Performance analysis method for model-based irrigation strategies under uncertainty

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.