Mapping Agricultural Labor: What 2,000 Counties Reveal About Rural Work
- Nashad Carrington
- 4 days ago
- 10 min read
A 3-minute read from the LandConnect: Powered by GRŌ research desk.
Published May 20, 2026 · 16-min read · License: CC-BY-ND 4.0
Key takeaways
• U.S. farm employment has declined steadily since 1969, but the burden is uneven across roughly 2,000 rural counties.
• In a meaningful share of rural counties, farm work exceeds 20% of total employment — multiples of the national average.
• Program funding and certified-contractor supply often diverge at the county level, producing funded-but-unstaffed risk.
• The migrant share of crop workers has collapsed since the 1990s; most labor is now settled, changing how it can be deployed.
• A county-level join of funding, contractor capacity, and migration signals can forecast project feasibility before deadlines slip.
1. The County Map Problem
Picture a landowner in a southeast Missouri county, a USDA program administrator in Yakima, and a conservation contractor outside Lancaster, Pennsylvania. They are all staring at the same kind of map: a county outline shaded by funding obligations, project demand, and deadlines that will not move. Each is also looking at the same gap — the certified crews capable of doing the work do not always live within reach of the dollars committed to it. [3,5]
That gap is not the story of one county. It is the story of roughly two thousand of them.
Across the rural United States, work exists and money has been committed, but labor does not always land in the right place at the right time. The Federal Reserve Bank of Richmond, analyzing 2022 USDA Census of Agriculture data, found that farming continues to create outsize economic value in rural counties even as the national labor base contracts. [3,6] USDA itself describes farm labor as a phenomenon that no single survey can capture — which is why its Office of the Chief Economist explicitly stitches together multiple data sources to read the market. [4]
The real question is not whether U.S. farm labor has shrunk. It has, persistently, for half a century. [1] The question is whether the labor still here can be matched to the work still funded. Increasingly, that is a geographic problem — a county-by-county problem — and the data needed to solve it lives in places that have never been joined.
2. What This Article Measures
This piece works from four overlapping county-level layers:
• Labor indicators — USDA NASS Farm Labor Survey, the Census of Agriculture’s county profiles, and BLS occupational employment data. [5,7,14]
• Program funding records — FSA and NRCS payment archives and the conservation programs that move dollars to counties on predictable cycles. [4]
• Contractor availability — certified contractor directories, including TSP designations, service radius, and current bandwidth.
• Migration-linked labor patterns — Department of Labor NAWS research and state-level analyses such as California’s ALRB review. [8,10]
Each of these is independently valuable. Joined at the county level, they become a working map of where conservation and farm work can credibly be staffed — and where it cannot. LandConnect, built by GRŌ:FARM, exists precisely to make that join. It does not replace USDA’s data sources. It connects them to live contractor capacity so program participants can see, county by county, whether the work they have funded is actually deliverable.
3. County-Level Labor Analysis
Three patterns repeat across the county map: agricultural intensity, labor dependency, and seasonal volatility. Each is uneven. Each shapes how a contractor or landowner experiences the market.
Nationally, agricultural production has been a steadily smaller share of U.S. employment since the late 1960s. [1] But the national line obscures the local reality. The Richmond Fed’s 2025 analysis of 2022 Census of Agriculture data documents that in farm-dependent rural counties, agriculture remains a foundational industry — sometimes the foundational industry — providing a multiple of jobs relative to the U.S. average. [3,6]
Reading the same data at the county level reframes the conversation. In a meaningful number of rural counties, farm employment exceeds 20 percent of total employment — not as an outlier statistic, but as the day-to-day economic base. [3] When a county is that dependent on agriculture, the supply of qualified labor and the schedule of conservation work are not abstract concerns; they are local economic infrastructure.
Seasonal volatility complicates the picture further. Planting, mid-season conservation practice installation, post-harvest cover-crop establishment, and prescribed burns all compete for the same finite crews inside narrow windows. [5,15] The University of Missouri Extension’s 2025 review of state agricultural work needs makes the point directly: county-level capacity does not move with funding announcements; it moves with people, equipment, and seasonal availability. [16]
4. Funding vs. Contractor Supply
The central hypothesis of this analysis is straightforward: counties that receive more program dollars should also have more certified contractors available to do the work. The data complicates the assumption.
Approached as a market-efficiency question rather than a procurement question, the relevant signals are:
• Funding per county — the dollar volume of FSA, NRCS, and adjacent program obligations. [4]
• Certified contractor count per county — including TSPs and specialty trades qualified for conservation practice installation.
• Lag time between award and project start — a direct measure of execution friction.
• Service radius — the practical distance a contractor will travel, which determines whether one county’s surplus capacity can flow to a neighbor’s deficit.
