Category: Farm Management

For most cow-calf operations, winter feed is the single largest cost and one of the biggest drivers of overall profitability. As we move through fall, hay feeding is becoming a major focus: some operations are preparing, some have just started, and some have been feeding hay for quite some time. The cost of winter feeding primarily depends on three things: the cost of producing or purchasing hay, the number of days hay is fed, and how efficiently that hay is stored and fed.

At this point in the year, most hay has already been produced or purchased. If an operator does feel they need to purchase additional hay, it likely makes sense to source that hay soon. Hay prices tend to climb through the winter in years when supplies are short. It is also wise to have a little extra hay on hand since no one can predict how hard the winter will be or how early cattle can get back on spring pastures.

The number of days hay is fed depends largely on pasture conditions, and those conditions are poor in many areas. Much of the country has been dealing with drought for several months, and pastures across the US are generally in unfavorable shape. Farmers who are fortunate enough to still have grazable forage should use it as efficiently as possible. Culling open cows and poor producers can also stretch available pasture a little further. However, with calf prices at historically strong levels, most producers will likely want to keep bred cows, and cows with calves, through the winter.

How efficiently hay is stored and fed also has a major impact on winter feed costs, even though those losses can be hard to see and measure. A ton of hay costs the same whether it is consumed or wasted. Practical steps to cut waste often include storing bales off the ground and under cover, using hay rings or feeders, and feeding hay in quantities that cattle will consume relatively quickly. The right mix depends on the operation, but even modest changes can reduce waste.

To show the value of reducing waste, the table at the end of this article estimates daily hay cost on a per cow basis. The estimates assume a 1,300-lb cow consumes 2.25% of her body weight each day, or a little over 29 pounds. It is the waste rate that determines how much hay must be fed for the cow to reach that level of intake. The table shows daily costs at hay values of $80, $100, and $120 per ton and at waste rates of 15%, 25%, and 35%. To estimate a cow’s total hay cost for the winter, multiply the daily cost by the number of days you expect to feed hay. For example, at $1.95 per day, total winter feed costs over 120 days would be $234 per cow.

While one can use the table below to examine the potential cost savings related to the cost of purchasing or producing hay, the savings related to storage and feeding losses are likely more relevant at this point in the year. With hay valued at $100 per ton, cutting the waste rate from 35% to 25% saves an estimated $0.30 per cow per day, and cutting it from 25% to 15% saves about $0.23 per cow per day. These numbers can add up quickly. As an illustration, a 50-cow herd feeding hay for 120 days per year would save about $1,800 over the winter by reducing waste from 35% to 25%.

What makes sense for one operation won’t necessarily make sense for another. Some large operations may be able to justify a significant investment in hay storage and feeding systems to drive waste rates down sharply. Smaller operations may need to focus on cheaper ways to achieve smaller reductions in losses. Regardless of size, every operation should know what it costs to carry its cowherd through the winter and look for ways to lower that cost by limiting storage and feeding losses.

Estimated Daily Winter Hay Cost Per Cow, by Hay Value and Waste Rate

		Hay Cost Per Ton
		$80 per ton	$100 per ton	$120 per ton
Storage and Feeding Losses	15%	$1.38	$1.72	$2.06
	25%	$1.56	$1.95	$2.34
	35%	$1.80	$2.25	$2.70
Assumes 1300 lb. cow consumes 2.25% of body weight per day

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Recommended citation format: Burdine, Kenny. “The Most Expensive Days Are Near: Managing Winter Hay Costs for Cow-Calf Operations.” Southern Ag Today 6(43.1). October 19, 2026. Permalink

Authors: Authors: Adam N. Rabinowitz, Associate Professor and Extension Economist, Agricultural Economics and Rural Sociology, Auburn University  

Sthefani Oliveira, Research Associate, Agricultural Economics and Rural Sociology, Auburn University

When planning for the crop year, farmers create budgets and form expectations of costs that will occur during the growing season. With row crop harvesting in full swing, one critical cost is piling up faster than expected with every pass of the field. Diesel fuel is the most significant consumable input during row crop harvesting. It powers the combine, tractor, cotton picker, and peanut diggers. It is also a critical input to transporting grain to the elevator, cotton to the gin, and peanuts to the buying point. Last year, farmers set budget expectations for this growing season that included fall harvest, but diesel prices across the South have since risen about 75%. 

