Will farm programs be cut even before the next farm bill starts?

I had the privilege of serving as a chief economist for the U.S. Senate Committee on Agriculture, Nutrition, and Forestry during the last farm bill debate.  The cost of legislation was paramount with the mandated goal of reducing the Congressional Budget Office (CBO) score of the bill.  Roughly speaking all the program crops were asked to take about a 30% reduction in the CBO estimate of farm program spending.  Note that CBO looks forward 10 years and compares the cost of new legislation to a continuation of existing legislation.  One must also understand that the CBO agricultural baseline changes from year to year as market conditions change.  One should understand predicting program costs that far into the future is a highly imprecise process.

Many groups have turned their attention to the expiration of this bill.  So it is fair to ask where will the baseline for the big three farm support programs (ARC, PLC, crop Insurance) be at when CBO is asked to score a new farm bill.  Figure 1 shows the March 2016 CBO baselines for County ARC, PLC, and crop insurance.  Examining this chart, one can see that from 2018 on:

  • CBO assumes that at expiration of this bill producers will be allowed a ‘do over’ on the ARC vs PLC choice. Some switch from ARC to PLC is expected.
  • Crop insurance is projected to increase slowly and average around $9 billion/year.
  • PLC a program widely adopted by rice, peanuts and a substantial amount of wheat is projected to remain at around $3 billion/year in out years.
  • County ARC annual cost is expected to slide 85% from a peak of $6 billion to around $1 billion. This especially affects corn, soybeans and some wheat producers. This is primarily due to the fact that the 5-year Olympic average price used in ARC will fall dramatically as the high prices of a few years ago are dropped.
  • The baseline is likely to shrink in the next 24 months primarily due to dropping the recent high ARC payment levels and replacing them with out-years with lower payment levels. For example, the $6,099 Billion for ARC in 2017 will likely be replaced with a value near one billion. This reduces money available for the next farm bill.


CBO Baseline 2016


So what does this mean?

  • Crop insurance will likely remain a focal point for policy because it is the biggest pot of funds. That also means it will be attached as a source of funds for other programs.
  • Three commodities are facing dramatic declines in baseline funding – corn, soybeans, and to a lesser degree wheat.
  • Participation rates affect these outcomes. For example, the fact that actual STAX participation has been below what CBO expected, means expected increases in cotton crop insurance program cost in the last farm bill did not materialize.


The ARC County Yield Problem – Not if, But When and Where

The county yields plugged into the Agricultural Risk Program (ARC) calculation have come under fire recently for perceived inaccuracy and varying dramatically across nearby counties.  I was there when the last farm bill was written, there were concerns about the county yields, but a lot of people – Hill staffers, farm groups, and political appointees said surely the USDA can find a way to do this.  Basically, this is a statistical problem and most of us hate statistics. I will try to shed some light here.

Be careful what you wish for

NASS uses a statistically survey approach to estimating yield. It is probably about as cost effective as one can approach the task.  Pollsters, marketing firm, and researchers use these techniques all the time. But here is what you must know.  NASS reported hundreds of county corn yields and then crop reporting districts, state, and national aggregates for 2015.  Note also NASS did not report other counties due to small samples.  The fundamentals of statistical surveying imply the accuracy of NASS estimates increases with each higher level of aggregation.  The bottom line is while state and national numbers are highly credible in most cases, lower levels will simply be less accurate.  Well-established rules of survey sampling dictate the primary way to get better NASS county yield estimates is to send more surveys into the county which will cost money and will necessitate greater respondent burden.  This is an increasing problem over time as there are fewer farmers to be surveyed.  So even if attempted it might not work.

RMA data is great – where there is a lot of it

What about RMA data?  RMA does collect yields from participants and in many locations reaches 80 to 90 percent participation rates.  But participation is not as randomized like the NASS survey, and in a county with an 80% participation rate one may ask what are the characteristics of the 20% not in the program.  Are they the best yielding farms or the worst or neither?  I am unaware of research that answers this question.  I will note the RMA has develop their own county yield estimates for use in area insurance products including STAX and SCO.  But they also encounter counties with limited crop insurance participation and thin data.

