Last week we looked at steer price seasonal patterns in Mississippi (view that post HERE). This week, we are examining the same story but for heifer prices. The back story from last week about why seasonality matters is the same for heifers as it is for steers. Rather than repeat it, I’m going to focus on some seasonal differences between steers and heifers.
The graph above is a seasonal price index that shows how much monthly average prices differ from annual average prices. This is calculated by dividing each month’s average price by the average annual price. Next, the monthly average across the years of data is calculated to obtain an average price index. The price index calculated in this article has a base value of 1. This implies that if a given months price index is 1, the average price in that month is equal to the average annual price. If a monthly index value is 1.05, then the average price in that month is five percent higher than the annual average.
Mississippi heifer prices over the past 7 years in Mississippi generally follow the same pattern as steer prices – higher prices in the early Spring months and lower prices in the Fall. A few key differences stand out though when we look at specific weight classes. The percentage range is larger with heifers for some lasses. 500-600 pound steers range from 7% higher than average in March to 8% lower than average in October. For heifers, the range is 6% high and 12% low — an 18% range in a “normal” year. The length of time those five-weight heifers are seasonally lower on average also lasts longer than for steers as November is even lower than October. The “Low” for each weight class is lower for heifers than it is for steers. Remember, these “Lows” are relative to the annual average price for each sex.
There are a few caveats that are worth mentioning here. In general, there are more steers sold in each weight group than heifers and thus the price data each week is probably a little accurate for steers than heifers. If there are few heifers traded in a week but those few are really good (or bad), that can strongly influence those prices. Because this analysis was done over seven years, those issues are outweighed by values other years and no single week has a huge impact on the index.
As we begin to digest the details of the 2018 Farm Bill, I was given the opportunity to provide the webinar discussing the provisions of the bill. More details will come out over time. We will continue work to provide timely information as the bill is implemented.
So we have a new Farm Bill if, as all signs suggest, the President signs the new legislation. The bill is estimated to cost $428 billion over the next 5 years and $867 over the next 10 years according to the Congressional Budget Office (CBO). Over 76% of the cost is estimated to be for the Nutrition title of the bill. CBO estimates the bill will spend about $1.5 billion more than continuing existing legislation. This is in contrast to the $23 billion cut in the last Farm Bill.
The largest cut occurred in the Rural Development title by tighten some Rural Utility Services programs. The Miscellaneous title has the greatest increase among the various titles, and contained funding for an animal disease vaccine bank, funding for feral hog eradication and Beginning farmer assistance.
- Farmers will be allowed to switch between the Agriculture Risk Coverage and Price Loss Coverage programs starting in 2019 and again in 2021, 2022 and 2023.
- Payment limits remain at $125,000 and AGI limit at $900,000.
- Expands program payments to nieces, nephews, and cousins.
- Suspends ARC and PLC payments on land entirely in grass or pasture since 2009
Agricultural Risk Coverage (ARC)
- ARC-County guarantee will be:
- Trend adjusted
- Increased by substituting “transitional” T-yield of 80 percent of the county T-yield – up from 70 percent.
- Will use RMA data and create separate dryland and irrigated yield for each counties
Price Loss Coverage (PLC)
- One time update of PLC payment yields = 90% of 2013-2017 ( not allowed to change more than 10% from the 2008-2012 national average)
- Payment = 85% x Base acres x base yield x [Reference price – maximum of loan rate or Market Year Average (MYA) price]
- Cotton is now eligible through seed cotton program
- Reference prices are unchanged but may rise if market prices rise over time by up to 15%
| PLC Reference Price
||Maximum Effective Reference Price
- Programs are largely unchanged except loan rates are increased as follows:
||2014 Farm Bill
||2018 Farm Bill
||$0.45 ‐ $0.52/lb
||$0.45 ‐ $0.52/lb
- The Conservation Stewardship Program (CSP) takes cuts to fund more EQIP which is increased by $275 million.
