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Sieber, S.; Amjath-Babu, T.S.; Jansson, T.; Müller, K.; Tscherning, K.; Graef, F.; Pohle, D.; Helming, K.; Rudloff, B.; Saravia-Matus, B.S.; Gomez y Paloma, S. |
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Title |
Sustainability impact assessment using integrated meta-modelling: Simulating the reduction of direct support under the EU common agricultural policy (CAP) |
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Journal Article |
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Year |
2013 |
Publication |
Land Use Policy |
Abbreviated Journal |
Land Use Policy |
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Volume |
33 |
Issue |
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Pages |
235-245 |
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Keywords |
SIAT; CAP; sustainability; impact assessment; land use change; trade off analysis; model; Netherlands; systems |
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Abstract |
Assessing the impact of macro-level policy driven land use changes on regional sustainability is an important task that can facilitate complex decision making processes of introducing reforms. The research work demonstrates the ability of Sustainability Impact Assessment Tool (SIAT), a meta-model, in conducting ex ante spatially explicit cross sectoral impact assessments of changes in common agricultural policy (CAP). The meta-model is able to appraise impacts of CAP amendments on land use and their repercussions on multiple indicators of sustainability. The presented study comprehensively analyses the possible impacts of discontinuing direct financial support to farmers under CAP. The simulations of the meta-model are able to reveal the land use changes both at EU and regional levels as well as to bring forth the subsequent changes in a number of indicators representing the regional sustainability (for five case study regions). In a nutshell, the simulations indicate that a reduction in direct support brings in general, a decrease in farmed area, an increase in forested land, less fluctuation in natural vegetation coverage, increase in abandoned arable land area and negligible changes in built-up area despite regionally diverging land use trends. The simulated changes in sustainability indicators for the study regions in consequence to these land use changes show that the discontinuation of subsidies evokes responses that are in general climate friendly (reduction in methane and N2O emissions, diminishing energy use and reduction in global warming potential), economically beneficial (increase in gross value of agriculture) and socially desired (decrease in unemployment rate) as well as environmentally harmful (increase in pesticide use). Even though the appraisals of diversity indicators such as forest deadwood and farmland birds are not conclusive for all regions, the changes are positive for the former indicator and slightly negative for the latter in general. The trade-offs among these regional sustainability indicators using their directional associations are also presented for a comprehensive assessment of the impacts. (C) 2013 Elsevier Ltd. All rights reserved. |
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0264-8377 |
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TradeM |
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MA @ admin @ |
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4479 |
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Author |
Murat, M.; Malinowska, I.; Hoffmann, H.; Baranowski, P. |
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Title |
Statistical modelling of agrometeorological time series by exponential smoothing |
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Journal Article |
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Year |
2016 |
Publication |
International Agrophysics |
Abbreviated Journal |
International Agrophysics |
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30 |
Issue |
1 |
Pages |
57-65 |
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Keywords |
exponential smoothing; meteorological time series; statistical forecasting; daily temperature records; weighted moving averages; climate-change; prediction; forecasts; state; weather |
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Abstract |
Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error. |
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0236-8722 |
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CropM, ft_macsur |
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MA @ admin @ |
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4728 |
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Author |
Müller, C. |
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Title |
African lessons on climate change risks for agriculture |
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Journal Article |
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Year |
2013 |
Publication |
Annual Review of Nutrition |
Abbreviated Journal |
Ann. Rev. Nutr. |
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33 |
Issue |
1 |
Pages |
395-411 |
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Keywords |
Africa/epidemiology; *Climate Change/economics; Crops, Agricultural/economics/*growth & development; Diet/adverse effects/economics; Forecasting; *Global Health/economics/trends; Humans; Malnutrition/economics/epidemiology/prevention & control; *Models, Theoretical; Risk; Soil/chemistry; Water Resources/economics |
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Climate change impact assessments on agriculture are subject to large uncertainties, as demonstrated in the present review of recent studies for Africa. There are multiple reasons for differences in projections, including uncertainties in greenhouse gas emissions and patterns of climate change; assumptions on future management, aggregation, and spatial extent; and methodological differences. Still, all projections agree that climate change poses a significant risk to African agriculture. Most projections also see the possibility of increasing agricultural production under climate change, especially if suitable adaptation measures are assumed. Climate change is not the only projected pressure on African agriculture, which struggles to meet demand today and may need to feed an additional one billion individuals by 2050. Development strategies are urgently needed, but they will need to consider future climate change and its inherent uncertainties. Science needs to show how existing synergies between climate change adaptation and development can be exploited. |