The United States Department of Agriculture Agricultural Marketing Service publishes regular estimates of #2 diesel fuel for farm use in Alabama and Oklahoma. The U.S. Energy Information Administration also publishes estimates of the retail price of #2 diesel fuel at the Gulf Coast. Figure 1 shows the price in Oklahoma at the beginning of September was $4.72 per gallon, while in Alabama it was $4.86 per gallon. Meanwhile, the retail price at the Gulf Coast was $5.75 per gallon. This was up from the average January-February 2026 price of $2.68 per gallon in Oklahoma, $2.73 per gallon in Alabama, and $3.33 per gallon at the Gulf Coast. Those early 2026 prices were the lowest observed prices in several years, concluding a relatively stable period from the fall of 2024 through February 2026. That changed in March because of the conflict with Iran and continued war between Russia and Ukraine. Prices have continued to rise since.

Focusing on the four major row crops grown throughout the South, Table 1 shows the number of acres that are expected to be harvested this fall. Roughly 32 million acres of corn, cotton, peanuts, and soybeans are expected to be harvested across the 13 southern states. We also calculated estimated diesel use during harvest, in gallons per acre, for each of these crops. Not all southern states included in the acreage numbers publish estimates of diesel use for harvest equipment. To account for this, along with variations in machinery efficiency and farms within a state, we compute an Olympic average of the diesel use from the available estimates to apply across the South. 

The estimated diesel price increased by approximately $2.20 per gallon, based on the average of the Alabama, Oklahoma, and Gulf Coast prices and the change from January/February ($2.91/gal) to September ($5.11/gal). This represents roughly a 75% increase relative to the diesel prices producers observed during the budgeting period. As shown in the last two columns of Table 1, the harvest cost increase from the rise in diesel prices is estimated at $214 million for the four row crops discussed. This is a harvest-acreage-weighted average increase of $6.67 per acre across the South, ranging from $5.56 per acre for corn to $18.83 per acre for peanuts. The main reason peanut diesel use and cost per acre are much higher than for other crops is that peanuts require two passes for harvest. The first pass is for digging and inverting the peanuts so they may dry, while the second pass is for picking. 

The estimates presented here should be viewed as conservative because they exclude diesel used for transporting harvested commodities, which depends upon the distance to the delivery point. Several states (Alabama, Arkansas, Georgia, Louisiana, North Carolina, Oklahoma, Kentucky, and Texas) have issued directives to temporarily halt enforcement of penalties for using tax-exempt off-road diesel on public roads. This can reduce the price for transporting commodities by the amount of the federal and state taxes. Meanwhile, Georgia has also suspended the state excise tax on regular gasoline and on-road diesel. At the federal level, President Trump issued an executive order on October 5 that temporarily allows the use of off-road diesel on U.S. highways in addition to deferring the federal excise tax of 24.4 cents a gallon. There is still further federal action necessary to provide forgiveness of deferred taxes.  

Some might counter that the price for these crops has also increased since planting expectations were formed. While this is true, the reality is that the increase was from an extremely low price point that for many was already below cost of production. Thus, one should not see market price increases as an offset to unexpectedly high diesel prices, but instead as continued pressure on already tight margins. Margins that get squeezed even more when one considers the increases in diesel costs for post-harvest transportation necessary for marketing. For more insight on marketing of row crops, check out the Wednesday Southern Ag Today articles. 