- Up to 1/2 of the money can be used for livestock operations.
- The Conservation Reserve Program (CRP) will be expanded from 24 million to 27 million acres, with 2 million acres reserved for grasslands.
- CRP payment rates will be capped to keep them below local rental rates.
- Increase the basic administrative fee for catastrophic coverage
- Buy-up crop Insurance subsidies are unchanged
- Participants in Seed Cotton Program not allowed to purchase STAX
- Allows enterprise units to cross county lines
- Hemp will be a covered commodity
- Directs RMA to study
- Intermittent flooding on rice
- Tropical hurricane coverage
I am really proud of our undergraduate research program at Mississippi State. This past year Alba Collart and I mentored Shea Gould an undergraduate researcher who has now joined our Department as a grad student. In her undergraduate research she asked a representative sample of 465 U.S. adults their preferences for allocating funds to the programs authorized in a farm bill. The survey focused on four broad categories of USDA spending – farm programs, conservation, nutrition, and a broad category subsuming all other USDA activities.
As Farm Bill conferees meet, the House of Representatives and Senate Bills are estimated to spend near current levels over the next ten years — essentially a zero-sum game of shifting priorities. Consistent with the zero-sum game being played in Congress right now, we asked respondents to reallocate the USDA pie rather than shrinking or adding to USDA spending.
We find the average U.S. adult. U.S adults desire to spend about the same as currently spend supporting farmers – in fact they support a slightly increased shared of the total USDA budget – from 18 to 19%. Though slight in percentage terms, this increase corresponds to an annual increase of $1.4 billion.
American adults desire to see USDA spending a smaller share of its budget on nutrition assistance programs. Reductions in nutrition spending were reallocated to the other three categories. Even when a subset of survey respondents were shown current levels of spending on nutrition programs they still wanted to spend less, but chose to cut less. Interestingly, spending on nutrition program have dropped significantly without a legislative change due in large part to an improving economy.
The biggest percentage change desired by American adults is that they would increase conservation programs from 7% to 22% of the total USDA budget. That is a three-fold increase. Our results show that survey respondents primarily wanted to take funding from nutrition programs.
Finally, the survey findings indicate a desire for increased spending on “other programs” which were summarized as encompassing research, marketing and regulatory activities, rural development, and food safety. Because the survey addressed such a broad set of areas, it did not dig deeper into desired spending in subcategories. We hope to do follow up research on what was in the ‘other program’ category that led to a desire for increased funding?
The latest USDA Cattle on Feed Report was released on Friday and showed some positive news for prices for the rest of Fall and into early 2019. The inventory is still large. Indeed it is another “record-large” total as 11.4 million head in feedlots is the largest October 1st total since the COF series began in 1996. However, it is what was contained in this report relative to pre-report expectations that provided some price support as futures prices showed strength in Monday trading.
Placements of cattle into feedlots were 2.05 million head during September. Importantly, this is 4.6% lower than September 2017. Perhaps even more importantly, this is about 5% lower than was expected pre-report. A lower placement and supply number than expected is what led to some market strength on Monday.
Relative to 2017, the number of heifers in the mix is 11% larger at 4.3 million head. The number of steers is up a more modest 2.3% at 7.1 million head. The story over the past few months has been of lower placement weights and that was again the case in September. This is an interesting dynamic that ultimately has an impact on lower average slaughter weights.
Fed Cattle Marketings were 3.6% lower than September of 2017. While this number is lower, it was well anticipated going into the report.
The combination of the anticipated marketing rate and the lower than expected placement rate led the inventory in feedlots number to be lower than expected. However, it is still a big number which is likely to keep a cap on market price potential in the near-term. It is encouraging to prices that it seems like there may be fewer supplies than expected, but the calf crop is still larger than a year ago and we still have to work through these large supplies. The combination of a large number of heifers in the mix and continued high cow-slaughter numbers do provide credence to the projection of a flattening total herd number over the next year or two.
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.
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.
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.
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.
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.