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Margin, Price, Revenue in the Farm Bill Commodity
Program Context
8
May
2023
By
Carl
Zulauf, Department of Agricultural,
Environmental and Development Economics
Ohio State University, Nick Paulson, Gary
Schnitkey, and Jonathan Coppess, Department
of Agricultural and Consumer Economics
University of Illinois
The increase in cost to produce crops since 2020 has focused attention
on the net return margin between gross revenue and cost of
production. This analysis finds that change in revenue has a notably
closer relationship to change in margin than does change in price. US
commodity programs use either price (i.e. Price Loss Coverage (PLC))
or revenue (Agriculture Risk Coverage (ARC) to trigger payments. This
analysis implies revenue is a better policy variable than price for
addressing financial stress due to low or negative margins.
Data and Methods
The analysis uses the cost of production data set maintained by USDA,
ERS (US Department of Agriculture, Economic Research Service). It
currently covers the 1975-2022 crop years. Crops included in the data
set since 1975 are barley, corn, cotton, oats, peanuts, rice, sorghum,
soybeans, and wheat. We use the cost of production data for the US.
This data can also be thought of as the cost of production for the
average producer of the crop in the US.
Net return margin equals gross revenue minus total economic cost of
production (See Data Note 1). USDA, ERS assigns a cost to all inputs,
including an opportunity cost for owned land and unpaid labor. Hence,
it is an economic cost of production. We specifically calculate the
share of the change in margin explained by the change in price and
change in revenue. Explained share, or explanatory power, is the
squared correlation between change in margin and change in price or
revenue. Change is examined instead of level because price and
revenue have trended higher over time. Trends in variables can result
in misleading correlations. Moreover, change is often of more
interest, such as when margin declines notably. The explanatory power
calculated in this analysis is unconditional since there is only 1,
not multiple explanatory variables. Commodity programs are also
unconditional since they use a single metric, price (i.e. PLC) or
revenue (i.e. ARC) to trigger payments.
Individual Crop -1-Year Change
For each of the 9 crops, change in revenue explains a higher, often
notably higher, share of change in margin than does change in price
(see Figure 1). Explanatory power of change in revenue ranges from 83
percentage points (pp) higher for sorghum (88% -5%) and 65 pp higher
for cotton to 14 and 9 pp higher for rice and oats, respectively. For
comparative purposes, explanatory power for change in yield and total
cost per acre was also computed. They are much lower, especially
relative to change in revenue.
![](images/images_06_26_2023/05082023_fig1-768x558.png)
Average Relationship – 1-Year Change
Explanatory power for the individual crops was averaged to obtain the
general relationship for the 9 crops. The general relationship is of
interest because many features of commodity programs are similar
across program commodities. On average, year-to-year change in
revenue explains a notably higher share of the year-to-year change in
margin than does year-to-year change in price: 85% vs. 44% (see
Figure 2).
![](images/images_06_26_2023/05082023_fig2-768x558.png)
5-Year Change
To examine the sensitivity of the above results, a correlation was
calculated for 5-year change (such as, 2015 to 2020) instead of
year-to-year change (such as, 2015 to 2016). Change in input and
output prices can follow different time paths with input price change
often lagging output price change (see, for example, farmdoc
daily April 4, 2023). Differential price change can result
in differential change in margin over different time periods. Five
years is also a common farm bill length. Perspective is thus provided
on explanatory power for a shorter (1 year) and longer period (5
years).
For 8 crops, 5-year change in revenue has a higher explanatory power
of the 5-year change in margin than does the 5-year change in price.
Explanatory power of the 5-year change in revenue ranges from 57 pp
higher for peanuts (77% – 20%) to 6 pp higher for soybeans (82% –
76%). The exception is oats, for which explanatory power is 3 pp
higher for 5-year change in price (63% – 60%).
![](images/images_06_26_2023/05082023_fig3-768x558.png)
On average for the 9 crops, change in revenue continued
to explain a notably higher share of change in margin than does change
in price, but the average difference narrowed from 41 pp (85% – 44%)
to 24 pp (78% – 54%) (see Figure 4). Average explanatory power for
change in total cost and yield per acre remains much lower.
![](images/images_06_26_2023/05082023_fig4-768x558.png)
Discussion
Using US data for 9 large acreage field crops,
change in revenue is found to more closely track change in net
return margin than does change in price over the 47 year period,
1975-2022.
For the 9 crops as a group, change in revenue, on average,
explains between 78% and 85% of the change in margin, a high
explanatory power especially for a single variable relationship.
Change in price explains, on average, 44% to 54% of the change in
margin. The range in explanatory power for revenue and price
reflects change over 1 and 5 year periods.
Change in revenue explained a higher share of the change in
margin, often a much higher share of the change in margin, than
did change in price for 17 of the 18 crop and length of change
observations examined in this analysis (9 crops and 2 changes of 1
and 5 years).
The closer relationship of change in revenue with change in margin
should not come as a surprise. Revenue is a more complete measure
of returns than price since revenue also includes yield.
The finding has an important implication for the current farm bill
debate. If Congress wishes to increase the efficacy with which
current commodity programs address financial stress due to low or
negative net return margins, revenue is a better policy instrument
than price.
Data Note
USDA, ERS cost of production data includes both
revenue from the primary product, such as grain, and secondary
products, such as straw. Except for cotton, we use revenue only
from the primary product, i.e. its price times its yield, since
commodity programs only cover the primary product. For cotton,
seed cotton is the program crop, which includes both the value of
cotton lint and cottonseed. For a more detailed discussion of
USDA, ERS cost of production data, see the 2022
article by Zulauf, Langemeier, and Schnitkey in the Journal
of the American Society of Farm Managers and Rural Appraisers.
References and Data Sources
Paulson, N., G. Schnitkey, S. Sellars, C. Zulauf
and J. Baltz. “Update
on Growth Rates of Fertilizer, Pesticide and Seed Costs Over Time.” farmdoc
daily (13):62, Department of Agricultural and Consumer
Economics, University of Illinois at Urbana-Champaign, April 4,
2023.
US Department of Agriculture, Economic Research Service. May
2023. Cost of Production. https://www.ers.usda.gov/data-products/commodity-costs-and-returns/
Zulauf, C. M. Langemeier, and G. Schnitkey. 2022. U.S. Crop
Profitability and Farm Safety Net Payments since 1975. Journal
of the American Society of Farm Managers and Rural Appraisers.
Pages 60-69. https://www.asfmra.org/resources/asfmra-journal/2022journal
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