Category: Specialty Topics

Authors: Wes Brondos, Graduate Research Assistant, Department of Agricultural and Resource Economics, James Mingie, Research Specialist, Department of Agricultural and Resource Economics, Sreedhar Upendram, Associate Professor, Department of Agricultural and Resource Economics

Drinking water systems across the South face increasing repair, replacement, and upgrade costs. State Revolving Funds (SRFs) are an important source of affordable financing. Under the Drinking Water State Revolving Fund (DWSRF), the U.S. Environmental Protection Agency (EPA) provides capitalization grants to states, which combine federal and state matching funds to provide loans and other assistance to eligible water systems. Since states have flexibility in setting loan terms, the rate a community pays can vary substantially across state lines.

A comparison of nine Southern states – Alabama, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, and Tennessee – shows that DWSRF rates differ both in level and structure. Figure 1 summarizes the range between standard and most-discounted published rates. Some states rely mainly on a single benchmark or formula, while others use multiple discount tiers based on household income, fiscal capacity, or other affordability measures. Consequently, a state’s standard rate may not reflect the financing available to a lower-income or financially constrained community.

Figure 2 illustrates the standard DWSRF rates in the nine Southern states. Mississippi has the lowest standard DWSRF rates at 1.95 percent with no discount tiers, followed by North Carolina and Louisiana at 2.00 percent and 2.45 percent, respectively. Alabama’s rate includes an interest rate of 0.10 percent and a yearly fee of 2.65 percent that adds up to a rate of 2.75 percent.

Tennessee’s standard DWSRF rate is the highest of the nine, at 3.66 percent without discounts. The state sets a quarterly base rate and applies discounts based on an Ability-to-Pay Index (ATPI). For July-September 2026, Tennessee’s standard rates range from 3.29 percent for 20-year loans to 3.74 percent for 30-year loans, while qualifying communities receive lower rates. While Tennessee’s loan interest range starts at 1.03 percent for a five-year loan, South Carolina’s interest rates start at 2 percent for a five-year loan. States such as Kentucky and South Carolina also use tiered approaches while states such as Georgia use relatively flat rates or formulas tied to market conditions or local income. Fees, loan terms, principal forgiveness, and assistance for disadvantaged communities can further change borrowing costs. Lower financing costs can reduce debt-service pressure on utilities and lessen the need for customer rate increases.

Communities also have federal financing alternatives. USDA Rural Development’s Water and Waste Disposal program serves eligible rural communities, generally those with populations of 10,000 or fewer, and offers repayment terms of up to 40 years. For July-September 2026, USDA’s poverty, intermediate, and market rates are 2.875, 3.750, and 4.750 percent. Community Development Block Grant (CDBG) assistance is primarily related to grant funding, while HUD’s Section 108 program allows eligible communities to leverage future CDBG allocations through loan guarantees.

The key takeaway is that there is no single consistent SRF rate across the South. Water systems should compare the rates they are eligible to receive and consider fees, repayment terms, service area limitations and the potential for available subsidies. For policymakers, the comparison highlights a choice of three sources to seek water infrastructure funds and enable water systems to choose between simpler, broadly uniform pricing and targeted discounts that direct assistance toward communities with greater financial need. Based on the information presented, water systems can strategize which funding source to target to get better terms on their loans and avail the maximum discounts to repair, replace and upgrade water systems. As a consequence, the savings can be potentially passed on to consumers with lower rate increases for water infrastructure upgrades.

Figure 1. Published DWSRF standard and discounted rate ranges in nine Southern states, July 2026.

Figure 2. Standard Drinking Water State Revolving Fund Rates across the nine Southern States, July 2026.

Sources: U.S. Environmental Protection Agency, Drinking Water State Revolving Fund program overview; state SRF rate sheets and Intended Use Plans from Alabama ADEM, Florida DEP, Georgia GEFA, Kentucky Infrastructure Authority, Louisiana DEQ/DOH, Mississippi Department of Health/MDEQ, North Carolina DEQ, South Carolina Rural Infrastructure Authority, and Tennessee Department of Environment and Conservation; USDA Rural Development, Water and Waste Disposal Loan and Grant Program; and U.S. Department of Housing and Urban Development, Section 108 Loan Guarantee Program. Tennessee rates shown are State Fiscal Year 2027 first-quarter rates, updated July 22, 2026; USDA rates are for July-September 2026.

