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Author |
Strauss, F.; Moltchanova, E.; Schmid, E. |
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Title |
Spatially explicit modeling of long-term drought impacts on crop production in Austria |
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Journal Article |
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Year |
2013 |
Publication |
American Journal of Climate Change |
Abbreviated Journal |
American Journal of Climate Change |
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Volume |
2 |
Issue |
3 |
Pages  |
1-11 |
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Keywords |
Long-Term Drought Modeling; Dry Day Index; Biophysical Impacts; Spatial Variability; EPIC; Austria |
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Abstract |
Droughts have serious and widespread impacts on crop production with substantial economic losses. The frequency and severity of drought events may increase in the future due to climate change. We have developed three meteorological drought scenarios for Austria in the period 2008-2040. The scenarios are defined based on a dry day index which is combined with bootstrapping from an observed daily weather dataset of the period 1975-2007. The severity of long-term drought scenarios is characterized by lower annual and seasonal precipitation amounts as well as more sig- nificant temperature increases compared to the observations. The long-term impacts of the drought scenarios on Aus- trian crop production have been analyzed with the biophysical process model EPIC (Environmental Policy Integrated Climate). Our simulation outputs show that—for areas with historical mean annual precipitation sums below 850 mm— already slight increases in dryness result in significantly lower crop yields i.e. depending on the drought severity, be- tween 0.6% and 0.9% decreases in mean annual dry matter crop yields per 1.0% decrease in mean annual precipitation sums. The EPIC results of more severe droughts show that spring and summer precipitation may become a limiting factor in crop production even in regions with historical abundant precipitation. |
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2167-9495 |
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CropM, ft_macsur |
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no |
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MA @ admin @ |
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4507 |
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Dono, G.; Cortignani, R.; Doro, L.; Giraldo, L.; Ledda, L.; Pasqui, M.; Roggero, P.P. |
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Title |
Adapting to uncertainty associated with short-term climate variability changes in irrigated Mediterranean farming systems |
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Journal Article |
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Year |
2013 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
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Volume |
117 |
Issue |
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Pages |
1-12 |
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Keywords |
changed climate variability; dsp; epic; adaptation; water management; irrigation; simulating impacts; co2 concentration; crop production; productivity; maize; yield; growth; model; photosynthesis; agriculture |
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Abstract |
Short-term perspectives appear to be relevant in formulating adaptation measures to changed climate variability (CCV) as a part of the European Rural Development Policy (RDP). Indeed, short-run CCV is the variation that farmers would perceive to such an extent that a political demand would be generated for adapting support measures. This study evaluates some relevant agronomic and economic impacts of CCV as modelled in a near future time period at the catchment scale in a rural district in Sardinia (Italy). The effects of CCV are assessed in relation to the availability of irrigation water and the irrigation needs of maize. The Environmental Policy Integrated Climate (EPIC) model was used to simulate the impact of key climatic variables on the irrigation water requirements and yields of maize. A three-stage discrete stochastic programming model was then applied to simulate management and economic responses to those changes. The overall economic impact of a simulated CCV was found to be primarily caused by reduced stability in the future supply of irrigation water. Adaptations to this instability will most likely lead to a higher level of groundwater extraction and a reduction in the demand for labour. Changed climate variability will most likely reduce the income potential of small-scale farming. The most CCV-vulnerable farm typologies were identified, and the implications were discussed in relation to the development of adaptation measures within the context of the Common Agricultural Policy of European Union. (C) 2013 Elsevier Ltd. All rights reserved. |
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0308521x |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4489 |
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Author |
Challinor, A.J.; Smith, M.S.; Thornton, P. |
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Title |
Use of agro-climate ensembles for quantifying uncertainty and informing adaptation |
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Journal Article |
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Year |
2013 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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170 |
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2-7 |
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Keywords |
Climate models; Crop models; Ensembles; Climate change; Adaptation; Food security; Climate variability; Uncertainty; Crop yield |
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► Introduces the special issue on Agricultural prediction using climate model ensembles. ► Discuss remaining scientific challenges. ► Develops distinction between projection- and utility-based ensemble modelling. ► Recommendations made RE modelling and the analysis and reporting of uncertainty. Significant progress has been made in the use of ensemble agricultural and climate modelling, and observed data, to project future productivity and to develop adaptation options. An increasing number of agricultural models are designed specifically for use with climate ensembles, and improved methods to quantify uncertainty in both climate and agriculture have been developed. Whilst crop–climate relationships are still the most common agricultural study of this sort, on-farm management, hydrology, pests, diseases and livestock are now also examined. This paper introduces all of these areas of progress, with more detail being found in the subsequent papers in the special issue. Remaining scientific challenges are discussed, and a distinction is developed between projection- and utility-based approaches to agro-climate ensemble modelling. Recommendations are made regarding the manner in which uncertainty is analysed and reported, and the way in which models and data are used to make inferences regarding the future. A key underlying principle is the use of models as tools from which information is extracted, rather than as competing attempts to represent reality. |
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2015-09-23 |
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0168-1923 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4690 |
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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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2014 |
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Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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45 |
Issue |
1 |
Pages |
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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0169-5150 |
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TradeM, ft_macsur |
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MA @ admin @ |
Serial |
4822 |
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Author |
Bindi, M.; Palosuo, T.; Trnka, M.; Semenov, M.A. |
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Title |
Modelling climate change impacts on crop production for food security INTRODUCTION |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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65 |
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3-5 |
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Crop production; Climate change impact and adaptation assessments; Upscaling; Model ensembles |
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Abstract |
Process-based crop models that synthesise the latest scientific understanding of biophysical processes are currently the primary scientific tools available to assess potential impacts of climate change on crop production. Important obstacles are still present, however, and must be overcome for improving crop modelling application in integrated assessments of risk, of sustainability and of crop-production resilience in the face of climate change (e.g. uncertainty analysis, model integration, etc.). The research networks MACSUR and AGMIP organised the CropM International Symposium and Workshop in Oslo, on 10-12 February 2014, and present this CR Special, discussing the state-of-the-art-as well as future perspectives-of crop modelling applications in climate change risk assessment, including the challenges of integrated assessments for the agricultural sector. |
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2016-10-31 |
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ISSN |
0936-577x |
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Editorial Material |
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CropM, ftnotmacsur |
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Call Number |
MA @ admin @ |
Serial |
4785 |
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