Author: John Robinson

Hedge funds or “managed money” refers to financial client money that is invested in commodity futures, stocks, bonds, and other investments. Figure 1 shows varying levels of hedge fund investment in ICE cotton futures over the last ten years, either positioned as bullish net longs (i.e., upward pointing green areas) or bearish net shorts (i.e., downward pointing green areas).  In contrast, the blue colored graphed area of Figure 1 reflects the more stable, long-only positioning of index funds. The latter tend to buy and hold nearby futures contracts, and then roll forward as those contracts mature.

Statistically, net long/short positioning of hedge funds is directly associated with higher/lower ICE cotton futures. Past statistical modeling indicates that a 1.9-cent decline in the most active cotton futures price was expected for every 10,000-contract decrease (or increase) in the hedge fund net long (net short) position (https://www.farmprogress.com/cotton/cotton-spin-hedge-funds-revisited ).  Looking at the most recent price decline, the hedge fund net long position peaked at 73,230 contracts on February 27, 2024, when nearby ICE cotton futures settled at 98.80 cents per pound.  This net long position changed to a 51,442 net short position, a total change of 124,672 contracts and associated with a 72.76-cent settlement on June 18 in nearby ICE cotton futures.  The previous statistical relationship implies that 23.56 cents of the total 26.04 cent decline in nearby ICE cotton futures is associated with bearish hedge fund adjustment, all other things being equal. 

How long will the current net short position last?  Figure 1 highlights that net short positioning is less frequent than net long positioning.  Over the ten years graphed in Figure 1, only 156 weekly observations involved net short positioning while 370 weekly observations involved net long positioning.  Practically speaking, speculating on the size of an establishing, growing crop during the summer is a bit of a risky gamble.  This dynamic may encourage caution on the part of short speculators during the growing season.  Such behavior could also be the basis of a short covering rally in the event of reduced acreage expectations, bad weather, or negative production scenarios.  Short covering is where hedge fund managers buy back their outright short speculative positions as a result of changing (i.e., more bullish) expectations and/or pre-set buy stop orders.  The liquidation of a large net short position can thus lead to a cascade of buying, referred to as a short covering rally.

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Robinson, John. “The Net Short Hedge Fund Position in ICE Cotton Futures.” Southern Ag Today 4(29.1). July 15, 2024. Permalink

During April, the USDA National Agricultural Statistics Service (NASS) announced the suspension of selected statistical reports and processes, including the annual objective yield sampling effort for U.S. cotton.  The latter was a process of field sampling of squares and bolls in major cotton producing states, from which an objective estimate of yield was calculated.  This estimate presumably informed the monthly U.S. cotton production forecasts, beginning with the August forecast, with potential to confirm or contradict the earlier (May, June, July) forecasts that are traditionally based on historical average yield and abandonment, along with subjective opinion.  It should be noted that in 2019, the start date for objective yield sampling for most of the U.S. was moved from August to September. 

The delaying of the objective yield survey process, followed by its announced elimination, has potential for increasing the forecasting error of U.S. cotton production, which has implications for market planning and even price volatility.  Figure 1 provides a description of the variability of USDA NASS monthly forecasts of U.S. cotton, by crop year.  For example, the year 2012 in Figure 1 graphs the standard deviation (roughly 200,000 bales) around the average of forecasted cotton production from the May 2012 initial monthly forecast through the following April (2013) forecast of 2012 cotton production.  

The graphed points in Figure 1 represent the variation associated with revised production estimates.  Through 2018, the public data collection and publication of information to inform the production estimates was the same.  Over this period, the variability of those monthly estimates within a given crop year fluctuated between 200,000 and one million bales around the average production for that year.  This appears as a fairly narrow, sideways pattern.  

Since 2019, there has been a marked increase in variability of the monthly production forecasts around average crop year production for the last five years.  This variability reached 1.7 million bales in 2020, and 1.6 million in 2023. The increase in production forecast variability since 2019 is visually correlated with the delay of objective field sampling for most of the U.S. Cotton Belt until September.  It is unknown whether there is any causal influence.  However, the complete elimination of cotton objective yield sampling represents an even larger disruption of previously available information.  Our hypothesis is that confirming information on the size of the U.S. crop will have to wait until data from NASS surveys of ginning, as well as bale counts from USDA classing offices, are finalized in the winter. The absence of in-season, objective yield information adds to market uncertainty with likely price implications. 

