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Pirttioja, N.; Carter, T.R.; Fronzek, S.; Bindi, M.; Hoffmann, H.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, M.; Acutis, M.; Asseng, S.; Baranowski, P.; Basso, B.; Bodin, P.; Buis, S.; Cammarano, D.; Deligios, P.; Destain, M.F.; Dumont, B.; Ewert, F.; Ferrise, R.; François, L.; Gaiser, T.; Hlavinka, P.; Jacquemin, I.; Kersebaum, K.C.; Kollas, C.; Krzyszczak, J.; Lorite, I.J.; Minet, J.; Minguez, M.I.; Montesino-San Martin, M.; Moriondo, M.; Müller, C.; Nendel, C.; Öztürk, I.; Perego, A.; Rodríguez, A.; Ruane, A.C.; Ruget, F.; Sanna, M.; Semenov, M.A.; Slawinski, C.; Stratonovitch, P.; Supit, I.; Waha, K.; Wang, E.; Wu, L.; Zhao, Z.; Rötter, R.P. |
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Title |
Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces |
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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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87-105 |
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Keywords |
climate; crop model; impact response surface; IRS; sensitivity analysis; wheat; yield; climate-change impacts; uncertainty; 21st-century; projections; simulation; growth; region |
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Abstract |
This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development. |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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no |
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MA @ admin @ |
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4662 |
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Hlavinka, P.; Kersebaum, K.C.; Dubrovský, M.; Fischer, M.; Pohanková, E.; Balek, J.; Žalud, Z.; Trnka, M. |
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Title |
Water balance, drought stress and yields for rainfed field crop rotations under present and future conditions in the Czech Republic |
Type |
Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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Volume |
65 |
Issue |
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Pages |
175-192 |
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Keywords |
crop growth model; evapotranspiration; soil; climate change; climate-change scenarios; spring barley; wheat production; winter-wheat; model; impacts; europe; uncertainties; simulation; strategies |
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Continuous crop rotation modeling is a prospective trend that, compared to 1-crop or discrete year-by-year calculations, can provide more accurate results that are closer to real conditions. The goal of this study was to compare the water balance and yields estimated by the HERMES crop rotation model for present and future climatic conditions in the Czech Republic. Three locations were selected, representing important agricultural regions with different climatic conditions. Crop rotation (spring barley, silage maize, winter wheat, winter rape) was simulated from 1981-2080. The 1981-2010 period was covered by measured meteorological data, while 2011-2080 was represented by a transient synthetic weather series from the weather generator M& Rfi. The data were based on 5 circulation models, representing an ensemble of 18 CMIP3 global circulation models, to preserve much of the uncertainty of the original ensemble. Two types of crop management were compared, and the influences of soil quality, increasing atmospheric CO2 and adaptation measures (i. e. sowing date changes) were also considered. Results suggest that under a ‘dry’ scenario (such as GFCM21), C-3 crops in drier regions will be devastated for a significant number of seasons. Negative impacts are likely even on premium-quality soils regardless of flexible sowing dates and accounting for increasing CO2 concentrations. Moreover, in dry conditions, the use of crop rotations with catch crops may have negative impacts, exacerbating the soil water deficit for subsequent crops. This approach is a promising method for determining how various management strategies and crop rotations can affect yields as well as water, carbon and nitrogen cycling. |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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MA @ admin @ |
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4663 |
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Author |
Lardy, R.; Bellocchi, G.; Martin, R. |
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Title |
Vuln-Indices: Software to assess vulnerability to climate change |
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Journal Article |
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2015 |
Publication |
Computers and Electronics in Agriculture |
Abbreviated Journal |
Computers and Electronics in Agriculture |
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114 |
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53-57 |
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climate change; Java; vulnerability indices; pasture simulation-model; integrated assessment; environmental-change; change impacts; system |
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Vuln-Indices Java-based software was developed on concepts of vulnerability to climate change of agro-ecological systems. It implements the calculation of vulnerability indices on series of state variables for assessments at both site and region levels. The tool is useful because synthetic indices help capturing complex processes and prove effective to identify the factors responsible for vulnerability and their relative importance. It is suggested that the tool may be plausible for use with stakeholders to disseminate information of climate change impacts. (C) 2015 Elsevier B.V. All rights reserved. |
