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Pasqui, M., Quaresima, S., Tomozeiu, R., Dono, G., Doro, L., Cortignani, R., et al. (2014). A comprehensive climate characterization of the Oristano (Sardinia) regional pilot case study. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: In order to assess probability distributions of critical response variables in a full crop modelling system, a complete climate characterization has been implemented to identify principal variability components in the Oristano (Sardinia) regional pilot study area with a particular emphasis on current vs near future climate.
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Minet, J., Laloy, E., Tychon, B., & François, L. (2014). Bayesian inference of a dynamic vegetation model for grassland. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: As a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler. CARAIB is a mechanistic model that calculates the carbon assimilation of the vegetation as a function of the soil and climatic conditions, and can thus be used for simulating grassland production under cutting or grazing management. Bayesian model inversion was performed at 4 grassland sites across Europe: Oensingen, CH; Grillenburg, DE; Laqueuille, FR and Monte-Bodone, IT. Four daily measured variables from these sites: the Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Evapotranspiration (ET) and Soil Water Content (SWC) were used to sample 10 parameters related to rooting depth, stomatal conductance, specific leaf area, carbon-nitrogen ratio and water stresses. The maximized likelihood function therefore involved four objectives, whereas the applied Bayesian framework allowed for assessing the so called parameter posterior probability density function (pdf), which quantifies model parameter uncertainty caused by measurement and model errors. Sampling trials were performed using merged data from all sites (all-sites-sampling) and for each site (site-specific sampling) separately. The derived posterior parameter pdfs from the all-sites sampling and site-specific sampling runs showed differences in relation with the specificities of each site. Analysis of these distributions also revealed model sensitivity to parameters conditioned on the measured data, as well as parameter correlations.
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Lellei-Kovács, E., Barcza, Z., Hidy, D., Horváth, F., Ittzés, D., Ittzés, P., et al. (2014). Application of Biome-BGC MuSo in managed grassland ecosystems in the Euro-Mediteranean region. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Simulation of the biogeochemical cycles of extensively and intensively managed grasslands and croplands are of particular interest due to the strong connection between ecosystem production, animal husbandry and food security. In the frame of MACSUR LiveM activities, we conducted a series of „blind tests” (i.e. uncalibrated model simulations with previously optimized model) on differently managed grasslands within Europe and Israel. We used the latest version of Biome-BGC MuSo model, the modified version of the widely used biogeochemical Biome-BGC model. Biome-BGC MuSo contains structural improvements, development of management modules, and the extension of the model to simulate herbaceouos ecosystem carbon and water cycles more faithfully. The studied ecosystems were meadows and pastures located in a variety of climate zones from the Atlantic sector to Central Europe, including Mediterranean sites. Managements were intensive and extensive grazing or mowing with or without different kind of fertilizers. Under similar options we simulated ecosystem variables, e.g. Gross Primary Production (GPP) and Net Ecosystem Exchange (NEE). Our experiences show that different sites have different sensitivity to the parameters (maximum root depth, soil parameters, etc.), but overall the model provided realistic fluxes. Experiences gained during the blind tests led us to further improve the model. Biome-BGC MuSo is available as a standalone model in personal computers, but also through virtual laboratory environment and Biome-BGC Projects database (http://ecos.okologia.mta.hu/bbgcdb) developed within the BioVeL project (http://www.biovel.eu). Scientific workflow management, web service and desktop grid technology can support model optimization in the so-called „calibrated runs” within MACSUR.
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Lacetera, N., Vitali, A., Bernabucci, U., & Nardone, A. (2014). Relationships between temperature humidity index, mortality, milk yield and composition in Italian dairy cows. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The aim of this presentation is to illustrate the activities performed by the LiveM-Task L1.2. group based at the University of Tuscia, Viterbo, Italy. Three different pluriannual databases were built to perform retrospective studies aimed at establishing the relationships between temperature humidity index (THI) and parameters of interest for dairy cow farms. The THI combines temperature and humidity in a single value and has been widely used to quantify heat stress in farm animals. The first database was built to assess the relationships between THI and mortality over a 6 yr period (2002-2007); the second one was a 7 yr database (2001-2007) which was built to establish the relationships between THI and milk yield; the last database included THI, milk somatic cell counts, total bacterial counts, fat and protein percentages data collected over a 7 yr period (2003-2009). The analysis of the three databases provided several equations which demonstrated and quantified an increase of mortality, reduction of milk yield and a worsening of milk quality in hot environment. Results of these analyzes authorized speculations about risks for dairy cows and their productivity in a warming planet. Furthermore, the same results are being utilized by economists also working within MACSUR at the University of Tuscia for an integrated study aimed at establishing the economic impact of climate change in the dairy sector. Combining this information with climate change regional scenarios might permit prediction of the impact of global warming and identification of adaptation measures that are appropriate for specific contexts.
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Kipling, R. P., Don, A., & Topp, K. (2014). Assessing the availability of data on grassland Carbon sequestration in Europe. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The role of grasslands in the sequestration of atmospheric Carbon represents an important benefit of extensive livestock systems based on permanent grasslands. The accurate modelling of such systems is key to understanding their potential in mitigating GHG emissions, and this in turn relies on access to high quality data. Here, the availability of Carbon sequestration data for EU grasslands is investigated, using information gathered from reviews of journal papers and EU project outputs. The challenges involved in providing information on datasets to modellers are discussed, and the next steps in the gathering and sharing of meta-data are defined.
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