| Home | [61–70] << 71 72 73 74 75 76 77 78 79 80 >> [81–90] |
|
Haas, E. (2015). Responses of soil N2O emissions and nitrate leaching on climate input data aggregation: a biogeochemistry model ensemble study (Vol. 5).
Abstract: Numerical simulation models are increasingly used to estimate greenhouse gas (GHG) emissions at site to regional scales and are outlined as the most advanced methodology (Tier 3) for national emission inventory in the framework of UNFCCC reporting.Low resolution simulations needs less effort in computation and data management, but details could be lost during data aggregation associated with high uncertainties of the simulation results. This aggregation effect and its uncertainty will be propagated with the simulations. This paper aims to study the aggregation effects of climate and soil input data on soil N2O emissions and nitrate leaching by comparing different biogeochemistry models. We simulated two 30-year cropping systems (winter wheat and maize monocultures) under nutrient-limited conditions. Input data (climate and soil) was based on a 1 km resolution aggregated on resolutions of 10, 25, 50, and 100. In the first step, the soil data was kept homogenous using representative soil properties while climate data was used on all different scales. In the second step, the climate data was kept homogeneous while soil initial data was used on all different scales. Finally in the third step we have used spatially explicit climate and soil data on all different scales. We analyzed the N2O emissions per unit of crop yield as well as the nitrate leaching on the annual average as well as on daily resolution to study pulsing events for all scenarios and on all scales. The study presents an analysis of the influence of data aggregation.The study gives an indication on adequate spatial aggregation schemes in dependence on the scope of regionalization studies addressing the quantification of losses of reactive nitrogen from managed arable systems. No Label
|
|
Bannink, A. (2015). Trade-offs of dietary N-reducing dietary measures on enteric methane emission and P excretion in lactating cows (Vol. 5).
Abstract: The dairy sector may expand by over 2% per annum with expiration of the milk quota system in countries with a major and intensive dairy sector. Such expansion will increase pressure to further reduce on-farm nitrogenous emission per unit of milk produced even more. A straightforward N-reducing measure is the manipulation of the cow diet resulting in a lower excretion of ammoniacal N excreted with urine in particular. However, dietary N-reducing measures also affect enteric methane emissions and P excretion. For an integral evaluation of the consequences of N-reducing dietary measures on on-farm emissions, the trade-offs between N emissions and P and methane emissions at the cow level need to be taken into account. Therefore, a simulation study was performed to simulate the consequence of various N-reducing and/or P-reducing dietary measures (altered grassland management, grass silage replaced by low-N feeds, increased concentrate allowance) on enteric methane emission and on N and P excretion. Results indicate a large scattering, but there was a trend of higher methane emissions with lower N excretion was significant. Specific measures had a synergistic effect on emissions such as the exchange of maize for grass silage. The present detailed model evaluations may aid in quantifying the extent of trade-offs between various types of emissions at the cow level, but also prove to be relevant when evaluating consequences of management options taken at the farm scale. No Label
|
|
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
|
|
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
|
|
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
|