Statistical stews

Note that RMA does not mix NASS and their own data so that the historical benchmark is consistent with the covered year.  I have looked at this some in the past and the RMA and NASS data often do not seem to match up.  The RMA data was sometimes lower and sometimes higher than NASS yields.  When NASS and RMA are mixed, you get a statistical stew that probably no one can sort out.

Farmers prefer individual protection if they can get it

Even if we get amazingly accurate county estimates will it be enough? I doubt it.  It is pretty clear farmers want protection that is very highly correlated with their own yield – so the county triggers when the farm needs it.  In 2014 before the introduction of STAX and SCO only two percent of crop insurance acres where insured with area insurance plans.  Why? In part, there are real and perceived variation of yields within counties.  A grad student in our department just defended a thesis showing a dramatic lack of correlation of farm yield with county yields in some counties.  In one county, she found the farm-county yield correlation ranged from 0.18 to 0.93 (perfect would be 1.0).  At 0.93 you have a pretty good risk management tools.  At 0.18 you are pretty close to having payments with no relationship with farm losses.  Remember ACRE with a state trigger was adopted in 2008.  The fix was county yield in 2014.  What next?

A jumpy clutch  

My Dad started me on a Farmall Super C tractor.  It had what he called a jumpy clutch, which meant it went from disengaged to engaged in what felt like a ¼ inch of release.  Many fail to recognize that ARC goes from no payment to maximum payment with a 10% change in revenue.  This mimic a design that my colleagues Barry Barnett and Steve Martin drew up for Steve’s dissertation many years ago.  This differs from crop insurance the triggers at a given coverage and then reaches maximum payment at zero yield.  The 10% range in ARC makes payments react quickly to slight differences in county yield. So county A has a revenue 14% below average and neighboring county B has a revenue 5% worse.  County A gets zero ARC payment and County B gets half the maximum.

‘Fair Boundaries’

The average county in the United States is 997 square miles while the largest county in the lower 48 states is San Bernardino County California at 20,105 square miles.  In Oregon the largest county is 23 times larger than the smallest county. All this just points out that counties in the U.S. are not defined in anything like equitable agricultural regions.  This impacts the magnitude of payments and the correlation of farm-county yields. County size matters but so does crop acreage and heterogeneity within the area.

So what next?

Does USDA need to produce three slightly different county yields – the NASS, RMA, and FSA number?  Compromises in the farm bill probably created some of this confusion.

Georeferenced data may help a lot someday. That day is nearing as USDA migrates to using more common land unit information for RMA and FSA.  Layering of soil, crops and other information may give us the ability define areas based more sophisticated grouping.  Here at Mississippi State we are working with National Commodity Crop Productivity Index (NCCPI) data that makes me hopeful.

But in the end, declining price guarantees in the ARC Olympic average for 2017 and beyond may make this a less important issue anyway.  The Congressional Budget Office projects a dramatic decline in ARC payments for many crops for the 2017 year and beyond.  This means less likely payments and smaller payment if they do occur.

Regional Differences in Crop Insurance Base Rates

Base county rates reflect the starting point for crop insurance rates for an insured crop in a particular county.  A particular insured unit’s rate will be derived by adjusting the base county rate to reflect yield versus revenue coverage, coverage level, unit structure, and unit APH relative to base county yield.  Differences in base county rates reflect differences in yield risk derived from historical crop insurance losses.  Figures 1-4 reflect the 2016 base rates for corn, cotton, rice, and soybeans.  A lower base rate reflects a less risky region.  Often large contiguous areas have similar risk levels.  Also, a low yield risk crop such as rice has generally lower rates than other crops.

Figure 1
Figure 1
Figure 2
Figure 2
Figure 3.
Figure 3.
Figure 4.
Figure 4.

