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Mansouri, M. (2013). Modeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods. In P. Masegosa, C. Villacorta, S. Cruz-Corona, M. Garcia-Cascales, J. Lamata, & A. Verdegay (Eds.), (pp. 112–137). Exploring Innovative and Successful Applications of Soft Computing. Hershey PA: IGI Global.
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Virkajärvi, P., Jing, Q., Bélanger, G., Baron, V., Bonesmo, H., & Young, D. (2013). Modeling grassland with CATIMO – focus on the second cut. (Vol. 9(1), pp. 9–13).
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Doltra, J., Olesen, J. E., Báez, D., Louro, A., & Chirinda, N. (2015). Modeling nitrous oxide emissions from organic and conventional cereal-based cropping systems under different management, soil and climate factors. European Journal of Agronomy, 66, 8–20.
Abstract: Mitigation of greenhouse gas emissions from agriculture should be assessed across cropping systems and agroclimatic regions. In this study, we investigate the ability of the FASSET model to analyze differences in the magnitude of N2O emissions due to soil, climate and management factors in cereal-based cropping systems. Forage maize was grown in a conventional dairy system at Mabegondo (NW Spain) and wheat and barley in organic and conventional crop rotations at Foulum (NW Denmark). These two European sites represent agricultural areas with high and low to moderate emission levels, respectively. Field trials included plots with and without catch crops that were fertilized with either mineral N fertilizer, cattle slurry, pig slurry or digested manure. Non-fertilized treatments were also included. Measurements of N2O fluxes during the growing cycle of all the crops at both sites were performed with the static chamber method with more frequent measurements post-fertilization and biweekly measurements when high fluxes were not expected. All cropping systems were simulated with the FASSET version 2.5 simulation model. Cumulative soil seasonal N2O emissions were about ten-fold higher at Mabegondo than at Foulum when averaged across systems and treatments (8.99 and 0.71 kg N2O-N ha(-1), respectively). The average simulated cumulative soil N2O emissions were 9.03 and 1.71 kg N2O-N ha(-1) at Mabegondo and at Foulum, respectively. Fertilization, catch crops and cropping systems had lower influence on the seasonal soil N2O fluxes than the environmental factors. Overall, in its current version FASSET reproduced the effects of the different factors investigated on the cumulative seasonal soil N2O emissions but temporally it overestimated emissions from nitrification and denitrification on particular days when soil operations, ploughing or fertilization, took place. The errors associated with simulated daily soil N2O fluxes increased with the magnitude of the emissions. For resolving causes of differences in simulated and measured fluxes more intensive and temporally detailed measurements of N2O fluxes and soil C and N dynamics would be needed. (C) 2015 Elsevier B.V. All rights reserved.
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Bindi, M., Palosuo, T., Trnka, M., & Semenov, M. A. (2015). Modelling climate change impacts on crop production for food security INTRODUCTION. Clim. Res., 65, 3–5.
Abstract: Process-based crop models that synthesise the latest scientific understanding of biophysical processes are currently the primary scientific tools available to assess potential impacts of climate change on crop production. Important obstacles are still present, however, and must be overcome for improving crop modelling application in integrated assessments of risk, of sustainability and of crop-production resilience in the face of climate change (e.g. uncertainty analysis, model integration, etc.). The research networks MACSUR and AGMIP organised the CropM International Symposium and Workshop in Oslo, on 10-12 February 2014, and present this CR Special, discussing the state-of-the-art-as well as future perspectives-of crop modelling applications in climate change risk assessment, including the challenges of integrated assessments for the agricultural sector.
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Kersebaum, C., Kollas, C., Bindi, M., Nendel, C., Ferrise, R., Moriondo, M., et al. (2014). Modelling complex crop rotations and management across sites in Europe with an ensemble of models..
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