As AI expands, rural areas are increasingly targeted for new data centers. According to the Pew Research Center, while 87% of existing AI data centers are in urban areas, 67% of newly planned facilities are sited in rural counties (Seets &amp; Radde, 2026). Local opinions about rural data centers are mixed. Some residents view them as a last chance for sustained economic growth, while others argue that they require substantial amounts of water and energy while providing limited local benefits (Nickelsburg, 2025). Although construction of a large facility produces a massive one time economic boost, questions remain about the longer term operational impacts on rural counties. How much do data centers actually help rural communities?



To explore this question, this article examines a “what-if” scenario in which rural counties add 100 jobs with the same national average productivity and work hours across three sectors: data centers, automobile manufacturing, and food manufacturing, with the latter two representing traditional rural manufacturing industries (Figure 1). Would a data center generate more economic value than food manufacturing or automobile manufacturing? To answer this question, an IMPLAN input-output (IO) model was used to develop scenarios. In plain terms, this model tracks how economic activity flows through a local economy, capturing not just the direct jobs in an industry, but also the ripple effects generated through purchases of intermediate inputs and services from suppliers (indirect effects), and household spending of employees in the sectors (induced effects) (IMPLAN, 2024). The analysis focuses on single year operational impacts only, rather than construction phase or long term project effects. While large scale facilities typically generate substantial one time construction impacts, capturing multi year outcomes would require a dynamic modeling framework, which is beyond the scope of this study. Accordingly, the analysis focuses on the direct, indirect, and induced impacts associated with annual facility operations.



This scenario covers five regions: the state of Arkansas, a diversified benchmark region, and the four most rural counties in the state: Calhoun County in the south, Newton County in the north, Woodruff County in the east, and Dallas County in the south central region. These counties were selected based on U.S. Census Bureau data (U.S. Census Bureau, 2023) to represent the lowest population density counties across geographically distinct areas of Arkansas. In addition, each scenario is standardized to 100 direct jobs, defined as full-time equivalent (FTE) positions (Clouse, 2024), with total output determined based on national average output per job. This approach ensures comparability across sectors and regions and enables a direct comparison of sectoral impacts. While a standard data center typically requires significantly less daily operational labor than traditional manufacturing, this standardized baseline effectively isolates and compares how each industry’s structural linkages perform within rural economies.



Figure 1. Conceptual Framework



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At the state level, data centers perform quite strongly (Table 1). Their output multiplier (1.64) is nearly identical to that of food manufacturing (1.65) and higher than that of automobile manufacturing (1.52), suggesting that data centers generate a comparable level of total economic activity per dollar of output. Interestingly, the state model shows data centers scoring a slightly higher employment multiplier than food manufacturing. This happens because, on a statewide scale, the massive corporate operations and tech support networks required to run these facilities are large enough to trigger significant job creation activities and impacts across the broader Arkansas economy.



Table 1. Economic Multipliers for Data Centers and Selected Manufacturing Industries by Region