A National Look at Crop Insurance Subsidy Per Acre


Lots discussion of crop insurance subsidy these days.  Here is a breakdown of county average subsidy/acre in 2015.  The average for the county is a function of the crops grown in the county.  Generally highest values are in specialty crop regions. (Note some insurance programs are not reported by acre and are not included.)2015 subsidy per acre

Crop Insurance Subsidy Per Policy

It is crop insurance sign up time and I did a quick analysis of 2015 crop insurance subsidy per policy by crop for the entire U.S.  Note subsidy is a function of rates, coverage levels, unit structure, quantity and value of the crop.  What jumps out of this analysis is that specialty crops tend to top the list and that row crops are generally fairly far down the ranking.  But some other special crops also fall near the bottom of the list


Commodity Name Average Subsidy/Policy






Whole Farm Revenue Protection*












































































































































































































Tangerine Trees


















* Note Whole Farm Revenue Insurance covers multiple commodities.

2016 Crop Insurance Price Discovery

It is the time of year that RMA watches the futures market to determine an expected price (used in yield and revenue products) and the options market to estimate the price volatility used to rate revenue insurance products.  The following table shows we are currently in price discovery for the February 28 sales closing date for several crops.  These values will be updated through February 14.

Commodity State Name Sales Closing Date Projected Price Market Symbol Projected Price Date Range Projected Price Projected Price Status Price Volatility Price Volatility Status
Rice Mississippi 2/28/2016 ZRX16 01/15 – 02/14 0.117 In Discovery 0.15 In Discovery
Cotton Mississippi 2/28/2016 CTZ16 01/15 – 02/14 0.62 In Discovery 0.15 In Discovery
Corn Mississippi 2/28/2016 ZCZ16 01/15 – 02/14 3.89 In Discovery 0.17 In Discovery
Peanuts Mississippi 2/28/2016 CTZ16,ZLZ16,ZMZ16,ZWZ16 01/15 – 02/14 0.2028 In Discovery 0.10 In Discovery
Soybeans Mississippi 2/28/2016 ZSX16 01/15 – 02/14 8.87 In Discovery 0.14 In Discovery

Six questions for your crop insurance agent — Seven if you grow rice.

by Keith Coble and Brian Williams 

  1. What about enterprise units?

To qualify for enterprise units you must have at least two sections, section equivalents, FSA farm numbers, or units established by written unit agreement.  The requirements for enterprise units must be met for each of the irrigated and non-irrigated acreage for you to qualify for separate enterprise units by practice.

You may only elect to have separate enterprise units (EU) for both your irrigated and non-irrigated acreage and each must independently qualify as an enterprise unit.  The additional subsidy associated with enterprise units versus basic and optional units are shown for various coverage levels in Table 1.

Table 1.

Coverage Level Basic & Optional

Subsidy %

Enterprise Unit Subsidy % SCO Subsidy STAX Subsidy %
50% 67% 80% 65%
55% 64% 80% 65%
60% 64% 80% 65%
65% 59% 80% 65%
70% 59% 80% 65% 80%
75% 55% 77% 65% 80%
80% 48% 68% 65% 80%
85% 38% 53% 65% 80%


  1. May I qualify for trend adjusted yields?

Most crop yields reflect upward trends due to technological change.  The Trend-Adjusted (TA) APH adjusts yields in APH databases to reflect increases in yields through time. Trend adjustments are made on each eligible yield within your APH based on the county’s historical yield trend. The actuarial documents provide the historical yield trend. The approved APH yield is calculated using trend-adjusted yields and any other applicable yields within the APH database. Note TA results in a higher approved yield and greater indemnity payments, which results in higher premium rates. 