Plotting funding against contractor count surfaces a recognizable shape: a heavy cluster of counties where the two move together, plus a long tail of counties that have either money without crews or crews without sustained funding. The lag-time dimension is where the cost of a poor match becomes visible — counties with thin contractor coverage routinely show award-to-start lags more than twice those of well-served counties.
USDA’s own labor data sources page and the NASS Farm Labor Survey program form the baseline for any work in this area; the Ag Census Web Maps then make it possible to test any pattern against a county geography. [4,5,6] Where LandConnect’s internal dataset adds value is in the contractor layer: linking funding signals to real-world execution capacity, county by county.
5. Migration Patterns and Deployable Labor
Agricultural labor is mobile, but not infinitely mobile. The Department of Labor’s NAWS Research Report No. 17 documents that most U.S. crop workers are settled — they live in or near the area they work — while a smaller and shrinking share are migrants who follow the crop. [10] California’s ALRB analysis of NAWS data reaches the same conclusion for the state with the largest agricultural labor force in the country: migrants are now a distinct, smaller subgroup with different travel patterns and different seasonal availability than settled workers. [8]
Three implications follow. First, migration is a labor-routing issue, not just a demographic issue: it determines whether a county can pull labor from outside its own workforce during peak demand. Second, travel distance and seasonal length govern how far any individual crew can realistically be deployed. [9] Third, housing, transportation, employer practice, and immigration policy all influence whether labor actually arrives when needed. [11]
The most important journalistic distinction in this section is between labor that exists on paper and labor that can be deployed in practice. A county may have a nominally adequate workforce in census data while having far fewer workers actually available for scheduled conservation work in a given week. That distinction maps directly onto the contracting history visible inside LandConnect: counties with high deployable labor close out projects on time; counties with high paper labor but low deployable labor accumulate delays.
6. Predictive Modeling for Labor Needs
Predictive analytics in agriculture has historically focused on yield, weather, and commodity prices. [12,13] The same logic transfers cleanly to labor planning once the right county-level inputs are available.
A practical labor-feasibility model uses five inputs:
1. Historical program funding by county and program type. [4]
2. County-level farm output, acreage, and operation profiles from the Census of Agriculture. [7]
3. Contractor availability and observed response time.
4. Seasonal labor migration windows derived from NAWS and state analyses. [8,10]
5. Local wage and employment trends from BLS and the NASS Farm Labor Survey. [5,14]
Those inputs feed four practical outputs: probability of labor shortage by county and season; expected contractor coverage gaps for a given practice mix; lead time required to secure crews ahead of program deadlines; and a “funded-but-unstaffed” risk score, identifying counties where execution risk is highest.
The point of the model is not to forecast labor volume in the abstract. It is to forecast project feasibility — whether a specific set of practices, funded on a specific schedule, can credibly be installed by available crews. That framing makes the output usable for landowners deciding whether to apply, for agency staff scheduling contracts, and for contractors planning their own seasons.
7. The Policy and Market Intersection
U.S. farm labor supply has been under long-term pressure. The National Council of Agricultural Employers documents persistent shortages reported by growers even as wages and benefits rise. [2] The farmdoc series shows the underlying decline in national farm employment going back to 1969. [1] The Census of Agriculture and the Farm Labor Survey confirm that the squeeze is uneven, concentrated in counties whose production is already labor-intensive. [5,7]
That long-term pressure shows up directly in conservation contracting. When contractor supply is thin or geographically mismatched, the cost is missed deadlines, returned obligations, and lost program throughput. AgAmerica’s review of farm-labor shortage impacts traces the cascade from production to procurement to compliance. [17]
This is also where the institutional case for a county-level platform becomes strongest. USDA itself relies on multiple labor data sources — the Farm Labor Survey, the Current Population Survey, the Census of Agriculture, and NAWS — precisely because no single one is sufficient. [4,5] A county-by-county execution view is the missing layer that turns those federal datasets into operational decisions.
8. Where LandConnect Fits
LandConnect is not a labor-shortage solution. The labor shortage is structural, and no platform will reverse it. What LandConnect does is reduce friction in the layer above the shortage: surfacing capacity that already exists, in the county where it is needed, before a deadline is missed.
Three capabilities drive that:
1. Find certified contractors by county and project type. The directory is structured around TSP designations, practice standards, and service radius — the attributes that determine whether a contractor can actually be assigned.
2. Match funding opportunities to available labor capacity. Awards and program obligations are joined to contractor bandwidth so program staff and landowners can see, at award time, whether a county can credibly absorb the work.
3. Anticipate shortages before deadlines slip. The labor-feasibility model flags counties where funded projects are at risk of going unstaffed, in time to act on the warning.
These capabilities lean on the same federal data the rest of the field already uses — the 2022 Census of Agriculture web maps, the NASS Farm Labor Survey, and USDA OCE’s labor affairs catalog. [5,6] The differentiation is the contractor layer and the county-level join, not a competing data source.
9. The Next Advantage
Rural work is becoming more data-driven, and the advantage will accrue to the counties, agencies, and contractors who learn to read county signals early. The Richmond Fed’s analysis already makes clear that farming creates outsize value in rural areas; the emerging predictive-analytics literature in agriculture demonstrates the methods are available; USDA’s catalog of labor data sources confirms that the inputs exist. [3,4,13]
The counties with the best outcomes over the next decade will not necessarily be the ones with the most money. They will be the ones that align funding, workforce, and timing — and that align them visibly enough that contractors, landowners, and agency staff can act on the same picture.
10. Sources & Methodology
Methodology: charts in the web edition labeled Figures 1, 2, 4 are derived from public, cited sources and reflect publicly reported series at the time of writing. Figure 3 is illustrative of patterns in the LandConnect internal dataset (FSA and NRCS payment archives joined with the certified-contractor directory). Figure 5 is a schematic of the model architecture. Figure 6 is a live aggregate from the LandConnect contractor pipeline. All federal data sources are cited inline; nothing in this article relies on proprietary identifiers, individuals, or operations.
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Citations
[1] farmdoc daily, University of Illinois. Schnitkey et al., Changes in Farm Employment, 1969 to 2021 (2023). https://farmdocdaily.illinois.edu/2023/07/changes-in-farm-employment-1969-to-2021.html
[2] National Council of Agricultural Employers. NCAE, Declining Farm Labor Supply (research brief).https://www.ncaeonline.org/ag-labor-research/declining-farm-labor-supply/
[3] Federal Reserve Bank of Richmond. Regional Matters, Farming Creates Value in Rural Areas (2025). https://www.richmondfed.org/region_communities/regional_data_analysis/regional_matters/2025/farming_creates_value_rural_areas
[4] USDA Office of the Chief Economist. Labor Affairs — Data Sources. https://www.usda.gov/about-usda/general-information/staff-offices/office-chief-economist/labor-affairs/data-sources
[5] USDA NASS. Guide to NASS Surveys: Farm Labor. https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Farm_Labor/
[6] USDA NASS. 2022 Census of Agriculture — Web Maps Overview. https://www.nass.usda.gov/Publications/AgCensus/2022/Online_Resources/Ag_Census_Web_Maps/Overview/
[7] USDA NASS. Census of Agriculture (program landing). https://www.nass.usda.gov/AgCensus/
[8] California Agricultural Labor Relations Board. California Farm Labor Force: An Analysis of NAWS Data (2018). https://www.alrb.ca.gov/wp-content/uploads/sites/196/2018/05/CalifFarmLaborForceNAWS.pdf
[9] National Center for Farmworker Health. Facts About Farmworkers (Jan 2023). https://www.ncfh.org/wp-content/uploads/2025/04/facts_about_farmworkers_fact_sheet_1.10.23-1.pdf
[10] U.S. Department of Labor. National Agricultural Workers Survey — Research Report No. 17. https://www.dol.gov/sites/dolgov/files/ETA/naws/pdfs/NAWS%20Research%20Report%2017.pdf
[11] Farm Aid. Immigration and the Food System (fact sheet). https://www.farmaid.org/blog/fact-sheet/immigration-and-the-food-system/
[12] Saiwa. Predictive Analytics in Agriculture. https://saiwa.ai/sairone/blog/predictive-analytics-in-agriculture/
[13] Meegle. Predictive Analytics in Agriculture. https://www.meegle.com/en_us/topics/predictive-analytics/predictive-analytics-in-agriculture
[14] U.S. Bureau of Labor Statistics. Occupational Outlook Handbook: Agricultural Workers. https://www.bls.gov/ooh/farming-fishing-and-forestry/agricultural-workers.htm
[15] Cornell Cooperative Extension, Erie County. Agricultural Labor Management. https://erie.cce.cornell.edu/agriculture/ag-labor-management
[16] University of Missouri Extension. Missouri Economy: Ag Work Needs (Jul 2025). https://extension.missouri.edu/media/wysiwyg/Extensiondata/Pro/ExCEED/Docs/MissouriEconomy_AgWorkNeeds_v6i9_21Jul2025.pdf
[17] AgAmerica. The Impact of the Farm Labor Shortage. https://agamerica.com/blog/the-impact-of-the-farm-labor-shortage/
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Suggested attribution: "Mapping Agricultural Labor" by Nashad Carrington, originally published by GRO:FARM at landconnect.mygro.co/reports/mapping-agricultural-labor. Republished under CC-BY-ND 4.0.
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