Figure 1. Number 2 Diesel Prices: Alabama, Oklahoma, and Gulf Coast: February 2022-September 2026

Table 1. Estimated Increase in Cost of Diesel for Harvesting Southern Row Crops

Crop	Expected Harvest (1,000 acres)	Estimated Diesel Use for Harvest (gal/acre)	Harvest Cost Increase from Diesel	Cost Increase Per Acre
Corn (grain)	9,424	2.53	$52,438,089	$5.56
Cotton	7,540	4.38	$72,550,508	$9.62
Peanuts	1,334	8.56	$25,124,055	$18.83
Soybeans	13,785	2.11	$63,818,990	$4.63
Unexpected Harvest Cost Increase			$213,931,642	$6.67

References:

U.S. Department of Agriculture, Agricultural Marketing Service. Alabama Production Cost Report (Weekly). Available at: https://mymarketnews.ams.usda.gov/viewReport/3051

U.S. Department of Agriculture, Agricultural Marketing Service. Oklahoma Production Cost Report (Bi-Weekly). Available at: https://mymarketnews.ams.usda.gov/viewReport/3621

U.S. Energy Information Administration, Weekly Gulf Coast No 2 Diesel Retail Prices. Available at:

https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=EMD_EPD2D_PTE_R30_DPG&f=W

U.S. Department of Agriculture, Crop Production, Available at:

https://esmis.nal.usda.gov/publication/crop-production

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Rabinowitz, Adam, and Sthefani Oliveira. “Harvest Cost Increases Tightening Margins for Southern Row Crop Farmers.” Southern Ag Today 6(42.1). October 12, 2026. Permalink

Authors: Falin Sun, Ph. D Student, Auburn University and Sunjae Won, Assistant professor, University of Arkansas.

Irrigation plays very different roles across the Southern region. According to the 2022 Census of Agriculture, Arkansas, Texas, and Mississippi account for the largest shares of irrigated farmland, while Virginia, Kentucky, and Alabama irrigate comparatively little. These differences reflect the region’s diverse production systems and water resources, making irrigation an important farm-management issue across the South.

Figure 1 compares irrigated farms and irrigated acreage across Southern states. States with the largest irrigated acreage do not necessarily have the greatest number of irrigated farms. For example, Mississippi’s irrigated land is concentrated on relatively few, larger farms, whereas Georgia has irrigation distributed across many more operations. This contrast illustrates how farm size, crop mix, and production systems influence irrigation adoption beyond acreage alone.

Figure 2 highlights where irrigated acreage changed between 1997 and 2022. Declines in West Texas and parts of Oklahoma are consistent with long-term pressure on the Ogallala Aquifer and declining groundwater availability (Hornbeck and Keskin, 2014). In the Rio Grande Valley, unreliable surface-water supplies, increasing municipal water demand, and urban expansion have reduced irrigation opportunities, while improvements in irrigation efficiency do not necessarily reduce total agricultural water use (Ward and Pulido-Velazquez, 2008). Florida experienced substantial declines following the widespread impacts of citrus greening, which has significantly reduced citrus production since the mid-2000s (Bové, 2006). In contrast, counties across the Mississippi Delta and South Georgia recorded notable increases in irrigated acreage, reflecting the availability of relatively shallow groundwater and growing use of irrigation to reduce production risks associated with variable rainfall and periodic drought (USDA ERS, 2022).

These regional areas of transition highlight the difficult farm-management and risk-management decisions of shifting to, or away from, irrigated production. The contrasting trends shown in Figure 2 suggest that producers adapt irrigation decisions to local water availability, production systems, and economic conditions. In water-constrained areas, improving irrigation efficiency and adjusting crop choices may provide greater benefits than expanding irrigated acreage. In regions where irrigation continues to grow, producers should carefully evaluate whether additional investments will remain profitable under changing rainfall patterns, rising pumping costs, and future water availability. Extension programs can further support these decisions by providing region-specific recommendations on irrigation scheduling, technology adoption, and long-term water management.

Figure 1. Irrigated farms and irrigated acreage in Southern states, 2022.

Figure 2. Change in irrigated agricultural land by county, 1997-2022.

References

U.S. Department of Agriculture, National Agricultural Statistics Service. 2022. 2022 Census of Agriculture. https://www.nass.usda.gov/AgCensus/

U.S. Department of Agriculture, Economic Research Service. 2022. Trends in Irrigated Agriculture Reveal Sector’s Ability To Adapt to Evolving Climatic, Resource, and Market Conditions. Amber Waves. https://www.ers.usda.gov/amber-waves/2022/january/trends-in-irrigated-agriculture-reveal-sector-s-ability-to-adapt-to-evolving-climatic-resource-and-market-conditions

U.S. Department of Agriculture, Economic Research Service. 2026. Irrigation & Water Use. https://www.ers.usda.gov/topics/farm-practices-management/irrigation-water-use/

Bové, J. M. 2006. Huanglongbing: A destructive, newly-emerging, century-old disease of citrus. Journal of Plant Pathology 88(1): 7-37.