Figure 1. Standard Deviation of Monthly U.S. Cotton Production Forecasts (May to April), By Crop Year.

Robinson, John. “Public Information and the Variability of U.S. Cotton Production Forecasts.” Southern Ag Today 4(19.1). May 6, 2024. Permalink

It is important for farmers to have accurate knowledge of their costs of production.  Having a historical baseline of production costs gives producers a standard for managing their operation.  Accurate knowledge of production costs is also the basis for developing a marketing plan, i.e., identifying break-even price levels to target your price risk management or selling.

There are tools available to assist producers.  There are commercial software products that provide useful database management and financial calculations.  Some Extension agricultural economists provide support using standard accounting programs like Quickbooks.  Extension agricultural economists in major cotton producing states also publish planning budgets, often in spreadsheet formats, to guide producers in developing their own customized cost and returns estimates. Lastly, the USDA Economic Research Service (ERS) also conducts regular grower surveys of production costs, by region, and publishes research reports based on this information (Figure 1).

Figure 1 summarizes annual data on U.S. average annual cotton production costs.  The data depict two measures of historical profitability:  1) short run profitability, reflected as the value of cotton production less specified variable costs, and 2) long run profitability, calculated as the value of cotton production less specified variable and fixed costs.  The value of production shown does not include farm program payments or crop insurance indemnities.

On the face of it, these data reflect U.S. cotton as a marginal proposition.  While there appears to be an economic rationale to operate in the short run, and partially contribute to fixed costs, the long run profitability implications of U.S. cotton appear poor. The possible implications for long term viability of U.S. cotton include the following.  1) The cotton growing operations that will be left are likely of a scale that implies lower than average fixed costs, particularly machinery costs. This may involve beneficial leasing terms that are unavailable to smaller scale producers.  2)  The larger scale operations may also benefit from volume discounts on purchases of variable and capital inputs.  3) Some operations may generate above average value of production.  On the yield side, this perhaps is being achieved by the early adopters of yield enhancing technology and production systems.  On the price side this could involve better risk management and marketing that captures some of the upside price risk that is available in most years. Lastly, the picture implied by Figure 1 reinforces the need for the buffering effects of federal farm programs and crop insurance. 

Figure 1. Cotton U.S. Net Return Cost Trend

References and Resources

Beginning Quick Books Online Training for Farmers and Ranchers. https://amarillo.tamu.edu/files/2023/08/QuickBooks-Online-Course-Flyer.pdf.

Texas A&M AgriLife Extension. Cotton Budgets. https://agecoext.tamu.edu/resources/crop-livestock-budgets/by-commodity/cotton/.

University of Georgia. Department of Agricultural and Applied Economics. Budgets. https://agecon.uga.edu/extension/budgets.html.

University of Arkansas Cooperative Extension Service. Crop Budgets for Arkansas. https://www.uaex.uada.edu/farm-ranch/economics-marketing/farm-planning/budgets/crop-budgets.aspx.

USDA Economic Research Service. Commodity Costs and Returns. https://www.ers.usda.gov/data-products/commodity-costs-and-returns/.

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Robinson, John. “U.S. Cotton Cost Trends and Implications.” Southern Ag Today 3(46.1). November 13, 2023. Permalink

For spring planted crops like cotton, a key market influence in the fall season is the refinement of the national production forecast.  U.S. cotton production forecasts are published monthly by USDA’s National Agricultural Statistics Service (NASS).  For example, the current U.S. cotton production forecast is 13.13 million bales.^[1]  The forecast is expressed in standard 480-pound bale equivalents (or statistical bales).  Actual physical bales (or running bales) tend to weigh closer to 500 pounds, so analysts typically use conversion factors following USDA, e.g., 1.0275 statistical bales for every running bale.

Acreage and Production Data. Between May and July, NASS’s forecast of cotton production is based on surveyed planted acreage and assumed historical averages for yield and abandonment. Beginning in August, the production forecast incorporates grower survey data on acreage, yield, and abandonment.  Also beginning in August, the South Texas cotton forecast incorporates data from “objective yield surveys” which involve field sampling of boll counts and weights.  In September, this field sampling expands to include all of Texas, Arkansas, Georgia, and Mississippi, with repeat monthly sampling through December.  Lastly, the production forecast is occasionally adjusted in the fall months based on crop insurance information (USDA Risk Management Agency) and/or certified acres data (USDA Farm Service Agency).

