Strong Domestic Demand for Beef

Domestic consumer demand for beef was very good in the first quarter of 2018 and also for the second quarter of 2018. The beef demand index chart above shows an index increase of about a half percent (2017 was 85.8 and 2018 was 86.2). We use index values because beef demand is difficult to measure and understand. Both price and quantity matter to demand. For example, 2015 was one of the lowest years for beef consumption per person but it was actually a relatively strong year for beef demand. That is because beef prices were high. Therefore, this index approach attempts to account for both pieces of the equation.

The stronger demand is a positive shift in the estimated demand relationship. What happened in the first quarter was classical, though not always obvious, economics. Compared to a year earlier, the Consumer Price Index deflated retail beef price (“all fresh” price as calculated by USDA’s Economic Research Service) was 2.1% above a year ago while the per capita disappearance (retail weight) slipped by a much less than expected 0.1%. So, the demand profile increased year-over-year but was below 2015 and 2016. Importantly, that demand measure for 2018’s first quarter was the third best since 1992.

Looking ahead regarding U.S. consumer beef demand, the question is will U.S. economic growth slow-down? More specifically the concern is if this slowdown will occur as early as the second half of 2019, since production/breeding decisions that would impact cattle supplies during that timeframe are already in place. The other demand concern is large domestic supplies of competing meats and poultry, specifically pork and chicken, and their impact on beef demand. Besides the export market being a factor, as to how much is available in the domestic market, an economic slow-down tends to influence demand for beef more than competing sources of animal proteins, which are less expensive.

This post includes information from the Livestock Marketing Information Center.

Income and Meat Demand

The figure above comes from an interesting new article published in the American Journal of Agricultural Economics. The authors use consumption data for seven food categories in more than 100 countries (including the U.S.) to see how food demand changes with income and population. In particular, let’s look at the area shaded red in the figure which refers to meat and seafood. The figure shows that as income increases, consumers demand less starchy staples and more meat and seafood among others. Within this meat and seafood category is beef.

Not only do consumers demand more meat, but also more food in general. Note that at a per capita annual income of $500, consumers food demands are just below 2,000 calories per day and very little meat and seafood. For consumers with incomes greater than $25,000, demand increases to over 3,000 calories with significantly larger meat and seafood demand. Those may sound like low annual incomes to U.S. readers, but the average for the countries used was just over $15,000. Here is a link to per capita incomes around the world if you’re interested.

This research is especially insightful for beef and other protein producers. This figure explains why growing middle classes in other countries can boost beef sales. Think of a country with a low but growing per-capita income (hello China at $17,000). Now project out what demand for meat will be for that country over the next decade or more as incomes rise. The authors take a stab at this, too. They project that between 2010 and 2050, demand for meat and seafood will double due to income and population effects. While increased population matters, the biggest driver for this category is projected to be the income effect.

We’ve all heard (and probably used) the projection of needing to feed 10 billion people worldwide by the year 2050 as support for agriculture in general. However, for animal protein producers, that number is compounded by the expected rise in incomes as countries develop. This also suggests that perhaps the biggest demand growth for meat will occur outside the U.S. – in countries that have the most room to grow their income.

Heifers and Beef Production

To follow up on last week’s article (available HERE), this week we’ll dig a little deeper into the beef production picture.  This week’s article comes from Dr. Derrell Peel at Oklahoma State University. It sheds some light on the increased role of heifers in the total beef production system. Total cattle slaughter has outpaced year-ago levels for most of 2018. The mix of steers and heifers plays an important role in the total amount of beef produced because heifers are generally lighter than steers. However, as Dr. Peel points out below, the gap between heifer weights and steer weights has shrunk. Heifer dressed weights for the past 12 months averaged just 7.5% lighter than steer dressed weights. Continue reading for a more in-depth analysis of the growing role of heifers in beef production.   

The heifer contribution to beef production depends on both heifer slaughter and heifer carcass weights.  Heifer slaughter varies cyclically with additional heifer retention during herd expansion and reduced retention during liquidation, thus providing much of the variation in beef production in cattle cycles.  Heifer slaughter as a percent of total steer and heifer (yearling) slaughter has averaged about 37 percent on an annual basis for the past 45 years, though heifers averaged less than 30 percent of yearling slaughter prior to 1965.

