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Technische Universität Dresden (2015)

Simulation-Optimization of the Management of Sensor-Based Deficit Irrigation Systems

Kloß Sebastian

Titre : Simulation-Optimization of the Management of Sensor-Based Deficit Irrigation Systems

Auteur : Kloß Sebastian

Université de soutenance : Technische Universität Dresden

Grade : Doctor rerum naturalium (Dr. rer. nat.) 2015

Résumé partiel
Current research concentrates on ways to investigate and improve water productivity (WP), as agriculture is today’s predominant freshwater consumer, averaging at 70% and reaching up to 93% in some regions. A growing world population will require more food and thus more water for cultivation. Regions that are already affected by physical water scarcity and which depend on irrigation for growing crops will face even greater challenges regarding their water supply. Other problems in such regions are a variable water supply, inefficient irrigation practices, and over-pumping of available groundwater resources with other adverse effects on the ecosystem. To face those challenges, strategies are needed that use the available water resources more efficiently and allow farming in a more sustainable way. This work focused on the management of sensor-based deficit irrigation (DI) systems and improvements of WP through a combined approach of simulation-optimization and irrigation experiments. In order to improve irrigation control, a new sensor called pF-meter was employed, which extended the measurement range of the commonly used tensiometers from pF 2.9 to pF 7. The following research questions were raised : (i) Is this approach a suitable strategy to improve WP ; (ii) Is the sensor for irrigation control suitable ; (iii) Which crop growth models are suitable to be part of that approach ; and (iv) Can the combined application with experiments prove an increase of WP ?

Mots clés  : Simulationsbasierte Optimierung, Wasserproduktivität, Mais, Defizitbewässerung, Pflanzenwachstumsmodellierung — simulation-based optimization, water productivity, maize, deficit irrigation, crop growth modeling

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Page publiée le 9 novembre 2016, mise à jour le 16 janvier 2020