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Assessment of COVID-19 impacts on U.S. counties using the immediate impact model of local agricultural production (IMLAP)

CONTEXT: The COVID-19 pandemic has resulted in immediate and wide impacts on human and agricultural systems. While some of the positive and negative impacts of COVID-19 on the environment and economies are emerging, there is not a comprehensive understanding of the potential impacts of COVID-19 on t...

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Detalles Bibliográficos
Autores principales: Haqiqi, Iman, Bahalou Horeh, Marziyeh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9186126/
https://www.ncbi.nlm.nih.gov/pubmed/35702605
http://dx.doi.org/10.1016/j.agsy.2021.103132
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author Haqiqi, Iman
Bahalou Horeh, Marziyeh
author_facet Haqiqi, Iman
Bahalou Horeh, Marziyeh
author_sort Haqiqi, Iman
collection PubMed
description CONTEXT: The COVID-19 pandemic has resulted in immediate and wide impacts on human and agricultural systems. While some of the positive and negative impacts of COVID-19 on the environment and economies are emerging, there is not a comprehensive understanding of the potential impacts of COVID-19 on the most vulnerable farmers. OBJECTIVE: The purpose of this study is to evaluate the immediate impacts of COVID-19 on agricultural and food systems in the United States. Our aim is to quantify the impacts on labor productivity in crops and livestock production considering the heterogenous vulnerability of different farmworkers. We are interested in measuring the production that is not realized due to COVID-19. METHODS: In this paper, we introduce IMLAP, Immediate impact Model of Local Agricultural Production. This model is an economic framework considering short-term agricultural production responses to economic, environmental, and policy changes. We investigate the potential impacts of COVID-19 on the farmers in the U.S. for each county with a special focus on female, Hispanic, black and African American, and small-scale producers. RESULTS AND CONCLUSIONS: Considering the impacts of COVID-19 on labor, the findings of this study suggest a decline in agricultural output in all the U.S. counties ranging from 1.18% to 7.14% of total production. Our simulation results show that counties with a higher number of small-scale farms, non-white farmers, and female-operated farms are the most vulnerable to COVID-19. Also, we argue that the stimulus policies and support packages must target these communities of producers to ensure that their livelihood is protected. The findings suggest that productivity growth (technological improvements) and international trade can eliminate the negative impacts of pandemics. SIGNIFICANCE: The proposed quantitative framework of this study is a simple yet novel model that empowers diverse research communities to provide a quick analysis of the impacts of unprecedented events. It offers a holistic framework to evaluate the response of agricultural production to changes in availability and productivity of labor, machinery & equipment, land, fertilizer, seeds, and other inputs. This study presents new foundations for agricultural research communities to provide solutions to agricultural resilience challenges and highlights the significance of demand drivers, technological growth, and international trade in strengthening the food system.
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spelling pubmed-91861262022-06-10 Assessment of COVID-19 impacts on U.S. counties using the immediate impact model of local agricultural production (IMLAP) Haqiqi, Iman Bahalou Horeh, Marziyeh Agric Syst Article CONTEXT: The COVID-19 pandemic has resulted in immediate and wide impacts on human and agricultural systems. While some of the positive and negative impacts of COVID-19 on the environment and economies are emerging, there is not a comprehensive understanding of the potential impacts of COVID-19 on the most vulnerable farmers. OBJECTIVE: The purpose of this study is to evaluate the immediate impacts of COVID-19 on agricultural and food systems in the United States. Our aim is to quantify the impacts on labor productivity in crops and livestock production considering the heterogenous vulnerability of different farmworkers. We are interested in measuring the production that is not realized due to COVID-19. METHODS: In this paper, we introduce IMLAP, Immediate impact Model of Local Agricultural Production. This model is an economic framework considering short-term agricultural production responses to economic, environmental, and policy changes. We investigate the potential impacts of COVID-19 on the farmers in the U.S. for each county with a special focus on female, Hispanic, black and African American, and small-scale producers. RESULTS AND CONCLUSIONS: Considering the impacts of COVID-19 on labor, the findings of this study suggest a decline in agricultural output in all the U.S. counties ranging from 1.18% to 7.14% of total production. Our simulation results show that counties with a higher number of small-scale farms, non-white farmers, and female-operated farms are the most vulnerable to COVID-19. Also, we argue that the stimulus policies and support packages must target these communities of producers to ensure that their livelihood is protected. The findings suggest that productivity growth (technological improvements) and international trade can eliminate the negative impacts of pandemics. SIGNIFICANCE: The proposed quantitative framework of this study is a simple yet novel model that empowers diverse research communities to provide a quick analysis of the impacts of unprecedented events. It offers a holistic framework to evaluate the response of agricultural production to changes in availability and productivity of labor, machinery & equipment, land, fertilizer, seeds, and other inputs. This study presents new foundations for agricultural research communities to provide solutions to agricultural resilience challenges and highlights the significance of demand drivers, technological growth, and international trade in strengthening the food system. Elsevier Ltd. 2021-05 2021-03-24 /pmc/articles/PMC9186126/ /pubmed/35702605 http://dx.doi.org/10.1016/j.agsy.2021.103132 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Haqiqi, Iman
Bahalou Horeh, Marziyeh
Assessment of COVID-19 impacts on U.S. counties using the immediate impact model of local agricultural production (IMLAP)
title Assessment of COVID-19 impacts on U.S. counties using the immediate impact model of local agricultural production (IMLAP)
title_full Assessment of COVID-19 impacts on U.S. counties using the immediate impact model of local agricultural production (IMLAP)
title_fullStr Assessment of COVID-19 impacts on U.S. counties using the immediate impact model of local agricultural production (IMLAP)
title_full_unstemmed Assessment of COVID-19 impacts on U.S. counties using the immediate impact model of local agricultural production (IMLAP)
title_short Assessment of COVID-19 impacts on U.S. counties using the immediate impact model of local agricultural production (IMLAP)
title_sort assessment of covid-19 impacts on u.s. counties using the immediate impact model of local agricultural production (imlap)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9186126/
https://www.ncbi.nlm.nih.gov/pubmed/35702605
http://dx.doi.org/10.1016/j.agsy.2021.103132
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