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Author |
Kersebaum, K.C.; Nendel, C. |
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Title |
Site-specific impacts of climate change on wheat production across regions of Germany using different CO2 response functions |
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Journal Article |
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Year |
2014 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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Volume |
52 |
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Pages |
22-32 |
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Keywords |
climate change; co2 effect; crop yield; water use efficiency; groundwater; modeling nitrogen dynamics; winter-wheat; carbon-dioxide; assessing uncertainties; agricultural crops; potential impact; enrichment face; elevated co2; soil; simulation |
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Impact of climate change on crop growth, groundwater recharge and nitrogen leaching in winter wheat production in Germany was assessed using the agro-ecosystem model HERMES with a downscaled (WETTREG) climate change scenario A1B from the ECHAM5 global circulation model. Three alternative algorithms describing the impact of atmospheric CO2 concentration on crop growth (a simple Farquhar-type algorithm, a combined light-use efficiency – maximum assimilation approach and a simple scaling of the maximum assimilation rate) in combination with a Penman-Monteith approach which includes a simple stomata conduction model for evapotranspiration under changing CO2 concentrations were compared within the framework of the HERMES model. The effect of differences in regional climate change, site conditions and different CO2 algorithms on winter wheat yield, groundwater recharge and nitrogen leaching was assessed in 22 regional simulation case studies across Germany. Results indicate that the effects of climate change on wheat production will vary across Germany due to different regional expressions of climate change projection. Predicted yield changes between the reference period (1961-1990) and a future period (2021-2050) range from -0.4 t ha(-1), -0.8 t ha(-1) and -0.6 t ha(-1) at sites in southern Germany to +0.8 t ha(-1), +0.6 t ha(-1) and +0.8 t ha(-1) at coastal regions for the three CO2 algorithms, respectively. On average across all regions, a relative yield change of +0.9%, +3.0%, and +6.0%, respectively, was predicted for Germany. In contrast, a decrease of -11.6% was predicted without the consideration of a CO2 effect. However, simulated yield changes differed even within regions as site conditions had a strong influence on crop growth. Particularly, groundwater-affected sites showed a lower vulnerability to increasing drought risk. Groundwater recharge was estimated to change correspondingly to changes in precipitation. The consideration of the CO2 effect on transpiration in the model led to a prediction of higher rates of annual deep percolation (+16 mm on average across all sites), which was due to higher water-use efficiency of the crops. In contrast to groundwater recharge, simulated nitrogen leaching varied with the choice of the photosynthesis algorithm, predicting a slight reduction in most of the areas. The results underline the necessity of high-resolution data for model-based regional climate change impact assessment and development of adaptation measures. (C) 2013 Elsevier B.V. All rights reserved. |
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1161-0301 |
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MA @ admin @ |
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4527 |
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Dumont, B.; Leemans, V.; Mansouri, M.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Parameter identification of the STICS crop model, using an accelerated formal MCMC approach |
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Journal Article |
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Year |
2014 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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52 |
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121-135 |
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Keywords |
crop model; parameter estimation; bayes; stics; dream; global sensitivity-analysis; simulation-model; nitrogen balances; bayesian-approach; generic model; wheat; prediction; water; optimization; algorithm |
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This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling. (C) 2013 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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MA @ admin @ |
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4520 |
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Ventrella, D.; Charfeddine, M.; Moriondo, M.; Rinaldi, M.; Bindi, M. |
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Title |
Agronomic adaptation strategies under climate change for winter durum wheat and tomato in southern Italy: irrigation and nitrogen fertilization |
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Journal Article |
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2012 |
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Regional Environmental Change |
Abbreviated Journal |
Reg Environ Change |
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12 |
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3 |
Pages |
407-419 |
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Keywords |
Modelling; Climate change; Agronomic adaptation strategies; Yield; Tomato; Winter durum wheat; air co2 enrichment; change scenarios; cropping systems; change impacts; simulation; agriculture; variability; increase; model; responses; Environmental Sciences & Ecology |
