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A model for plant lighting system selection.

D E Ciolkosz1, L D Albright, J C Sager

  • 1CDH Energy Corp., Cazenovia, New York, USA. dan@cdhenergy.com

Transactions of the ASAE. American Society of Agricultural Engineers
|November 22, 2002
PubMed
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This study presents a decision model using Multiple Attribute Utility Theory to select optimal plant growth lighting systems. The model reliably identifies the best lighting solution based on utility values derived from expert input and simulations.

Area of Science:

  • Agricultural Engineering
  • Decision Science
  • Horticultural Lighting

Background:

  • Selecting optimal lighting systems for plant growth is crucial for maximizing yield and efficiency.
  • Existing methods may not comprehensively integrate diverse factors like expert knowledge and performance data.

Purpose of the Study:

  • To develop and validate a decision model for selecting the most appropriate plant growth lighting system.
  • To apply Multiple Attribute Utility Theory (MAUT) for quantitative comparison of lighting options.

Main Methods:

  • A decision model based on Multiple Attribute Utility Theory (MAUT) was developed.
  • Expert input and performance simulations were integrated to calculate utility values for each system.
  • The model was applied to a greenhouse scenario for validation.
Keywords:
NASA Center KSCNASA Discipline Life Support Systems

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Main Results:

  • The model successfully ranked different lighting systems based on calculated utility values.
  • Parameter variation analyses confirmed the model's expected performance.
  • Sufficiently large utility differences among systems provided confidence in the selection order.

Conclusions:

  • The developed MAUT-based decision model is a valid and reliable tool for selecting optimal plant growth lighting systems.
  • The model's approach offers a robust framework for integrating complex decision factors in horticultural applications.
  • This methodology can enhance efficiency and effectiveness in greenhouse lighting management.