Snapshot: What ICE arrests looked like in farm counties last year 12%

By Sky Chadde0% Investigate Midwest29%

7/14/2026, 10:22:34 AM

BS Summary: This article contains 14 faulty reasoning types, including Anecdotal, Post Hoc (False Cause), and Appeal to Authority, with Ambiguity (Equivocation) as the most egregious example at 18.4% saturation with 134 hits. Analysis detected 585 faulty-reasoning hits from 727 analyzed words, generating a BS Score of 22.7% and a BS Rank of 12% (24,145 of 27,238 articles). This article is better (less manipulative) than 88.60% of the article peer group.

About a third of counties most dependent on farm labor for their economies had ICE arrests in 2025, more than in previous years, according to an analysis of federal data. 
The increase came as the Trump administration pursued mass deportations of immigrants throughout the country. 
The campaign largely focused on sending law enforcement agents to major cities, which resulted in the deaths of two U.S. citizens in Minneapolis (and less employment ). 
However, rural communities faced activity as well, such as a large raid in Idaho last year that threatened the harvest season. 
Increased ICE activity led to concerns about farms losing their labor force. 
In March, Michigan State University researchers released the results of a survey they conducted of California farmers in late 2025 and early 2026. 
Less than 1% of surveyed farmers reported losing workers as a direct result of immigration enforcement on their farms. 
However, more than 14% reported losing workers over general fears of encountering ICE. 
It is unclear exactly how many workers in the agriculture industry  i.e, those harvesting crops, packaging meat or milking cows  have been affected by the Trump administration’s crackdown. 
Data showing the occupations of those arrested by ICE is not available. 
But rural corners of the U.S. that rely on farming have experienced more ICE activity than in previous years. 
According to the U.S. 
Department of Agriculture, 453 counties are considered “farming” counties. 
Last year, 145 of these counties saw at least one ICE arrest  the most since 2018, President Trump’s second year in office. 
The total number of arrests in “farming” counties also increased in 2025. 
Some well-known agricultural areas, such as western Wisconsin (dairies) and California’s Central Valley (specialty crops), are not included in the USDA’s definition. 
The federal data also does not show the ICE arrestees’ occupations. 
It appears many of the arrests in these counties occurred while people were already in custody, such as in a county jail. 
Overall, the number of ICE arrests in the U.S. has skyrocketed since Trump returned to office. 
More than 300,000 arrests were recorded in the data, more than double the number in any previous year over the past decade. 
To address labor shortage concerns, the Trump administration will allow dairy farms to access H-2A visa labor, Bloomberg reported last month . 
The industry, which milks cows year-round, was not qualified to bring in labor from overseas because the visa is meant for short-term, seasonal jobs, such as harvesting crops. 
House Agriculture Chair G.T. 
Thompson, a Pennsylvania Republican, has proposed codifying the change into law, according to Politico . 
More and more workers come to the U.S. temporarily through the program every year . 
HOW WE ANALYZED THE DATA 
To identify “farming” counties, we relied on the USDA’s definition . 
The department defines “high farming concentration counties” as those where at least a fifth of the county’s average earnings came from farming, or where at least an average of 17% of a county’s labor force worked in farming over a three-year period. 
Under that definition, there are 453 “farming” counties, mostly in the center of the country. 
We then compared the list of USDA “farming” counties to the locations of ICE arrests since 2015. 
For the arrests, we used data obtained by the Deportation Data Project , which sued ICE for the records. 
The data includes arrest locations and goes back to 2023. 
For more context, we used similar data that the University of Washington Center for Human Rights obtained and released publicly. 
That data also includes arrest locations for the years 2015 through 2023. 
The locations are imprecise. 
For instance, many arrests in Chicago are listed as “Chicago general area,” making it difficult to assess location trends in that city. 
The Deportation Data Project also cautioned that arrest locations did not appear to be consistently entered into the database. 
Data Harvest (formerly Graphic of the Week) is Investigate Midwest’s way of making complex agricultural data easy to understand. 
