Optimization methods for permaculture
Bueres y Dominguez, Lisa
Promotor(s) : Louveaux, Quentin
Date of defense : 26-Jun-2023/27-Jun-2023 • Permalink :
|Optimization methods for permaculture
|Translated title :
|[fr] Méthodes d'optimisation appliquées à la permaculture
|Bueres y Dominguez, Lisa
|Date of defense :
|Committee's member(s) :
|Number of pages :
[en] multistage stochastic programming
[en] precision farming
|Engineering, computing & technology > Computer science
|Target public :
|Université de Liège, Liège, Belgique
|Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
|Master thesis of the Faculté des Sciences appliquées
[en] For the last 20 years, the concern for climate change has increased significantly among the population. Global warming is accelerating, the state of the planet is deteriorating, and we urgently need to do something about it. In agriculture in particular, many changes must be done to reach sustainability and resilience. Many methods exist but optimisation has long been used in precision farming as a tool for decision support. Given the random nature of the natural events that occur in agriculture, particularly weather and plant growth, stochastic optimisation is the most appropriate.
The aim is therefore to develop a new multistage stochastic optimisation model for decision support. This work describe the implementation the model. It takes into account weather from the last 30 years to the 20 next as scenarios in order to produce results over a sufficiently long period of time to study the impact of global warming on the production. It also includes forecast yields of three crops : tomatoes, strawberries and potatoes, as parameters depending on the weather. Each plant is modelled according to a model validated in the literature. The implementation of the optimisation model is performed by starting with a small deterministic one and developing it with the final version.
Optimal solutions to the model are computed, thus demonstrating the feasibility and providing a decision strategy maximising the quantity harvested according to the weather. The impact of some variations of the model or in the data are also analysed. The results of model shows the relevance for the problem and the impact of the global warming on the production. This thus brings a new interesting approach to optimisation in precision farming.
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