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

Autores
Agost, Lisandro; Velázquez, Guillermo Ángel
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: Agost, Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentina
Fil: Velázquez, Guillermo Ángel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Geografía, Historia y Ciencias Sociales. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Geografía, Historia y Ciencias Sociales; Argentina
Materia
LAND USE MODEL
GENETICALLY MODIFIED CROP
FOREST
AGRICULTURE PESTICIDE USE
CONTAMINATION
HEALTH
INDEX
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/234499

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spelling Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United StatesAgost, LisandroVelázquez, Guillermo ÁngelLAND USE MODELGENETICALLY MODIFIED CROPFORESTAGRICULTURE PESTICIDE USECONTAMINATIONHEALTHINDEXhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1America 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.Fil: Agost, Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; ArgentinaFil: Velázquez, Guillermo Ángel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Geografía, Historia y Ciencias Sociales. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Geografía, Historia y Ciencias Sociales; ArgentinaElsevier2024-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/234499Agost, Lisandro; Velázquez, Guillermo Ángel; Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States; Elsevier; Ecological Informatics; 81; 4-2024; 1-121574-9541CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1574954124001298?via%3Dihubinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecoinf.2024.102587info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:09:43Zoai:ri.conicet.gov.ar:11336/234499instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:09:43.376CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States
title Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States
spellingShingle Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States
Agost, Lisandro
LAND USE MODEL
GENETICALLY MODIFIED CROP
FOREST
AGRICULTURE PESTICIDE USE
CONTAMINATION
HEALTH
INDEX
title_short Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States
title_full Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States
title_fullStr Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States
title_full_unstemmed Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States
title_sort Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States
dc.creator.none.fl_str_mv Agost, Lisandro
Velázquez, Guillermo Ángel
author Agost, Lisandro
author_facet Agost, Lisandro
Velázquez, Guillermo Ángel
author_role author
author2 Velázquez, Guillermo Ángel
author2_role author
dc.subject.none.fl_str_mv LAND USE MODEL
GENETICALLY MODIFIED CROP
FOREST
AGRICULTURE PESTICIDE USE
CONTAMINATION
HEALTH
INDEX
topic LAND USE MODEL
GENETICALLY MODIFIED CROP
FOREST
AGRICULTURE PESTICIDE USE
CONTAMINATION
HEALTH
INDEX
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv 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.
Fil: Agost, Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentina
Fil: Velázquez, Guillermo Ángel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Geografía, Historia y Ciencias Sociales. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Geografía, Historia y Ciencias Sociales; Argentina
description 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.
publishDate 2024
dc.date.none.fl_str_mv 2024-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/234499
Agost, Lisandro; Velázquez, Guillermo Ángel; Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States; Elsevier; Ecological Informatics; 81; 4-2024; 1-12
1574-9541
CONICET Digital
CONICET
url http://hdl.handle.net/11336/234499
identifier_str_mv Agost, Lisandro; Velázquez, Guillermo Ángel; Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States; Elsevier; Ecological Informatics; 81; 4-2024; 1-12
1574-9541
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S1574954124001298?via%3Dihub
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecoinf.2024.102587
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
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instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
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