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0199-9885 1545-4312 |
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CropM |
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Call Number |
MA @ admin @ |
Serial |
4496 |
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Author |
Milford, A.B.; Le Mouel, C.; Bodirsky, B.L.; Rolinski, S. |
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Title |
Drivers of meat consumption |
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Journal Article |
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Year |
2019 |
Publication |
Appetite |
Abbreviated Journal |
Appetite |
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Volume |
141 |
Issue |
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Pages |
Unsp 104313 |
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Keywords |
Meat consumption; Nutrition transition; Climate change mitigation; Cross-country analysis; nutrition transition; food; sustainability; globalization; countries; future; health; income; price |
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Increasing global levels of meat consumption are a threat to the environment and to human health. To identify measures that may change consumption patterns towards more plant-based foods, it is necessary to improve our understanding of the causes behind the demand for meat. In this paper we use data from 137 different countries to identify and assess factors that influence meat consumption at the national level using a cross-country multivariate regression analysis. We specify either total meat or ruminant meat as the dependent variable and we consider a broad range of potential drivers of meat consumption. The combination of explanatory variables we use is new for this type of analysis. In addition, we estimate the relative importance of the different drivers. We find that income per capita followed by rate of urbanisation are the two most important drivers of total meat consumption per capita. Income per capita and natural endowment factors are major drivers of ruminant meat consumption per capita. Other drivers are Western culture, Muslim religion, female labour participation, economic and social globalisation and meat prices. The main identified drivers of meat demand are difficult to influence through direct policy intervention. Thus, acting indirectly on consumers’ preferences and consumption habits (for instance through information, education policy and increased availability of ready-made plant based products) could be of key importance for mitigating the rise of meat consumption per capita all over the world. |
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2020-02-14 |
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English |
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ISSN |
0195-6663 |
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TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
5224 |
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Author |
von Lampe, M.; Willenbockel, D.; Ahammad, H.; Blanc, E.; Cai, Y.; Calvin, K.; Fujimori, S.; Hasegawa, T.; Havlik, P.; Heyhoe, E.; Kyle, P.; Lotze-Campen, H.; Mason, d’C., Daniel; Nelson, G.C.; Sands, R.D.; Schmitz, C.; Tabeau, A.; Valin, H.; van der Mensbrugghe, D.; van Meijl, H. |
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Why do global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison |
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Journal Article |
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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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45 |
Issue |
1 |
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3-3 |
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Keywords |
Computable general equilibrium; Partial equilibrium; Meta-analysis; Socioeconomic pathway; Climate change; Bioenergy; Land use; Model; intercomparison; land-use change; food demand; crop productivity; climate-change; future |
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Recent studies assessing plausible futures for agricultural markets and global food security have had contradictory outcomes. To advance our understanding of the sources of the differences, 10 global economic models that produce long-term scenarios were asked to compare a reference scenario with alternate socioeconomic, climate change, and bioenergy scenarios using a common set of key drivers. Several key conclusions emerge from this exercise: First, for a comparison of scenario results to be meaningful, a careful analysis of the interpretation of the relevant model variables is essential. For instance, the use of real world commodity prices differs widely across models, and comparing the prices without accounting for their different meanings can lead to misleading results. Second, results suggest that, once some key assumptions are harmonized, the variability in general trends across models declines but remains important. For example, given the common assumptions of the reference scenario, models show average annual rates of changes of real global producer prices for agricultural products on average ranging between -0.4% and +0.7% between the 2005 base year and 2050. This compares to an average decline of real agricultural prices of 4% p.a. between the 1960s and the 2000s. Several other common trends are shown, for example, relating to key global growth areas for agricultural production and consumption. Third, differences in basic model parameters such as income and price elasticities, sometimes hidden in the way market behavior is modeled, result in significant differences in the details. Fourth, the analysis shows that agro-economic modelers aiming to inform the agricultural and development policy debate require better data and analysis on both economic behavior and biophysical drivers. More interdisciplinary modeling efforts are required to cross-fertilize analyses at different scales. |
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2016-10-31 |
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ISSN |
0169-5150 |
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TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4822 |
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