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Eitzinger, J.; Thaler, S.; Schmid, E.; Strauss, F.; Ferrise, R.; Moriondo, M.; Bindi, M.; Palosuo, T.; Rotter, R.; Kersebaum, K.C.; Olesen, J.E.; Patil, R.H.; Saylan, L.; Caldag, B.; Caylak, O. |
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Title |
Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria |
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Journal Article |
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Year |
2013 |
Publication |
Journal of Agricultural Science |
Abbreviated Journal |
J. Agric. Sci. |
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Volume |
151 |
Issue |
6 |
Pages |
813-835 |
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Keywords  |
simulate yield response; climate-change scenarios; central-europe; nitrogen dynamics; high-temperature; future climate; elevated co2; soil; growth; variability |
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Abstract |
The objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity. |
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0021-8596 |
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CropM |
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MA @ admin @ |
Serial |
4601 |
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Author |
Perego, A.; Giussani, A.; Sanna, M.; Fumagalli, M.; Carozzi, M.; Alfieri, L.; Brenna, S.; Acutis, M. |
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Title |
The ARMOSA simulation crop model: overall features, calibration and validation results |
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Journal Article |
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Year |
2013 |
Publication |
Italian Journal of Agrometeorology |
Abbreviated Journal |
Italian Journal of Agrometeorology |
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3 |
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23-38 |
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Keywords |
simulation model; crop growth; water dynamics; nitrogen leaching; performance assessment; nitrogen dilution curve; field-scale; soil; systems; maize; water; dynamics; growth; winter; evaporation |
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Abstract |
ARMOSA is a dynamic simulation model which was developed to simulate crop growth and development, water and nitrogen dynamics under different pedoclimatic conditions and cropping systems in the arable land. The model is meant to be a tool for the evaluation of the impact of different crop management practices on soil nitrogen and carbon cycles and groundwater nitrate pollution. A large data set collected over three to six years from six monitoring sites in Lombardia plain was used to calibrate and validate the model parameters. Measured meteorological data, soil chemical and physical characterizations, crop-related data of different cropping systems allowed for a proper parameterization. Fit indexes showed the reliability of the model in adequately predicting crop-related variables, such as above ground biomass (RRMSE=11.18, EF=0.94, r=0.97), Leaf Area Index maximum value (RRMSE=8.24, EF=0.37, r=0.72), harvest index (RRMSE=19.4, EF=0.32, r=0.74), and crop N uptake (RRMSE=20.25, EF=0.69, r=0.85). Using two different one-year data set from each monitoring site, the model was calibrated and validated, getting to encouraging results: RRMSE=6.28, EF=0.52, r=0.68 for soil water content at different depths, and RRMSE=34.89, EF=0.59, r=0.75 for soil NO3-N content along soil profile. The simulated N leaching was in full agreement with measured data (RRMSE=26.62, EF=0.88, r=0.98). |
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2038-5625 |
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CropM, ftnotmacsur |
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Call Number |
MA @ admin @ |
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4612 |
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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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Year |
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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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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Volume |
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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Author |
Calanca, P.; Semenov, M.A. |
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Local-scale climate scenarios for impact studies and risk assessments: integration of early 21st century ENSEMBLES projections into the ELPIS database |
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Journal Article |
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Year |
2013 |
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Theoretical and Applied Climatology |
Abbreviated Journal |
Theor. Appl. Climatol. |
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113 |
Issue |
3-4 |
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445-455 |
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stochastic weather generators; regional climate; lars-wg; daily; precipitation; models; simulation; europe; temperature; variability; heatwaves |
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We present the integration of early 21st century climate projections for Europe based on simulations carried out within the EU-FP6 ENSEMBLES project with the LARS-WG stochastic weather generator. The aim was to upgrade ELPIS, a repository of local-scale climate scenarios for use in impact studies and risk assessments that already included global projections from the CMIP3 ensemble and regional scenarios for Japan. To obtain a more reliable simulation of daily rainfall and extremes, changes in wet and dry series derived from daily ENSEMBLES outputs were taken into account. Kernel average smoothers were used to reduce noise arising from sampling artefacts. Examples of risk analyses based on 25-km climate projections from the ENSEMBLES ensemble of regional climate models illustrate the possibilities offered by the updated version of ELPIS. The results stress the importance of tailored information for local-scale impact assessments at the European level. |
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0177-798x 1434-4483 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4484 |
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