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Recommended citation format: Brondos, Wes, James Mingies, and Sreedhar Upendram. “The Headline Rate Does Not Tell the Whole Story: Water Infrastructure Financing Across the South.” Southern Ag Today 6(38.5). September 18, 2026. Permalink

Independent rural grocery stores have been in decline due to factors such as competition from newer, smaller-footprint rivals, buying-power disadvantages relative to larger chains, rural population loss, and the aging out of independent grocery store owners. This decline is leading some small towns to explore starting cooperatively owned grocery stores, as history shows that many cooperatives were formed to serve rural areas where private investment failed. Examples include farmers’ cooperatives, rural electric and telephone cooperatives, and the Farm Credit System.

Recently, the South Carolina Center for Cooperative Development surveyed a small town to assess interest in a cooperatively owned grocery store and found interesting results.

The Positive Findings

In general, the survey found substantial enthusiasm for a grocery store. The survey drew 773 complete responses from a population center of about 9,000 people. Most participants (84%) either lived in the city center or were within a 20-minute drive. Participants’ shopping habits showed that they use a variety of food outlets, with many respondents (~80%) shopping at major grocery chains and discount stores. Average monthly grocery spending ranged from $200 to $800, with a median of $400–$599.

When choosing where to shop, respondents ranked product quality first (80.7%), followed by price or value (62.7%) and product availability, selection, and variety (48.9%). When asked which product offerings were the most important, fresh produce was ranked highest, followed by meat and seafood, dairy products, organic or natural foods, and prepared foods. Locally sourced products seemed to matter more than organically sourced products for most participants: 88.6% rated local sourcing at least moderately important, while 65.7% rated organic sourcing at least moderately important.

Respondents also viewed the store as more important than a typical retail outlet, like an “anchor store” that drives foot traffic for a struggling main street. A deli or café was the most desired additional service, followed by online ordering and pickup, cooking demonstrations, community events, delivery, and nutritional education. Respondents also emphasized accessibility, parking, operating hours, professional management, and a welcoming atmosphere.

The Lukewarm Findings

Familiarity with the cooperative concept was limited. Sixty-nine percent said they were either only slightly familiar or not familiar at all with the model. Membership interest was more measured. About 61% were somewhat or very interested in becoming member-owners. Of those asked about investing, roughly three-quarters considered a one-time stock purchase of less than $300 reasonable. Engagement interest was limited, with interest in serving on a board or committee split evenly at 50% “yes” and 50% “no.”

The sample was also comparatively affluent, with about 70% reporting household incomes of $100,000 or more, suggesting that the findings may not fully reflect all income groups in the wider community.

Next Steps and Sources for Assistance

Cooperative groceries can work, though they typically do not resemble traditional grocery stores. While the survey findings point to a potentially promising market, long-term success depends on clear education (and engagement) with the cooperative model, and thoughtful collaboration with existing farmers, farm markets, and other local businesses. Potential cooperative “owners-as-customers” must also be realistic about the variety of products offered, as grocery store margins are low and inventory costs high.

The South Carolina Center for Cooperative and Enterprise Development is an example of how most states have resources to help rural towns with this issue. If you are looking to start a cooperative, many land-grant universities have extension personnel and cooperative specialists who can help with these items.  Also, there are national organizations available to help cooperative grocery stores, once a cooperative has been established.

For more information:

Food Co-op Initiative  https://fci.coop

National Co-op Grocers  https://www.ncg.coop

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Recommended citation format: Richards, Steve. “Can Cooperatives Help Reverse the Decline in Rural Grocery Stores?” Southern Ag Today 6(37.5). September 11, 2026. Permalink

Authors: Le Chen, Christopher N. Boyer, and S. Aaron Smith – University of Tennessee 

Ad hoc payments have been used in times of disaster or unforeseen market forces to help producers. Historically, these payments were relatively small compared to the payments received by producers through the Farm Bill safety net, but recently ad hoc payments have been at record high levels. There have been several Southern Ag Today articles discussing a variety of topics about ad hoc payments such as the structures and the assistance they provide (Link, Link, Link). These payments are in addition to the Farm Bill safety net programs, Agricultural Risk Coverage (ARC) and Price Loss Coverage (PLC), that have primarily existed to stabilize farm income from various shocks.  Both types of programs are designed to support farm income and reduce financial stress. But do they actually help farmers stay current on their loans — and are they equally effective? A new study published in Agricultural Finance Review provides an analysis to jointly evaluate ad hoc payments (MFP and CFAP) and farm bill support programs (ARC and PLC) using actual payment timing and amounts.

