January is a Great Time to Complete the Farm Balance Sheet

Eric Richer, OSUE Fulton County

 The balance sheet is a “snap shot” in time of your farm’s financial position, including what assets you own and how they are financed. The balance sheet is also known as the net worth statement. When completed precisely and timely, the balance sheet and corresponding ratios can be a very valuable tool to determine farm financial health. The balance sheet objectively measures farm business growth, liquidity, solvency, and risk capacity.

Categorizing Balance Sheet Items

The assets and liabilities on the balance sheet (including the financing of the assets) are used to determine the equity, or net worth, of the farm owner. The owner’s equity is used by lenders and insurers to determine a farm business’ value.  There are two ways to calculate the owner’s equity, or net worth. The first simply subtracts the liabilities from the assets:

Assets – Liabilities = Owner’s Equity

The second calculation adds the owner’s equity with liabilities to determine the assets:

Liabilities + Owner’s Equity = Assets

Terms of Assets and Liabilities

Beyond the broad categories of either an asset or liability, a balance sheet categorizes items into “time compartments” or terms of useful life. Useful life is a term for the amount of time an item can be utilized for the farm business. Depreciation allocates the cost of this asset over its useful life. Both assets and liabilities can be categorized into current, intermediate, and long, or fixed, terms of useful life.

Assets – Current assets can be converted to cash in one year or less. Common current assets are cash, growing crops, harvested crop inventory, market livestock, accounts receivable, and other similar items. Intermediate assets have an assumed useful life or depreciable value of one to ten years. Common intermediate assets are breeding livestock, machinery and equipment, titled vehicles, and not-readily-marketable bonds and securities. Long term, or fixed, assets are typically permanent items with value—depreciable or not—for more than ten years and include farmland, buildings, farmsteads, and other similar items.

Liabilities – Current liabilities are obligations that are due and payable in the next twelve months. Most common current liabilities include accounts payable (bills), credit card bills, operating lines of credit, accrued interest, and the current portion of principal on loans due this year. Intermediate liabilities are obligations that due to be paid back within one to ten years and are usually associated with intermediate farm assets on the left side of the balance sheet. Common intermediate liabilities are the principal remaining on machinery and equipment loans or breeding livestock purchases. Finally, long term, or fixed, liabilities are debts with terms greater than ten years like the principal balance remaining on a farmland or building mortgage.

Assets: Market Value vs. Cost Value

Market value – Today’s market values minus selling costs are used to determine market value. For example, a fully depreciated 15-year-old tractor certainly has a current market value greater than zero. A realistic current market value for this tractor can be obtained with an appraisal, or by looking at current sales of similar tractors online. Similarly, farmland bought 30 years ago likely has a different current market value today. In general, lenders may prefer the use of current market values in a balance sheet for asset valuation.

Cost value – The net book value, or the cost of the item minus accumulated depreciation, is the cost value. For example, a fully depreciated 15-year-old tractor has a cost value of $0 in a cost based balance sheet. No appraisal is needed; only record the cost minus accumulated depreciation. Farmland (a non-depreciable, long term asset) purchased 30 years ago has a balance sheet value of the purchase cost.  In general, accountants prefer cost value balance sheets as a more clear reflection of business success, based on business decisions rather than inflation, depreciation, or appreciation of investments.

In a precisely completed balance sheet, the cost value and the market value columns usually produce different total asset values.

Keys to Completing the Balance Sheet

Several keys can help farmer improve their accuracy, effectiveness, and efficiency for completing year-end balance sheets.

  • Complete the balance sheet on the same date each year, usually as of December 31st. The information will never be more accurate than immediately after the end of the year.
  • Inventory all assets, including standard weight and measure units (ie. Lbs, head, bushels, bales, etc).
  • Utilize current market prices for crop and livestock inventories.
  • Calculate cost value for growing crops.
  • Include government payments and insurance indemnities yet to be received in accounts receivable.
  • Apply conservative breeding livestock values, avoiding large year-to-year changes.
  • Maintain a separate, easy-to-update depreciation schedule for depreciable assets.

Balance Sheet Tools

Balance Sheet Ratios to Evaluate Financial Health

The scorecard uses these three accounting statement to determine financial ratios and measurements to benchmark a farm operation against acceptable industry standards.


Hachfeld, G. A., D.B. Bau, C.R. Holcomb. 2016. Balance Sheet. Farm Financial Series, #1, University of Minnesota Extension.