Hornbeck, Richard, and Pinar Keskin. 2014. The Historically Evolving Impact of the Ogallala Aquifer: Agricultural Adaptation to Groundwater and Drought. American Economic Journal: Applied Economics 6(1): 190-219.

Ward, Frank A., and Manuel Pulido-Velazquez. 2008. Water Conservation in Irrigation Can Increase Water Use. Proceedings of the National Academy of Sciences 105(47): 18215-18220.

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Recommended citation format: Sun, Falin. “Irrigation Footprints Across the Southern Region.” Southern Ag Today 6(41.1). October 5, 2026. Permalink

Highly pathogenic avian influenza (HPAI) has received significant attention in recent years because of its devastating impact on poultry operations. A confirmed outbreak typically requires rapid depopulation to prevent the virus from spreading to neighboring farms. However, disease outbreaks are only one of many risks that can affect grower income. In reality, some of the costliest revenue losses stem from everyday equipment failure.

A recent example from a broiler farm highlights this risk. The farm experienced a failure in a water line connection, causing extensive flooding in one house. Approximately 15,000 birds, or about 65% of the flock in that house, were lost overnight. With less than a week remaining before harvest, the loss represented more than 100,000 pounds of marketable live weight, approximately a 17% production loss for the flock.

Based on the example farm’s historical settlement data, the lost pounds reduced flock gross revenue by approximately $7,854. However, the financial impact did not end there. Feed consumed by the birds that were lost remained in the production expense calculations, but those costs were spread over fewer pounds delivered. As a result, the farm’s cost of production increased, likely lowering its ranking within the tournament settlement system and pushing incentive pay from a bonus to a penalty.

The combined effect is an estimated gross revenue loss of $9,778. Under typical assumptions, with loan obligations equal to 50% of gross revenue and operating expenses representing 30% of gross revenue, an average flock would have generated about $9,115 in net return. Instead, the affected flock produced an estimated net loss of $663 (Table 1).

The consequences may extend beyond a single flock. Excess moisture from the flooding could require the affected house to remain empty for an entire flock cycle to allow the house’s dirt pad to dry properly. If so, the operation could lose roughly 25% of its gross revenue from the next flock while continuing to make full loan payments. This could result in an annual loss of over $12,000 in net return for this farm with 2 out of 5 flocks being affected. Even with lender flexibility, recovering from a loss of this magnitude could take years.

Many insurance policies would not cover this type of income loss because it resulted from a plumbing failure rather than a covered peril event. This example illustrates why poultry growers should include contingency reserves in their financial planning. It also highlights the need to explore risk-management tools and insurance products designed to address the unique revenue risks faced by contract poultry growers, even though they do not own the birds they raise.

Table 1. Estimated Financial Impact of a Water Line Failure on a Broiler Farm

Scenario	Pounds Delivered	Final Pay ($/lb.)	Total Gross Revenue	Total Expenses	Net Return
Average Flock	595,734	$0.0765	$45,574	$36,459	$9,115
Catastrophe Flock	493,734	$0.0725	$35,796	$36,459	-$663

The event reduced flock gross revenue from an estimated $45,574 for an average flock to $35,796, a decline of more than 21%, demonstrating how a single event can significantly affect farm profitability.

