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Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

B. Dumont, V. Leemans, M. Mansouri, B. Bodson, J.-P. Destain, M.-F. Destain
2014 Environmental Modelling & Software  
Acknowledgements 784 The authors would like to thank the SPW (DGARNE) for its financial support for the project entitled 785 'Suivi en temps réel de l'environnement d'une parcelle agricole par un réseau  ...  152 STICS soil-crop model.  ...  STICS is a generic soil-216 crop model that can simulate a broad range of crops. It simulates the water, carbon and N dynamics in 217 the soil-plant-atmosphere system on a day-by-day basis.  ... 
doi:10.1016/j.envsoft.2013.10.022 fatcat:epxmz3ympzhrbptzexmdpdpzjm

Calibration and evaluation of the STICS soil-crop model for faba bean to explain variability in yield and N2 fixation

Gatien N. Falconnier, Etienne-Pascal Journet, Laurent Bedoussac, Anthony Vermue, Florent Chlébowski, Nicolas Beaudoin, Eric Justes
2019 European Journal of Agronomy  
The STICS model was chosen for its agro-environmental purpose  ...  Soil-crop models provide quantitative information to evaluate these processes and help to design innovative cropping system including legumes.  ...  of soil mineral N.  ... 
doi:10.1016/j.eja.2019.01.001 fatcat:v3ihswgf2nh2nkl4iiybimnyzm

Modeling sugar cane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values

A. Valade, P. Ciais, N. Vuichard, N. Viovy, N. Huth, F. Marin, J.-F. Martiné
2014 Geoscientific Model Development Discussions  
Published by Copernicus Publications on behalf of the European Geosciences Union. A. Valade et al.: Modeling sugarcane yield with a process-based model Geosci.  ...  Model Dev., 7, 1225-1245, 2014 www.geosci-model-dev.net/7/1225/2014/ Geosci. Model Dev., 7, 1225-1245, 2014 www.geosci-model-dev.net/7/1225/2014/  ...  We acknowledge Philippe Peylin for his help with Bayesian optimization and Patrick Brockmann for his help with data visualization. Edited by: H. Sato  ... 
doi:10.5194/gmdd-7-1197-2014 fatcat:a655zuuffjbgteieiat22vnnei

Modelling of Drainage and Hay Production over the Crau Aquifer for Analysing Impact of Global Change on Aquifer Recharge

Albert Olioso, Rémi Lecerf, Antoine Baillieux, André Chanzy, Françoise Ruget, Olivier Banton, Patrice Lecharpentier, Fabienne Trolard, Anne-Laure Cognard-Plancq
2013 Procedia Environmental Sciences  
We implemented a modelling system combining the STICS crop model, used in a distributed mode, and the MODFLOW aquifer model for analysing the evolution of hay production, aquifer recharge and water level  ...  The modelling system was implemented for several scenarios concerning climate and land use evolutions, as well as water availability for irrigation, in a close future (2025)(2026)(2027)(2028)(2029)(2030  ...  This corresponds to a specific combination of input variables (climatic data, initial soil variables, crop input data, parameters for soil and crop functioning) and is termed a USM: Unit of SiMulation.  ... 
doi:10.1016/j.proenv.2013.06.078 fatcat:i7zw47442vhlvldytogwlk5cxq

Dynamic Modeling of Crop–Soil Systems to Design Monitoring and Automatic Irrigation Processes: A Review with Worked Examples

Jorge Lopez-Jimenez, Alain Vande Vande Wouwer, Nicanor Quijano
2022 Water  
validation from experimental data, estimation of non-measured variables and sensor fusion, and predictive control based on cropsoil and weather models.  ...  In this review, the different elements of an automatic control system are described, including the mathematical modeling of the cropsoil systems, instrumentation and actuation, model identification and  ...  Acknowledgments: The first author acknowledge the support by COLCIENCIAS grant 727. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/w14060889 fatcat:zkpjlx6kwjc67eb2dovc6wnala

The carbon balance of European croplands: A cross-site comparison of simulation models