<figure class="wp-block-table is-style-stripes has-small-font-size"><table><tbody><tr><td>Region</td><td>Sector</td><td>Output Multiplier (Total economic activity per $1 of direct output)</td><td>Employment Multiplier (Total jobs supported per 1 direct job)</td><td>Tax impact per Output ($ tax revenue per $1 of output)</td></tr><tr><td rowspan="3">Arkansas State</td><td>Data Center</td><td>1.64</td><td>2.54</td><td>0.11</td></tr><tr><td>Food Manufacturing</td><td>1.65</td><td>2.49</td><td>0.07</td></tr><tr><td>Auto Manufacturing</td><td>1.52</td><td>5.22</td><td>0.05</td></tr><tr><td rowspan="3">Calhoun County</td><td>Data Center</td><td>1.13</td><td>1.27</td><td>0.10</td></tr><tr><td>Food Manufacturing</td><td>1.19</td><td>1.78</td><td>0.05</td></tr><tr><td>Auto Manufacturing</td><td>1.06</td><td>1.52</td><td>0.03</td></tr><tr><td rowspan="3">Dallas County</td><td>Data Center</td><td>1.20</td><td>1.51</td><td>0.11</td></tr><tr><td>Food Manufacturing</td><td>1.22</td><td>1.77</td><td>0.05</td></tr><tr><td>Auto Manufacturing</td><td>1.07</td><td>1.83</td><td>0.03</td></tr><tr><td rowspan="3">Newton County</td><td>Data Center</td><td>1.23</td><td>1.94</td><td>0.13</td></tr><tr><td>Food Manufacturing</td><td>1.19</td><td>2.21</td><td>0.06</td></tr><tr><td>Auto Manufacturing</td><td>1.06</td><td>1.97</td><td>0.03</td></tr><tr><td rowspan="3">Woodruff County</td><td>Data Center</td><td>1.22</td><td>1.66</td><td>0.11</td></tr><tr><td>Food Manufacturing</td><td>1.26</td><td>1.62</td><td>0.06</td></tr><tr><td>Auto Manufacturing</td><td>1.27</td><td>3.24</td><td>0.05</td></tr></tbody></table><figcaption class="wp-element-caption">Note 1: Data derived from the 2024 IMPLAN structural matrix using Type SAM multipliers to capture operational impacts only (excluding initial construction). Note 2: To benchmark data center operations, three sectors, Data processing, hosting, and related services (Sector 418), All other food manufacturing (Sector 98), and Automobile and light duty motor vehicle manufacturing (Sector 324), are modeled in IMPLAN Note 3: Standardizing to 100 jobs isolates differences in economic structure and local linkages across industries. Because industries differ in output per worker, the results reflect structural relationships rather than equal total investment levels. Note 4: Employment multipliers reflect the magnitude of indirect (supply-chain) and induced (household-spending) effects captured within each region. Differences across regions and sectors arise from variation in local supply-chain linkages, leakage, and the labor intensity of industries receiving spillover effects, rather than output multipliers alone.</figcaption></figure>



In rural counties, however, the pattern becomes more constrained. Output multipliers generally decline as economic activity leaks to surrounding regions, but data centers still produce output levels broadly comparable to manufacturing sectors. The key difference emerges in employment. Data centers generally produce fewer employment spillovers than manufacturing industries. This gap highlights a more limited ability to support broader local employment, suggesting weaker and less consistent integration into rural economic systems. Why does this happen? It really comes down to how rural economies are structured. Rural counties typically have smaller labor pools and fewer local suppliers. Data centers often rely on technically specialized workers, hardware, and external services, so some of the spending flows out to other regions. Conversely, manufacturing industries are more likely to hire locally and purchase inputs from nearby businesses, which helps keep dollars circulating within the community.



That said, there is one area where data centers consistently stand out: tax impact. Across all regions, data centers generate more tax impact per dollar of output than the other sectors. So even if not all the economic activity stays local, there can still be meaningful benefits for public finances. However, this result must be viewed with caution. In the real world, data center developers frequently condition their investments on massive local tax abatements or utility waivers (Goldman, 2026; Quinn, 2026; Plautz &amp; Tomich, 2026). Therefore, the actual revenue flowing to a rural county&#8217;s public purse may be significantly lower than the theoretical baseline modeled here.



In addition, data centers can place significant demands on energy and water infrastructure, creating capacity challenges for smaller rural systems, particularly in resource dependent agricultural areas. These infrastructure constraints can limit the net local benefits of data centers and may require additional public or utility investment to support long-term operations.



The bottom line is not that data centers are “better” or “worse,” but that their impact depends heavily on local conditions. For rural communities considering data center investments, the key question is not just how large the investment is, but how much of that activity actually stays and circulates locally. Overall, the simulation suggests that data centers can generate output comparable to traditional industries in more diversified economies, but their local economic integration in rural areas is more limited.



References



Clouse, C. (2024, November 8). Employment in IMPLAN. IMPLAN Support.



<a href="https://support.implan.com/hc/en-us/articles/30779951167771-Employment-in-IMPLAN">https://support.implan.com/hc/en-us/articles/30779951167771-Employment-in-IMPLAN</a>



Goldman, S. (2026, March 26). Meta AI data center Hyperion in Louisiana. Fortune.



<a href="https://fortune.com/2026/03/26/meta-ai-data-center-hyperion-louisiana">https://fortune.com/2026/03/26/meta-ai-data-center-hyperion-louisiana</a>



IMPLAN. (2025, August 12). What is IMPLAN? IMPLAN Blog.



