Estimating COVID-19 Impacts on the Broiler Industry

Introduction

Millions of words have likely been written about the impacts of the COVID-19 pandemic on the economy and agriculture, in particular. Across agriculture, impacts have ranged from euthanized livestock to rotted crops; disruptions in meat processing, restaurants, and grocery stores; and volatile prices (Lusk et al. 2020). The COVID-19 impacts continue to ripple back and forth through our economy, especially in livestock agriculture.

This article focuses on the chicken/broiler industry. Similar to other livestock sectors, the broiler sector faced detrimental disruptions due to COVID-19. In February 2020, USDA forecasted 45.8 billion pounds of broiler meat production in 2020 and an annual average price of $0.87 per pound (U.S. Department of Agriculture, Office of the Chief Economist (USDA, OCE) 2020). The same report in August 2020 showed significant declines from February with a 2020 production forecast of 44.7 billion pounds and an annual average price of $0.704 per pound. The most dramatic decrease occurred in the second quarter due to COVID-19 demand shocks and processing disruptions. USDA estimated second quarter of 2020 production was 4.5% (or 519 million pounds) lower and the price was 29% (or $0.28 per pound) lower than the second quarter forecasts in the February 2020 report.

While it is clear that COVID-19 caused detrimental impacts on the broiler industry as a whole, the industry exhibits features that led to unique impacts within the industry and especially on growers. Perhaps most importantly, almost all production is contracted to growers who do not own the birds. This differs from many other producers of agricultural commodities. Additionally, industry output consists of highly specialized products depending on retail outlet and production is fast compared to other animals, a matter of weeks, which allows for relatively quick adjustment.

These industry attributes create difficulty in understanding the impacts of the pandemic on broiler producers and crafting policy responses using traditional methods. For example, the Coronavirus Food Assistance Program (CFAP) did not provide assistance to broiler growers because it did not provide funding for producers who did not own the livestock (Coronavirus Food Assistance Program (CFAP) 2020). Yet the losses to many broiler growers were significant due to the impact of reduced flock numbers or where flock destruction occurred.

This paper examines the broiler industry and analyzes the impact of COVID-19 on broiler growers. The paper is organized with an overview of the market going into the COVID-19 pandemic, a review of broiler market structure, an analysis of grower impact, and implications for policy response.

The Market Early in 2020

Broiler production was very strong to start 2020. First-quarter slaughter for 2020 was about 5.4% greater than during the first quarter 2019 (U.S. Department of Agriculture, National Agricultural Statistics Service (USDA, NASS) 2020). Eggs set for broilers and chicks placed for broiler growout were well above the year before during the first quarter, setting the stage for continued year-over-year growth into the second quarter (U.S. Department of Agriculture, Economic Research Service (USDA, ERS) 2020). Weekly chicks placed were 2%–4% above 2019 throughout March. First quarter broiler production was 8.2% above the year before, outpacing slaughter due to heavier bird weights.

These strong production numbers reversed when the COVID-19 impacts began. The closure of restaurant dining rooms affected consumer demand for chicken while workforce disruptions affected the chicken supply chain. By the week ending April 25, egg sets were 8.3% below the same week the year before (USDA, ERS 2020). Chicks placed for broiler growout declined 12.9% from the year before by the week of May 2. Young chicken (broiler) ready-to-cook production in May declined 7.1% from the previous May (U.S. Department of Agriculture, National Agricultural Statistics Service (USDA, NASS) 2020). Heavier average weights moderated the effects of reduced slaughter. It was not until late June when chick placement for growout returned to levels from a year ago.

These industry-level impacts were felt differently across participants. It is common for different integrators and specific plants within integrated companies to concentrate on different retail chicken outlets. The whole bird, deboned, and cut-up chicken markets have different bird size specifications. Processing plants and the growers who supply them focus on different retail markets, allowing them to specialize on specific bird sizes, for example anything from a four- to nine-pound bird. That means that the impact of COVID-19 has had differential impacts not only on companies who may be more heavily dependent on one market, but also on specific production complexes who supply these companies. Growers producing different bird weights may face different impacts from restaurant sector closures, for instance.