Langemeier, M. R. 2011. Balance Sheet—A Financial Management Tool. MF-291, Department of Agricultural Economics, Kansas State University Extension. Available online at: www.agmanager.info

OSU Extension to Host Two Northwest Ohio Farm Transition Programs

by: Eric Richer, OSU Extension Fulton County & Sarah Noggle, OSU Extension Paulding County

Are you interested in starting the conversation for a successful farm transition to the next generation?  OSU Extension in Northwest Ohio is holding two separate but identical farm transition meetings to assist farmers in navigating the farm transition process.

The first night will focus on the senior generation (all are invited) including estate and Medicaid planning, communication through the process, farm financial affairs and vision/management transition. The second night will focus on the next generation (all are invited) including entity formation and use in transition planning, a recap of wills & trusts, accounting implications like capital gains, gifting and share valuation, and committing to the process. Local legal and accounting professionals will be teaching sessions along with local county Extension educators.  For either program location, the cost is $20 per farm entity for both nights and including refreshments and materials.

In Fulton County, the 2-night program will be held at the Robert Fulton Ag Center, 8770 State Route 108, Wauseon, OH 43567 on January 28th and February 11th from 6:30-9:00 pm. If you are interested in the Fulton County program, download the registration form at www.go.osu.edu/fultonagprograms2020 or visit www.fulton.osu.edu. Pre-registration closes Friday, January 24th.

In Paulding County, the 2-night program will be held at the Paulding County Extension Office, 503 Fairgrounds Drive, Paulding, OH 45879 on February 20th and 27th from 6:30-9:00 pm. If you are interested in the Paulding County program, visit www.paulding.osu.edu for registration details. Pre-registration closes February 6.

Planning for the Future of Your Farm Program Planned in Tuscarawas Country

by: Chris Zoller, Extension Educator, ANR

A two-evening “Planning for the Future of Your Farm” program will be held February 12 and 19 from 7:00 pm to 9:30 pm each evening.  The program will be held at the Village of Tuscarawas Community Center on Cherry Street in Tuscarawas.

David Marrison, OSU Extension Educator, Agriculture and Natural Resources, Coshocton County, will discuss developing the next generation of managers, family communications, providing income for multiple generations, keeping your farm competitive, and preparing for the unexpected.  These topics will be discussed the evening of February 12.

The evening of February 19 will feature Peggy Hall, Attorney and OSU Extension Ag Law Specialist, and Robert Moore, Attorney, Wright and Moore Law.  Peggy and Robert will discuss farm business structures, estate and transfer strategies, trusts, life insurance, tax planning, and much more.

Registration for the program is $25 per person or $35 per family.  Please make your check payable to OSU Extension-Tuscarawas County, 419 16th St. SW, New Philadelphia, OH 44663.  Please RSVP by February 5.  Questions may be directed to Chris Zoller at 330-339-2337 or zoller.1@osu.edu.


Ohio Farm Custom Rate Survey 2020

Barry Ward, Leader, Production Business Management, OSU Extension, Agriculture & Natural Resources

 A large number of Ohio farmers hire machinery operations and other farm related work to be completed by others. This is often due to lack of proper equipment, lack of time or lack of expertise for a particular operation.  Many farm business owners do not own equipment for every possible job that they may encounter in the course of operating a farm and may, instead of purchasing the equipment needed, seek out someone with the proper tools necessary to complete the job. This farm work completed by others is often referred to as “custom farm work” or more simply “custom work”. A “custom rate” is the amount agreed upon by both parties to be paid by the custom work customer to the custom work provider.

Custom farming providers and customers often negotiate an agreeable custom farming machinery rate by utilizing Extension surveys results as a starting point. Ohio State University Extension collects surveys and publishes survey results from the Ohio Farm Custom Survey every other year. This year we are updating our published custom farm rates for Ohio.

We need your assistance in securing up-to-date information about farm custom work rates, machinery and building rental rates and hired labor costs in Ohio.

This year we have an online survey set up that anyone can access. We would ask that you  respond even if you know only a few rates.  We want information on actual rates, either what you paid to hire custom work or what you charged if you perform custom work. Custom Rates should include all ownership costs of implement & tractor (if needed), operator labor, fuel and lube. If fuel is not included in your custom rate charge there is a place on the survey to indicate this.

 You may access the survey at: ohio farm custom rates survey 2020

Or: https://osu.az1.qualtrics.com/jfe/form/SV_7WN0eNQz3VO41nv

The deadline to complete the survey is March 31, 2020.