<a href="https://blog.implan.com/what-is-implan">https://blog.implan.com/what-is-implan</a>



Nickelsburg, M. (2025, August 17). AI is driving a data center boom in rural America. Locals



are divided on the benefits. NPR. <a href="https://www.npr.org/2025/08/17/nx-s1-5461467/ai-is-driving-a-data-center-boom-in-rural-america-locals-are-divided-on-the-benefits">https://www.npr.org/2025/08/17/nx-s1-5461467/ai-is-driving-a-data-center-boom-in-rural-america-locals-are-divided-on-the-benefits</a>



Plautz, J., &amp; Tomich, J. (2026, May 2). Data centers used to be a prize. States are having second



thoughts. Politico. <a href="https://www.politico.com/news/2026/05/02/data-centers-states-tax-incentives-00891184">https://www.politico.com/news/2026/05/02/data-centers-states-tax-incentives-00891184</a>



Quinn, S. (2026, May 22). Google data center plan raises tax transparency questions in rural



Missouri. Missouri Independent. <a href="https://missouriindependent.com/2026/05/22/google-data-center-plan-raises-tax-transparency-questions-in-rural-missouri/">https://missouriindependent.com/2026/05/22/google-data-center-plan-raises-tax-transparency-questions-in-rural-missouri/</a>



Seets, S., &amp; Radde, K. (2026, April 13). Most new data centers in the U.S. are coming to rural



areas. Pew Research Center. <a href="https://www.pewresearch.org/short-reads/2026/04/13/most-new-data-centers-in-the-us-are-coming-to-rural-areas/">https://www.pewresearch.org/short-reads/2026/04/13/most-new-data-centers-in-the-us-are-coming-to-rural-areas/</a>



U.S. Census Bureau. (2023, September). County-level urban and rural information for the 2020



Census. <a href="https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html">https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html</a>



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Recommended citation format: Seo, Frank. &#8220;<a href="https://southernagtoday.org/2026/07/27/data-centers-vs-factories-do-ai-facilities-truly-benefit-rural-economies-evidence-from-an-arkansas-simulation/">Data Centers vs. Factories: Do AI Facilities Truly Benefit Rural Economies? Evidence from an Arkansas Simulation.</a>&#8221; Southern Ag Today 6(32.5). August 7, 2026. <a href="https://southernagtoday.org/2026/07/27/data-centers-vs-factories-do-ai-facilities-truly-benefit-rural-economies-evidence-from-an-arkansas-simulation/">Permalink</a>

Data Centers vs. Factories: Do AI Facilities Truly Benefit Rural Economies? Evidence from an Arkansas Simulation

The Senate Agriculture Committee is slated to markup The Agricultural Act of 2026 today (August 6, 2026), bringing Congress one step closer to long-anticipated passage of a new farm bill. During the markup, committee members propose, debate, and vote on amendments to legislation before sending it to the full Senate for consideration. The House passed its version of the farm bill, the Farm, Food, and National Security Act of 2026, on April 30th. Once the Senate passes its version, the two chambers can establish a conference committee to resolve the differences between the two bills. Then, the conferenced bill will go back to the House and Senate for votes, and, if passed, will go to the President to be signed into law.



Although there are several steps to go before a new farm bill is enacted, the Senate Agriculture Committee markup is an important step in the right direction. While some farm bill programs are permanently authorized and would continue without new legislation, authorization of other programs expires and must be reauthorized to continue either in a new farm bill or an extension of the existing farm bill. The most recent farm bill, passed in 2018, has already been extended three times – this is the closest Congress has been to getting a new farm bill across the finish line since the expiration of the 2018 Farm Bill on September 30, 2023.



Table 1 highlights some of the farm bill changes proposed by the House and Senate Agriculture Committees and major policy differences between the two chambers’ farm bill text. Significant updates to farm safety net provisions were included in the One Big Beautiful Bill Act last summer; therefore, most changes in current legislation focus on other farm bill titles.



