TRAINING.

Overview

After participating in the course, you will be able to:

• have a basic understanding about spatial variability of environmental variables (groundwater table, soil properties, yields)
• have a detailed knowledge of how to use R-software to characterize spatial variability.
• have a working knowledge of how to work with R software producing maps and diagrams.

Description

One of the most important aspects of farm and landscape design is understanding and characterizing spatial variability of environmental variables. Soil properties, water table level, and yields are often highly spatially heterogeneous. In this way, designing a precision irrigation system or a water management system requires information on how soil properties and water availability changes spatially. Therefore, producing maps of environmental variables is primordial for water, soil and crop management for those involved in the fam and landscape design. The focus of this course is to provide the necessary information for mapping and characterizing environmental variables using the open-source software R.

R software is one of the most used software for data analysis and data sciences along with Python. R is a language and environment for statistical computing and graphics. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. This training course teaches using R to characterize spatial variability and create maps. During the course, the concepts of spatial characterization will be highlighted through several simple and practical examples using real datasets available for students.

The subject materials are designed for practicing professionals who want to refresh and upgrade their knowledge of spatial variability, mapping and automatic reporting.

Course Outline:

• Spatial variability
• Spatial interpolation
• Introduction to R for spatial analyses
• Practical considerations

Who Should Attend:

Agriculture and Environmental Engineers • Project Engineers and Managers • Consultants • Designers • Operation and Maintenance personnel • Developers • Planners

Special Features & Requirements

All software is free and user-friendly. Trainees will be able to use them after this course for their cases.

Please note: You can check other time zones here.

Syllabus

Day 1

Introduction to R language

R syntax and Rstudio

• R functionality, packages, functions
• Data structures, vectors, matrix, data frames and lists

Reading and Writing Data

• Reading and writing data sets
• Data frame basics

Plots and Figures

• Basic plots with R
• Output report quality figures

Day 2

Spatial Variability

• Working with spatial data
• Variograms and spatial structure

Spatial Interpolation

• Kriging
• Thin plate splines
• Triangulation

Mapping

• Producing maps with R
• Superposing information on the same map
• Dynamic mapping with leaflet

Instructor

Silvio J. Gumiere, Ph.D.

Professor Gumiere studied mechanical engineering with a major in fluid and thermodynamics. He obtained a master’s degree in hydrological sciences from Sao Paulo University in 2003. In 2009 he obtained his Ph.D. in hydrological and erosion modelling from the SupAgro, Montpellier France. In 2011 he became a professor at the department of soil sciences at Laval University. He teaches soil physics, hydrology and drainage as well as solute transport in porous media. Since 2006 he works on various aspects soil erosion, hydrology and soil physics as well as on the development of modelling tools for water and soil management in agricultural systems. His research projects go from increasing crop water efficiency to understanding the impact of agricultural activities on the hydrological processes of watersheds soil erosion modelling at various scales including particle tracking and image processing. All these processes include the application of R-based numerical, statistical and geostatistical methods, such as time series analyses, image and signal processing, erosion modelling and spatial hydrology and spatial interpolation methods. He is a Guest Editor for several special issues on hydrological modelling and machine learning techniques for solving applied science problems in hydrology, soil sciences, soil hydrology and environmental journals.