During periods of herd expansion, the heifer percentage of yearling slaughter drops to roughly 31 percent and during periods of herd liquidation, heifers will contribute about 40 percent to total yearling slaughter.  Most recently, a twelve month moving average of monthly heifer slaughter percentage bottomed at 31.4 percent in mid-2016 during aggressive herd expansion.  Back in 2001, cyclical liquidation of the beef herd resulted in a heifer slaughter percentage of 40.3 percent.  Most of the period from 1995-2013 was herd liquidation and the average heifer percentage of yearling slaughter was 38.2 percent.  The beef cow herd expanded from 2014 -2017 and the heifer slaughter percentage averaged 33.4 percent during that period.  Most recently, heifer slaughter has increased to an annual average of 34.3 percent of yearling slaughter as heifer retention slows down.

The evolution of heifer carcass weights is even more interesting.  Both steer and heifer carcasses have trended up for about 50 years.  For example, heifer carcasses averaged 564 pounds in 1967 and 811 pounds in 2017.  Heifer carcass weights have increased relative to steers over that period.  Heifer carcasses averaged 84 percent of steer carcass weights until the 1970s; reaching 85 percent consistently by 1978.  Heifer carcasses reached 86 percent of steers weights by 1982 and in just five years, from 1982 to 1987 shot up to 90 percent of steer carcass weights.  By 1993, heifer carcasses were 91 percent of steer weights and by 1996 were 92 percent of steers.  The percentage hovered around 92 percent until 2009, when it reached 92.2 percent, and increased to 92.3 percent in 2010.  Heifer carcass weights have continued to inch up relative to steer weights. In December, 2017, the annual average heifer carcass weight reached 92.4 percent of steer weights for the first time and in the most recent months of February and March, 2018, the twelve month moving average of heifer carcass weight as a percent of steer carcass weight was a new record of 92.5 percent.

Clearly, the industry continues to feed heifers more and more efficiently over time.  There may, however be a downside.  Research at Oklahoma State University has shown that big carcasses lead to big beef cut sizes which may limit demand.  Anecdotal indications from the industry suggest that for a number of years, some markets for beef products have specified heifer sources to ensure smaller product sizes.  The problem now is that heifer carcass weights in 2018 are the same size as steer carcasses were in 2005.  Heifer carcass weights appear to have provided a buffer against big steer carcasses for the past decade or more but that may be coming to an end.  It may be that cattle and carcass weights can physically continue to get bigger but there is a very real question of the demand implications and economic consequences of continued growth in steer and heifer carcass weights.

Rating Price Risk in Crop Insurance Markets

Barry Goodwin, Ardian Harri, Rod Rejesus and I just published a paper in the American Journal of Agricultural Economics examining the use of the Black-Scholes implied volatilities in rating crop revenue insurance.  For those not familiar with futures option implied volatility, it is derived from observed option premiums and known parameters of the option contract. Under certain assumptions it is the price volatility implied by the price of the option contract.

To rate a revenue contract one needs both an expected price and a volatility associated with that expected price.  Needing an expected price is rather obvious, but many forget that the price volatility estimate profoundly affects premium rates. In 2017 the premium associated with these revenue insurance policies was approximately $7.68 billion dollars.  Just a few years ago both expected corn and soybean prices and volatilities were much higher than today.  For example, in 2010 the price volatility used by the USDA Risk Management Agency (RMA) for Midwest corn was 0.28 while in 2017 it was 0.19. This decline in volatility has reduced premium rates and the amount of subsidy in the program.

Current USDA RMA methods are based upon a pre-signup average of futures closings and Black–Scholes (BS) implied volatilities calculated from “near–to–the–money” options for the harvest time contracts.  We focus on options and futures markets during the period of time used by RMA for price discovery (i.e., planting and harvest time pricing).

We find that the Black-Scholes model works well when there is robust trading during the pricing period. We also conclude there is strong support for using a forward looking implied volatility rather than a backward looking historical based volatility.  We also determine there is merit in using a third party source of volatility rather than some less transparent model.  However, the contracts for which significant violations of the assumptions inherent in the BS model tend to be for thinly traded crops.