Table 1. Comparison of House-Passed and Senate-Proposed Farm Bill Provisions



<figure class="wp-block-table is-style-stripes has-small-font-size"><table><tbody><tr><td>Title</td><td>Provision</td><td>Current Law</td><td>House-Passed Bill</td><td>Senate-Proposed Bill</td></tr><tr><td>I – Commodities</td><td>Farm storage facility loans</td><td>USDA has authority to provide loans to producers of certain storable commodities to construct or upgrade storage and handling facilities.</td><td>Adds authority for USDA to provide loans for producers to construct or upgrade storage facilities for propane that is primarily used for agricultural production.</td><td>Adds the authority for USDA to provide loans for producers to construct or upgrade storage facilities for both propane and fertilizer that are primarily used for agricultural production.</td></tr><tr><td>I – Commodities</td><td>Livestock eligibility for USDA&#8217;s natural disaster assistance programs</td><td>Defines livestock eligible for USDA&#8217;s natural disaster assistance programs.</td><td>No comparable provision.</td><td>Expands the definition of livestock to include unweaned livestock for agricultural disaster assistance.</td></tr><tr><td>II – Conservation</td><td>Feral Swine Eradication and Control Program</td><td>The 2018 Farm Bill established the Feral Swine Eradication and Control Pilot Program.</td><td>Codifies the Feral Swine Eradication and Control Pilot as a program and increases funding for FY2025-FY2031 to $150 million. Requires NRCS and APHIS to contract with one or more land grant universities to assist with the program.</td><td>Codifies the Feral Swine Eradication and Control Pilot as a program with the same funding increase as the House bill. Does not include the requirement to contract with land-grant universities.</td></tr><tr><td>III – Trade</td><td>Trade promotion program funding</td><td>Includes funding for trade promotion programs such as the Market Facilitation Program (MAP) and the Foreign Market Development Program (FMD).</td><td>Increases mandatory funding from $255 million to $500 million (FY2027), then $533 million annually (FY2028-FY2031).</td><td>Increases mandatory funding to $515 million (FY2027), then $533 million annually (FY2028-FY2031).</td></tr><tr><td>IV – Nutrition</td><td>Timing of state cost share for SNAP</td><td>Beginning FY2028, states must pay a share of SNAP benefit costs if their payment error rates are 6% or higher.</td><td>No change from current law.</td><td>Delays state cost share start to FY2029. Increases cost share rate for states whose error rate is still 10% or higher beginning FY2031.</td></tr><tr><td>V – Energy</td><td>Year-round sale of E15</td><td>Sale of E15 (a fuel blend of 15% ethanol and 85% gasoline) generally not permitted during summer months (June 1 to September 15).</td><td>No comparable provision. (In May, the House passed H.R. 1346 which would allow for the sale of E15 year-round)</td><td>Makes nationwide summer E15 sales permanently legal by amending the Reid vapor pressure requirements in the Clean Air Act.</td></tr><tr><td>XII – Miscellaneous</td><td>Standards for movement of livestock-derived products in interstate commerce (such as California&#8217;s Proposition 12)</td><td>No comparable provision.</td><td>Prohibits states from enacting or enforcing a production standard as a condition for sale or consumption on livestock-derived products not produced in that state.</td><td>No comparable provision.</td></tr></tbody></table></figure>



It will be interesting to see whether the Senate Agriculture Committee votes to report the bill to the full Senate today. Will the recent additions to the Senate text (inclusion of year-round E15 and delays to state cost sharing for SNAP) gain enough Democrat support to pass the bill out of committee? Or will we see another extension of the 2018 Farm Bill as its expiration approaches at the end of September?



References:



Congressional Research Services. (2026, July 29). The 2026 Farm Bill: Comparison of the House and Senate Bills with Current Law. (CRS Report No. R48918) https://www.congress.gov/crs-product/R48918



Farm, Food, and National Security Act of 2026, H.R. 7567, 119th Congress (2026) https://www.congress.gov/bill/119th-congress/house-bill/7567



The Agricultural Act of 2026, S. ___, 119th Congress. (2026) https://www.agriculture.senate.gov/agricultural-act-of-2026-farm-bill-20



To amend the Clean Air Act with respect to the ethanol waiver for Reid Vapor Pressure under that Act, and for other purposes, H.R. 1346, 119th Congress. (2026) https://www.congress.gov/bill/119th-congress/house-bill/1346



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Recommended citation format: Stewart, Natalie. &#8220;<a href="https://southernagtoday.org/2026/07/27/comparison-of-house-and-senate-farm-bill-provisions-ahead-of-todays-senate-farm-bill-markup/">Comparison of House and Senate Farm Bill Provisions Ahead of Today&#8217;s Senate Farm Bill Markup.</a>&#8221; Southern Ag Today 6(32.4). August 6, 2026. <a href="https://southernagtoday.org/2026/07/27/comparison-of-house-and-senate-farm-bill-provisions-ahead-of-todays-senate-farm-bill-markup/">Permalink</a>