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0168-1699 |
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LiveM, ft_macsur |
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MA @ admin @ |
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4648 |
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Lorite, I.J.; García-Vila, M.; Santos, C.; Ruiz-Ramos, M.; Fereres, E. |
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Title |
AquaData and AquaGIS: Two computer utilities for temporal and spatial simulations of water-limited yield with AquaCrop |
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Journal Article |
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2013 |
Publication |
Computers and Electronics in Agriculture |
Abbreviated Journal |
Computers and Electronics in Agriculture |
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96 |
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227-237 |
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Keywords |
software tool; aquacrop; crop simulation model; geographic information system; spatial aggregation; fao crop model; irrigation management; iberian peninsula; southern spain; climate models; impacts; program; europe; system |
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The crop simulation model AquaCrop, recently developed by FAO can be used for a wide range of purposes. However, in its present form, its use over large areas or for applications that require a large number of simulations runs (e.g., long-term analysis), is not practical without developing software to facilitate such applications. Two tools for managing the inputs and outputs of AquaCrop, named AquaData and AquaGIS, have been developed for this purpose and are presented here. Both software utilities have been programmed in Delphi v. 5 and in addition, AquaGIS requires the Geographic Information System (GIS) programming tool MapObjects. These utilities allow the efficient management of input and output files, along with a GIS module to develop spatial analysis and effect spatial visualization of the results, facilitating knowledge dissemination. A sample of application of the utilities is given here, as an AquaCrop simulation analysis of impact of climate change on wheat yield in Southern Spain, which requires extensive input data preparation and output processing. The use of AquaCrop without the two utilities would have required approximately 1000 h of work, while the utilization of AquaData and AquaGIS reduced that time by more than 99%. Furthermore, the use of GIS, made it possible to perform a spatial analysis of the results, thus providing a new option to extend the use of the AquaCrop model to scales requiring spatial and temporal analyses. (C) 2013 Elsevier B.V. All rights reserved. |
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0168-1699 |
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CropM |
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MA @ admin @ |
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4609 |
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Author |
Mansouri, M.; Dumont, B.; Destain, M.-F. |
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Title |
Modeling and prediction of nonlinear environmental system using Bayesian methods |
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Journal Article |
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Year |
2013 |
Publication |
Computers and Electronics in Agriculture |
Abbreviated Journal |
Computers and Electronics in Agriculture |
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92 |
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16-31 |
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Keywords |
state and parameter estimation; variational filter; particle filter; extended kalman filter; nonlinear environmental system; leaf area index and soil moisture model; extended kalman filter; state-space models; parameter-estimation; particle filters; navigation; tutorial; tracking |
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An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination of the model parameters. In these cases, estimating these variables or parameters from other easily obtained measurements can be extremely useful. This work addresses the problem of monitoring and modeling a leaf area index and soil moisture model (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), particle filter (PF), and the more recently developed technique variational filter (VF). Specifically, two comparative studies are performed. In the first comparative study, the state variables (the leaf-area index LAI, the volumetric water content of the soil layer 1, HUR1 and the volumetric water content of the soil layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of number of estimated model parameters on the accuracy and convergence of these techniques are also assessed. The results of both comparative studies show that the PF provides a higher accuracy than the EKF, which is due to the limited ability of the EKF to handle highly nonlinear processes. The results also show that the VF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the VF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The results of the second comparative study show that, for all techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. However, the VF can still provide both convergence as well as accuracy related advantages over other estimation methods. (C) 2013 Elsevier B.V. All rights reserved. |
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0168-1699 |
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CropM |
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MA @ admin @ |
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4495 |
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