This leads to a really interesting question.  Does crop revenue insurance which protects against low prices, as well as, low yields reduce the number of natural pre-season hedgers in the futures and options market? If so we have something of a catch-22.

I know that up-side price protection makes revenue insurance more conducive to pre-harvest hedging than straight revenue insurance. But is does also have a substitution effect (Coble, Heifner, Zuniga JARE 2000).  In the end I think the prima facie evidence is that corn and soybeans have had robust preseason price data and these two crops have among the highest levels of crop revenue protection insurance participation.  Conversely, rice arguably has had the most severe price data problems and yet has relatively low crop insurance participation.  Finally, note also that crop insurance does not affect the natural longs in the market.  However, we are left with the question of how to utilize the data from a thin market such as rice.

Lastly, there is another closely related question we did not address.  Are historical models good enough to rate crop revenue insurance when there is no futures and options market?  This deserves more research given the demand for revenue insurance in those markets is obvious since a functioning price risk market often does not exist.

The risk of a trade disruption for American Agriculture

Risk remains one of the salient features of commodity agriculture. We usually discuss weather or market price risk, but we also need to be mindful of policy risk. Macro-economic policy in the 1980s and more recently the Renewable Fuel Policy of 2007 are examples of policy decisions that shocked commodity prices. When I teach risk management, I tell students that risk = probability of a bad event x the severity of the event. Much of what challenges us in risk management is how to deal with the low probability but severe negative event.

Current discussion regarding NAFTA, recent withdrawal from the Trans-Pacific Partnership, and other looming threats make trade disruption increasingly probable events. However, if there is a sudden trade disruption in our crop sector, what happens to U.S. producers? Most economist suggest the price of our agricultural commodities could fall, perhaps dramatically. Trade disruptions may occur for a variety of reasons such as disease outbreaks, a trading partner’s economic turmoil, or war.
So here is the question. To what extent would our current farm safety net mitigate a sudden shock to crop prices due to a trade disruption?

Crop Insurance – Based on planted acres, crop insurance would protect against a price shock only if it occurred during the growing season. Since price guarantees are reset with the futures markets every year, a new lower equilibrium futures price in the following year would be used to value insurance losses. Thus, the economic adjustments to lower price levels would be unprotected by this program.
Agricultural Risk Coverage – To the extent base acres match planted acres, ARC would mitigate the decline in price, but would only cover a 10% band of crop value. ARC would provide some protection over the next few years, but the Olympic average used in ARC would gradually adjust over a 5 year period.

Price Loss Coverage – uses base acres as does ARC and protects against prices below a legislatively set reference price. If a medium term price shift occurred (3-5 years), these programs would provide a significant protection but at a high budgetary cost.

Ultimately, our farm safety net is not designed for such a shock. Maintaining trade flows and reducing barriers to trade has a strong economic justification. There are clear benefits to consumers and agricultural producers.

The Use of Enterprise Units in Crop Insurance

The 2008 Farm bill provided for an alternative level of crop insurance subsidies for Enterprise Units relative to Basic and Optional Units.  As you can see in Table 1 the subsidy for enterprise units are sometimes as much as 20% higher than for the same coverage with basic and optional unit structures.

Coverage Level Basic & Optional

Subsidy %

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

A brief review of unit structures is as follows:

  • Basic unit – All insurable acreage of the insured crop in the county on the date coverage begins for the crop year: (1) In which a producer has 100 percent crop share; or (2) Which is owned by one person and operated by another person on a share basis.
  • Optional Unit – Subdivision of basic unit.
  • Enterprise unit – All insurable acreage of the same insured crop or all insurable irrigated or non-irrigated acreage of the same insured crop in the county in which a producer has a share.

Importantly, because enterprise units are aggregated from basic units the base rates for enterprise units are generally lower than for the basic units from which they are aggregated.  Thus, higher subsidies and lower rates lead to significantly lower producer paid premiums for enterprise units.