Comparison of House and Senate Farm Bill Provisions Ahead of Today’s Senate Farm Bill Markup

The first forecast of corn yields by the USDA is expected to be released with the&nbsp;Crop Production&nbsp;report on August 12, 2026. However, most southern corn has passed through silking, the crop’s most critical yield-determining period. One can then look at the USDA&nbsp;Crop Progress and Condition Layers&nbsp;current condition ratings to gain a meaningful signal of likely yield outcomes. According to crop condition data for the week ending July 26, most southern corn entered the second half of July in fair-to-good condition—3.56 on a scale from very poor (1) to excellent (5). The average masks a sharp divide between the mid-South and the Carolinas. While weather can still affect kernel weight, much of the crop’s yield potential, and damage from June and early-July heat and moisture stress, has already been determined.&nbsp;



As shown in Figure 1, conditions were near good from Arkansas through Mississippi and into northern Alabama and Tennessee. Alabama was good (4.18), while Tennessee and Arkansas were near good (3.98 and 3.96, respectively). The strongest county ratings clustered in northern Alabama and the Tennessee Valley. Conditions weakened farther east. South Carolina was below fair (2.88), and North Carolina had the region’s lowest rating, between poor and fair (2.81). The lowest-rated counties were concentrated in eastern North Carolina’s Coastal Plain, where much of the state’s corn acreage is located.



Figure 2 shows the corn condition by state throughout the growing season. Both Carolinas were near the middle of the regional range in late spring before deteriorating during June and early July amid repeated heat and limited, uneven rainfall. Because the decline overlapped silking, the Carolinas are increasingly likely to experience below-trend yields and reduced production, especially in eastern North Carolina. In contrast, stronger mid-South conditions point to comparatively favorable yields.&nbsp;



These lower expected yields may matter more for local markets than for national corn prices because Southern production is small relative to the Corn Belt. The Carolinas, like many other southern states, have livestock and poultry sectors and often rely on corn shipped from other regions. A smaller local harvest could increase demand for imported grain, strengthen local basis, and raise delivered feed costs. Mid-South producers, by contrast, head toward harvest with a near-good crop and no comparable supply concerns.



Figure 1. County-average corn condition for the week ending July 26, 2026, and 2025 corn acreage across the South.



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="1849" height="1408" src="https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-1.png" alt="" class="wp-image-44562" srcset="https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-1.png 1849w, https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-1-1024x780.png 1024w, https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-1-768x585.png 768w, https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-1-1536x1170.png 1536w" sizes="auto, (max-width: 1849px) 100vw, 1849px" /></figure>



Figure 2. Acreage-weighted state corn condition through the 2026 season.



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="1883" height="1250" src="https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-2.png" alt="" class="wp-image-44563" srcset="https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-2.png 1883w, https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-2-1024x680.png 1024w, https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-2-768x510.png 768w, https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/08/6.32.3-2-1536x1020.png 1536w" sizes="auto, (max-width: 1883px) 100vw, 1883px" /></figure>



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Recommended citation format: Lee, Seunghyun. &#8220;<a href="https://southernagtoday.org/2026/07/27/southern-corn-conditions-and-yield-expectations/">Southern Corn Conditions and Yield Expectations.</a>&#8220; Southern Ag Today 6(32.3). August 5, 2026. <a href="https://southernagtoday.org/2026/07/27/southern-corn-conditions-and-yield-expectations/">Permalink</a>

Southern Corn Conditions and Yield Expectations

Authors: Matthew Marchant, Ph.D. Student, Department of Agricultural Economics and Agribusiness



Jada Thompson, Associate Professor, Department of Agricultural Economics and Agribusiness



Ryan Loy, Assistant Professor and Extension Economist, Department of Agricultural Economics and Agribusiness



Highly pathogenic avian influenza (HPAI), commonly called bird flu refers to the H5N1 clade 2.3.4.4b strain of avian influenza, which was detected in the United States in 2022. The term “highly pathogenic” refers to the virus’s ability to spread quickly and destructively, killing more than 75% of birds it infects (World Organisation for Animal Health, 2026). Because the virus is so deadly in commercial poultry, best practice requires the depopulation of flocks in which the disease is detected. Primarily, this is the humane response, as the entire flock will perish if given enough time. Depopulation also stops the reproduction of the virus, protecting farmers, consumers, and the public. Additionally, it allows farmers to minimize down time, returning farms to production as soon as possible, after appropriate cleaning and disinfection. Though necessary, these population losses lead to economic consequences which extend well beyond just individual farms, contributing significantly to elevated egg prices (Mitchell et al., 2024).