Ohio State University Extension and the Ohio Soybean Council Energy Study: Understanding the Impact of Demand Charges & Power Factor in Agriculture

Farmers have long explored options to provide energy savings associated with their agricultural operations. Ohio State University Extension and the Ohio Soybean Council have partnered to provide research-based data driven tools to help Ohio farmers assess and navigate various energy infrastructure investment options for their farm. Specifically, the project team is interested in learning more about your experience and interest in implementing energy management strategies such as peak demand reduction, power factor correction, and/or the integration of solar generation systems to reduce electricity costs on your farm.

Farmers with commercial rate structures that charge for peak demand and poor power factor can implement equipment and management strategies to reduce electricity costs, thus increasing long-term profitability. However, very little is known about the economic feasibility of investing in equipment to reduce peak electric demand charges in agriculture. To determine the economic feasibility of implementing energy management strategies it is important to simultaneously study the real costs of installing new equipment, ongoing risks, challenges, as well as understanding how these improvements will influence the calculations of a farms electric bill a comprehensive manner.

If you are an Ohio farmer and interested in participating, you may click the survey link below to participate in this voluntary study. The survey will take less than 5 minutes and is designed to determine the overall level of interest in implementing energy management strategies such as peak demand reduction, power factor correction, the integration of solar generation systems to reduce electricity costs on your farm and to identify individuals who have experience with on-farm energy management strategies to summarize benefits and challenges. This project will provide our research team with data to identify actionable recommendations that will inform future Extension outreach and education programs.

If you have additional questions regarding this study please contact Eric Romich, Ohio State University Extension Field Specialist, at 419-294-4931 or by e-mail at: (romich.2@osu.edu).

Survey Link: https://osu.az1.qualtrics.com/jfe/form/SV_4MaQn34JafSQlQ9

OSU Extension to Offer Lunch and Learn Webinars

By: Chris Bruynis, Extension Educator

In the age of multi-tasking and convenience, OSU Extension is offering a lunch and learn webinar series for farmers. We have arranged for eight topic and speakers to provide a webinar every Wednesday starting on Wednesday, February 5, 2020 and concluding March 25, 2020. Join us for eight consecutive Wednesdays for this educational series starting at 11:45 am and lasting 1.5 hours. Learn important risk management information during this lunch and learn series from top industry, private sector, and university experts important to the success of farm businesses in 2020 and beyond.

The topics that will be covered include:

February 5:         Using Financial Statements/Ratios to Make Informed Financial Decisions

February 12:      Farm Law 101: Leasing and Financing Agreements

February 19:      Grain Contracts and Markets: What to Use When

February 26:      Where to Start with Workers Compensation Benefits

March 4:             Meeting with a Lender: What Numbers are Important

March 11:           Estate Planning: What are the Tools and Options

March 18:           Grain Marketing Strategies for 2020

March 25:           Tips for Recruiting, Hiring, and Retaining Farm Business Employees

Farmers interested in participating should register at http://go.osu.edu/fm2020 by January 31, 2020.  At this website you can access detailed information on the speakers and the learning objectives for each session. There is also a registration link for the webinar at this site. The cost for all eight topics is $25 per registration and must be paid with credit card at time of registration.

Any question can be directed to Chris Bruynis or Marianne Guthrie at 740-702-3200 or email bruynis.1@osu.edu. We hope this program series will be beneficial to your farm business, whether you attend all the topic presentations or just some of them.

Farm Bill Meetings to be held across Ohio

Click here for complete article with locations of meetings

Ohio State University Extension and the USDA Farm Service Agency in Ohio are partnering to provide a series of educational Farm Bill meetings this winter to help producers make informed decisions related to enrollment in commodity programs.

The 2018 Farm Bill reauthorized the Agriculture Risk Coverage (ARC) and Price Loss Coverage (PLC) safety net programs that were in the 2014 Farm Bill. While the ARC and PLC programs under the new farm bill remain very similar to the previous farm bill, there are some changes that producers should be aware of.

Farm Bill meetings will review changes to the ARC/PLC programs as well as important dates and deadlines. Additionally, attendees will learn about decision tools and calculators available to help, which program best fits the needs of their farms under current market conditions and outlook.

Enrollment for 2019 is currently open with the deadline set as March 15, 2020. Enrollment for the 2020 crop year closes June 30, 2020. Producers can enroll for both 2019 and 2020 during the same visit to an FSA county office. Producers have the opportunity to elect to either ARC or PLC for the 2019 to 2023 crop years, with the option to change their program election in 2021, 2022, and 2023.

