The bottom-line supply and demand number is stocks-to-use (s/u). I’ve long said stocks-to-use is the Readers’ Digest version of supply and demand, in that this one number can tell us the bullishness, bearishness, or neutrality of a market’s fundamentals. I’ve also argued endlessly over the years with economists, my point being there should be a strong positive correlation between stocks-to-use and cash price. Given this premise, I’ve developed my system between the two for all three major markets (wheat, corn, and soybeans) with the r-squared[I]for all three near 100%. In all cases I’m using the cmdty National Cash Price Indexes (weighted national average cash prices from Barchart), and in wheat that means HRW, SRW, and HRS have been weighted to reflect US production of all wheat supplies. The Darin Newsom Analysis, Inc. (DNAI) stocks-to-use numbers are calculated at the end of every month, and then compared to the previous month and the previous year. The DNAI numbers may not agree with subsequent USDA report estimates, but that is understandable given the DNAI numbers are real (based on national average cash prices) rather than imaginary (based on…I have no idea).

WHEAT: As May came to an end, the curtain closed on the the 2021-2022 marketing year for wheat (despite my argument spring wheat’s marketing year should be from September through August). You’ll see I’ve broken out the final marketing year stocks-to-use chart for wheat, with the daily average price of $8.36 correlating to 18% (red marker), leaving every year prior (at least back through 2014-2015) in the dust. This chart highlights how historically tight US available stocks-to-use at the end of 2021-2022, but yet still paling in comparison to what we seen in corn and soybeans. For the record, the 2020-2021 marketing year ended with a figure of 39.1% based on an average cash price of $5.27. Looking ahead to 2022-2023 the situation doesn’t look to get much better given the dryness across the US Southern Plains (HRW), the wetness over much of the Northern Plains (HRS), while SRW saw near ideal conditions for much of the spring. A toast and round of Auld Lang Syne to wheat. Let’s see what the new year brings.

CORN: The 2021-2022 daily average of the NCPI though the end of May increased to $6.27, a gain of 18 cents from the previous month and $1.44 above last year at this time. The 2021-2022 price correlates to available stocks-to-use of 8.2% as compared to last month’s 8.4% and last year’s 10.3%. The series of monthly corn supply and demand charts remains some of my favorites as they clearly show monthly tightening of supplies in relation to demand. This also ties in with the strength of national average basis with the Barchart National Corn Basis Index calculated at 6.3 cents under July futures Tuesday afternoon (May 31) as compared to this week’s previous 5-year high weekly close (not counting 2021) coming in at 24.8 cents under. Given how tight US corn supplies are in relation to continued strong demand, all three legs (feed, ethanol, and exports), I’m still of the opinion noncommercial traders will return as buyers. For the record, based on the NCPI the US situation is closing in on what we saw at the end of 2011-2012 ($6.48, 7.9%).

SOYBEANS: The 2021-2022 daily average of the NSPI though the end of May jumped to $14.05, closing the gap on the final marketing year calculation for 2012-2013 of $14.52. This month’s average price calculation was an increase of 27 cents from the previous month and up $1.59 from the previous year. While the attached chart shows the US situation remains incredibly tight, the lack of a longer track record for the NSPI leaves out the highest marketing year of 2011-2012. For this I’ve gone back to the DTN National Soybean Index (see attached soybean chart), and we see the monthly close of $16.5109 trails only what was seen in late 2011-2012. That year, the NSI posted monthly closes of $16.84 (July) and $17.32 (August), with an ending stocks-to-use calculation of 2.0%. The May 2022 close of $16.51 correlates to available stocks-to-use of 2.4%.

[i] R-squared is defined as “a statistical measure of fit that indicates how much variation of a dependent variable is explained by the independent variable in a regression model.” (Investopedia). In my world, it is how closely related two (or more) variables are, in this case national average cash price and stocks-to-use.