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Recommended citation format: Brothers, Dennis. “Broiler Grower Revenue Risks Can Come from Many Directions.” Southern Ag Today 6(40.1). September 28, 2026. Permalink

Authors: Dr. Hunter D. Biram, Assistant Professor and Extension Agricultural Economist, University of Arkansas, Mr. Enil Serrano Puerto, Ph.D. Student, University of Kentucky, Dr. Grant Gardner, Assistant Extension Professor, University of Kentucky

The Federal Crop Insurance Program (FCIP) has been a standard in farm risk management with nearly 500 million acres insured across row crops, forages, and specialty crops, resulting in up to $192 billion in insured liability in 2024, or 78% of the total value of U.S. crops (USDA-RMA and USDA-ERS, 2026). Despite its popularity as a risk management tool, the question of the best coverage level remains each year. Because premium rates are capped at annual increases of 20%, base premiums tend to change very little from year to year. However, the expected insurance price used to calculate coverage is influenced by futures market prices. As a result, insurance costs can fluctuate based on changes in commodity prices and the mix of crop acres planted by producers. This often leaves farmers with the question of how much insurance to buy or whether to renew with the same coverage from the year before. In response, a large suite of tools has been developed by university extension services. A total of 13 decision aids have been developed by universities from across the U.S., with 9 focusing on farm programs (i.e., Agriculture Risk Coverage and Price Loss Coverage) administered by the Farm Service Agency (FSA), and 4 focusing on federal crop insurance programs administered by the Risk Management Agency (RMA) (Serrano, Gardner, and Biram, Forthcoming). We add a decision aid to this suite of tools that provides analysis for both FSA farm programs and federal crop insurance programs, the Crop Insurance Decision-Maker (CIDM). The CIDM is a free, web-based decision aid that provides expected revenue net of production expenses and insurance premiums paid under scenarios with and without crop insurance. After analyzing multiple scenarios, the CIDM highlights the risk management option with the highest expected net return as a potential optimal coverage choice. The tool further provides analysis of farmer risk preferences by including data for farmers who are risk-averse and are concerned about extreme weather and pest pressure lowering expected net returns.

In an article published in the latest edition of the Journal of the American Society of Farm Managers and Rural Appraisers (JASMFRA), Serrano, Gardner, and Biram (2026) provide an explanation for CIDM, how to interpret the results in the decision aid, how the results were generated, and where it fits in the greater suite of farmer decision aids. In most instances, results follow those of Biram et al. (2022), who show that even in the presence of ARC and PLC, the optimal decision in most cases is to choose 80-85% coverage. Biram et al. (2022) show that the base premium rate drives the coverage level, with higher base premiums resulting in increasing cost of insurance, and therefore lower optimal coverage levels. 

We suggest here that another driver of optimal coverage is the ratio of the farm-level yield expectation, measured by the Actual Production History, to the county-level yield expectation measured by the county Reference Yield determined by RMA. Using Arkansas and Kentucky as examples, Figures 1 and 2 plot optimal coverage levels for risk-averse farmers across various yield ratio levels based on Serrano, Gardner, and Biram (2026). We first note that in all instances, except for cotton grown in one county in Arkansas, purchasing some level of crop insurance is always preferred to not purchasing any crop insurance at all (see Figures 1-2). We also find that the optimal coverage level is at least 70% or greater when the farm-level yield expectation is at least half that of the county yield expectation for Arkansas (Figure 1) or the farm yield expectation is at least 60% of the county yield expectation in Kentucky.

Since these general results are across multiple combinations of states, counties, and coverages, we direct farmers and other users involved in the crop insurance purchase process to consult the CIDM for optimal coverage in a specific county.

Figure 1. The Relationship Between the Yield Ratio and Optimal Coverage Level in Arkansas

Figure 2. The Relationship Between the Yield Ratio and Optimal Coverage Level in Kentucky

References

Biram, H. D., Coble, K. H., Harri, A., Park, E., & Tack, J. (2022). Mitigating price and yield risk using revenue protection and agriculture risk coverage. Journal of Agricultural and Applied Economics, 54(2), 319-333.

Serrano, E., Gardner, G., & Biram, H.D. (2026). Enhancing Crop Insurance Decisions with Data-Driven Tools. Journal of the American Society of Farm Managers and Rural Appraisers.

United States Department of Agriculture, Economic Research Service. (Accessed 2026). Farm income and wealth statistics.

United States Department of Agriculture, Risk Management Agency. (Accessed 2026). Revised premium ratings for corn and soybeans: Frequently asked questions.