To find out about upcoming meetings, and get information about the Farm Bill, visit go.osu.edu/farmbill2019

Corn, Soybean, and Wheat Yield Trends by Ohio County, 1972-2018

by: Carl Zulauf, Robert Dinterman, and Ben Brown, Ohio State University, November 2019

Click here to access full report complete with figures

Yield growth is the primary source of increased production of crops in Ohio and most of the US.  Most land that can be cropped is being cropped.  Understanding historic yield trends is thus important to an informed understanding of Ohio agriculture.  This article examines trends in corn, soybean, and wheat yields since 1972 at the Ohio state level and across Ohio counties.  These three crops composed 87% of Ohio harvested crop acres in the 2017 Census of US Agriculture.  Trend yield is higher for corn than soybean and wheat, both in terms of bushel / acre and percent of yield.  Trend yields vary across Ohio counties, particularly for corn.  Implications are drawn for Ohio crop agriculture, with a particular point of interest being the implication for the CAUV (Current Agricultural Use Value) program that taxes farm land at its agricultural use value rather than its appraised value.

Analysis:  Yield per harvested acre is analyzed.  Source for the data is USDA, NASS (US Department of Agriculture, National Agricultural Statistics Service).  The analysis starts with the 1972 crop and ends with the 2018 crop.  It spans 47 years that include periods of prosperity, financial stress, and tight profit margins.  Not all counties have 47 years of observations for each crop.  It was decided a county should have at least half or 24 years of observations to be included in the analysis.  This decision reflects (1) consideration of the power of statistical tests, (2) that 24 years is a “natural break” in the distribution of number of county yield observations, and (3) a feeling that it seems reasonable to require yields for at least half of all years in order to have confidence in a county’s estimated trend yield.  Counties with 24 years of harvested yields total 86, 78, and 69 for corn, soybeans, and wheat, respectively.  The county yield trends were tested for statistical difference from the yield trend for Ohio.  For additional discussion of the analytical procedures, see the Data Note.

Corn Yield Trend:  Ohio linear corn yield trend is +1.76 bushel / year over 1972- 2018 (see Figure 1).  In comparison, average of the 86 county yield trends estimated for corn is +1.62 bushel / year.  Since the state yield is the average of county yield weighted by the amount of production in the county, the higher state trend yield suggests counties with more corn production had a higher yield trend.

County corn yield trend ranged from +0.66 (Carroll County) to +2.14 (Clinton County) (see Figures 1 and 2).  When examining the range of values, it is useful to assess if the extreme values are outliers.  Examination of the county corn yield trends suggests that both Carroll and the county with the next lowest trend (Belmont – +0.71) are outliers as the next lowest yield trend is +1.09 for Monroe County.

Individual county yield trends were tested for statistically significant deviation from Ohio’s yield trend (see Date Note).  Thirty-five (41%) of county corn yield trends deviated from the state yield trend with the commonly-used 95% level of statistical confidence (see Figure 2).  Corn yield trend was above (below) the state corn yield trend in 9 (26) counties.  It was thus almost three times more likely for statistically significant county yield trends to be below than above the Ohio trend yield. Counties with a statistically significant lower trend have a tendency to be in eastern Ohio (see Figure 2).  Statistically significant higher corn yield trends have a tendency to be in southwestern and central Ohio.

Soybean Yield Trend:  Ohio linear soybean trend yield is +0.48 bushel per year over 1972-2018, the same as the average of the 78 county trend yields estimated for soybeans (see Figure 3).  Unlike corn, this comparison does not suggest county soybean yield trend varied with amount of county production.

County soybean yield trend ranged from +0.23 (Lawrence County) to +0.59 (Fairfield County) (see Figures 3 and 4).  Lawrence County may be an outlier as the next lowest soybean yield trend was Summit County at +0.30 bushel per year.

Statistically significant deviation from the state yield trend was far less common for soybeans than corn.  Only 10 (13%) of county soybean yield trends deviated from the state yield trend with 95% statistical confidence (see Figure 4).  Five were below and 5 were above the state trend.  The small number of counties with statistically significant deviations from the state yield trend calls for caution in making regional categorization of these deviations.  Given this caveat, the 5 counties with trend yield above the Ohio trend yield are in central Ohio.

