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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/234499
Ver los metadatos del registro completo
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network_name_str |
CONICET Digital (CONICET) |
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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) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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1846083245016350720 |
score |
13.216834 |