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Grosz, B. (2015). The implication of input data aggregation on upscaling of soil organic carbon changes (Vol. 5).
Abstract: In regionalization studies the spatial resolution of driving data is often restricted by data availability or limited computational capacity. Method and level of spatial driver aggregation in upscaling studies are sources of uncertainty and might bias aggregated model results. The suitability of upscaled model results using aggregated driving data depends on both the sensitivity of the model to these model drivers and the scale of interest to which the model output will be aggregated. An important component of soil plant atmosphere systems is the soil organic matter content influencing GHG emissions and the soil fertility of croplands.The implications of driver aggregation schemes on different system properties of croplands have been examined in a scaling exercise within the joint research project MACSUR. In this study, meteorological driving data and data on soil properties on several aggregation levels have been used to calculate the organic carbon change of cropland soils of North Rhine-Westphalia with an ensemble of biogeochemical models.The results of this scaling exercise show that the aggregation of meteorological data has little impact on modeled soil organic carbon changes. However, model uncertainty increases slightly with decreasing scale of interest from NUTS 2 level to smaller grid cell size. Conversely, the aggregation of soil properties resulted in high uncertainty ranges constraining the predictable scale of interest for all models. The study gives an indication on adequate spatial aggregation schemes in dependence on the scope of regionalization studies addressing soil organic carbon changes. No Label
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Dono, G. (2015). The economic impact of changes in climate variability on milk production in the area of Grana Padano (Vol. 5).
Abstract: Climate variability (CV) normally influences production and farm management, and climate change (CC) has precisely the effect of changing this variability. Thus, models that estimate the economic impact of CC, integrating with climatic models, agronomic, and livestock, must represent the implications of this variability on farm management. This study describes an economic model based on Discrete Stochastic Programming (DSP) which assesses the impact of CC on milk production in the Grana Padano area. The model is based on 23 farm typologies from FADN that represent 856 farms in Piacenza and Cremona, two of the most important provinces for Grana Padano production. The results of the model were projected at the regional scale. The climate scenarios, current and future, are generated with a Regional Atmospheric Modeling System. The forage production under these scenarios is estimated with the EPIC agronomic model. Estimates on milk production and livestock mortality are based on studies conducted in the Po valley. The nutritional needs of the cattle are estimated with the CNCPS model. Probability distribution functions (PDF) express the relations between the CV and the productive variables under both climate scenarios. These PDFs represent the expectations of farmers on the productive-climate variability in the DSP model, which is PMP calibrated based on land distribution observed in a reference year. Comparing the model results in the two scenarios indicates the effects of CC, given the opportunity to adapt the use of resources and techniques of cultivation. The structure of the model, and its economic results are presented and discussed, along with the strengths and weaknesses of this approach. No Label
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Dono, G. (2015). Climate change impact on production and income of Mediterranean farming systems: a case study (Vol. 5).
Abstract: Adaptation to climate change calls for local responses. The impact of a 2020-30 climate scenario was assessed on a 54,000 ha Mediterranean district characterized by a variety of farming systems (FS), ranging from low-input rainfed (42% of the district area and 16% of the district net income) to high-input irrigated. Climate was generated with a Regional Atmospheric Modelling System nested into a full coupled atmosphere-ocean global simulation model, under the A1B emission scenario. Crop responses to climate were assessed using EPIC after calibration. The Temperature Humidity Index was used to assess the impact on dairy cow milk yield. Farmer choices were simulated on 13 representative FS by an hybrid model of supply, territory and farm. The adaptive choices were simulated through Discrete Stochastic Programming, fed by probability distribution functions output of crop and animal models. The expected decrease in spring rainfall (-33%) will affect hay-crop production and the net income (NI) of rainfed livestock farms (-5 to -12%). The increased summer temperature will affect dairy cows NI up to -5.9%. Rice production is expected to increase up to +10%. Overall, the NI of irrigated and rainfed farms will be -2.1% and -5.4% of the current NI respectively, with livestock FS being the most affected and rice and horticultural FS the most resilient. Results will provide an ideal mediating object for engaging policy makers and stakeholders in designing visionary adaptive strategies. No Label
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Dell’Unto, D. (2015). Modeling the effects of Climate Change on dairy farms: an integration of livestock and economic models (Vol. 5).
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Dalgaard, T. (2015). Models for regional scale farming system evaluation of climate change mitigation options and environmental impact assessment (Vol. 5).
Abstract: The aim of the present paper is to exemplify and discuss the importance of farm scale modeling in relation to The EU Joint Programming Initiative (JPI-FACCE) knowledge hub on Agriculture, Food Security and Climate Change project.In particular, livestock production systems include complex interactions, with non-linear relationships between input factors, production, emissions, local climate as well as natural resources (e.g. soil types, rotational land versus permanent grasslands etc.). Moreover, management options pursued by the different types of farmers and other relevant decision makers are important to integrate. Consequently, results of regional scale impact assessments depend on the farming systems model approach, the approach to upscale results, and the inclusion of the relevant stakeholders and decision makers at the scales considered.Different farming systems models are reviewed, including the existing dynamic and static biophysical models. Finally, procedures for upscaling and validity testing of synthesized model results at regional scales are presented. Based on a discussion of these procedures, recommendations for hot-spot analyses in farming systems with regard to integrated climate change adaptation and mitigation for a sustainable food production are synthesized, and the potentials for integration of recommended policies and farm management options into overarching models in order to assess their impact on the regional to global scales are discussed. No Label
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