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Agricultural crops are affected by climate change due to the relationship between crop development, growth, yield, CO2 atmospheric concentration and climate conditions. In particular, the further reduction in existing limited water resources combined with an increase in temperature may result in higher impacts on agricultural crops in the Mediterranean area than in other regions. In this study, the cropping system models CERES-Wheat and CROPGRO-Tomato of the Decision Support System for Agrotechnology Transfer (DSSAT) were used to analyse the response of winter durum wheat (Triticum aestivum L.) and tomato (Lycopersicon esculentum Mill.) crops to climate change, irrigation and nitrogen fertilizer managements in one of most productive areas of Italy (i.e. Capitanata, Puglia). For this analysis, three climatic datasets were used: (1) a single dataset (50 km x 50 km) provided by the JRC European centre for the period 1975-2005; two datasets from HadCM3 for the IPCC A2 GHG scenario for time slices with +2A degrees C (centred over 2030-2060) and +5A degrees C (centred over 2070-2099), respectively. All three datasets were used to generate synthetic climate series using a weather simulator (model LARS-WG). Adaptation strategies, such as irrigation and N fertilizer managements, have been investigated to either avoid or at least reduce the negative impacts induced by climate change impacts for both crops. Warmer temperatures were primarily shown to accelerate wheat and tomato phenology, thereby resulting in decreased total dry matter accumulation for both tomato and wheat under the +5A degrees C future climate scenario. Under the +2A degrees C scenario, dry matter accumulation and resulting yield were also reduced for tomato, whereas no negative yield effects were observed for winter durum wheat. In general, limiting the global mean temperature change of 2A degrees C, the application of adaptation strategies (irrigation and nitrogen fertilization) showed a positive effect in minimizing the negative impacts of climate change on productivity of tomato cultivated in southern Italy. |
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1436-3798 1436-378x |
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MA @ admin @ |
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4480 |
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Bassu, S.; Brisson, N.; Durand, J.-L.; Boote, K.; Lizaso, J.; Jones, J.W.; Rosenzweig, C.; Ruane, A.C.; Adam, M.; Baron, C.; Basso, B.; Biernath, C.; Boogaard, H.; Conijn, S.; Corbeels, M.; Deryng, D.; De Sanctis, G.; Gayler, S.; Grassini, P.; Hatfield, J.; Hoek, S.; Izaurralde, C.; Jongschaap, R.; Kemanian, A.R.; Kersebaum, K.C.; Kim, S.-H.; Kumar, N.S.; Makowski, D.; Müller, C.; Nendel, C.; Priesack, E.; Pravia, M.V.; Sau, F.; Shcherbak, I.; Tao, F.; Teixeira, E.; Timlin, D.; Waha, K. |
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Title |
How do various maize crop models vary in their responses to climate change factors |
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Journal Article |
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Year |
2014 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
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20 |
Issue |
7 |
Pages |
2301-2320 |
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Keywords |
Carbon Dioxide/metabolism; *Climate Change; Crops, Agricultural/growth & development/metabolism; Geography; Models, Biological; Temperature; Water/*metabolism; Zea mays/*growth & development/*metabolism; AgMIP; [Co2]; climate; maize; model intercomparison; simulation; uncertainty |
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Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. |
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1354-1013 |
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MA @ admin @ |
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4510 |
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Francone, C.; Cassardo, C.; Richiardone, R.; Confalonieri, R. |
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Sensitivity Analysis and Investigation of the Behaviour of the UTOPIA Land-Surface Process Model: A Case Study for Vineyards in Northern Italy |
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Journal Article |
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2012 |
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Boundary-Layer Meteorology |
Abbreviated Journal |
Boundary-Layer Meteorology |
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144 |
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3 |
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419-430 |
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energy balance; hydrological balance; land-surface model; morris method; vegetation cover; vitis vinifera l.; atmosphere transfer scheme; environmental-models; energy-balance; uncertainty; simulation; canopy |
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We used sensitivity-analysis techniques to investigate the behaviour of the land-surface model UTOPIA while simulating the micrometeorology of a typical northern Italy vineyard (Vitis vinifera L.) under average climatic conditions. Sensitivity-analysis experiments were performed by sampling the vegetation parameter hyperspace using the Morris method and quantifying the parameter relevance across a wide range of soil conditions. This method was used since it proved its suitability for models with high computational time or with a large number of parameters, in a variety of studies performed on different types of biophysical models. The impact of input variability was estimated on reference model variables selected among energy (e.g. net radiation, sensible and latent heat fluxes) and hydrological (e.g. soilmoisture, surface runoff, drainage) budget components. Maximum vegetation cover and maximum leaf area index were ranked as the most relevant parameters, with sensitivity indices exceeding the remaining parameters by about one order of magnitude. Soil variability had a high impact on the relevance of most of the vegetation parameters: coefficients of variation calculated on the sensitivity indices estimated for the different soils often exceeded 100 %. The only exceptions were represented by maximum vegetation cover and maximum leaf area index, which showed a low variability in sensitivity indices while changing soil type, and confirmed their key role in affecting model results. |
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0006-8314 1573-1472 |
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MA @ admin @ |
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4470 |
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