Bird Flu in the Flyway



The U.S. poultry industry encountered H5N2 HPAI in 2014, which was mostly spread house-to-house through contaminated materials, trucks, and laborers. This prompted sweeping changes in biosecurity culture that still exist today. These changes, along with a single warm summer, helped to end the outbreak by June 2015. Since the 2022 introduction of a host-adapted H5N1 virus, poultry growers in the Mississippi Flyway have faced increased disease risk, despite the wide adoption of these enhanced biosecurity measures. This most recent outbreak has spread instead by external introduction, rather than in the same fashion as in 2014. In this case, “host-adapted” refers to the virus’s ability to persist in wild bird populations. Whereas previous HPAI events ravaged both poultry and wild birds, the current H5N1 2.3.4.4b classification is less likely to kill wild bird species (Patyk et al., 2023; Thompson et al., 2025). This means that waterfowl can become infected and still survive migration, spreading the disease across the flyway.



What is the Mississippi flyway?



Migratory birds follow predictable migratory corridors, called flyways. The Mississippi flyway is an economically important flyway, covering parts of more than fifteen states and two Canadian provinces which include commercial broiler, turkey, and egg production. Migratory fowl spend the warm summer months in the breeding grounds of the northern Midwest, leaving in the fall to ride cold fronts south towards over-wintering habitat in the Gulf States. For southern states like Arkansas and Louisiana, the regularity of this migration is a large source of off-farm income, as waterfowl hunters flock to the Mississippi Delta. The U.S. Fish and Wildlife Service estimates that migratory bird hunters in Arkansas spend nearly $300 million annually on lodging, equipment, food, and transportation, over the state’s 72-day waterfowl season (U.S. Fish &amp; Wildlife Service, 2022). Birds begin moving north again in the spring, often returning to the same breeding region year after year. Aside from its incredible biodiversity during migrations, the wetland habitat along the Mississippi River acts as a natural flood barrier year-round, holding surplus water as it drains towards the river.



Examining the geographic distribution of HPAI detections over time reveals a clear seasonal pattern that mirrors waterfowl migration. Figure 1 shows the number of detections, by county, for each month over the course of the outbreak. During the summer months, detections are sparse and concentrated in the northern reaches of the flyway, where birds spend the breeding season. As fall migration begins, HPAI’s geographic footprint expands southward, spanning nearly the entire flyway from October through May.



Figure 1: Mississippi Flyway HPAI Detections by Month, 2022-2025, with fitted latitudinal mean and IQR



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="1298" height="1408" src="https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/07/6.32.2-1-1.png" alt="" class="wp-image-44551" srcset="https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/07/6.32.2-1-1.png 1298w, https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/07/6.32.2-1-1-944x1024.png 944w, https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/07/6.32.2-1-1-768x833.png 768w" sizes="auto, (max-width: 1298px) 100vw, 1298px" /></figure>



What does this mean?



Understanding seasonality affords producers a predictable window each year to prepare before peak exposure periods arrive.



Before Peak Season



Inspect and repair perimeter fencing, netting, and housing gaps that could allow wildlife to access farm premises. Evaluate water and feed delivery systems to ensure they too are isolated from outside contact. Review all available extension publications, updating biosecurity protocols where necessary while maintaining coverage status with livestock indemnity programs. Conduct training and review these protocols with all farm workers and establish relationships with your veterinarian and state animal health officials before an emergency arises. Budget for increased biosecurity expenditures and most importantly, ensure you have a financial emergency plan, should you face depopulation.



During Peak Season



Vigilantly adhere to enhanced biosecurity protocols without exception. Monitor flocks closely for behavioral changes, decreased feed and water consumption, or sudden mortality. Limit all non-essential personnel traffic and enforce strict entry and exit biosecurity for all visitors and vehicles. Report any unusual mortality to your veterinarian or the USDA at 1-866-536-7593.



Year-Round



Stay current with USDA APHIS detection reports to understand active disease pressure in your region. Participate in state and federal livestock indemnity programs and know your coverage.



