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Using explanatory crop models to develop simple tools for Advanced Life Support system studies.

J Cavazzoni1

  • 1Bioresource Engineering, Department of Plant Biology and Pathology, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA. cavazzoni@aesop.rutgers.edu

Advances in Space Research : the Official Journal of the Committee on Space Research (COSPAR)
|April 26, 2005
PubMed
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Mathematical models are crucial for Advanced Life Support systems. This study developed simplified biomass production models using complex explanatory models to estimate daily canopy gas exchange and crop yields.

Area of Science:

  • Biomass production modeling
  • Advanced Life Support systems

Background:

  • System-level analyses for Advanced Life Support necessitate integrated mathematical models for processes like biomass production and waste management.
  • Explanatory (mechanistic) models offer a robust foundation for system models but can be too complex for direct implementation.

Purpose of the Study:

  • To develop practical, system-level models for biomass production within Advanced Life Support.
  • To translate complex mechanistic understanding into simplified, usable equations for estimating key crop growth parameters.

Main Methods:

  • Utilized explanatory models to derive parameters for simpler, multivariable polynomial equations.
  • Focused on biomass production, specifically canopy gas exchange, harvest index, and production scheduling.
Keywords:
NASA Discipline Life Support SystemsNon-NASA Center

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Main Results:

  • Generated polynomial equations suitable for estimating daily changes in canopy gas exchange and harvest index.
  • Developed models applicable to both nominal and off-nominal (challenging) growing conditions.
  • Provided a method for estimating production scheduling based on derived parameters.

Conclusions:

  • Simplified models derived from explanatory models are practicable for system-level analysis in Advanced Life Support.
  • These models enable estimation of critical biomass production variables, supporting system design and operation.
  • The approach facilitates the integration of biomass production dynamics into overall life support system simulations.