In 2019, the largest broiler producer, Tyson Foods, representing nearly 21% of the US broiler market and 38.3 million head, produced six-pound birds on average. By contrast, Sanderson Farms, the third largest producer representing 9% of the broiler market and 12.0 million head, produced 8.17-pound birds on average (WATT 2020). In terms of sales outlets, Pilgrim's Pride, the second largest U.S. broiler producer, with 17% market share and 30.3 million head, shipped an estimated 47% of production to grocery stores and 53% of production volume to food service (WATT 2020). Perdue, the fourth largest US broiler producer (7% market share and 12.8 million head), was dependent on grocery store outlets for about 74% of production volume and on food service for an additional 19% of volume. Ninety-five percent of Wayne Farms, the seventh largest producer (5% and 7.6 million head), volume was food service. In addition, the destination market is also important with close to 18% of US broiler production targeted for international markets and individual firms varying in how much of their production is destined abroad (e.g., 4% for Pilgrim's Pride and 15% of volume for Koch) (USDA, ERS 2020; WATT 2020). Given the very different types of markets and bird size produced, the range of impacts on growers could range from very little to the loss of significant numbers of birds or flocks for the year through longer out times.

Structure of the US Broiler Industry

Modern broiler production developed in the 1950s, from the growth of dual-purpose birds (i.e., for both meat and eggs) into concentrated genetic selection for meat type and egg type birds. Broiler production in the United States accounts for 70% of all poultry production value. US broiler production operates as a fully integrated system where integrators are responsible for birds, feed, and technical assistance and producers are contracted to grow and mange birds suppling the land, facilities, labor, and management. The broiler industry is broadly characterized by two primary sectors: breeding and growout. Breeding birds are those that are raised for hatching eggs through the primary breeding and parent stages to produce broiler chicks for growout. Growout is the final stage and where broilers are raised for harvest. The broiler industry functions under a contract growing system with 99% of broilers and broiler breeders grown under contract (U.S. Department of Agriculture, National Animal Health Monitoring System 2010). While these sectors are linked in the broiler production chain, they differ in meaningful ways such as management, length of production, housing types, manure management, geographical concentrations, value of birds, and product produced (i.e., eggs compared to carcasses). In order to understand these general sectors of the broiler industry, broiler breeding and growout are discussed separately below.

Estimating Farm-Level Impacts

Due to the integrated nature of contract broiler production, farmers growing birds under contract do not generally face price risk. Contracts specify the terms for grower compensation. While these terms may include some variable component (e.g., a “tournament”-based premium structure or a seasonal energy cost component), base compensation is generally fixed relatively firmly. This is not to say, however, that contract broiler growers have no revenue risk. Adverse changes in key production parameters can result in significant financial risk for contract growers despite the relative stability of the compensation rate.

Stochastic Budget Analysis of Flock Losses

The preceding exercise is a useful way to isolate the effects of a change in key production parameters (in this case, average number of flocks per year) on expected values for key financial metrics (in this case, net cash flow). However, to fully appreciate how changes in production parameters impact the financial viability of an operation, a more comprehensive and realistic evaluation of financial outcomes is in order. Such evaluation is a challenge because the data needed to define distributions of and correlations between production parameters (and, incidentally, compensation rates) is proprietary. Still, anecdotal sources of data, such as growers themselves and agricultural lenders, coupled with judicious assumptions about distributional parameters provide a starting point for more robust risk evaluation.

Table 1 presents a detailed enterprise budget for a hypothetical four-house broiler operation.3 This budget is used to develop a stochastic simulation of net cash flow in order to evaluate grower financial risk associated with relevant production parameters. Production variables will be assumed to follow a triangular distribution as described in table 2, with correlations between the variables as described in table 3.4 Production variables are simulated using the procedure described in Clements, Mapp, and Eidman (1971), with the exception that the information in the rank correlation matrix rather than the covariance matrix is used to derive correlations, consistent with the approach described in Anderson, Harri, and Coble (2009).