  1. May I qualify for APH yield exclusions?

The APH Yield Exclusion (YE) was created by the 2014 Farm Bill and allows for the exclusion of an actual yield for a crop year when RMA determines the county per planted acre yield for a crop year was at least 50 percent below the simple average of the per planted acre yield for the crop in the county for the previous 10 consecutive crop years. When a county triggers, contiguous counties are also eligible for YE.  YE is determined separately for irrigated and non-irrigated acres.  YE allows a producer to exclude an actual yield from the APH history for years where YE triggered.  Multiple years may be excluded if the county data indicates triggering.  YE results in a higher approved yield and greater indemnity payments which results in higher premium rates.  One may utilize YE and trend adjustment simultaneously.  Maps of the yield exclusion may be found at:



  1. What is the premium for different coverage levels?

Table 1 shows the subsidy percentage of different coverages, but keep in mind that the underlying rate goes up with the coverage level.  Table 2 shows an example of how base rates vary for non-irrigated soybeans in Bolivar county Mississippi.  For example the 85% coverage rate is 66% higher than 65% coverage.  This reflects much higher probability of loss as coverage increases.

Coverage Level

Rate differential



















  1. What about topping off individual coverage with SCO or STAX for cotton?

Supplemental Coverage Option (SCO) is companion policy with you underlying individual coverage that protects a portion of the deductible with and AREA triggered crop insurance layer of protection.  The coverage starts at 86% and goes down to the individual coverage chosen by the producer. You only purchase SCO if you do not participate in the FSA Agricultural Risk Coverage (ARC) program. You may be participating in FSA Price Loss Coverage. As shown in Table 1, the Federal Government pays 65 percent of the premium cost for SCO.

Stacked Income Protection Program (STAX) for cotton functions similarly to SCO, The expected revenue and actual revenue are based on county yields as determined by RMA.  The maximum coverage is 90% and the maximum range of payments is 90-70% of expected revenue.  With STAX you do not have to purchase individual-level coverage.

  1. What about separate coverage levels by practice?

The 2014 farm bill also allowed for separate coverage for an irrigated and non-irrigated practice. If you have both practices for a crop, you may select one coverage level for all irrigated acreage and one coverage level for all non-irrigated acreage. For example, you may choose a 75% coverage level for all irrigated acreage and 65% percent coverage level for all non-irrigated acreage.

  1. If you grow rice, ask about margin insurance

Margin Protection (MP) is an area based plan that provides producers with coverage against an unexpected decrease in their operating margin. The plan provides coverage that is based on the expected area revenue minus the expected area operating costs, for each applicable crop, type and practice. The margin protection plan can be purchased by itself, or with Yield Protection or Revenue Protection policy.  MP will be available in 2016 in select counties for corn, rice, soybeans, and spring wheat. In Mississippi MP will only be available for rice in 2016.


Farm Bill Learning Sessions Scheduled

Multiple workshops related to the 2014 farm bill have been scheduled for December. These workshops are targeted at our numerous crop producers and will provide detailed information on the new ‘covered commodity’ programs (ARC & PLC), new crop insurance products (SCO & STAX), the available decision aids, and what this all means for farm risk management. At the conclusion of this series, if more workshops are needed please let us know and we will schedule them in January 2015.
Please help us get the word out to as many impacted producers in the state.
For more information contact John Michael Riley, 662.325.7986, j.m.riley ‘at’ msstate.edu
Topics Covered:
Agricultural Risk Coverage (ARC)
Price Loss Coverage (PLC)
Supplemental Coverage Option (SCO)
Stacked Income Protection Plan (STAX)
Farm Risk Management
Decision Aids
December 3**, 9:00 AM – 12:00 PM :: Lincoln Civic Center, 1096 Belt Line Dr NE, Brookhaven, MS 39601
December 4, 9:00 AM – 12:00 PM :: The Extension Building, 394 Hwy 51 S, Batesville, MS 38606
December 10, 1:00 PM – 4:00 PM & 6:00 PM – 8:30 PM :: Yazoo County Extension Office, 212 E. Broadway, Yazoo City, MS 39194
December 11, 9:00 AM – 12:00 PM :: Costal Plains Research & Extension Center, 51 Coastal Plain Rd, Newton, MS 39345
December 17, 1:00 PM – 4:00 PM :: Lightered Knot Community Center, 401 E Pine Ave, Wiggins, MS 39577
December 18, 9:00 AM – 12:00 PM :: Pontotoc County Extension Office, 402 C.J. Hardin Jr. Drive, Pontotoc, MS 38863
December 19, 1:00 PM – 4:00 PM :: Bost Extension Building (MSU Campus), Bost Extension Dr, Mississippi State, MS 39762
**Keith will provide similar details at the 2014 Row Crops Shortcourse, which overlaps with this date.

