Introduction | Prepare your marteloscope field data in the Excel template | Software installation | Installing Java | Installing Capsis | Installing R and Rstudio | Installing the R package | Using the R package | Testing the package on a virtual workshop on the marteloscope of Prelenfrey | Create a directory and an R project to simulate a marteloscope workshop on the marteloscope of your choice | Run the script 01_start.R | Run the script 02_test.R | Testing the package on your own marteloscope | Create a directory and an R project to simulate a marteloscope workshop on the marteloscope of your choice - Before each exercise | Run the script 01_start.R - Before each exercise | Run the script 02_test.R to test a random marking | Prepare the field tablets - Before going to the field | Make a virtual field marking on your tablet – On the field | Run the script 03_workshop.R to simulate the field marking - After the marking | Evaluate the simulations on your marteloscope | The D-Day : Performing a real marteloscope field and simulation workshop | Preparing your computer and your tablets before the workshop | Import marking files, make the simulations and produce the reports | Other simulation projects with Samsara2 | References

Introduction | Overview of the workflow | Defining the calibration problem | Calibrated parameters | Observations used for calibration | Objective function | Rationale | Mathematical formulation | Normalization | Weighting | Handling sparse and dense observations | Internal split-sample validation | Principle | Constructing the groups | Interpretation of one split | Screening the parameter space | Parameter bounds | Candidate generation | Candidate evaluation | From screening to local optimization | Selecting distinct starting candidates | Initializing Nelder-Mead | Local optimization with Nelder-Mead | Role of Nelder-Mead | Bounds during local optimization | Outputs retained for each split | Selecting the final parameter set | Why the best split-specific optimum is not necessarily the final choice | Practical final-selection strategies | Methodological reference | Conclusion

Introduction & objectives | Project setup | Minimal model inputs | Creation of the input dataset | Reference simulation (rainfed maize) | Defining irrigation strategies | Threshold-based irrigation strategies (TBIS) | Irrigation turns and operational flexibility | Multi-period strategies and phenological targeting | Building the input object for all strategies | Simulation of all strategies | Water and growth dynamics | Soil water reserve and hydric stress | Biomass and yield dynamics | Total Water – Yield relationship | Main patterns | Strategy objectives and interpretation | Interpreting the water–yield relationship as a production function | Discussion and conclusions

Introduction | The Optirrig model in a nutshell | What is the OptirrigCORE package? | Let’s Get Started! | Prerequisites | Step 1 : Install the OptirrigCORE Package | Step 2 : Load Climate & Irrigation data | Step 3 — Describe the simulation configuration | Which crops and soils are available? | Step 4 — Run the simulation | Step 5 — Visualize the outputs | Wrap-up

🚀 Prérequis | 🔧 Fonctionnement général de l’outil | 📁 L’ordonnanceur – Structure et champs à renseigner | 🧪 Exécution type dans R | 📊 Sorties générées | 📎 Notes utiles | 🏁 Contact / Contributions

Introduction | Run | Crop | Soil | Yield | Economics | Irrigation (option)

Introduction | Crop | Water Balance | Yield/Economics | Irrigation

The study case | Network | GRiwrmInputsModel object | Implementation of the regulation controller | The supervisor | The control logic function | The controller | Running the simulation | Exploring results

Why modeling Ungauged station in a semi-distributed model? | Presentation of the study case | Using Ungauged stations in the airGRiwrm model | Generation of the GRiwrmInputsModel object | Calibration of the model integrating ungauged nodes | Run the model with the optimized model parameters | References

Presentation of the study case | Conversion of a gauging station into a release spot | Modification of the GRiwrm object | Generation of the GRiwrmInputsModel object | Calibration of the new model | Run of the model with this newly calibrated parameters | Plotting of the results | References

Presentation of the case study | Network configuration | Irrigation objectives and flow demand at intakes | Restriction of irrigation in case of water scarcity | Minimal environmental flow at the intakes | Restriction rules | Implementation of the model | Implementation of the regulation controller | The supervisor | The control logic function | The controller | Running the simulation | References

Load library | Preparation of function inputs | GRiwrmInputsModel object | GRiwrmRunOptions object | GRiwrmInputsCrit object | GRiwrmCalibOptions object | Calibration | Run the model with the optimized model parameters | Plot the results for each basin

For getting started several materials are available: | A slide-show of a training on airGRiwrm in French | The vignettes provided with the package with example of use on the Severn River (UK) | An example of use on a large network of 25 nodes on the Seine River (France)

Rapid description of the Integral Projection Model | Simulations input | Define a species | Define a forest | Running simulations | Customizing the simulations | Initialisation step | Recruitment delay | Multiple species | References

Get started | How it works | Listing the APIs searchable with the hubeau R package | Available endpoints for the "qualite_rivieres" API | List of arguments by endpoint | Extracting physico-chemical data | Availability of stations in the Côte d'Or department | Retrieving nitrate concentration in river water of the Côte d'or department | Extraction and analysis of data station by station | Objectives | Selection of stations available for analysis | Statistical analysis of samples

Getting started | Retrieving the data | Sites | Surveys | Observations | Graphical output of monthly indicator

Getting started | Retrieving the data | Sites | Time series | Tidying the data | Plotting | Mapping

Simulations input | Define a species | Default scenario | Presentation | Modulation | Uneven scenario | Theory | Monospecific case | Harvest proportion | Harvest curve | Harvesting algorithm | Multispecific case | Abundance-based preference | Favoured species | Examples | Even scenario | References

Simulations input | Define a species | Disturbance | Definition of disturbance | Impact on population | Simulations | Multispecific simulations | Linear stabilizing effect of species mixture | References

Decision rules for the Dimensions approach | Decision rules for the Properties approach

Description of the example used in this tutorial | Semi-distributed network description | Observation time series | Generation of the GRiwrmInputsModel object | References

New version (starting with IDEATools 3.0.0) | How can I modify / translate the colored trees ? | Previous version

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