A review of RMA participation data from 2009-2016 reveals the choices farmers have made.  The results are reported by crop.  For the six major row crops enterprise units have covered at least 27% of acres since 2009.  However, it appears enterprise units are far more popular for corn and soybeans than the other four crops.  More than ½ of corn and soybean acres have been insured with enterprise units.  In contrast, ½ of wheat acres have been insured at the optional unit level.

Percent of 2009-2016 Acres Insured with Basic, Enterprise, or Optional Units
Crop Optional Unit Basic Unit Enterprise Unit
Corn 28% 18% 53%
Cotton 42% 25% 33%
Rice 12% 54% 33%
Sorghum 34% 38% 29%
Soybeans 28% 20% 52%
Wheat 50% 23% 27%

Source: USDA RMA County Summary Data

The Four Percent Rule of Crop Insurance

With a looming farm bill debate, crop insurance stands as the largest single component of the crop farm safety net.  The program provides risk protection from low yield or revenue in return for a premium that producers pay.  These premiums were subsidized by the USDA on average about 63% across all programs in 2016.  The total cost of the subsidy in 2016 was approximately $5.85 Billion.  Figure 1 provides a bit of historical perspective on acres insured and total crop insurance subsidy.  Beginning in the early 1990s a series of legislative changes increased subsidy levels and acreage insured has trended up as well.  We note that recent declines in subsidy primarily result from reduced crop value as prices decline from historic highs.

Figure 1

In the next farm bill debate the amount of subsidy for crop insurance is likely to be a topic of discussion.  A frequent question posed to economist sounds something like this, “If we change the subsidy structure what will happen to crop insurance participation.”  This question has been asked and answered numerous times.  In most, but not all studies, the conclusion has been that crop insurance demand is inelastic.  That is, the percent change in participation will be less than a percentage change in subsidy.  However, many of those studies are older and may reflect a different era of crop insurance.

In this report, we examine some key data associated with RMA corn and soybean program participation.  We do not estimate an elasticity, but rather show evidence of a consistent pattern in in how much farmers are willing to pay for crop insurance.  We use the dramatic changes in crop value between 2011 and 2016 and variation in riskiness across regions to show a remarkable constant in crop insurance demand.  We find that across periods of high and low crop value and across regions of low and high risk – corn and soybean farmers are willing to pay out-of-pocket no more than four percent of the expected value of the crop.  If this is true, it has implications for the demand for crop insurance when subsidy is changed.  We do not provide a theoretical explanation for this finding but believe it may be tied to the degree of risk aversion and farmer budget constraints.

We begin by examining variation across region in the base county premium rate.  The maps in figure 2 and 3 show wide variation the level of yield risk across growing regions.  While most producers purchase revenue insurance, regional variation in premium rates are largely driven by yield risk.

Figure 2

Figure 3

Next we examine the amount of insurance chosen by corn and soybean producers.  Figures 4 and 5 reflect the acre-weighted average coverage level chosen in each county.  We use coverage level to represent the amount of insurance chosen by those who participate in the program.  When figures 4 and 5 are compared to figures 2 and 3, a pattern begins to emerge. Areas of the country with lower per-acre base premium rates also tend to purchase higher coverage levels than areas with higher base premium rates.

Figure 4

Figure 5

Figures 6 and 7 show the 2016 average producer paid premium per acre for corn and soybeans by county.  Note that producer premium is a function of the coverage level, rate, and value of the crops.  In general, low risk-high yield regions pay similar premiums per acre as higher risk-lower yielding regions.  Having said that the lower coverage levels chosen in many higher risk regions results in lower producer paid premium per acre.  Finally, the maps show that producer paid premium for soybeans are generally lower than for corn.  This is in part due to lower per acre expected crop value.

Figure 6

Figure 7

Figures 8 and 9 divides the average producer paid premium by the insured value of the crop to compute the percentage of expected crop value farmers opt to pay in producer paid premium.  This reveals our primary finding.  As can be seen in both figures, the majority of counties are shown to pay between one and four percent of the value the crop in 2016.  Thus, we find that farmers appear to be willing to pay a premium of about four percent of crop value and no more.

Figure 8

Figure 9

To test the robustness of our results in 2016 we also conduct the same analysis using data from 2011.  These results are shown in figures 10 and 11. Note that higher crop price in 2011 resulted in expected crop revenue more than 30% higher in that year than in 2016.  However the premium paid as a percent of crop value maps look quite similar to that of 2016.

