Authors:
Ilya Ioslovich
and
Raphael Linker
Affiliation:
Faculty of Civil and Environmental Engineering and Technion–Israel Institute of Technology, Israel
Keyword(s):
Irrigation Scheduling, Optimal Control.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Engineering Applications
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Planning and Scheduling
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
Water stress is one of the most influential factors contributing to crop yield loss. The importance of the irrigation
constantly increases because of water scarcity and growing demand for agricultural production worldwide.
Previously, an approach using empirical water production functions and analytic optimal control methodology
has been developed for optimal irrigation scheduling. Such an approach based on numerical optimal control
is an alternative to common irrigation scheduling based on agronomy practice. Nowadays, more complex
dynamic crop simulation models, such as the FAO AquaCrop model, predict crop responses to different irrigation
strategies and climates. The state variables of the AquaCrop model include crop characteristics, such
as biomass, and soil water content in up to 12 soil layers. In this paper the numerical optimal control scheme
for irrigation scheduling and crop water production function development is described and demonstrated using
this model and the TOMLAB o
ptimization library. Maize crop in Foggia, Italy, for season of the year 2000, is
used as an illustrative case study.
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