Ginnings Data.  All U.S. cotton is ginned following harvest.  Surveys of gins are performed by USDA NASS who then publishes a monthly forecast of cumulative bales ginned as of that month.  For example, the number of actual U.S. bales ginned as of September 1 was forecasted at 484,450 running bales.^[2]  This converts to 497,772 statistical bales, which is 4% of USDA’s September production forecast for this year’s crop.  This is a seasonally normal percentage of the total U.S. crop.  

Classing Data.  All U.S. cotton bales are sampled for fiber quality.  It follows that the most accurate, albeit unfolding, measure of cotton production is the cumulative count of bales classed by USDA’s Agricultural Marketing Service (AMS).  These data are reported weekly as running bales.  Through the week ending September 8, 2023, AMS reports cumulative classings of 531,866 running bales of upland cotton and no pima cotton.^[3]  This converts to 546,492 statistical bales of all cotton which, unexpectedly, exceeds NASS’s ginned bale forecast.  It is assumed that the weekly count of classed bales is always the more accurate measure.

Reconciliation/Refinement.  The ginnings and classings data are published until the conclusion of ginning season, typically in the first quarter of the year following harvest. NASS will then reconcile their production forecast with final cumulative ginning and classing numbers (the latter two being converted to statistical bales).  The effect of this reconciliation is the refining of NASS’s production forecast to its final estimate.  Figure 1 shows that August production estimates have been between 15% below and almost 20% over final estimates with a lot of variation.  Each monthly refinement in production estimates occurred until final estimates were achieved by May for the last twelve crop years (2011 through 2022).  The refinement concluded by April in eight out of the twelve crop years and was finished in March in six out of the twelve crop years.  In general, the tightening pattern of percent deviations from the final production forecast reflects the influence of the updated USDA data flow.

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^[1]  USDA-NASS.  2023.  Crop Production 09/12/2023.

^[2]  USDA-NASS.  2023.  Cotton Ginnings 09/12/2023.

^[3]  USDA-AMS.  2023.  Cotton Weekly Quality Report by State 9/8/2023.

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Robinson, John. “USDA Refining of Forecasted U.S. Cotton Production.” Southern Ag Today 3(38.1). September 18, 2023. Permalink

It is frequently challenging to forecast crop production because of varying local weather and crop conditions.  In the case of upland cotton, the situation is further complicated by plant biology. The major row crop substitutes to U.S. cotton include corn, sorghum, wheat, soybeans, and peanuts. The first three of these are annual grasses, while the latter are annual legumes. The reproductive strategy of these annuals is to produce as much seed (yield) as allowed by weather and soil conditions. As a result, aggregate yields of annual crops are often thought to be well correlated with weekly crop condition rates. 

In contrast, upland cotton is relatively more complicated in its growth habits and resulting predictability.  The reason is that cotton is a perennial in its native tropical environment, akin to a crepe myrtle bush.  When grown as an annual crop in temperate regions, cotton plants can still shift back and forth between vegetative and reproductive growth.  This mixed and indeterminate growth habit is illustrated by a poor visual inspection of the weekly cotton crop condition and the resulting Texas average cotton yield (Figure 1).  The Texas aggregate situation is even further complicated by the range of planting dates, from March-April in southern Texas to May-June in northwestern Texas.  Such phenomena in Texas can have a major influence on U.S. production estimates since Texas represents over half of U.S. planted acreage (USDA NASS). 

The situation in 2023 is further complicated by a mid-year shift in the weather.  The first quarter of 2023 saw severe drought conditions over much of Texas (Figure 2, left panel).  Widespread and repeated rains since April have reportedly helped developing crops in southern Texas while complicating planting in northwestern Texas.  How much the latter will contribute to more or less cotton production is unknown. It is possible that the State will realize prevented planting from drought preceding prevented planting from too much rain.  With much of the growing season still ahead, further shifts in weather may also occur.  It may not be until early fall before we have reliable forecasts of the resulting production outcome.

Figure 1. Texas Cotton Crop Condition Index

Figure 2. U.S. Drought Monitor Maps from March 7, 2023, and June 6, 2023

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Robinson, John. “Difficulty in Forecasting 2023 U.S. Cotton Production.” Southern Ag Today 3(24.1). June 12, 2023. Permalink