The Big Picture



A host-adapted virus in a migratory population means recurring seasonal risk for the foreseeable future. However, that seasonality also gives producers, researchers, and communities time to plan ahead. Understanding when and where the disease is likely to appear turns an unpredictable force into a manageable calendar of preparation and vigilance. Extension agents and agricultural educators can use these materials to help producers understand local risk windows and connect with available resources. Community preparedness, which begins at the farm level, remains the most effective tool available against this disease.



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References



Mitchell, J. L., Thompson, J. M., &amp; Malone, T. (2024). Biological lags and market dynamics in vertically coordinated food supply chains: HPAI impacts on U.S. egg prices. Food Policy, 126, 102655. https://doi.org/10.1016/j.foodpol.2024.102655



Patyk, K. A., Fields, V. L., Beam, A. L., Branan, M. A., McGuigan, R. E., Green, A., Torchetti, M. K., Lantz, K., Freifeld, A., Marshall, K., &amp; Delgado, A. H. (2023). Investigation of risk factors for introduction of highly pathogenic avian influenza H5N1 infection among commercial turkey operations in the United States, 2022: A case-control study. Frontiers in Veterinary Science, 10. https://doi.org/10.3389/fvets.2023.1229071



Thompson, J. M., Patyk, K., Fields, V., Branan, M., Delgado, A., &amp; Roesler, K. (2025). Biosecurity investment impacts on highly pathogenic avian influenza control in United States commercial turkey operations. Preventive Veterinary Medicine, 244. https://doi.org/10.1016/j.prevetmed.2025.106624



U.S. Fish &amp; Wildlife Service. (2022). Economic Analysis of the Migratory Bird Hunting Regulations for the 2022-2023 Season. U.S. Department of the Interior.



World Organisation for Animal Health. (2026). 10.4 Infection with Highly Pathogenicity Avian Influenza Viruses. In Terrestrial Animal Health Code (33rd ed.).



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Recommended citation format: Marchant, Matthew, Jada Thompson, and Ryan Loy. &#8220;<a href="https://southernagtoday.org/2026/07/27/bird-flu-on-the-move-understanding-seasonal-hpai-risk-in-the-mississippi-flyway/">Bird Flu on the Move: Understanding Seasonal HPAI Risk in the Mississippi Flyway.</a>&#8221; Southern Ag Today 6(32.2). August 4, 2026. <a href="https://southernagtoday.org/2026/07/27/bird-flu-on-the-move-understanding-seasonal-hpai-risk-in-the-mississippi-flyway/">Permalink</a>

Bird Flu on the Move: Understanding Seasonal HPAI Risk in the Mississippi Flyway

The U.S. row crop sector is currently under significant economic pressure. Lower commodity prices, high input costs, policy uncertainty, and ongoing trade disruptions have combined to create a challenging operating environment for producers. It is clear that all producers are currently facing a challenging farming environment. However, we also observe meaningful differences in outcomes and performance within this same difficult environment. Some farms remain relatively stable. Others experience severe financial stress. Some adjust quickly, while others struggle to respond.



Why? Traditional discussions of agricultural risk tend to focus on external shocks such as price volatility, drought, policy changes, or broader economic downturns. These factors certainly matter. They shape the environment in which farms operate. However, identical shocks do not produce identical outcomes. The difference is often not the shock itself, but the decisions that are made in response to uncertainty.



Risk, by itself, does not generate outcomes, decisions do. Between an external shock and its eventual outcome lies a chain of decision processes. Producers must recognize emerging risks, interpret available information, evaluate alternatives, and choose a course of action. These decisions ultimately shape whether a farm remains resilient or experiences distress.



Consider a decline in commodity prices. Two farms may face the same market conditions. Yet one farm may recognize the threat earlier, have stronger financial reserves, or possess better market information. Another may have limited flexibility or delayed responses. The result is different outcomes despite facing the same shock.



This perspective suggests a shift in risk management away from simply identifying threats. Equally important is understanding how individuals and organizations process uncertainty and transform information into action. Rather than asking only, “What risks exist?” we should also ask, “How are decisions made in the face of risks as well as when the shocks emerge?”



