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Nol L.,CAH Vilentum University of Applied Sciences | Verburg P.H.,VU University Amsterdam | Moors E.J.,Wageningen University
Journal of Environmental Management | Year: 2012

Better insight in the possible range of future N 2O emissions can help to construct mitigation and adaptation strategies and to adapt land use planning and management to climate objectives. The Dutch fen meadow landscape is a hotspot of N 2O emission due to high nitrogen inputs combined with moist peat soils due to land use change. Socio-economic developments in the area are expected to have major impacts on N 2O emission. The goals of this study are to estimate changes in N 2O emissions for the period 2006-2040 under three different scenarios for the Dutch fen meadow landscape (rural production, rural fragmentation, and rural multifunctionality) and to quantify the share of different emission sources. Three scenarios were constructed and quantified based on the Story-And-Simulation approach. The rural production and the rural fragmentation scenarios are characterized by globalization and a market-oriented economy; in the rural production scenario dairy farming has a strong competitive position in the study region, while under the rural fragmentation scenario agriculture is declining. Under the rural multifunctionality scenario, the global context is characterized by regionalization and stronger regulation toward environmental issues. The N 2O emission decreased between 2006 and 2040 under all scenarios. Under the rural production scenario, the N 2O emission decreased by 7%. Due to measures to limit peat mineralization and policies to reduce agricultural emissions, the rural multifunctionality scenario showed the largest decrease in N 2O emissions (44%). Under the rural fragmentation scenario, in which the dairy farming sector is diminished, the emission decreased by 33%. Compared to other uncertainties involved in N 2O emission estimates, the uncertainty due to possible future land use change is relatively large and assuming a constant emission with time is therefore not appropriate. © 2011 Elsevier Ltd. Source


Verburg P.H.,VU University Amsterdam | Neumann K.,Wageningen University | Nol L.,CAH Vilentum University of Applied Sciences
Global Change Biology | Year: 2011

Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscapes. © 2010 Blackwell Publishing Ltd. Source


Kabourkova E.,Mendel University in Brno | Slama P.,Mendel University in Brno | Corten H.,CAH Vilentum University of Applied Sciences
Journal of Animal and Veterinary Advances | Year: 2015

The objective of this manuscript was to evaluate the effect of ambient temperature on the uterus flushing out during the embryo transfer process. The study was carried out on dairy farms and was performed from January 7 to November 19, 2010. The milking cow donors flushed out during hotweather were compared with donors that were flushed out during cold weather. It focused on the number of embryos obtained and the ambient temperature. The number of embryos obtained was recorded during the embryo transfer process. The diree groups of milking cows were measured during high ambient temperature, medium ambient temperature and low ambient temperature while they were in heat. The experiment was performed on the Holstein breed of dairy cow. The experiment focused on tire viable embryos present in the flushed liquid. The number of embryos obtained while the cows were in heat fluctuated with the difference in ambient temperatures, being the lowest during high ambient temperature. The number of embryos was high during medium ambient temperature and during low ambient temperature. Low ambient temperature had no significant effect on the number of embryos. Because high ambient temperature lias a significant negative effect on the number of embryos obtained, it seems necessary to protect milking cows that are in heat against high ambient temperatures. © Medwell Journals, 2015. Source


Nol L.,CAH Vilentum University of Applied Sciences | Heuvelink G.B.M.,Wageningen University | Veldkamp A.,Wageningen University | de Vries W.,Wageningen University | Kros J.,Wageningen University
Geoderma | Year: 2010

Nitrous oxide (N2O) emission from agricultural land is an important component of the total annual greenhouse gas (GHG) budget. In addition, uncertainties associated with agricultural N2O emissions are large. The goals of this work were (i) to quantify the uncertainties of modelled N2O emissions caused by model input uncertainty at point and landscape scale (i.e. resolution), and (ii) to identify the main sources of input uncertainty at both scales. For the Dutch western fen meadow landscape, we performed a Monte Carlo uncertainty propagation analysis using the INITIATOR model. The Monte Carlo analysis used novel and state-of-the-art methods for estimating and simulating continuous-numerical and categorical input variables, handling spatial and cross-correlations and analyzing spatial aggregation effects. Spatial auto- and cross-correlation of uncertain numerical inputs that are spatially variable were represented by the linear model of coregionalization. Bayesian Maximum Entropy was used to quantify the uncertainty of spatially variable categorical model inputs. Stochastic sensitivity analysis was used to analyze the contribution of groups of uncertain inputs to the uncertainty of the N2O emission at point and landscape scale. The average N2O emission at landscape scale had a mean of 20.5kg N2O-N ha-1 yr-1 and a standard deviation of 10.7kg N2O-N ha-1 yr-1, producing a relative uncertainty of 52%. At point scale, the relative error was on average 78%, indicating that upscaling decreases uncertainty. Soil inputs and denitrification and nitrification inputs were the main sources of uncertainty in N2O emission at point scale. At landscape scale, uncertainty in soil inputs averaged out and uncertainty in denitrification and nitrification inputs was the dominant source of uncertainty. This was partly because these inputs were assumed constant across areas with the same soil type and land use, which is probably not very realistic. Experiments at landscape scale are needed to assess the spatial variability of these fractions and analyze how a more realistic representation influences the uncertainty budget at landscape scale. This research confirms that results from uncertainty analyses are often scale dependent and that results for one scale cannot directly be extrapolated to other scales. © 2010 Elsevier B.V. Source


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