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Mathematical programming for the economic analysis of agriculture

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Prerequisites

This course requires knowledge of mathematics at the level of a Bachelor of Science in Economics.


Programme

You will find :
You will find in this e-learning module
– A self-study course designed for students, post-graduates and researchers who wish to learn how to model agricultural economics and environmental problems, how to understand these models and to interpret their results -> unrestricted access
Course outline
Introduction
Sequence 1 : Introduction to mathematical programming using GAMS
Unit 1.1 : Constrained optimization (lesson 1-3)
Unit 1. 2 : Discover GAMS (Lesson 4-8)
Unit 1.3 : Primal problem, dual problem (Lesson 9-10)
Unit 1.4 : Application exercise
Sequence 2 : The farm model
Unit 2.1 : Enriching the base model (lesson 11-14)
Unit 2.2 : Multi-periodical decisions in an annual model (lesson 15-17)
Unit 2.3 : Simulation of a public policy (lesson 18-20)
Sequence 3 : Risk and time factors in models
Unit 3.1 : Modelling risk (Lesson 21-26)
Unit 3.2 : Modelling time (Lesson 27-29)


Acquired skills

'This course aims to enable students to master mathematical programming techniques and to know how to use them to build optimisation models applied to the analysis of agricultural economics issues.
It is largely based on practical work that allows students to acquire modelling skills in order to understand research work using this type of method and also to have sufficient knowledge to use them in a personal research project. The practical work is carried out using the GAMS (General Algebraic Modelling System) language, which is the most widely used language in this type of modelling and whose basics will therefore also be taught;
The teaching is oriented towards the construction of optimisation models applied to different issues of agricultural economics. In particular, modelling issues raised by current policy challenges will be addressed: environmental impacts of agricultural activities, uncertainty and farmers' risk preferences.


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