Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States

Abstract

America is the continent with the largest area of genetically modified crops, the United States being the leading producer. Numerous studies show a panorama of potential exposure from agricultural pesticide use for this types of crops near to cities across a vast region of the United States. For the reasons mentioned above, we have chosen to investigate the following issues in this study: How does the implementation of an indexbased spatial modeling tool effectively rank the proximity of peri-urban crops, and what factors impact its effectiveness across diverse peri-urban agricultural landscapes? To address these questions, the research employs the Crop Proximity Index (CPI) model in various cities across the Midwest region of the United States. Six hundred and seventy cities in the state of Iowa were selected, and their peripheries were analysed using weighted perimeter rings, from 0 to 2000 m. The Crop Proximity Index was used to simulate a model of proximity to crops by considering the spatial quantification occupied by agriculture, forest cover, shrubs, pastures and buffer zones. This index varies from 0 to 1 and serves to rank the cities under study. It was estimated that a Crop Proximity Index equal to or >0.8 is a good approximation to a model with less proximity of crops and that only 62 cities (9%) meet this condition. Some 457 cities (68%) have CPIs equal to or <0.5 due to the large areas of crops and the low peripheral forest levels. The CPI is an index that makes it possible to obtain vital exploratory data in order to focus on future research that would determine how the proximity of agro-industrial crops has possible negative consequences for the environment and human health in greater detail.