Food and Agriculture Vulnerability Index Dashboard

Source: https://ag.purdue.edu/department/agecon/foodandagvulnerabilityindex.html Parent: https://ag.purdue.edu/department/agecon/undergraduate-program/scholarships.html

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​Purdue Food and Agriculture Vulnerability Index

in collaboration with Microsoft

The Department of Agricultural Economics at Purdue University, in collaboration with Microsoft, has created an online dashboard, built on top of Microsoft Azure and Power BI platforms, to quantify the potential risk to the supply of agricultural products as a result of farm and agricultural worker illnesses from COVID19. Vulnerability of the supply of an agricultural commodity to risks such as COVID-19 depend on the number of farmers and agricultural workers affected, the location of affected workers, the crops and animals that are grown in the most impacted regions, and the degree to which production is concentrated in a particular geographic region. By combining data on the number of COVID cases in each U.S. county with the county’s total population, U.S. Department of Agriculture data on the number of farmers and hired farm workers in each county, and data on agricultural production of each county, and estimate of the share of agricultural production at risk can be computed.

USING THE DASHBOARD

To use the dashboard, click on one of the agricultural commodities at the top of the table. The pie-chart shows the estimated percent of total U.S. production of the selected commodity potentially at risk because of COVID19 farmer and farm worker illnesses. The table below shows the estimated number of agricultural worker COVID19 case by state, the level of production of the commodity in each state (in bushels, pounds, acres, or number of head, depending on the commodity), the estimated loss in productivity because of worker illnesses, and the percent of total production in the state potentially lost due to illnesses. The map shows the total number of COVID cases in each state. To drill into the county-level detail, click on a state to see a more detailed county-level map with the estimated number of farm and agricultural workers with COVID-19 along with other county-level statistics on production.

This work was financially supported by a grant from the Foundation for Food and Agriculture Research under award number – Grant ID: DSnew-0000000017. The content of this tool are solely the responsibility of the authors and does not necessarily represent the official views of the Foundation for Food and Agriculture Research

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Please note: this is a work in progress and we are working to add new data and eliminate errors. If you have questions about the tool or project, contact Jayson Lusk at 765.494.4191 or jlusk@purdue.edu.

RELATED PUBLICATIONS

A related paper, "Farmer and farm worker illnesses and deaths from COVID-19 and impacts on agricultural output," was published on PLOS ONE, April 28, 2021 and is available at: doi.org/10.1371/journal.pone.0250621

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Purdue University news release -- 5/20/2020​

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Data, Calculations, and Definitions -- available here

DATA SOURCES

• COVID-19 confirmed case are from Johns Hopkins:

• https://coronavirus.jhu.edu/us-map

• County-level total population is for 2019 from the U.S. Census Bureau:

• https://www.census.gov/data/datasets/time-series/demo/popest/2010s-counties-total.html

• County-level data on number of farmers is from the 2017 Census of Agriculture (measured as the numbers of “producers”):

• https://quickstats.nass.usda.gov/

• County-level data on number of hired farm laborers is from the 2017 Census of Agriculture (measured as an expense associated with “labor, hired – number of workers”):

• https://quickstats.nass.usda.gov/

• County-level production for each commodity is from the 2017 Census of Agriculture:

• https://quickstats.nass.usda.gov/

CALCULATIONS & DEFINITIONS

• j indexes each county
• TCCj is the total number of confirmed cases of COVID19 in county j
• AWjare the number of agricultural farm workers in county j, defined as the sum of the number of “producers” and “hired labor” in the county
• POPjis the total population of county j
• The expected number of agricultural workers with COVID, or “Ag Workers COVID”, AW_CVDj, in county j is AW_CVDj .= TCCj ( AWj/ POPj ). The assumption is that agricultural workers in a county contract COVID19 at a rate equal to that of everyone else in the county, implying that the expected number of ag worker COVID cases is equal to the share of the total population that is an agricultural worker multiplied by the total number of COVID cases.
• yjk is the production of commodity k in county j (units depend on the commodity in question).
• Labor productivity for commodity k in county j is: LaborPjk = yjk / AWj. This is the amount of commodity k produced per agricultural worker in the county.
• If AW_CVDj agricultural workers are ill, then there is the potential lost productivity, LaborPjk, of commodity k is:  LPjk = AW_CVDj * ( LaborPjk )
• Total estimated lost production in a state or for the country as a whole with J total counties is determined by summing over counties: TOTAL_LPk = ΣJj =1 LPjk.
• Total production of a commodity in a state or for the country as a whole with J total counties is determined by summing over counties: TOTAL_yk = ΣJj =1 yjk .
• The potential percent of lost production from COVID for commodity k is: ( TOTAL_LPk / TOTAL_yk ) * 100.