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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
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Banse, M. (2015). What drives meat consumption? Combining cross-country analysis with an applied trade model (Vol. 5).
Abstract: In a cross country analysis using national data for both OECD and developing countries, we estimate a regression model with different coefficients for different drivers for per capita meat consumption. The model contains data from approximately 125 countries (depending on the variables included) on meat consumption and production, relative size of agricultural area and pasture and meadows, PPP adjusted consumer prices for meat (and for food as control variable), PPP adjusted GNI per capita, HDI, degree of urbanisation, religion and geographical/cultural belonging.A regression analysis has been conducted, using OLS with data from 2011 and an aggregation of all meat types as the dependent variable. In the results all of the mentioned variables have a significant impact on meat consumption.Based on a first scenario analysis which has been presented on a TradeM Workshop of MACSUR in September 2014, this paper will extend the approach of an estimated cross-country analysis to improve the demand elasticities in the MAGNET model for meat and meat products. Further other demand determining factors of meat consumption, e.g. behavioural change towards less meat consumption (vegetarian or vegan) derived from the regression analysis will be fed into the MAGNET model. This extended approach will help to analyse the resulting market effects of a changing demand pattern for meat. MAGNET will provide insights in consequences on supply and international trade for meat and meat products.The aim of this combined approach is to further explore the relationship between production and consumption, and to what extent the one is driving the other. Based on the application of the panel data method for a detailed demand analysis with the combination of the feedback from the supply and trade side based on the MAGNET model we will be able to provide a tool which is able to address the important questions of demand responses under different adaptation or mitigation strategies towards clime change, such as tax measures like fat taxes. This extended tool also contributes to an improved decision making process of policy makers under different options to respond to climate change issues – not only with regard to the supply side of agricultural production but also to the consumption side. No Label
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Baranowski, P. (2015). Multifractal analysis of meteorological time series to assess climate impact on chosen regions of Europe (Vol. 5).
Abstract: Over the last decades modelling of climate change through the analysis of empirical meteorological data has become of great interest. The standard approach gives satisfactory results only in the climatic zones with extreme dynamics of climate change, thus there is need to develop and apply more subtle methods such as fractal analysis and chaotic evolution analysis of the atmospheric system. The scaling analysis of meteorological time series is complicated because of the presence of localized trends and nonstationarities. The objective of this study was to characterize scaling properties (i.e. statistical self-similarity) of the daily air temperature, wind velocity, relative air humidity, global radiation and precipitation through multifractal detrended fluctuation analysis on data from 31 years for stations located in Finland, Germany, Poland and Spain. The empirical singularity spectra indicated their multifractal structure. The richness of the studied multifractals was evaluated by the width of their spectrum, indicating considerable differences in dynamics and development. The log-log plots of the cumulative distributions of all the studied absolute and normalized meteorological parameters tended to linear functions for high values of the response, indicating that these distributions were consistent with the power law asymptotic behaviour. Additionally, we investigated the type of multifractality that underlies the q-dependence of the generalized Hurst exponent, by analysing the corresponding shuffled and surrogate time series. The results suggest that MFDFA is valuable for assessing the change of climate dynamics. No Label
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Bellocchi, G. (2015). Fuzzy-logic based multi-site crop model evaluation (Vol. 5).
Abstract: The most common way to evaluate simulation models is to quantify the agreement between observations and simulations via statistical metrics such as the root mean squared error and the linear regression coefficient of determination. It is agreed that the aggregation of metrics of different nature intro integrated indicators offers a valuable way to assess models. Expanded notions of model evaluation that have recently emerged, based on the trade-off between properties of the model and agreement between predictions and actual data under contrasting conditions, integrate sensitivity analysis measures and information criteria for model selection, as well as concepts of model robustness, and point to expert judgments to explore the importance of different metrics. As a FACCE MACSUR CropM-LiveM action, a composite indicator (MQIm: Model Quality Indicator for multi-site assessment) was elaborated, by a group of specialists, on metrics commonly used to evaluate crop models (with extension to grassland models) while also integrating aspects of model complexity and stability of performances. The indicator, based on fuzzy bounds applied to a set of weighed metrics, was first revised by a broader group of modellers and then assessed via questionnaire survey of scientists and end-users. We document a crop model evaluation in Europe and assess to what extent the MQIm reflects the main components of model quality and supports inferences about model performances. No Label
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Biewald, A. (2015). Ethical aspects in the economic modeling of water policy options (Vol. 5).
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