A spatial directivity-based sensitivity analysis to farmland quality evaluation in arid areas.

Area of Science:

• Environmental Science
• Geographic Information Science
• Decision Science

Background:

• Multi-criteria decision-making (MCDM) is crucial for resource and environmental evaluation.
• Traditional sensitivity analysis in MCDM often omits spatial information, limiting robustness understanding.
• Existing methods lack spatial directivity for uncertainty in evaluation results.

Purpose of the Study:

• To propose a novel spatial measurement approach for sensitivity analysis in MCDM.
• To introduce the spatial barycenter model (SBM) to provide spatial directivity of uncertainty.
• To apply the SBM to farmland quality evaluation (FQE) in an arid region.

Main Methods:

• Developed and applied a spatial barycenter model (SBM) for sensitivity analysis.
• Utilized mean of the absolute average change rate (MACR) for numerical sensitivity.
• Employed SBM for spatial sensitivity analysis of farmland quality factors.

Main Results:

• Numerical sensitivity analysis (MACR) identified soil organic matter and irrigation capacity as key factors.
• Spatial sensitivity analysis (SBM) highlighted accumulated temperature (AT) and precipitation as most influential.
• SBM indicated that farmland quality index is most sensitive to increased AT in a northwesterly direction.

Conclusions:

• The SBM offers a computationally inexpensive method for spatial sensitivity analysis in MCDM.
• This approach provides direct spatial insights into uncertainty, enhancing understanding of evaluation results.
• Findings support improved agricultural production layout and sensitivity analysis comprehensiveness.