Wheat Yield Trend:  Ohio’s linear wheat yield trend is +0.76 bushel per year over 1972-2018, nearly identical to the average of the 69 county yield trends estimated for wheat (see Figure 5).  Similar to soybeans and unlike corn, this comparison does not suggest wheat county yield trend varied with amount of county production.

County wheat yield trend ranged from +0.38 (Carroll County) to +0.98 Pickaway County) (see Figures 5 and 6).  There did not appear to be any obvious outlier county wheat yields.

Twenty (29%) of the county wheat yield trends deviated from Ohio’s wheat yield trend with 95% statistical confidence (see Figure 6).  As with corn, it was more common for a county yield trend that differed from the Ohio yield trend with statistical significance to be above than below Ohio’s trend (13 vs. 7).  No clear regional category of deviations from the state wheat trend yield is apparent.  Counties with significant deviations from the state trend are dispersed across Ohio (see Figure 6).

Comparing Yield Trend across Crops:  Comparing yield trend across corn, soybeans, and wheat is complicated by their different yield levels.  Given the use of regression analysis, one useful measure of yield level is the estimated intercept value for 1972.  These intercepts for Ohio corn, soybeans, and wheat are 87, 29, and 40 bushels/acre, respectively.  Taking the ratio of Ohio trend yield to the Ohio intercept finds that yield grew fastest for corn (2.0%) and slowest for soybeans (1.7%) (see Figure 7).  The difference may seem small, but it is an annual difference that has extended over 47 years.

Another useful comparison is to examine the relative variation in county yield trends by crop.  One such measure is the ratio of the standard deviation of county yield trends to the average county yield trend.  Using the values in Figures 1, 3, and 5, the so-called coefficient of variation ratio is 18% for corn, 12% for soybeans, and 16% for wheat.  Eliminating the two outlier county yield trends for corn reduces its coefficient of variation to 15%.  The coefficient of variation thus suggests that soybean yield trends varied less across Ohio counties than did corn and wheat yield trends.

Summary Observations:

►   Linear yield trend is higher for Ohio corn than soybeans, with wheat in between.

►   Among the three crops, soybean yield trends differ the least across Ohio’s counties.

►   County yield trends are more likely to deviate from Ohio’s yield trend with statistical significance for corn than for soybeans.

►   Only readily-apparent regional patterns in yield growth are a higher probability of slower yield growth for corn in eastern Ohio and faster yield growth for corn in central and southwestern Ohio.

►   Corn’s differential yield trends have likely differentially impacted profitability of crop agriculture across Ohio’s counties.

►   Statistically significant differences in county yield growth from state yield growth pose a potential policy issue for Ohio’s CAUV (Current Agricultural Use-Value) Program.  CAUV determines assessed value for a majority of agricultural land in Ohio.  It uses a net-income approach partially based on a soil type’s yield potential for corn, soybeans, and wheat.  Potential yield for a soil type partially comes from the state’s most recent comprehensive soil survey (Zobeck, Gerken, and Powell, 1983). This yield value, from the early 1980s, is then adjusted based on the state-wide trend in harvested yield for each of the three crops.  The significant differences between county and state-wide yield trends raises the potential issues of whether or not the use of state-wide yield trends to adjust a soil productivity index dating to the early 1980s continues to be appropriate policy and thus if an update of the soil productivity index may be an appropriate policy option.

Data Note:  The statistical method used for this analysis is multiple linear regression.  Unit of observation is a county-year in Ohio from 1972 to 2018.  Statewide yield is included as well.  Dependent variable is county yield (for corn, soybeans, or wheat) for a given year.  It is regressed on time, measured as a count of years starting with 1972 equal to zero.  A county specific intercept and a county specific annual trend are estimated.  The statistical test of interest is if a county specific annual yield trend is statistically different from the statewide annual yield trend for a given crop.  Since the county specific annual trend and statewide annual trend are both estimated coefficients, an F-Test is constructed with the null hypothesis that the two trend coefficients are equal to each other.  An F-test rejection of a null hypothesis is a function of both the difference between the two estimated coefficients and the estimated standard error of the coefficients.

References and Data Source:

US Department of Agriculture, National Agricultural Statistics Service.  (April 2019).  2017 Census of Agriculture:  United States  Summary and State Data. Volume 1, Geographic Area Series, Part 51.  AC-17-A-51   www.agcensus.usda.gov

Zobeck, TM, JC Gerken, and KL Powell. 1983. “Ohio Soils with Yield Data and Productivity Index.” Ohio State Univeristy Cooperative Extension Service.  Bulletin 685.