What We Found

Using FSA payment data from 2015 to 2022, we examined how each payment type affected two key indicators of farm financial stress: total non-real estate farm debt and loan delinquency rates — both short-term (30–89 days past due) and longer-term (90 or more days past due). The results reveal important differences between programs:

• ARC payments were associated with significant reductions in short-term loan delinquencies (30–89 days past due) and also increased total operating debt — suggesting that ARC payments improved farm liquidity and helped producers service their short-term obligations;
• PLC payments reduced longer-term delinquencies (90 or more days past due) — indicating that PLC provides a more sustained form of financial support that helps producers avoid falling seriously behind on loans;
• CFAP payments — the emergency COVID-19 assistance program — also significantly reduced short-term delinquencies, suggesting emergency payments can provide effective near-term financial relief when deployed quickly; and
• MFP payments — the trade war relief program — actually increased total farm debt without reducing delinquencies, suggesting that MFP was less effective at alleviating financial stress and may have encouraged borrowing rather than debt repayment.

What This Means for Farm Policy

Not all government payments are created equal when it comes to reducing farm financial stress. Farm Bill safety net programs show evidence of reducing loan delinquencies while the study finds less evidence with ad hoc emergency programs. These findings are directly relevant to ongoing Farm Bill and agricultural policy discussions in 2026. With ARC and PLC recently reauthorized through the One Big Beautiful Bill with an additional 30 million base acres allocated nationwide, these payments are expected to provide more assistance in the future. These Farm Bill safety net programs appear to function as intended: providing income support that translates into improved loan performance and farm financial stability. As policymakers consider the future ad hoc payments (Link), these results are important when thinking about the intended purpose, design, and potential impacts of these ad hoc payments. 

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Recommended citation format: Chen, Le, Christopher N. Boyer, and S. Aaron Smith. “Ad hoc and Farm Bill payments – Can these payments alleviate financial stress?“ Southern Ag Today 6(36.5). September 4, 2026. Permalink

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 & 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

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

Region	Sector	Output Multiplier (Total economic activity per $1 of direct output)	Employment Multiplier (Total jobs supported per 1 direct job)	Tax impact per Output ($ tax revenue per $1 of output)
Arkansas State	Data Center	1.64	2.54	0.11
	Food Manufacturing	1.65	2.49	0.07
	Auto Manufacturing	1.52	5.22	0.05
Calhoun County	Data Center	1.13	1.27	0.10
	Food Manufacturing	1.19	1.78	0.05
	Auto Manufacturing	1.06	1.52	0.03
Dallas County	Data Center	1.20	1.51	0.11
	Food Manufacturing	1.22	1.77	0.05
	Auto Manufacturing	1.07	1.83	0.03
Newton County	Data Center	1.23	1.94	0.13
	Food Manufacturing	1.19	2.21	0.06
	Auto Manufacturing	1.06	1.97	0.03
Woodruff County	Data Center	1.22	1.66	0.11
	Food Manufacturing	1.26	1.62	0.06
	Auto Manufacturing	1.27	3.24	0.05

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 & Tomich, 2026). Therefore, the actual revenue flowing to a rural county’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.

https://support.implan.com/hc/en-us/articles/30779951167771-Employment-in-IMPLAN

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

https://fortune.com/2026/03/26/meta-ai-data-center-hyperion-louisiana

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

https://blog.implan.com/what-is-implan

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

are divided on the benefits. NPR.
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

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

thoughts. Politico. https://www.politico.com/news/2026/05/02/data-centers-states-tax-incentives-00891184

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

Missouri. Missouri Independent.
https://missouriindependent.com/2026/05/22/google-data-center-plan-raises-tax-transparency-questions-in-rural-missouri/

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

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

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

Census. https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html

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Recommended citation format: Seo, Frank. “Data Centers vs. Factories: Do AI Facilities Truly Benefit Rural Economies? Evidence from an Arkansas Simulation.” Southern Ag Today 6(32.5). August 7, 2026. Permalink

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.  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”.  That equity is typically redeemed into cash by the cooperative at a later date and is thus referred to as “revolving equity”.  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.  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.  In reality, the ownership element of the cooperative business model is more complicated relative to other firms.  Agricultural cooperatives create great benefits in keeping markets competitive and improving the financial results of their farmer-owners.  I view cooperatives as a better, but not necessarily simpler, business structure.

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Kenkel, Phil. “What is Different About Financing a Cooperative?” Southern Ag Today 6(31.5). July 31, 2026. Permalink