Conclusions

While we find quite robust results, it is not clear why producers seem to spend such a constant percent of crop value on crop insurance.  Most likely it is related to the out of pocket cost associated with this program and the perceived benefits. We suggest that models of insurance demand consider the possibility of a budget constraint on crop insurance demand.  Ultimately, the consistency of these results suggests that if crop insurance costs rose past four percent of expected crop value, the producers would reduce insurance expenditure – most likely by reducing coverage levels.

Figure 10

Figure 11

Where have the Generic Acres Gone? Where will they go?

As the cotton industry asks for a new Title I program in the next Farm Bill, the question of what becomes of generic base becomes a central issue.  In this report, we summarize a USDA/FSA report summarizing the program crops to which generic base has been applied in 2015.  Note that an acre of generic base applied to a program crop then receives the payments associated with that crop.  Nationally 10.6 million acres of generic base were applied to program crops in 2015.  Of that total 33% were applied to soybean acres.  However, the Mid-south pulled up the national average where over 60% of generic acres were applied to soybean acres.

2015 Crops Planted on Generic Acres Percent of Planted Generic Acres
Total Acres WHEAT RICE-LONG GRAIN CORN GRAIN SORGHUM PEANUTS SOYBEANS
Grand Total 10,675,012 22.9% 0.2% 19.0% 14.0% 8.7% 33.2%
TEXAS 3,281,424 39.2% 0.0% 18.4% 34.9% 3.7% 0.7%
MISSISSIPPI 1,316,641 3.9% 0.3% 21.1% 3.6% 2.0% 68.9%
ARKANSAS 1,020,523 5.4% 1.3% 14.7% 10.0% 0.7% 67.7%
GEORGIA 871,613 7.3% . 16.5% 1.7% 59.0% 13.6%
LOUISIANA 830,276 4.8% 0.1% 30.3% 2.7% 0.0% 61.9%
NORTH CAROLINA 643,072 13.3% . 18.2% 1.7% 7.2% 58.9%
TENNESSEE 609,802 10.7% . 20.6% 7.8% 0.0% 60.8%
OKLAHOMA 455,071 89.8% . 1.5% 4.6% 0.6% 1.2%
ALABAMA 407,582 15.7% . 19.6% 1.8% 24.7% 36.5%
MISSOURI 351,708 8.0% 0.6% 16.2% 7.1% 0.3% 67.7%
CALIFORNIA 276,897 51.9% . 27.8% 8.4% . .
SOUTH CAROLINA 251,367 11.2% . 29.9% 1.9% 19.3% 36.8%[1]

 

In Texas, the state with the most generic acres, wheat and grain sorghum captured the highest percentage of generic acres.  Nationally wheat was the second highest percentage of generic acres pulled up by states like Oklahoma and California where almost 90% and 52% of generic acres went to wheat, respectively.

Many have suggested generic base was moving to peanuts.  Nationally, only 8.7% of generic base has moved to peanuts.  However, that shift is more common in Southeastern states where peanuts are a larger player such as Alabama, South Carolina and especially Georgia where 59% on generic base went to cotton.

Final thoughts

  • With projected declines in soybean and corn ARC payments in the future, perhaps these crops will become less attractive for generic base planting.
  • With the cotton industry push for a cottonseed program, what will become of the generic base is a key question for the farm bill.
  • Generic base was born of the ‘planted acre versus base acre’ debate during the last farm bill and will be integral to that conversation again.