The Case for Looking at the ARC-IC (ARC-Individual) Program Option

by: Carl Zulauf and Ben Brown, Ohio State University, and Gary Schnitkey, Krista Swanson, Jonathan Coppess, and Nick Paulson, University of Illinois at Urbana-Champaign, October 2019

Click here for the complete article as PDF

ARC-IC (Agriculture Risk Coverage – Individual) has received less attention than ARC-CO (ARC – County) and PLC (Price Loss Coverage).  ARC-IC is operationally more complex, thus harder to explain and understand.  It pays on only 65% of program base acres while ARC-CO and PLC pay on 85% of base acres.  Nevertheless, ARC-IC is worth considering if an FSA farm has one or more of the appropriate production attributes.  These attributes include (1) 100% prevent plant acres on a FSA farm, (2) high year-to-year production variability, (3) much higher farm than ARC-CO and PLC yields, and/or (4) acres planted to fruits and vegetables.  The prevent plant attribute is more relevant than normal in 2019.

ARC-IC Overview

►   ARC-IC is a whole farm program option based on the average experience of all covered program commodities planted on the ARC-IC farm.

►   ARC-IC applies to all base acres of all covered commodities on an ARC-IC farm.  It is not elected on a commodity-by-commodity basis.

►   An ARC-IC farm is the sum of a producer’s share in all FSA farms he/she enrolls in ARC-IC in a state.

►   All payment entities on an FSA farm must elect to enroll in ARC-IC.

►   ARC-IC makes a payment if average actual revenue/acre of all covered commodities planted on the ARC-IC farm is less than 86% of the ARC-IC farm’s average benchmark revenue/acre.

►   Revenue/acre for a covered commodity and year is (ARC-IC farm yield times US market year price).

►   ARC-IC farm benchmark revenue/acre equals the sum of the 5-year Olympic average revenue/acre for each covered commodity weighted by current year acres planted to a covered commodity.

►   ARC-IC actual revenue/acre equals the sum of actual revenue/acre for each covered commodity that was planted weighted by current year acres planted to a covered commodity.

►   Payment is made on 65% of total base acres on an ARC-IC farm times ARC-IC payment/acre.

►   Payment/acre is capped at 10% of the ARC-IC farm benchmark revenue/acre.

►   NOTE: Payment depends on program commodities that are planted.

►   NOTE: Prevent plant acres are included in ARC-IC revenue calculations ONLY IF 100% of an ARC-IC farm’s initially reported covered commodities are approved as prevent plant.

►   NOTE: Only initially planted covered commodity and approved double crop acres are included in the revenue calculations.  Any subsequently planted crops are not included in the calculations.

When ARC-IC should be considered:  A farm production attribute must compensate for ARC-IC’s fewer payment acres (65% vs. 85% of base acres for ARC-CO and PLC).  Such attributes include:

(1)  All of an ARC-IC farm’s initially planted covered commodities are approved as prevent plant.  Current year revenue is zero since production is zero, resulting in a payment/acre equal to the payment cap of 10% of ARC-IC benchmark revenue/acre.  In contrast, if any acre is planted to any covered commodity, payment is based on revenue/acre for the planted acre(s).  Given the prevalence of prevent plant acres in 2019, examples are provided below.  To underscore the key point, payment in this situation requires the ARC-IC farm has prevent plant for all covered program commodities on all base acres.

(2)  Production is highly variable from year to year on the ARC-IC farm.  High variability increases the likelihood of ARC-IC payment.  High variability is most likely when1 crop is grown and 1 FSA farm makes up the ARC-IC farm.  ARC-IC averages across crops and FSA farms.  Variability declines as more than 1 crop is grown and/or more than 1 FSA farm makes up the ARC-IC farm.

(3)  ARC-IC benchmark yield is (much) higher than ARC-CO benchmark yield and PLC farm payment yield.  Assuming 1 covered commodity and same percent payment rate for both ARC programs, ARC-IC benchmark yield needs to be more than 30% higher than the county benchmark yield for ARC-IC to pay more than ARC-CO.  Other situations result in different breakeven yields.

