Academic literature on the topic 'STICS soil-crop model'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'STICS soil-crop model.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "STICS soil-crop model":
Ravelojaona, Nomena, Guillaume Jégo, Noura Ziadi, Alain Mollier, Jean Lafond, Antoine Karam, and Christian Morel. "STICS Soil–Crop Model Performance for Predicting Biomass and Nitrogen Status of Spring Barley Cropped for 31 Years in a Gleysolic Soil from Northeastern Quebec (Canada)." Agronomy 13, no. 10 (September 30, 2023): 2540. http://dx.doi.org/10.3390/agronomy13102540.
Bourdin, F., F. J. Morell, D. Combemale, P. Clastre, M. Guérif, and A. Chanzy. "A tool based on remotely sensed LAI, yield maps and a crop model to recommend variable rate nitrogen fertilization for wheat." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 672–77. http://dx.doi.org/10.1017/s2040470017000887.
Valdés-Gómez, Héctor, Florian Celette, Iñaki García de Cortázar-Atauri, Francisco Jara-Rojas, Samuel Ortega-Farías, and Christian Gary. "Modelling soil water content and grapevine growth and development with the stics crop-soil model under two different water management strategies." OENO One 43, no. 1 (March 31, 2009): 13. http://dx.doi.org/10.20870/oeno-one.2009.43.1.806.
Tribouillois, Hélène, Julie Constantin, and Eric Justes. "Analysis and modeling of cover crop emergence: Accuracy of a static model and the dynamic STICS soil-crop model." European Journal of Agronomy 93 (February 2018): 73–81. http://dx.doi.org/10.1016/j.eja.2017.12.004.
Wallach, D., S. Buis, P. Lecharpentier, J. Bourges, P. Clastre, M. Launay, J. E. Bergez, M. Guerif, J. Soudais, and E. Justes. "A package of parameter estimation methods and implementation for the STICS crop-soil model." Environmental Modelling & Software 26, no. 4 (April 2011): 386–94. http://dx.doi.org/10.1016/j.envsoft.2010.09.004.
Valade, A., P. Ciais, N. Vuichard, N. Viovy, A. Caubel, N. Huth, F. Marin, and J. F. Martiné. "Modeling sugarcane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values." Geoscientific Model Development 7, no. 3 (June 30, 2014): 1225–45. http://dx.doi.org/10.5194/gmd-7-1225-2014.
Valade, A., P. Ciais, N. Vuichard, N. Viovy, N. Huth, F. Marin, and J. F. Martiné. "Modeling sugar cane yield with a process-based model from site to continental scale: uncertainties arising from model structure and parameter values." Geoscientific Model Development Discussions 7, no. 1 (January 31, 2014): 1197–244. http://dx.doi.org/10.5194/gmdd-7-1197-2014.
Saadi, Sameh, Elizabeth Pattey, Guillaume Jégo, and Catherine Champagne. "Prediction of rainfed corn evapotranspiration and soil moisture using the STICS crop model in eastern Canada." Field Crops Research 287 (October 2022): 108664. http://dx.doi.org/10.1016/j.fcr.2022.108664.
Sow, Sidy, Yolande Senghor, Khardiatou Sadio, Rémi Vezy, Olivier Roupsard, François Affholder, Moussa N’dienor, et al. "Calibrating the STICS soil-crop model to explore the impact of agroforestry parklands on millet growth." Field Crops Research 306 (February 2024): 109206. http://dx.doi.org/10.1016/j.fcr.2023.109206.
Demestihas, Constance, Daniel Plénet, Michel Génard, Iñaki Garcia de Cortazar-Atauri, Marie Launay, Dominique Ripoche, Nicolas Beaudoin, et al. "Analyzing ecosystem services in apple orchards using the STICS model." European Journal of Agronomy 94 (March 2018): 108–19. http://dx.doi.org/10.1016/j.eja.2018.01.009.
Dissertations / Theses on the topic "STICS soil-crop model":
Ravelojaona, Nomena. "Evaluation of STICS model performance for long-term simulation of biomass production and nitrogen nutrition of spring barley and timothy cultivated in two important agricultural regions in Quebec (Canada)." Electronic Thesis or Diss., Bordeaux, 2023. http://www.theses.fr/2023BORD0503.