Martin Wattenbach, Oliver Sus, Nicolas Vuichard, Simon Lehuger, Pia Gottschalk, Longhui Li, Adrian Leip, Mathew Williams, Enrico Tomelleri, Werner Leo Kutsch, Nina Buchmann, Werner Eugster (+10 others)
2010 Agriculture, Ecosystems & Environment  
Here, we present a multi-site model comparison for four cropland ecosystem models namely the DNDC, ORCHIDEE-STICS, CERES-EGC and SPA model.  ...  ORCHIDEE-STICS and the SPA demonstrate the best performance, nevertheless, they are not able to simulate full crop rotations under consideration of multiple management.  ...  For example, SPA has no maize and ORCHIDEE-STICS no winter barley implementation yet, and CERES-EGC produces no estimate of R eco .  ... 
doi:10.1016/j.agee.2010.08.004 fatcat:is2vykizszcu3fbk7j44ydg2xq

Simultaneous Calibration of Grapevine Phenology and Yield with a Soil–Plant–Atmosphere System Model Using the Frequentist Method

Chenyao Yang, Christoph Menz, Helder Fraga, Samuel Reis, Nelson Machado, Aureliano C. Malheiro, João A. Santos
2021 Agronomy  
Reliable estimations of parameter values and associated uncertainties are crucial for crop model applications in agro-environmental research.  ...  The results indicate that the best-estimated parameters show a satisfactory predictive performance, with a mean bias of −2 to 4 days for phenology and −232 to 159 kg/ha for yield.  ...  Finally, all authors are grateful for ADVID and their associates, for collaborations in data collections and provisions under the Viticulture Observatory of the DDR.  ... 
doi:10.3390/agronomy11081659 fatcat:4jlbptxcazfnvnussn4cpa6fxa

Hi-sAFe: A 3D Agroforestry Model for Integrating Dynamic Tree–Crop Interactions

Christian Dupraz, Kevin J. Wolz, Isabelle Lecomte, Grégoire Talbot, Grégoire Vincent, Rachmat Mulia, François Bussière, Harry Ozier-Lafontaine, Sitraka Andrianarisoa, Nick Jackson, Gerry Lawson, Nicolas Dones (+7 others)
2019 Sustainability  
The model couples the pre‐existing STICS crop model to a new tree model that includesseveral plasticity mechanisms responsive to tree–tree and tree–crop competition for light, water,and nitrogen.  ...  Hi‐sAFe is a mechanistic,biophysical model designed to explore the interactions within agroforestry systems that mix treeswith crops.  ...  Acknowledgments: Hi-sAFe model design and development would not have been possible without the very active support of two teams: the CAPSIS INRA team (François de Coligny) and the STICS INRA team (Marie  ... 
doi:10.3390/su11082293 fatcat:vz3espplbfdzvnnh3nyncpvlr4

Modelling the mineralisation of plant residues on the soil surface

Axel Berkenkamp, Eckart Priesack, Jean Charles Munch
2002 Agronomie  
The results show that the new model is able to describe the C and N turnover of crop residues in more detail, including the decrease of soil cover observed in the field. crop residues / decomposition /  ...  A general assumption in many agroecosystem C and N turnover models is that the decomposition of added organic matter starts at the moment of residue incorporation into the soil.  ...  First, we estimated the model parameters for the new soil C and N turnover model by taking the parameters of the previously used SOILN turnover model [27] as starting values for a new calibration.  ... 
doi:10.1051/agro:2002042 fatcat:hwb3ogw2fjddtklczx5h7ag3ra

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

M.H.J.P. Gunarathna, Kazuhito Sakai, M.K.N. Kumari, Manjula Ranagalage
2020 Agronomy  
We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the permanent wilting point (PWP) of Sri Lankan soils) for process-based crop models.  ...  However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models.  ...  A study of the functionality of PTFs is developed to estimate FC and PWP for APSIM crop modeling.  ... 
doi:10.3390/agronomy10020285 fatcat:jsiusvxegffc5mdqirrh5bez3m

Modeling soil organic carbon evolution in long-term arable experiments with AMG model