Table 1. Broiler Grower Enterprise Budget: Net Returns and Net Cash Flow

	Width	Length
House dimensions (feet)	60	600
Number of houses	4	Bird density (sq. ft./bird)	0.85
AVG. bird size (live weight, lbs.)	6.5	Flocks/year	5.30
Death & condem. loss (%)	5.5%	Payment rate ($/lb, live)	$ 0.070
Other payment ($/SQ. FT.)	$ -	Litter value ($/ton)	$ -
Square feet each house	36,000	Total square feet	144,000
Number of birds/house	42,353	Number of birds/flock	169,412
Birds placed/year	897,882

		Year 1
Item	Unit	Quantity	Price	Total
Cash receipts
Payments for birds delivered	PER POUND (LIVE)	5,515,242	$ 0.0700	$ 386,067
Variable costs
Labor	HOURS/HOUSE	668	$ 15.00	$ 40,068
Paid labor	%	0%		$ -
Electricity	/SQ. FT./FLOCK	763,200	$ 0.0375	$ 28,620
Fuel	House/flock	21	$ 350	$ 7,420
Supplies and miscellaneous	HOUSE/flock	21	$ 675	$ 14,310
Building & equipment repairs	House	4	$ 6,806	$ 27,225
Other equip. repairs & maint.	Farm	1	$ 2,500	$ 2,500
Crust out/windrow/ cleanout	/House/flock	21	$ 500	$ 10,600
Interest on operating capital	%	90,675	2.0%	$ 1,814
Total variable costs				$ 92,489
Fixed costs			Total investment
Building cost	$/House	$ 371,250	$ 1,485,000	houses
Equipment cost	$/House	$ 123,750	$ 495,000	equipment
Percent financed	%	100%
Depreciation on buildings	0.5	Salvage		$ 49,500
Depreciation on equipment	0.25	Salvage		$ 53,036
Depreciation on other equipment	0.25	Salvage		$ 5,357
Interest on buildings	15 Years	Annually	4.5%	$ 66,825
Interest on equipment	7 Years	Annually	5.5%	$ 27,225
Interest on other equipment	7 Years	Annually	5.5%	$ 2,750
Property taxes (houses)	Farm	1	$ 11,664	$ 11,664
Insurance	SQ. FT.	144,000	$ 0.0420	$ 6,048
Telephone/alarm	Farm	1	$ 1,500	$ 1,500
Total fixed costs				$ 223,905
Principal on buildings				$ 71,449
Principal on equipment				$ 59,877
Principal on other equipment				$ 6,048
Total loan payments (P&I)		Annual		$ 234,175
Return to unpaid labor, land, overhead, risk & MGT.		Annual		$ 69,674
Operator/family labor				$ 40,068
Return to land, overhead, risk & MGT.		Annual		$ 29,606
Net cash flow				$ 40,192

• Note: The bold values represent totals within the table.

Table 2. Triangular Distribution Parameters for Broiler Production Variables

	Most Likely	Minimum	Maximum
Days in birds	48	45	52
Days out (5.3 flocks)	20	18	23
Days out (4.3 flocks)	36	32	41
Stocking Density	0.85	0.80	0.90
Avg. Bird Weight	6.50	6.20	6.85
Death Loss	5.50%	3.75%	6.25%

Table 3. Correlation between Broiler Production Variables

	Days In	Density	Avg. Bird Weight	Death Loss
Days In	1.00	0.91	0.92	0.60
Density		1.00	0.96	0.63
Avg. Bird Weight			1.00	0.62
Death Loss				1.00

• Notes: Spearman correlation coefficients.

Net cash flow is used as the primary metric to assess financial risk in these simulations because grower operations are typically financed with minimal equity and for relatively long terms (seven to eight years on equipment; ten to fifteen years on barns). One of the major risks to a contract broiler grower's financial viability is that a disruption in production will reduce cash flow to the point that debt cannot be serviced (MacDonald 2014). It is assumed in these simulations that the contract grower is financed at 100% on buildings and equipment and is still in the repayment period on both.5 It is further assumed that all labor in the operation is operator/family labor.

The cumulative distribution function for net cash flow for an average of 5.3 and 4.3 flocks per year are presented in figure 1. This figure shows the risk environment facing contract broiler growers. The first key point to note is that in what should be considered the normal scenario of 5.3 flocks average per year, the median value for net cash flow is $43,503 per year.6 On this four-house operation, with 5.3 flocks per year, a grower could reasonably expect to be able to meet all financing obligations, pay out-of-pocket expenses, and have about $43,500 left for other obligations and family living. Further, in this scenario, the likelihood of experiencing negative cash flow (i.e., of leaving some payment and/or cash expense obligations unpaid) is low: actually, zero given the relatively narrow distributions of production parameters used here. This result should not be surprising. A major part of the appeal of contract broiler production is that it provides a reasonably reliable source of income to the farm operation (MacDonald 2014). These simulation results are consistent with that expectation.