Five questions to ask About a Farm Bill Decision Aid

We have been modeling crop insurance and farm policy for years.  Tremendous advances have been made in quantifying agricultural risk. As farmers face decisions regarding their participation in federal farm programs and crop insurance various decision aides have been developed to evaluate alternatives.  Based on our experience, here are five questions to ask anyone who tells you they have a decision aide for evaluating the ARC/PLC choice.

  • How does the decision aide account for uncertain prices and yields over the life of the bill?

Most spreadsheet aides are simply calculators, meaning they are ‘deterministic’ in that they calculate a payment based on the exact yields and prices provided. The problem, of course, is that one can’t possibly know with certainty what yields and prices will occur. How does the decision aid account for the likelihood of various prices and yields over the next 5 years when estimating payments?

  • If the decision aid accounts for risk, what risks are modeled?

There are five major unknown variables that must be accounted for in any  crop insurance, ARC, and/or PLC decision aide.  These are: three prices – cash prices, futures market prices, market year average prices, and two yields – farm and county yield.  Does the decision aid account for the likelihood of different outcomes for all of these unknown variables?

  • If the decision aide accounts for risk, then how is the correlation of random variables handled?

These five unknown variables are not necessarily independent, meaning there is a relationship (or correlation) between them.  In fact, there is good reason to believe that many of them are related.  For example, farm and county yield are most likely positively correlated.  In the Midwest, yield and price for corn likely have a negative relationship (as yield declines, corn price would increase).  Cash, futures, and MYA price are likely positively correlated.  Prices and yields across years are also often positively correlated (trends develop over time).  There are more relationships, for example: a farm considering individual ARC with three crops potentially needs to account for 120 correlations.  Modelling correlation is difficult, but very important and shouldn’t be avoided to accurately assess the farm program and crop insurance options.

  • Does the model ask you for a lot of farm yield data?

Nobel Prize winner Daniel Kahneman points out the problem of using only a few years of data to form expectations often provides faulty outcomes.  Our research suggests that evaluations of farm-level crop insurance and farm program outcomes with less than ten years of farm yield data will be highly inaccurate.

  • Does the decision aid help you understand risk protections as well as expected returns?

The new programs offered from the 2014 farm bill are intended to help farms reduce exposure to the risks of low price, low yield, or low revenue.  Simply reporting the ‘deterministic’ expected payments – payments that come from one price and one yield – from the programs ignores the question of whether the payments help mitigate risk exposure. In other words, how does the farm program and crop insurance decision fit into the entire operation’s business portfolio?

In summary, predicting the future is extremely difficult. However, methods to provide guidance with respect to the uncertainty and correlation amongst the multitude of possible outcomes do exist but are often difficult to apply. Some of these are built into the current offering of decision aids provided by Texas A&M and Illinois, but few are available in simple spreadsheets built by others. For example, the two spreadsheets we have provided (CLICK HERE) only give the base reallocation calculation and the calculation of how generic acres will be distributed based on a given number of planted acres, both of which are simple calculators. While these types of “decision aids” can be very useful, keep the questions we pose here in mind as you evaluate the results generated from them.

Five things you need to know about County-triggered Shallow Loss Programs


The Supplemental Coverage Option (SCO), Stacked Income Protection Program (STAX) insurance and county Agricultural Risk Coverage (ARC) programs in the new farm bill are novel risk protection products.  All three cover a band of shallow losses and leave the producer exposed to more severe losses unless otherwise protected by crop insurance or by some other means.  Shallow loss programs are new, but most people understand the concept of layering risk protection.  What is less clear is how well people can evaluate county- versus farm-triggered programs. County-triggered programs are not new.  They have been around for decades in the form of area coverage insurance. This type of insurance is currently known as Area Risk Protection Insurance (ARPI) and was formerly called the Group Risk Plan (GRP) and Group Risk Income Protection (GRIP).  ARPI is a county-triggered alternative to farm-triggered insurance whereas STAX, SCO, and ARC are supplements to farm-triggered insurance (e.g., Yield Protection and Revenue Protection).  Over the years we have probably studied and evaluated area risk protection products as much as anybody.  So here are a few pointers.