(4)  Fruits and vegetables (other than mung beans and pulse crops) or wild rice are planted on a FSA farm.  Payment base acres are 65% for ARC-IC vs. 85% for ARC-CO and PLC.  Non-payment acres are the remaining base acres:  35% for ARC-IC vs. 15% for ARC-CO and PLC.  Payment is reduced if fruits and vegetables (other than mung beans and pulse crops) or wild rice are planted on more than the non-payment acres.  ARC-IC has more acres that can be planted to fruits and vegetables (other than mung beans and pulse crops) or wild rice without losing program payments.  Note, base acres on the FSA farm are not altered in this situation.

ARC-IC Examples – role of prevent plant – Overview:  An ARC-IC farm with all yield information needed to calculate the ARC-IC benchmark revenue is assumed.  This information plus the US market year average price for crop years 2013-2017 and 2019 are in the top half of each table of values for each example.  The farm has 100 acres of cropland and program base, both composed of 60 acres of corn and 40 acres of soybeans.

Calculation of the ARC-IC benchmark revenue is a 3-step process.  In step 1, per acre revenue is calculated for each covered commodity (corn and soybeans in this case) for each of the 5 years in the benchmark calculation window.  In step 2, the Olympic average revenue per acre is calculated.  An Olympic average removes the high and low value before calculating the average of the remaining values.  For the example ARC-IC farm, the Olympic average revenue is $605 for corn and $475 for soybeans.  In step 3, the Olympic average revenues are weighted by the acres planted in the current year (2019 in this case) to covered commodities to determine an ARC-IC farm average revenue per acre.  This calculation for the example ARC-IC farm is:  (($605*60) + ($475*40)) / (60+40), or an ARC-IC benchmark revenue of $553 / acre.

ARC-IC Example 1 – no prevent plant acres:  Actual revenue / acre is $631 (166 bushels / acre times $3.80 / bushel) for corn and $423 (47 bushels / acre times $9.00 / bushel) for soybeans (see Table 1).  These individual crop values are weighted by acres planted to each covered commodity, resulting in an actual revenue / acre for the ARC-IC farm of $548 (($631*60) + ($423*40)) / (60+40).  Since actual revenue of $548 / acre exceeds the ARC-IC coverage revenue of $476 / acre (86% ARC-IC coverage level times benchmark revenue of $553 / acre), ARC-IC makes no payment.

ARC-IC Example 2 – some prevent plant acres: This example has 20 acres of corn prevent plant acres and lower 2019 yields (see Table 2).  Actual revenue / acre is $570 (150 bushels / acre times $3.80 / bushel) for corn and $378 (42 bushels / acre times $9.00 / bushel) for soybeans.  These individual crop values are weighted by acres planted to each program commodity, resulting in an actual revenue / acre for the ARC-IC farm of $522 (($570*60) + ($378*20)) / (60+20).  Only 80 acres is used in calculating actual ARC-IC revenue.  The 20 prevent plant acres are not included in calculating ARC-IC actual revenue.  Since actual revenue of $522 / acre exceeds ARC-IC coverage revenue of $493 / acre (86% ARC-IC coverage level times benchmark revenue of $573 / acre), ARC-IC makes no payment.  ARC-IC benchmark revenue and coverage revenue is higher in Example 2 than Example 1.  The reason is the combined impact on the weighted averages of (a) higher revenue per acre for corn than soybeans and (b) fewer acres planted to soybeans (20, not 40).

ARC-IC Example 3 – all prevent plant acres:  This example has no planted acres, with all initial planted covered commodity acres approved for prevent plant (see Table 3).  Actual revenue / acre is $0 since no initial covered commodity is planted.  Because no acres are planted to covered program commodities and all acres of initial planted covered commodities are approved as prevent plant, a benchmark revenue exists.  It equals the benchmark revenue in example 1 ($553 / acre).  ARC-IC makes a payment since actual revenue of $0 / acre is less than the ARC-IC coverage revenue of $476 / acre (86% ARC-IC coverage level times benchmark revenue of $553 / acre).  Payment is however capped at 10% of the benchmark revenue, or $55 per base acre ($553 times 10%).  Total ARC-IC payment is $5,500 ($55 per base acre times 100 base acres).

Summary Observations: 

►   Crop program choice rests on the production attributes of an FSA farm.

►   ARC-IC may be worth considering more often than commonly thought.

►   Farm production attributes which make ARC-IC potentially attractive include:

  • 100% of program base acres on a FSA farm are prevent plant acres,
  • high production variability from year to year on a FSA farm,
  • much higher farm than county or PLC yields on a FSA farm, and
  • fruits and vegetables are planted on a FSA farm.