Through engaging graphics, charts, and maps, we break down key trends to help readers quickly grasp the forces shaping farming, food systems, and rural communities. 
Want us to explore other data trends? 
Let us know here. 
The post Snapshot: What ICE arrests looked like in farm counties last year appeared first on Investigate Midwest . 
Article reasoning-pattern comparisonThis article: 2.1%Sky Chadde: 0.0%Investigate Midwest: 0.8%Confirmation Bias2.1%This article: 0.0%Sky Chadde: 1.6%Investigate Midwest: 2.0%Anchoring Bias0.0%This article: 0.0%Sky Chadde: 0.9%Investigate Midwest: 2.7%Availability Heuristic0.0%This article: 2.6%Sky Chadde: 0.0%Investigate Midwest: 1.0%Representativeness Heuristic2.6%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.1%Hindsight Bias0.0%This article: 3.0%Sky Chadde: 0.0%Investigate Midwest: 0.3%Overconfidence Bias3.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 3.6%Framing Effect0.0%This article: 0.0%Sky Chadde: 0.8%Investigate Midwest: 0.3%Loss Aversion0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.4%Status Quo Bias0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Sunk Cost Effect0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.3%Optimism Bias0.0%This article: 1.7%Sky Chadde: 0.0%Investigate Midwest: 0.5%Pessimism Bias1.7%This article: 5.9%Sky Chadde: 3.0%Investigate Midwest: 9.5%Negativity Bias5.9%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Self-Serving Bias0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.1%Fundamental Attribution Error0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Actor-Observer Bias0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.2%In-Group Bias0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Out-Group Homogeneity Bias0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Halo Effect0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Horn Effect0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Dunning-Kruger Effect0.0%This article: 5.4%Sky Chadde: 1.5%Investigate Midwest: 1.1%Recency Bias5.4%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Primacy Effect0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Blind-Spot Bias0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.2%Ad Hominem0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Straw Man0.0%This article: 7.7%Sky Chadde: 0.0%Investigate Midwest: 0.4%Appeal to Authority7.7%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.8%False Dilemma0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Slippery Slope0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Circular Reasoning0.0%This article: 2.6%Sky Chadde: 0.0%Investigate Midwest: 2.2%Hasty Generalization2.6%This article: 0.0%Sky Chadde: 1.9%Investigate Midwest: 0.2%Red Herring0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Bandwagon0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 2.3%Appeal to Emotion0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.1%Begging the Question0.0%This article: 8.0%Sky Chadde: 0.0%Investigate Midwest: 0.8%Post Hoc (False Cause)8.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.2%Tu Quoque0.0%This article: 4.1%Sky Chadde: 0.0%Investigate Midwest: 0.4%Burden of Proof4.1%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Appeal to Nature0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.6%Composition/Division0.0%This article: 9.6%Sky Chadde: 0.0%Investigate Midwest: 0.4%Anecdotal9.6%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%No True Scotsman0.0%This article: 18.4%Sky Chadde: 0.0%Investigate Midwest: 0.9%Ambiguity (Equivocation)18.4%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Gambler’s Fallacy0.0%This article: 1.8%Sky Chadde: 0.0%Investigate Midwest: 0.0%Middle Ground1.8%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Personal Incredulity0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Special Pleading0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Genetic Fallacy0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.4%Unattributed Quote0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 1.3%Quote-first Misdirection0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 1.1%Biased Writer Voice0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.4%Indoctrination0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.9%Politically Left Leaning Bias0.0%This article: 0.0%Sky Chadde: 0.0%Investigate Midwest: 0.0%Politically Right Leaning Bias0.0%This article: 7.6%Sky Chadde: 0.0%Investigate Midwest: 1.3%Attempt to Sell a Product or S…7.6%

727 words analyzed.

Speakers

2speakers2.6%attributed speech708writer words
Selected voice

Politico

100%flagged-word coverage
15 attributed words79% of attributed speech58% writer coverage
0%5.0%10.0%Attempt to Sell a Product -7.8 ptsWriter: 7.8%Politico: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.