated in the same area than when the farms were spread over the area. The relationship between distance to the village center and level of environmental contamination also differed between settings with a potential increased risk for inhabitants when farms are located inside the village. Maps of the risk of soil contaminated with oocysts were also derived from the model. CONCLUSION: The agent-based model provides a useful tool to assess the risk of contamination by T. gondii oocysts at a local scale and determine the most at risk areas. Moreover it provides a basis to investigate the spatial dynamics of pathogens with an environmental stage.

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Bioptipou1

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