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Wilson, A. J., & Gubbins, S. (2014). Modelling interactions between climate and livestock pathogen transmission. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Climate affects the transmission of livestock pathogens via multiple direct and indirect pathways. The impact of climate change on livestock pathogens is therefore complex and difficult to predict. Recent improvements in the availability of climatic data, the accumulation of epidemiological data and the development of Bayesian methodologies allow improved inferences to be made about the responses of pathogens to climate change. We discuss recent studies demonstrating these principles and present a proposal for future work using an extensively validated model for the transmission of bluetongue virus to forecast the potential consequences of predicted environmental changes to the expected impact of the disease and efficacy of current control strategies under a range of incursion scenarios.
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Kipling, P., Saetnan, R., van den Pol-van Dasselaar, A., Scollan, D., Bartley, D., Bellocchi, G., et al. (2014). Modelling interactions between climate and livestock pathogen transmission, Pirbright Institute, UK..
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Kipling, P., Saetnan, E., Scollan, D., Bartley, D., Bellocchi, G., Hutchings, J., et al. (2014). Modelling livestock and grassland systems under climate change..
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Fung, F. (2014). Modelling livestock and grassland systems under climate change..
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Hlavinka, P., Trnka, M., Kersebaum, K. C., Cermák, P., Pohanková, E., Orság, M., et al. (2014). Modelling of yields and soil nitrogen dynamics for crop rotations by HERMES under different climate and soil conditions in the Czech Republic. J. Agric. Sci., 152(02), 188–204.
Abstract: The crop growth model HERMES was used to model crop rotation cycles at 12 experimental sites in the Czech Republic. A wide range of crops (spring and winter barley, winter wheat, maize, potatoes, sugar beet, winter rape, oats, alfalfa and grass), cultivated between 1981 and 2009 under various soil and climatic conditions, were included. The model was able to estimate the yields of field crop rotations at a reasonable level, with an index of agreement (IA) ranging from 0.82 to 0.96 for the calibration database (the median coefficient of determination (R-2) was 0.71), while IA for verification varied from 0.62 to 0.93 (median R-2 was 0.78). Grass yields were also estimated at a reasonable level of accuracy. The estimates were less accurate for the above-ground biomass at harvest (the medians for IA were 0.76 and 0.72 for calibration and verification, respectively, and analogous medians of R-2 were 0.50 and 0.49). The soil mineral nitrogen (N) content under the field crops was simulated with good precision, with the IA ranging from 0.49 to 0.74 for calibration and from 0.43 to 0.68 for verification. Generally, the soil mineral N was underestimated, and more accurate results were achieved at locations with intensive fertilization. Simulated yields, soil N, water and organic carbon (C) contents were compared with long-term field measurements at Ne. mc. ice, located within the fertile Moravian lowland. At this station, all of the observed parameters were reproduced with a reasonable level of accuracy. In the case of the organic C content, HERMES reproduced a decrease ranging from c. 85 to 77 tonnes (t)/ha (for the 0-0.3 m soil layer) between the years 1980 and 2007. In spite of its relatively simple approach and restricted input data, HERMES was proven to be robust across various conditions, which is a precondition for its future use for both theoretical and practical purposes.
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