[1] Source: https://www.fsa.usda.gov/Assets/USDA-FSA-Public/usdafiles/arc-plc/pdf/2015%20and%202014%20Crops%20Planted%20on%20Generic%20Base%20Acres%20%20Oct%2024%202016.pdf

Sorting Through Big Ag Data Part 2

  • Many are concerned with the privacy issues associated with precision agriculture data. These are real concerns.  I have worked with private farm data all my career, but precision maps are a particularly difficult kind of data to protect as geo-referenced maps can be quickly matched with other geo-located files and identities revealed.  Because of the size of data and for security reasons, I think we will see this data storied securely and rather than sharing data sets, and analysts will come to the data, run analysis, and walk away with results only.
  • Many producers will interact/share data with various machinery companies, input suppliers, consultants and others. Some will want to lock you into their system, and many producers are faced with the challenge of moving information such as a field boundary map or fertility prescription to various users.  Producers need a safe place to store this data, and seamless means to move this information to fertilizer applicators, USDA/FSA.  I am proud that Mississippi State University is a founding member of the Ag Data Coalition that involves farm organizations, agribusiness firms, and universities committed to helping producers control and utilize their data.  I encourage you to check it out at http://agdatacoalition.org/
  • Getting advice from a black box. It seems that almost every input supplier now has an app or software that is designed to help farmers make decisions.  In some cases, these are going to be really useful farm management tools.  There are some that impress me a lot. I also believe that some are way to entice farmers to give data to service provider or to make it difficult to leave a service provider.  I am also struck by the similarity of the sales pitches made by some big data based decision aids and that of the market advisory services that have a great sales pitch but when evaluated, have no better results than flipping a coin.
  • I just helped organize a conference on big ag data at the Agricultural and Applied Economics meetings. We had big name speakers from many disciplines speak.  In the end, there are limits on what one can learn from unstructured ag data that may not include some really important variables.  Using machine learning to find correlations among millions of records can lead to new discovery, but it can also find spurious correlations that may lead to bad decisions.  We will still need well designed statistical studies to answer some big questions about causation.
  • Big data begs several policy questions such as rural broadband access, funding for research on the topic, and the role of USDA data collection. I once worked at USDA and saw the hard work that goes into collecting agricultural data that is unbiased and statistically sound.  However, over my entire career I have watched as hundreds of ‘experts’ make a living claiming to know better than the USDA reports.   What you can’t find is a multitude of studies assessing the accuracy of these experts like one can find for the USDA reports.  I think we are beginning to see a new wave of ‘we know better than USDA’ business models that charge a fee and have not been verified to really be better.  Let the buyer beware, and let’s remember why USDA was asked to do these reports – to insure everybody in the ag sector had equal access to market information.

Sorting Through Big Ag Data Part 1

Today, one of the biggest buzz phrases in agriculture is ‘big ag data.’  While precision agriculture has been around for some time, it’s important to recognize that the data produced by agricultural equipment, field sensors, and various other technologies may pose a variety of opportunities but may also disrupt some long-standing relationships as well.  I have had the opportunity to engage this topic in recent years by writing a soon to be released white paper as well as being a Board member of the Ag Data Coalition, which is bringing together industry, farm groups, and Universities, to develop producer controlled data storage.

  • Your farm data has value to you, but don’t buy all the hype. Your data has value to you if it helps you make decisions that make you more money. But remember, this will be true only if it really is better than what you already knew.  For example, does a variable rate technology map make a difference is a very uniform field?  Probably not.  Another possibility is if you get information faster. But again, is there a critical decision that makes use of the timelier data?
  • Many talk of the 4 V’s of big data – volume, velocity, variety, and veracity. Volume because of the sheer quantity of data being generated by our farms.  Velocity, reflecting the speed at which data arrives. Variety reflecting the wide array of data – images, sensor readings, numerical data etc.  Finally, veracity reflecting that much of the data produced is of poor quality due to a lack of calibration or some other reason.  Bad data= bad decisions.
  • Your farm data has value to others. Machinery companies can learn how to build a better combine, a seed dealer can learn how a variety performs in actual field conditions, or a company can use your neighbor’s data to benchmark your farm against peers.  However, Terry Griffin of Kansas State points out that data is not like most goods.  If I use a gallon of gas, you can’t have it.  It’s gone.  But I can use my farm data and I can give it or sell it to others and they can use it too.  I think we are beginning to see that it is often not individual data but aggregated data that may be what has the greatest value anyway.
  • The question of data ownership is a murky legal territory. What is paramount for producers to understand is that data contract and the associated fine print are important.  In many ways this is what determines who has access to your farms data.  I recommend you check out the Ag data transparency evaluator at http://www.fb.org/agdatatransparent/  Rather than ownership perhaps the useful concern is who has access to my data.