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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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Title |
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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Volume |
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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Abstract |
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 @ |
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4822 |
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Author |
Lotze-Campen, H.; von Lampe, M.; Kyle, P.; Fujimori, S.; Havlik, P.; van Meijl, H.; Hasegawa, T.; Popp, A.; Schmitz, C.; Tabeau, A.; Valin, H.; Willenbockel, D.; Wise, M. |
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Title |
Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison |
Type |
Journal Article |
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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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Volume |
45 |
Issue |
1 |
Pages |
103-116 |
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Keywords |
energy demand; agricultural markets; general equilibrium modeling; partial equilibrium modeling; model comparison; greenhouse-gas emissions; land-use; energy; productivity; scenarios; policies; capture; storage; system |
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Integrated Assessment studies have shown that meeting ambitious greenhouse gas mitigation targets will require substantial amounts of bioenergy as part of the future energy mix. In the course of the Agricultural Model Intercomparison and Improvement Project (AgMIP), five global agro-economic models were used to analyze a future scenario with global demand for ligno-cellulosic bioenergy rising to about 100 ExaJoule in 2050. From this exercise a tentative conclusion can be drawn that ambitious climate change mitigation need not drive up global food prices much, if the extra land required for bioenergy production is accessible or if the feedstock, for example, from forests, does not directly compete for agricultural land. Agricultural price effects across models by the year 2050 from high bioenergy demand in an ambitious mitigation scenario appear to be much smaller (+5% average across models) than from direct climate impacts on crop yields in a high-emission scenario (+25% average across models). However, potential future scarcities of water and nutrients, policy-induced restrictions on agricultural land expansion, as well as potential welfare losses have not been specifically looked at in this exercise. |
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0169-5150 |
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CropM, TradeM |
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MA @ admin @ |
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4532 |
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Author |
Müller, C.; Robertson, R.D. |
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Title |
Projecting future crop productivity for global economic modeling |
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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 |
Pages |
37-50 |
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Keywords |
climate change; crop modeling; agricultural productivity; land use; greenhouse-gas emissions; soil organic-carbon; sub-saharan africa; climate-change; elevated co2; land-use; system model; wheat yields; maize yields; agriculture |
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Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply 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. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10-38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change. |
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0169-5150 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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4533 |
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Montesino-San Martín, M.; Olesen, J.E.; Porter, J.R. |
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A genotype, environment and management (GxExM) analysis of adaptation in winter wheat to climate change in Denmark |
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Journal Article |
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Year |
2014 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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187 |
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1-13 |
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Winter wheat; Climate change; Adaptation; Uncertainty; Europe; food security; model hadgem1; physical-properties; regional climate; change impacts; field-scale; land-use; yield; nitrogen; variability |
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Wheat yields in Europe have shown stagnating trends during the last two decades, partly attributed to climate change. Such developments challenge the needs for increased production, in particular at higher latitudes, to meet increasing global demands and expected productivity reductions at lower latitudes. Climate change projections from three General Circulation Models or GCMs (UKMO-HadGEM1, INM-GM3.0 and CSIRO-Mk3.1) for the A1FI SIZES emission scenario for 2000 to 2100 were downscaled at a northern latitude location (Foulum, Denmark) using LARS-WG5.3. The scenarios accounted for changes in temperature, precipitation and atmospheric CO2 concentration. In addition, three temperature-variability scenarios were included assuming different levels of decreased temperature variability in winter and increased in summer. Crop yield was simulated for the different climate change scenarios by a calibrated version of AFRCWHEAT2 to model several combinations of genotypes (varying in crop growth, development and tolerance to water and nitrogen scarcity) and management (sowing dates and nitrogen fertilization rate). The simulations showed a slight improvement of grain yields (0.3-1.2 Mg ha(-1)) in the medium-term (2030-2050), but not enough to cope with expected increases in demand for food and feed. Optimum management added up to 1.8 Mg ha(-1). Genetic modifications regarding winter wheat crop development exhibit the greatest sensitivity to climate and larger potential for improvement (+3.8 Mg ha(-1)). The results consistently points towards need for cultivars with a longer reproductive phases (2.9-7.5% per 1 degrees C) and lower photoperiod sensitivities. Due to the positive synergies between several genotypic characteristics, multiple-target breeding programmes would be necessary, possibly assisted by model-based assessments of optimal phenotypic characteristics. |
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0168-1923 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4630 |
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Gomara, I.; Bellocchi, G.; Martin, R.; Rodriguez-Fonseca, B.; Ruiz-Ramos, M. |
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Title |
Influence of climate variability on the potential forage production of a mown permanent grassland in the French Massif Central |
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Journal Article |
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2020 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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280 |
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107768 |
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climate variability; grasslands; potential yield; climate services; forage production forecasts; french massif central; pasture simulation-model; dry-matter production; atmospheric; circulation; crop yield; SST anomalies; maize yield; managed grasslands; storm track; ENSO; impacts |
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Climate Services (CS) provide support to decision makers across socio-economic sectors. In the agricultural sector, one of the most important CS applications is to provide timely and accurate yield forecasts based on climate prediction. In this study, the Pasture Simulation model (PaSim) was used to simulate, for the period 1959–2015, the forage production of a mown grassland system (Laqueuille, Massif Central of France) under different management conditions, with meteorological inputs extracted from the SAFRAN atmospheric database. The aim was to generate purely climate-dependent timeseries of optimal forage production, a variable that was maximized by brighter and warmer weather conditions at the grassland. A long-term increase was observed in simulated forage yield, with the 1995–2015 average being 29% higher than the 1959–1979 average. Such increase seems consistent with observed rising trends in temperature and CO2, and multi-decadal changes in incident solar radiation. At interannual timescales, sea surface temperature anomalies of the Mediterranean (MED), Tropical North Atlantic (TNA), equatorial Pacific (El Niño Southern Oscillation) and the North Atlantic Oscillation (NAO) index were found robustly correlated with annual forage yield values. Relying only on climatic predictors, we developed a stepwise statistical multi-regression model with leave-one-out cross-validation. Under specific management conditions (e.g., three annual cuts) and from one to five months in advance, the generated model successfully provided a p-value<0.01 in correlation (t-test), a root mean square error percentage (%RMSE) of 14.6% and a 71.43% hit rate predicting above/below average years in terms of forage yield collection. This is the first modeling study on the possible role of large-scale oceanic–atmospheric teleconnections in driving forage production in Europe. As such, it provides a useful springboard to implement a grassland seasonal forecasting system in this continent. |
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2020-06-08 |
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LiveM, ft_macsur |
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MA @ admin @ |
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5233 |
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