►   If prevent plant is the production attribute of interest, all covered commodity acres on the ARC-IC farm must be prevent plant for ARC-IC to make a payment.

►   If high production variability is the production attribute of interest, ARC-IC is more attractive if only 1 FSA farm in a state with only 1 program commodity is elected into ARC-IC.  ARC-IC pays on the average experience across all program crops on all FSA farms in the ARC-IC farm.  Averaging across multiple crops and FSA farms usually reduces variability and thus payment probability.

►   Program sign up is for 2019 and 2020.  Expected payments in both years need to be considered for ARC-IC, ARC-CO, and PLC.  It is highly possible that the program with the highest expected payment will differ for 2019 and 2020, especially if ARC-IC has the highest expected payment for one year.

►   This article is not an argument for electing ARC-IC.  It is an argument for not dismissing ARC-IC without thinking about the individual FSA farm production attributes.


Table 1.  ARC-IC Example 1 – no prevent plant acres
  corn corn corn beans beans beans corn beans corn beans ARC-IC
yield MYA ARC-IC yield MYA ARC-IC plant plant prevent prevent farm
price revenue price revenue acres acres plant plant per
year / calculation acres acres acre
2013 155 $4.46 $691 43 $13.00 $559 60 40 0 0  
2014 169 $3.70 $625 47 $10.10 $475 60 40 0 0
2015 166 $3.61 $599 48 $8.95 $430 60 40 0 0
2016 172 $3.36 $578 52 $9.47 $492 60 40 0 0
2017 174 $3.40 $592 49 $9.33 $457 60 40 0 0
2019 166 $3.80 $631 47 $9.00 $423 60 40 0 0
Olympic average (’13-’17) $605 $475
benchmark revenue $553
revenue coverage (86%) $476
actual revenue $548
revenue loss $0
payment $0
total ARC-IC payment                     $0






Table 2.  ARC-IC Example 2 – some prevent plant acres
  corn corn corn beans beans beans corn beans corn beans ARC-IC
yield MYA ARC-IC yield MYA ARC-IC plant plant prevent prevent farm
price revenue price revenue acres acres plant plant per
year / calculation acres acres acre
2013 155 $4.46 $691 43 $13.00 $559          
2014 169 $3.70 $625 47 $10.10 $475
2015 166 $3.61 $599 48 $8.95 $430
2016 172 $3.36 $578 52 $9.47 $492
2017 174 $3.40 $592 49 $9.33 $457
2019 150 $3.80 $570 42 $9.00 $378 60 20 20 0
Olympic average (’13-’17) $605 $475
benchmark revenue $573
revenue coverage (86%) $493
actual revenue $522
revenue loss $0
payment $0
total ARC-IC payment                     $0


Table 3.  ARC-IC Example 3 – 100% prevent plant acres
  corn corn corn beans beans beans corn beans corn beans ARC-IC
yield MYA ARC-IC yield MYA ARC-IC plant plant prevent prevent farm
price revenue price revenue acres acres plant plant per
year / calculation acres acres acre
2013 155 $4.46 $691 43 $13.00 $559          
2014 169 $3.70 $625 47 $10.10 $475
2015 166 $3.61 $599 48 $8.95 $430
2016 172 $3.36 $578 52 $9.47 $492
2017 174 $3.40 $592 49 $9.33 $457
2019 0 $3.80 $0 0 $9.00 $0 0 0 60 40
Olympic average (’13-’17) $605 $475
benchmark revenue $553
revenue coverage (86%) $476
actual revenue $0
revenue loss $476
payment $55
total ARC-IC payment                     $5,500


Ohio CAUV Values Projected to Decline Through 2020

The Current Agricultural Use Valuation (CAUV) program allows farmland devoted exclusively to commercial agriculture to be taxed based on their value in agriculture, rather than the full market value, resulting in a substantially lower tax bill for the farmer.

The formula for CAUV values incorporates agricultural factors (soil types, yields, prices, and non-land costs for corn, soybeans, and wheat) to calculate the capitalized net returns to farming land based on the previous 5 to 10 years. CAUV underwent large-scale changes to its calculation in 2017 that was targeted to reduce the property tax burden of farmland.

A new report, Ohio CAUV Values Projected to Decline Through 2020, shows the projection of CAUV values though 2020. According to the study authors, OSU agricultural economists Robert Dinterman and Ani Katchova forecast a decrease in the assessed value of agricultural land to an average CAUV value of approximately $600 in 2020.

Access this report at: