Author: John Robinson

The February WASDE included an updated U.S. cotton balance sheet for 2024/25 (third column of numbers in the table below), with very minor month-over-month adjustments.  The supply side variables were unchanged from January, as were projected U.S. exports.  U.S. domestic use was cut 100,000 bales, which went straight to the bottom line of 100,000 additional ending stocks compared to last month.  This leaves U.S. ending stocks at a more bearish 4.9 million bales.

It is not surprising that the USDA left U.S. production unchanged from their January forecast.  As the ginning season winds down, cotton has two reliable measures to forecast production:  1) a count of actual physical bales (“running bales”) that are classed for fiber quality, and 2) a survey of running bales ginned.  Actual physical bales vary in weight but are around 500 pounds.  On the other hand, USDA-NASS cotton production forecasts and WASDE numbers are expressed in 480 pound “statistical bales.”  For conversion purposes, I assume a conversion factor of 1.02755 statistical bales for one running bale.

For the week ending February 6, USDA-AMS classing accounted for 13,855,096 running bales classed (or 14,236,804 statistical bales, about 99% of USDA-NASS’s production forecast). As of February 1, USDA-NASS also forecasted 13,961,700 running bales ginned (or 14,346,345 statistical bales, within about 60,000 bales of USDA-NASS’s production forecast).  So, the end of the 2024 crop processing is in sight, if not here, although they may sit on it until the final classing and ginning reports (typically in May). I don’t think there are any market changing surprises left on the production side that would affect prices moving forward.

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Robinson, John. “Late Season Indicators for 2024/25 U.S. Cotton Production.” Southern Ag Today 5(9.3). February 26, 2025. Permalink

Statistically generated near-term price forecasts are useful to compare/contrast with Extension ad hoc price estimates, USDA monthly price estimates, and trade price estimates.  Ongoing cotton price forecasting research at Texas A&M University provides some timely short-term (monthly average) price forecasts that shed light on the 2024 season.

As depicted in Figure 1, over the period January 2014 to August 2024, ICE No.2 cotton monthly average futures prices ranged from 53.75 cents per pound to 146.17 cents per pound, averaging 78.23 cents per pound. The first nine years of this data were used as the training sample for model construction, while the 2024 monthly average prices were used for out-of-sample forecasting using a structural econometric model described in the next paragraph.  

Structural econometric models consider the direct effects of specific variables on our dependent variable of interest:  ICE No. 2 cotton futures prices.  Our best working model specifies monthly average nearby ICE cotton futures as being explained by the monthly stocks-to-use ratio, real disposable personal income, real retail clothing sales, real personal consumption expenditures, the Michigan consumer sentiment index, seasonality indicator variables, and various qualitative factors.  We hypothesize that ICE NO.2 cotton futures prices are positively related to real retail clothing sales, real disposable personal income, real personal consumption expenditures, and the Michigan sentiment index but negatively related to the stocks-to-use ratio.  After estimating the model coefficients, the signs and magnitudes of the continuous variables in the model conform to prior expectations. This model accounts for roughly 96 percent of the variables in monthly average nearby ICE No.2 cotton futures prices.  The results indicate the absence of autocorrelation in the residuals.

Using our econometric model to forecast prices, on average over the out-of-sample period January 2024 to August 2024, the price forecasts deviated from the actual values by 7.75 cents per month (also known as the Mean Absolute Error), or roughly 9.7 percent (also known as Mean Absolute Percent Error). In other words, the out-of-sample ex post forecasts are, on average, higher than the actual monthly average nearby ICE cotton futures price.  This result continues with the most recent model forecasts:

• September 2024 Forecast: 76.87 cents per pound (Actual:  70.68 cents per pound)
• October 2024 Forecast: 78.66 cents per pound (Actual: 71.65 cents per pound) 
• November 2024 Forecast: 77.60 cents per pound (Actual: 70.03 cents per pound).

So, what does this mean?   The results of an otherwise well-fitting statistical model suggest what cotton growers already knew:  2024 was an abnormal year.   The nearly thirty-cent decline in monthly average nearby prices between March and August was a statistical anomaly that our model cannot predict.  

These kinds of things suggest there are atypical factors affecting price forecasts, which means adjustments need to be made by considering market forces that are not captured by economic modeling.  

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Robinson, John. “A Statistically Lousy Year for Cotton Prices.” Southern Ag Today 4(50.3). December 11, 2024. Permalink

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