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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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Volume |
3 |
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Pages |
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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no |
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MA @ admin @ |
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4612 |
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Rötter, R.P.; Palosuo, T.; Kersebaum, K.-C.; Angulo, C.; Bindi, M.; Ewert, F.; Ferrise, R.; Hlavinka, P.; Moriondo, M.; Olesen, J.E.; Takáč, J.; Trnka, M. |
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Title |
Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models |
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Journal Article |
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Year |
2012 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
133 |
Issue |
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Pages |
23-36 |
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Keywords |
Climate; Crop growth simulation; Model comparison; Spring barley; Yield variability; Uncertainty; change impacts; nitrogen dynamics; high-temperature; soil-moisture; elevated co2; ceres-wheat; data set; growth; drought; sensitivity |
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► We compared nine crop simulation models for spring barley at seven sites in Europe. ► Applying crop models with restricted calibration leads to high uncertainties. ► Multi-crop model mean yield estimates were in good agreement with observations. ► The degree of uncertainty for simulated grain yield of barley was similar to winter wheat. ► We need more suitable data enabling us to verify different processes in the models. In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha(-1)), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (-1159 kg ha(-1)). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction. |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4592 |
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Author |
Dumont, B.; Vancutsem, F.; Seutin, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Simulation de la croissance du blé à l’aide de modèles écophysiologiques: Synthèse bibliographique des méthodes, potentialités et limitations |
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Journal Article |
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Year |
2012 |
Publication |
Biotechnologie, Agronomie, Société et Environnement |
Abbreviated Journal |
Biotechnologie, Agronomie, Société et Environnement |
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163 |
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376-386 |
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Keywords |
crops; growth; soil; Triticum; wheats; calibration; optimization methods |
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Crop models describe the growth and development of a crop interacting with its surrounding agro-environmental conditions (soil, climate and the close conditions of the plant). However, the implementation of such models remains difficult because of the high number of explanatory variables and parameters. It often happens that important discrepancies appear between measured and simulated values. This article aims to highlight the different sources of uncertainty related to the use of crop models, as well as the actual methods that allow a compensation for or, at least, a consideration of these sources of error during analysis of the model results. This article presents a literature review, which firstly synthesises the general mathematical structure of crop models. The main criteria for evaluating crop models are then described. Finally, several methods used for improving models are given. Parameter estimation methods, including frequentist and Bayesian approaches, are presented and data assimilation methods are reviewed. |
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French |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4584 |
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Martre, P.; He, J.; Le Gouis, J.; Semenov, M.A. |
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Title |
In silico system analysis of physiological traits determining grain yield and protein concentration for wheat as influenced by climate and crop management |
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Journal Article |
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Year |
2015 |
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Journal of Experimental Botany |
Abbreviated Journal |
J. Experim. Bot. |
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Volume |
66 |
Issue |
12 |
Pages |
3581-3598 |
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Keywords |
Climate; *Computer Simulation; Crops, Agricultural/*growth & development/physiology; Edible Grain/*growth & development; Models, Biological; Nitrogen/metabolism; Plant Proteins/*metabolism; Plant Transpiration; Probability; *Quantitative Trait, Heritable; Soil/chemistry; Triticum/growth & development/metabolism/*physiology; Water/chemistry; Crop growth model; genetic adaptation; grain protein concentration; grain yield; interannual variability; sensitivity analysis; wheat (Triticum aestivum L.); yield stability |
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Genetic improvement of grain yield (GY) and grain protein concentration (GPC) is impeded by large genotype×environment×management interactions and by compensatory effects between traits. Here global uncertainty and sensitivity analyses of the process-based wheat model SiriusQuality2 were conducted with the aim of identifying candidate traits to increase GY and GPC. Three contrasted European sites were selected and simulations were performed using long-term weather data and two nitrogen (N) treatments in order to quantify the effect of parameter uncertainty on GY and GPC under variable environments. The overall influence of all 75 plant parameters of SiriusQuality2 was first analysed using the Morris method. Forty-one influential parameters were identified and their individual (first-order) and total effects on the model outputs were investigated using the extended Fourier amplitude sensitivity test. The overall effect of the parameters was dominated by their interactions with other parameters. Under high N supply, a few influential parameters with respect to GY were identified (e.g. radiation use efficiency, potential duration of grain filling, and phyllochron). However, under low N, >10 parameters showed similar effects on GY and GPC. All parameters had opposite effects on GY and GPC, but leaf and stem N storage capacity appeared as good candidate traits to change the intercept of the negative relationship between GY and GPC. This study provides a system analysis of traits determining GY and GPC under variable environments and delivers valuable information to prioritize model development and experimental work. |
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1460-2431 (Electronic) 0022-0957 (Linking) |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4567 |
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Author |
Semenov, M.A.; Mitchell, R.A.C.; Whitmore, A.P.; Hawkesford, M.J.; Parry, M.A.J.; Shewry, P.R. |
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Title |
Shortcomings in wheat yield predictions |
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Journal Article |
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2012 |
Publication |
Nature Climate Change |
Abbreviated Journal |
Nat. Clim. Change |
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2 |
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6 |
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380-382 |
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winter-wheat; elevated CO2; temperature; growth |
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Predictions of a 40–140% increase in wheat yield by 2050, reported in the UK Climate Change Risk Assessment, are based on a simplistic approach that ignores key factors affecting yields and hence are seriously misleading. |
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1758-678x 1758-6798 |
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Commentary |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4504 |
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