The future of agricultural risk management will not be determined solely by better tools available to producers, such as better forecasting tools, insurance products, policy tools, or market information systems. It will also depend on improving the decision processes that connect uncertainty to outcomes. In the next article, I will walk through what I call Liu’s 5Rs of Risk Management Model to explore how information systems, risk perception, behavioral factors, and structural constraints interact to shape real-world agricultural decisions and outcomes.



Figure 1. Same Risk, Different Outcomes: Where Is the Missing Link? (Designed with ChatGPT)



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="846" height="411" src="https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/07/6.32.1-1.png" alt="" class="wp-image-44549" srcset="https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/07/6.32.1-1.png 846w, https://sat-wp.afpc.tamu.edu/wp-content/uploads/2026/07/6.32.1-1-768x373.png 768w" sizes="auto, (max-width: 846px) 100vw, 846px" /></figure>



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Liu, Yangxuan. &#8220;<a href="https://southernagtoday.org/2026/07/27/agricultural-risk-is-not-a-shock-problemits-a-decision-problem/">Agricultural Risk Is Not a Shock Problem—It&#8217;s a Decision Problem.</a>&#8221; Southern Ag Today 6(32.1). August 3, 2026. <a href="https://southernagtoday.org/2026/07/27/agricultural-risk-is-not-a-shock-problemits-a-decision-problem/">Permalink</a>

Agricultural Risk Is Not a Shock Problem—It’s a Decision Problem

Most farmers and rural residents know that agricultural cooperatives are farmer-owned but don’t think much more about their structure or how they are financed. While all businesses are financed by a combination of debt and owner equity, there are fundamental differences in financing a cooperative relative to a typical investor-owned business.



Typical Business Financing is Separate from Operations



Most businesses in the U.S. are owned by one group of individuals, the investors, and do business with another group, the customers.&nbsp; Under this structure, it is logical to separate financing from operations. If the business is generating, or projected to generate, adequate profits, there should be a pool of outside investors willing to provide equity capital. This structure allows for business financing to be separate from operations and marketing.



Profits from typical businesses are returned to their owners and investors in the form of retained earnings, dividends, and stock buy-backs. Those profit distributions create the incentive for equity investment. Customers of these businesses (who are not investors) do not receive a share of the profits.



Cooperative Financing is Combined with Operations



Under the cooperative business model, cooperative customers are also owners. Due to this structure, identifying financing for a new cooperative cannot be separated from the process of identifying its customers. New cooperative organizers must simultaneously identify individuals to be both cooperative users and investors and established cooperatives must obtain all the capital they need for future expansion through their user-investors.



Cooperatives distribute profits to its owner-customers in proportion to use. Therefore, there is no direct benefit from owning equity in a cooperative, rather the rationale for equity ownership is related to the use of the cooperative. In traditional, open membership cooperatives, a portion of the profits are distributed in the form of equity, often called “stock patronage”.&nbsp; That equity is typically redeemed into cash by the cooperative at a later date and is thus referred to as “revolving equity”.&nbsp; Under this cooperative structure, equity ownership is accumulated as a by-product of using the cooperative. In other situations, such as processing cooperatives which are more capital intensive, the cooperative stock is combined with a usage right.&nbsp; Under that structure the equity investment is a prerequisite to use.



Understanding Cooperative Equity Financing



Agricultural cooperatives are an important part of our rural landscape, with many of our legacy cooperatives having been in business for over 100 years. Understanding cooperative structures for acquiring and managing equity is key for continuing the cooperative business model. Merging the roles of users and investors has major implications for cooperative operations and planning. Groups who are interested in forming a cooperative must not only analyze the customer base, but they must also determine whether potential customers are interested in investing in a user-owned business. Established cooperatives must create systems to match use and investment on a long-term basis.



Having two stakeholder groups, investors and customers, sounds complicated while merging those two groups sounds simple.&nbsp; In reality, the ownership element of the cooperative business model is more complicated relative to other firms.&nbsp; Agricultural cooperatives create great benefits in keeping markets competitive and improving the financial results of their farmer-owners.&nbsp; I view cooperatives as a better, but not necessarily simpler, business structure.



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Kenkel, Phil. &#8220;<a href="https://southernagtoday.org/2026/07/27/what-is-different-about-financing-a-cooperative/">What is Different About Financing a Cooperative?</a>&#8221; Southern Ag Today 6(31.5). July 31, 2026. <a href="https://southernagtoday.org/2026/07/27/what-is-different-about-financing-a-cooperative/">Permalink</a>

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