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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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Kipling, R. P., Saetnan, E. R., Van den Pol-van Dasselaar, A., & Scollan, N. G. (2014). Building modelling capacity for livestock systems: progress in LiveM. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: MACSUR provides an opportunity to connect disparate research groups and disciplines in livestock and grassland modelling. Within the livestock theme (LiveM) of MACSUR, grassland modelling capabilities have been significantly improved through joint modelling exercises, and grassland modellers have exploited their methodological overlaps with CropM to make important contributions to regional pilot studies. Animal health researchers have been contributing to the southern regional pilot, and modelling resources have been identified for livestock systems at the animal and farm-scales. Here, the priorities for the next steps for livestock and grassland modelling are discussed, and for the role of MACSUR in addressing the challenges facing the sector. While crop and grassland modelling deals with primary production, livestock modelling examines the complexity of secondary production. The unique position of livestock modelling presents challenges and opportunities. The diversity of livestock models (in scale and approach) makes model inter-comparisons and collaborative work challenging, while the range of variables involved in livestock systems provide many opportunities for increasing systemic efficiency and robustness to the impacts of climate change. Closer integration of experimental research and modelling teams also has the potential to increase the capability of livestock and grassland models to predict the impact of European adaptation strategies on livestock farming systems, and on the contribution of these systems to global food security.
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Olesen, J. E., Porter, J. R., & Christensen, J. H. (2014). Centre for Regional change in the Earth System. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Centre for Regionalchange in the Earth System (CRES, cres-centre.net) is funded by the DanishStrategic Research Council for the period 2009-2014 and is coordinated by theDanish Meteorological Institute. CRES has established a coordinated researcheffort aiming to improve societal preparedness for climate change, inparticular for Denmark. The overall objective of CRES is to extend knowledge ofand reduce the uncertainties surrounding regional climate change and itsimpacts and thereby support future climate change adaptation and mitigationpolicies. Some of the objectives that also have large synergies with theeffects in the CropM theme of MACSUR are a) to reduce uncertainty surroundingregional climate change and its impacts for the period 2020-2050 by improvingmodel formulation and process understanding; b) identify key changes andtipping points in the regional hydrological system, agriculture, freshwater andestuarine ecosystems caused by changes in seasonality, dynamics and extremeevents of precipitation, droughts, heat waves and sea level rise; c) quantifyconfidence and uncertainties in predictions of future regional climate and itsimpacts, by improving the statistical methodology and substance and byintegrating interdisciplinary risk analyses; d) interpret these results inrelation to risk management approaches for climate change adaptation andmitigation. Studies in CRES of particular interest to MACSUR include a)Estimation on generic crop model uncertainties in projection of climate changeimpacts on wheat year, b) Assessment of uncertainties in projected effects onwater balance, crop productivity and nitrate leaching of changes in land use,climate and assessment models.
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Zavalloni, M., Marconi, V., Viaggi, D., & Raggi, M. (2014). Climate change adaptation: a farm level model to assess investment decisions in water storage. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: One of the potential measures suggested to cope with the changes induced by Climate Change (CC) is the construction of rainwater harvesting reservoirs (Bozzola and Swanson, n.d.). The literature has focused mostly on the water allocation management but it overlooks at the structure of the investment decision. We analyse the investment decision in water storage facilities, for different farming specialization and under different climatic scenarios to assess the option value of the investment. We take an interdisciplinary approach integrating climate, agronomic and economic models. CC effects are assessed by a downscale of the A1B scenario of the IPCC (Tomozeiu et al., 2010). The resulting estimated temperatures and rainfall levels are then introduced in an agronomic model, which determines the irrigation water quantity and timing for a number of crops. Finally all these elements are included in a farm level economics model, DHYMORA (Viaggi et al., 2010). The model is applied to typical farm specializations in eastern Emilia-Romagna, including both annual and perennial crops.
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Mereu, V., Spano, D., Gallo, A., & Carboni, G. (2014). Climate change impacts and adaptation strategies evaluation on staple food crops in different agro-climatic zones. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Increasing temperatures, changed precipitation patterns and more frequent extreme events may lead to an increase in crop failure and to a substantial decrease of crop yields. The assessment of climate change impacts on agricultural sector has a particular interest to stakeholders and policy makers, in order to identify specific agricultural sectors and agro-climatic zones that could be more vulnerable to changes in climatic conditions and to develop the most appropriate policies to cope with these threats. For these reasons, the evaluation of climate change impacts for key crops in different agro-climatic zones was made exploring climate uncertainty and focusing on short period monitoring, which is particularly useful for food security and risk management. The analysis was made using the DSSAT-CSM (Decision Support System for Agrotechnology Transfer – Cropping System Model) software, version 4.5. Crop models implemented into DSSAT-CSM were used, for each selected crop, to evaluate climate change impacts on crop production. Multiple combinations of soils and climate conditions, crop management and varieties were considered for different agro-climatic zones. The climate impact was assessed using future climate projections, statistically and/or dynamically downscaled, for the specific areas. Direct and indirect effects of different CO2 concentrations, projected for the future periods, were separately explored to estimate their effects on crops. Finally, several adaptation strategies were evaluated with the aim to reduce the negative impact of climate change on crop production. The results of the study, analysed at local and regional scale, will be discussed.
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