Hugues Clivot, Jean-Christophe Mouny, Annie Duparque, Jean-Louis Dinh, Pascal Denoroy, Sabine Houot, Françoise Vertès, Robert Trochard, Alain Bouthier, Stéphanie Sagot, Bruno Mary
2019 Environmental Modelling & Software  
Model performance was little affected by the choice of plant C input estimation method, but was improved by a site specific optimization of SOC pool partitioning.  ...  AMG shows a good potential for predicting SOC evolution in scenarios varying in climate, soil properties and crop management.  ...  It was supported by the French Government (ANR-001-01) and the Genesys WP1 P13 Solebiom project. We also thank the four reviewers for their constructive evaluation of the manuscript.  ... 
doi:10.1016/j.envsoft.2019.04.004 fatcat:r7riqfsdufe4vorvxgrhinu6vu

Forecasting Sunflower Grain Yield by Assimilating Leaf Area Index into a Crop Model

Ronan Trépos, Luc Champolivier, Jean-François Dejoux, Ahmad Al Bitar, Pierre Casadebaig, Philippe Debaeke
2020 Remote Sensing  
The methods are based on the re-estimation of soil conditions and on the sequential update of crop model states using an ensemble Kalman filter.  ...  Forecasting sunflower grain yield a few weeks before crop harvesting is of strategic interest for cooperatives that collect and store grains.  ...  Acknowledgments: The authors thank Arterris and Val de Gascogne for their help in structuring the field network. They also thank A. Micheneau, H. Gibrin, B. Garric, Y. Fernandez and F.  ... 
doi:10.3390/rs12223816 fatcat:jckm3ivpuvbmtk6rytdykvf3eq

Ad Hoc Modeling in Agronomy: What Have We Learned in the Last 15 Years?

F. Affholder, P. Tittonell, M. Corbeels, S. Roux, N. Motisi, P. Tixier, J. Wery
2012 Agronomy Journal  
In other words, a search for the best trade-off between the heuristic role of a model and its utility for decision-making emerged.  ...  Hence the need arose to search for compromises between at one side the mathematical and numerical properties of crop models (parameterization, uncertainty, computing time), and at the other side the scope  ...  Our modeling practices, the idea of writing this article, and the way we discussed the positions found in the literature have all been influenced by their views based on their great experience in "model  ... 
doi:10.2134/agronj2011.0376 fatcat:3ycpwqioana5vfeexc4a5bogk4

Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform

P.-H. Cournède, Y. Chen, Q. Wu, C. Baey, B. Bayol, A. Chavarria-Krauser, L. Dupuy, M. Ptashnyk
2013 Mathematical Modelling of Natural Phenomena  
Finally, a new model, the LNAS model for sugar beet growth, is presented and serves to illustrate how the different methods in PYGMALION can be used for its parameterization, its evaluation and its application  ...  The C++ platform proposes a framework in which stochastic or deterministic discrete dynamic models can be implemented, and several efficient methods for sensitivity analysis, uncertainty analysis, parameter  ...  We are grateful to our research colleagues at ITB (French Institute for Sugar Beet Research) for providing experimental data for LNAS model evaluation.  ... 
doi:10.1051/mmnp/20138407 fatcat:d6ttydsmrjaithrgndwlgrooou

Meta-modeling methods for estimating ammonia volatilization from nitrogen fertilizer and manure applications

Maharavo Marie Julie Ramanantenasoa, Sophie Génermont, Jean-Marc Gilliot, Carole Bedos, David Makowski
2019 Journal of Environmental Management  
In this study, we assess the capabilities of various types of meta-models to emulate the complex process-based Volt'Air for estimating NH3 emission rates from N fertilizer and manure applications.  ...  However, these models require a large number of input variables and their implementation on a large scale requires long computation times that may restrict their use by public environmental agencies.  ...  The methods are based on different types of models whose parameters are estimated using an optimization algorithm implemented with R functions of specific R packages.  ... 
doi:10.1016/j.jenvman.2019.01.066 fatcat:bze4zdognzdypfltgcr4trrr7y
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