By contrast, if the grower experiences extended out times such that number of flocks fall to 4.3 on average, median cash flow falls to ($18,272). Moreover, the probability of experiencing negative cash flow increases dramatically: to around 95%. In other words, falling one full flock below normal in terms of average production makes it extremely difficult for a grower who is still in the repayment period on his or her loans to meet cash flow obligations, much less contribute to family living expenses.

Note that this assessment of the marginal value of a flock assumes that as the number of flocks received per year goes down, other production parameters remain stable. To the extent that a reduction in flocks might be accompanied by other changes that would further reduce production (e.g., lower stocking density) the results reported here would understate actual losses. In reality, an integrator wanting to reduce production substantially and on short notice (as has been the case with COVID-19) may be forced to adjust several production parameters at once. Thus, it may well be the case that as out-times increase, integrators also reduce stocking density and/or bird weight as a means of managing the shift to lower production. This would imply that the results here may be on the low side of reasonable expectations for grower losses associated with flock reductions. To illustrate, suppose that a shift from an average of 5.3 flocks per year to 4.3 flocks per year is associated with a 5% reduction in average stocking density. In that case, the median net cash flow associated with the one-flock loss would be ($32,544)—a decline of $76,047 from the average-5.3-flock median net cash flow.

Conclusions and Implications for Policy Responses

The structure of the broiler industry leads to complexities in designing farm-level policy responses to market downturns such as the one created by the COVID-19 pandemic. COVID-19 clearly led to losses for the broiler sector but it is much less clear on how to provide relief for broiler producers who were impacted. Producers of other livestock such as cattle and hogs are eligible for varying levels of direct payments per animal through CFAP. However, that policy design does not work well for targeting farm-level losses for poultry producers since the growers do not own the birds or control the timing and number of birds they grow.

Our model allows for a better understanding COVID-19 impacts on broiler growers. The farm-level estimates presented in this paper show the potential impacts of the most common sources of loss for broiler producers. If demand for chicken declines, the effect on contract growers will most likely be production slowdowns that lead to increased time between flocks or a reduction in stocking density. Both of these adjustments significantly impact broiler growers and can lead to negative annual returns. Our model provides the framework for policymakers to estimate losses for impacted producers.

Policies aimed at providing relief to the broiler sector face these challenges of estimating loss and utilizing a measurable variable to determine eligibility. Public data do not exist to determine how many producers were actually affected by production adjustments or the level by which they were impacted. One measurable variable is square feet of broiler house space. While relief payments based on square feet would be relatively straightforward to administer, legitimate concerns could be raised about equitable treatment of growers. In addition to the differing COVID-19 impacts across growers, there also exists considerable variation across growers in farm-level revenue per square foot of house space. This variation can arise from differing compensation structures and production structure such as bird weights and stocking density. Other policy instruments such as poultry loan forgiveness likewise face similar issues of potentially inequitable administrations when compared to losses.

A more targeted relief program would require information to identify producers impacted and the severity of those impacts. While contract broiler growers were not included in CFAP at the national level, state governments have considered relief for broiler producers. The governor of Alabama announced funding to establish the Alabama Agricultural Stabilization Program on August 11, 2020 which includes $4 million to poultry farmers (State of Alabama 2020). The Alabama Department of Agriculture and Industries proposes the poultry funds will be used to “provide direct payments to Alabama resident broiler farmers that have experienced above normal downtimes between flocks and have not been fully compensated for that downtime by their integrator” at a rate of $0.005 per square foot for each day of downtime above twenty-one days. Details on how producers must certify eligibility were not included in the announcement; but it could likely only be accomplished through revealing information from private agreements with integrators. Similar information would likely be needed for any potential national-level program that targets losses due to increased downtime and reduced stocking density. This information could be used in conjunction with our model to calculate relief payments. Requiring private documents for certification would allow for targeted relief but would be more cumbersome to administer than would a self-certification process.

The COVID-19 pandemic created significant disruptions in the broiler industry. The consumer-level disruptions affecting demand for chicken combined with processing disruptions uniquely impacted growers at the farm level in a manner that is difficult to identify and measure. The methodology presented above can be used to better understand the farm-level impacts of COVID-19 and in designing potential policy responses for this pandemic as well as for future disruptions.