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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Performance analysis method for model-based irrigation strategies under uncertainty.

F D Mondaca-Duarte1, M Heinen2, S van Mourik1

  • 1Wageningen University, Farm Technology Group, P.O. Box 16, 6700 AH Wageningen, the Netherlands.

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|October 22, 2020
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Summary
This summary is machine-generated.

Precise agricultural management is key to boosting food production while conserving water. This study introduces a method to analyze performance risks from uncertain soil and evapotranspiration data, ensuring reliable farming predictions.

Keywords:
DrainageMonte CarloRichards equation

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Area of Science:

  • Agricultural Science
  • Environmental Modeling
  • Soil Science

Background:

  • Increasing agricultural performance requires precise management to optimize water use and prevent crop stress.
  • Model-based predictions support farming decisions, but uncertainties in soil properties and evapotranspiration impact prediction accuracy.
  • Uncertainty in these factors can lead to performance loss and risks in food production.

Purpose of the Study:

  • To present a method and code for analyzing performance uncertainty in agricultural systems.
  • To quantify the risk of performance loss due to uncertainties in soil properties and evapotranspiration.
  • To enhance the reliability of model-based management support in farming.

Main Methods:

  • Utilized the De Graaf evapotranspiration model to simulate water loss.
  • Employed the EMMAN3G model, based on Richards' equation, for soil water dynamics.
  • Integrated these models to conduct a comprehensive performance uncertainty study.

Main Results:

  • Developed and demonstrated a robust method for uncertainty analysis in agricultural models.
  • Quantified the impact of soil property and evapotranspiration uncertainties on performance predictions.
  • Provided a framework for assessing the risk of performance loss in farming systems.

Conclusions:

  • The presented method effectively analyzes performance uncertainty stemming from environmental variables.
  • Accurate modeling of soil water and evapotranspiration is crucial for mitigating prediction risks.
  • This approach supports informed decision-making for sustainable and efficient agricultural production.