Spatial Structure and Scaling of Agricultural Networks

TL;DR

This study investigates whether the scale-free properties of global agricultural networks are consistent at regional and local scales, using Landsat and IKONOS imagery to analyze vegetation connectivity across diverse landscapes.

Contribution

The paper demonstrates that regional agricultural networks exhibit similar scale-free properties to global networks, extending understanding of spatial connectivity at multiple scales.

Findings

Vegetation networks show power law distributions at regional scales.

Similar scaling properties are observed across different spatial resolutions.

Network disruption analysis highlights differences in node removal effects.

Abstract

Considering agricultural landscapes as networks can provide information about spatial connectivity relevant for a wide range of applications including pollination, pest management, and ecology. Global agricultural networks are well-described by power law rank-size distributions. However, regional analyses capture only a subset of the total global network. Most analyses are regional. In this paper, we seek to address the following questions: Does the globally observed scale-free property of agricultural networks hold over smaller spatial domains? Can similar properties be observed at kilometer to meter scales? We analyze 9 intensively cultivated Landsat scenes on 5 continents with a wide range of vegetation distributions. We find that networks of vegetation fraction within the domain of each of these Landsat scenes exhibit substantial variability - but still possess similar scaling properties to the global distribution of agriculture. We also find similar results using a 39 km2 IKONOS image. To illustrate an application of spatial network analysis, we show an example of network disruption. We compare two networks with similar rank-size distributions that behave differently when nodes are progressively removed. We suggest that treating agricultural land cover as spatial networks can provide a straightforward way of characterizing the connectivity of complex spatial distributions of agriculture across a wide range of landscapes and at spatial scales relevant for practical agricultural applications.