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Bannink, A. |
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Trade-offs of dietary N-reducing dietary measures on enteric methane emission and P excretion in lactating cows |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-2 |
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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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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2117 |
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Banse, M. |
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What drives meat consumption? Combining cross-country analysis with an applied trade model |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-3 |
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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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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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2118 |
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Baranowski, P. |
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Multifractal analysis of meteorological time series to assess climate impact on chosen regions of Europe |
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2015 |
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FACCE MACSUR Reports |
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Sp5-4 |
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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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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2119 |
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Hoveid, Ø. |
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Prototype of stochastic equilibrium model of the food system |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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D-T2.5 |
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Food security is an issue of risk. If climate change is not responded to with diet, technology and/or policy changes, it may lead to reduced food security for the world population, in particular the poorer part which in longer periods may not afford to purchase food in sufficient quantity and quality. In order to improve the situation, certain policy changes may be required. In some cases are policy recommendations relatively obvious, while in other cases a deeper insight in the stochastic dynamics of food supply and storage is required to assess the consequences of policy proposals. The relatively obvious part is that farmers need be responsive in periods of low total production, so that sufficient supply restores quickly. Moreover, trade should allow local shortages to be covered. Many national policies with the goal of self-sufficiency aim in the opposite direction with stable prices and production and relatively less flexibility in production. The stochastic dynamics of food supply can be analysed in more detail with a dynamic stochastic general equilibrium model (DSGE). Although agriculture by nature is about taking decisions under uncertainty, quantitative stochastic dynamic models for policy analysis in agriculture have not yet emerged. The contribution in MACSUR is a formalization of a class of DSGE-s based on representation of biological processes managed with regard to outcomes due to uncertain nature. No Label |
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2115 |
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Palatnik, R.R. |
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Title |
Climate-dependent yields |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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D-T2.1 |
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In this report we summarize the contributions made by four groups to the subject of climate dependent yields. The first is by Waldemar Bojar, Leszek Knopik, Jacek Zarski, Cezary Slawinski, Piotr Baranowski and Wojciech Zarski on the subject of “the impact of extreme climate changes on the forecasted agriculture production”. It presents general characteristics of resources and outputs of agriculture in the Kujawsko-Pomorskie (K&P) and Lubelskie regions, based on statistical databases and the literature review. In this study, some statistically significant dependencies between the climatic parameters and yields of selected important crops in the abovementioned regions were worked out on the basis of empirical survey conducted in the University of Technology and Life Sciences and Institute of Agrophysics in Lublin. Efforts were taken to make integrated assessments of forecasted agricultural outputs influenced by climate extreme phenomena on the basis of the found dependencies’ yields – precipitation and the data coming from wide area model regional outputs such as prices, areas of farmland and yields. The second contribution is by Bojar W., Knopik L. and Zarski J. on the subject of “integrated assessment of business crop productivity and profitability to use in food supply forecasting”. It examines the proposals to build a model describing the amount of precipitation and taking into account periods without rain. This model is based on a mixture of gamma distribution and one point-distribution. The third contribution is by Iddo Kan on the Vegetative Agricultural Land Use Economic (VALUE) model. It discusses the sub-task with respect to crops of statistically estimating with statistical methods predictions of expected crop-yield contingent on climate, soil and production cost for use in existing trade models, or refined versions thereof, and how VALUE can contribute to this sub-task. The fourth contribution was made by Christoph Muller and Richard D. Robertson on the subject of “projecting future crop productivity for global economic modelling”. It supplies a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. No Label |
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MA @ admin @ |
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2114 |
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