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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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133 |
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23-36 |
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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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Dumont, B.; Vancutsem, F.; Seutin, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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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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2012 |
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Biotechnologie, Agronomie, Société et Environnement |
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Biotechnologie, Agronomie, Société et Environnement |
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163 |
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376-386 |
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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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Özkan, Ş.; Hill, J. |
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Implementing innovative farm management practices on dairy farms:a review of feeding systems |
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2015 |
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Turkish Journal of Veterinary and Animal Sciences |
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Turkish Journal of Veterinary and Animal Sciences |
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39 |
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1-9 |
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australia; dairy; double-cropping; feeding system; pasture-based; profitability; forage crop systems; south-west victoria; nutritive characteristics; interannual variation; botanical composition; herbage accumulation; growth-rates; pasture; australia; cows |
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The Australian dairy industry relies primarily on pasture for its feed supply. However, the variability in rainfall negatively affects plant growth, leading to uncertainty in dryland feed supply, especially during periods of high milk price. New feeding (complementary) systems combining perennial ryegrass with another crop and/or pasture species may have the potential to mitigate this seasonal risk and improve productivity and profitability by providing off-season feed. To date, the majority of research studying the integration of alternative crops into pasture-based systems has focused on substitution and utilization of alternative feed sources. There has been little emphasis on the impacts of integration of forage crops into pasture-based systems. This review focuses on pasture-based feeding systems in southeastern Australia and how transitioning of systems contributes to improved productivity leading to improved profitability for dairy farmers. |
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1300-0128 |
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LiveM |
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MA @ admin @ |
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4577 |
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Kersebaum, K.C.; Boote, K.J.; Jorgenson, J.S.; Nendel, C.; Bindi, M.; Frühauf, C.; Gaiser, T.; Hoogenboom, G.; Kollas, C.; Olesen, J.E.; Rötter, R.P.; Ruget, F.; Thorburn, P.J.; Trnka, M.; Wegehenkel, M. |
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Analysis and classification of data sets for calibration and validation of agro-ecosystem models |
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2015 |
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Environmental Modelling & Software |
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Env. Model. Softw. |
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72 |
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402-417 |
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field experiments; data quality; crop modelling; data requirement; minimum data; software; different climatic zones; soil-moisture sensors; spatial variability; nitrogen dynamics; crop models; systems simulation; wheat yields; elevated co2; growth; field |
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Experimental field data are used at different levels of complexity to calibrate, validate and improve agroecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation. (C) 2015 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4563 |
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Caubel, J.; García de Cortázar-Atauri, I.; Launay, M.; de Noblet-Ducoudré, N.; Huard, F.; Bertuzzi, P.; Graux, A.-I. |
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Broadening the scope for ecoclimatic indicators to assess crop climate suitability according to ecophysiological, technical and quality criteria |
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2015 |
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Agricultural and Forest Meteorology |
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Agricultural and Forest Meteorology |
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207 |
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94-106 |
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Climate suitability; Indicator-based method of evaluation; Ecoclimatic; indicator; Crop phenology; Crop ecophysiology; Crop management; Yield; quality; high-temperature; heat-stress; change scenarios; maize; wheat; growth; yield; agriculture; systems; time |
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The cultivation of crops in a given area is highly dependent of climatic conditions. Assessment of how the climate is favorable is highly useful for planners, land managers, farmers and plant breeders who can propose and apply adaptation strategies to improve agricultural potentialities. The aim of this study was to develop an assessment method for crop-climate suitability that was generic enough to be applied to a wide range of issues and crops. The method proposed is based on agroclimatic indicators that are calculated over phenological periods (ecoclimatic indicators). These indicators are highly relevant since they provide accurate information about the effect of climate on particular plant processes and cultural practices that take place during specific phenological periods. Three case studies were performed in order to illustrate the potentialities of the method. They concern annual (maize and wheat) and perennial (grape) crops and focus on the study of climate suitability in terms of the following criteria: ecophysiological, days available to carry out cultural practices, and harvest quality. The analysis of the results revealed both the advantages and limitations of the method. The method is general and flexible enough to be applied to a wide range of issues even if an expert assessment is initially needed to build the analysis framework. The limited number of input data makes it possible to use it to explore future possibilities for agriculture in many areas. The access to intermediate information through elementary ecoclimatic indicators allows users to propose targeted adaptations when climate suitability is not satisfactory. |
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0168-1923 |
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CropM |
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MA @ admin @ |
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4553 |
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