Abstract

Land evaluation supports the sustainable management of natural resources by bringing together evidence about soils, climate, vegetation, landform and socioeconomic conditions at multiple spatial scales.

As assessment questions have become more complex, established land-evaluation approaches have faced difficulty representing the interconnected effects of climate change, land-use change and economic conditions. Agro-ecological zoning offers one route for combining these variables, but long-duration simulation across large or data-poor regions can demand substantial data collection and may introduce uncertainty from many sources.

This study explores virtual territories as controlled analogues of field experiments for agro-ecosystem scenario analysis. A virtual territory is designed to have morphological, ecological and land-cover characteristics that are statistically similar to a selected real landscape. It is assembled as a coherent collection of geographic information system layers and linked databases.

Once the digital landscape meets those baseline conditions, researchers can vary climate, land use, physical settings and socioeconomic assumptions while keeping other experimental factors controlled. The approach makes factorial comparisons possible and provides flexible tools for generating realistic landscape variants.

Demonstrated application

The method is illustrated through erosion analysis under contrasting climate scenarios, connecting a virtual landscape with the revised universal soil loss equation and agro-ecological zoning.

What the method contributes

  • Controlled experimental conditions for comparing agro-ecosystem scenarios.
  • Combinations of climate, physical and socioeconomic assumptions in a factorial structure.
  • Coherent GIS layers that preserve relationships among landscape variables.
  • Efficient generation of plausible landscape configurations and variants.

Interpretive context

Virtual territories are not substitutes for observations from real fields. Their value lies in creating transparent experimental settings where interacting drivers can be varied systematically. The usefulness of a scenario therefore depends on the quality of the input layers, the validity of the statistical resemblance and the assumptions embedded in the chosen models.