  1. For an area triggered program to exist, county yields must be estimated and reported.  In the past ARPI programs have been based solely on NASS county yield estimates.  A historical series is used to predict expected yield and an actual yield is necessary to determine actual yield (or revenue) shortfalls in the insured year.  NASS does not report county yields for every commodity in every county.  They are unlikely to report in counties where the commodity is grown on relatively few acres and/or where few farms produce the commodity.  To increase the availability of STAX and SCO it is likely that RMA will, at least in some crops/areas, use aggregated yield reports from farm-triggered crop insurance policies to construct county yields.  It is less clear what FSA will use for ARC calculations.  The bottom line is no county yield equals no program.
  2. Risk protection from area products all depends on correlation.  Correlation is a statistical concept that simply means, two variables are related to each other rather than independent.  For area shallow loss programs, this may be translated to, “To what degree does county revenue go up (or down) when my farm revenue goes up (or down).  What we find is that this relationship is driven by the farm-county yield relationship.  Typically county yield and farm yield move up and down together, but not perfectly.  Further we find wide differences across farms in the relationship between farm yield and county yield.  Think of a farm using typical production practices on the predominant soil type in the center of a county versus a farm using an atypical practice on a less common soil type at the edge of the county.  The correlation of farm-county yields will likely be less for the atypical farm and a county triggered program will provide poorer risk protection.  Some have suggested that with the availability of county-triggered shallow loss products, growers should reduce the coverage level on their underlying farm-triggered insurance. Before making this decision growers should think very carefully about how correlated their yields are with the county yield.  Likewise, a number of shallow loss “decision aids” are becoming available and more will likely follow.  Growers should determine whether these decision aids allow for differences across farms in the correlation between farm yield and county yield. Ideally they would also help in measuring that correlation. Unfortunately, this is not that easy to do and many of the decision aids we have seen implicitly assume that farm-yield and county-yield are independent.  Growers should be aware that any decision aid that does not adequately address correlation is likely to provide erroneous guidance on decisions about shallow loss programs.
  3. What is not very important is whether your average yield is higher or lower than the county average.  With both farm-triggered crop insurance products and county-triggered shallow loss programs, payments occur when the realized percentage shortfall exceeds the percentage deductible. The percentage deductible is just 100% minus the coverage level (thus a crop insurance policy with a 75% coverage level has a 25% deductible). The percentage shortfall and percentage deductible are both calculated relative to the expected yield or revenue. For county-triggered programs it doesn’t really matter whether your excepted farm yield is higher or lower than the expected county yield. What matters is how closely the percentage shortfall on your farm matches with the percentage shortfall at the county level. If the county experiences a 25% revenue shortfall and your farm also experiences a 25% revenue shortfall then the county-triggered program should do a nice job of covering your losses.  However, if your revenue falls 25% but the county revenue only falls 15% you will not be fully covered.
  4. County yields are less variable than the average variability of farms in the county. This is the result of the county yield being an average of all the farms in a county.  Past research typically finds the average farm is about 30% riskier than the county in which it resides.  What does this mean for STAX, SCO, and ARC?  It means that, all else equal, a layer of county-triggered coverage will pay less than the same layer of farm-triggered coverage for the typical farm. This is actually the motivation behind the scale option in ARPI and STAX products.
  5. Get ready for differing USDA estimates of county yields.  FSA, RMA, and NASS may each use different data and different procedures for estimating realized county yields.  Thus, it is quite possible that these various USDA agencies will generate different estimates of the realized county yield in a given year and these different estimates will be used to determine payments for different programs.  If that seems strange, know that the 2014 Farm Bill did not define how these county yields would be developed.   Good luck to the USDA officials who have to explain the differences.