Spring barley (Hordeum vulgare L.) and timothy (Phleum pratense L.) are crops of prime economic importance for the province of Quebec and other regions with cold, humid continental climate (North America, Nordic countries, etc.). Soil-crop models are powerful tools for calculating, a wide range of agronomic and environmental variables, since they are designed to simulate the complex interactions between crops, water, and soil nitrogen (N) in the soil–plant–atmosphere continuum. Among other models, STICS is a process-based soil-crop model developed initially for temperate agropedoclimatic conditions. However, it can be adapted to conditions of other agrosystems.The objectives of this thesis were to analyze and extend the scope of application of STICS to these two economically important crops grown under agropedoclimatic conditions in the Province of Quebec, and to evaluate the model's predictive performance on long–term simulations. This thesis is a contribution to the study of the genericity of STICS for Quebec agrosystems. In addition to the climatic context, the originality of this work lies in the crops studied – spring barley and timothy – and the number of successive years of continuous simulations (without annual reinitialization). The predictive performances of STICS were analyzed for aboveground biomass production, N content and N export for i) a 31-year spring barley monoculture grown under two tillage systems and fertilized with two N sources (mineral N and liquid dairy manure); and ii) an 8-year timothy grassland, fertilized each year with four application N rates (0, 60, 120, 180 kg N ha-1). We used databases from two experimental field trials conducted by Agriculture and Agri-Food Canada.For the barley monoculture, the STICS calibration procedure required the adjustment of cultivar parameters in particular, thus confirming the genericity of most plant parameters defined in STICS. There is a good agreement between observed and predicted variables of interest with the various tillage systems and N sources during the 31 successive barley cropping years, but with greater dispersion for the N nutrition. Predictions of crop attributes were more accurate in years with rainfall close to the long-term average. For timothy grassland grown over 8 years, the agreement between observed and predicted values was satisfactory for the first harvest. STICS correctly simulated the positive effect of the N application rates on biomass production and plant N nutrition. Nevertheless, the predicted values were overestimated by the model in the absence of N fertilization. Except for this very specific situation, which is not representative of agronomic practices, STICS performed satisfactorily in the context of the two field experiments studied. In addition, STICS reproduced well decreasing trend in timothy productivity observed with the age of the sward. The results showed that this decrease in yield over time is strongly correlated with the reduction in metabolic reserve in the perennial organs.In conclusion, this thesis work has demonstrated the applicability and reliability of the STICS model for the long-term simulation of biomass production and N nutrition of spring barley and timothy under agropedoclimatic conditions in the Province of Quebec
Varella, Hubert Vincent. "Inversion d’un modèle de culture pour estimer spatialement les propriétés des sols et améliorer la prédiction de variables agro-environnementales." Thesis, Avignon, 2009. http://www.theses.fr/2009AVIG0638/document.
Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters,especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless,observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In my work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, I started my study with a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, I chose a Bayesian data assimilation method (because I have prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prio rinformation and the result with the posterior information provided by the Bayesian data assimilation method. The result of the parameter estimation show that the whole set of parameter has a better quality of estimation when observations on sugar beet are assimilated. At the same time, global sensitivity analysis of the observed variables to the 7 soil parameters have been performed, allowing me to build a criterion based on sensitivity indices (provided by the global sensitivity analysis) able to rank the parameters with respect to their quality of estimate. This criterion constitutes an interesting tool for determining which parameters it is possible to estimate to reduce probably the uncertainties on the predictions. The prediction of the crop behaviour when estimating the soil parameters is then studied. Indeed, the quality of prediction of agro-environmental variables of the STICS crop model (yield, protein of the grain and nitrogen balance at harvest) is determined by comparing the result of the prediction using the prior information on the parameters and the result using the posterior information. As for the estimation of soil parameters, the prediction of the variable is made on different climatic and agronomic conditions. According to the result of parameter estimation, assimilating observations on sugar beet lead to a better quality ofprediction of the variables than observations on wheat. It was also shown that the number ofcrop seasons observed and the number of observations improve the quality of the prediction
Book chapters on the topic "STICS soil-crop model":
Ebbisa, Addisu. "Application of Crop Modeling in Multi-Cropping Systems for Maximize Production and Build Resilient Ecosystem Services." In Resource Management in Agroecosystems [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.110742.
Varella, Hubert, Martine Guerif, and Samuel Buis. "Estimation of Soil Properties Using Observations and the Crop Model STICS. Interest of Global Sensitivity Analysis and Impact on the Prediction of Agro-Environmental Variables." In Advances in Geoscience and Remote Sensing. InTech, 2009. http://dx.doi.org/10.5772/8342.
Conference papers on the topic "STICS soil-crop model":
Lammoglia, S. K., Chanzy A, and Guerif M. "Characterizing soil hydraulic properties from Sentinel 2 and STICS crop model." In 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). IEEE, 2019. http://dx.doi.org/10.1109/metroagrifor.2019.8909266.