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%. Recently I’ve switched to the month end calculation of the DTN National Corn and Soybean Price Indexes based on a longer track record. I’m still working on getting the back data for wheat. By using the end of month number rather than it gives us a picture of the stocks-to-use situation at the end of each month, a system that should smooth out the wide changes seen at the end of a marketing year. This continues to be a work in progress, with the bottom line being stocks-to-use remain bullish for all three markets.

WHEAT: The end of June meant the end of the first month of the new 2022-2023 marketing year. Based on the Barchart National Wheat Price Indexes for HRW, SRW, and HRS and weighting the indexes based on 2021 production, the month end average price was $9.58. The end of May saw the average price come in at $10.92 with the lower price reflecting winter wheat bushels being sold during the 2022 harvest. The final average price for June 2021 was $6.63, reflecting how tight US stocks-to-use had become over the 2021-2022 marketing year. Bear with me as I hope to have stocks-to-use calculations for the wheat sector by the end of July.

CORN: The DTN National Corn Price Index (NCI, unweighted national average cash price) was calculated at $7.17 on June 30, 2022. This correlates to an end of month available stocks-to-use of 8.0% The end of May showed $7.51 and 7.6%. Did the US find more supplies during June? It’s possible more cash sales were made of tightly held bushels. I know a few of you have reported selling much of your remaining bushels during June, helping the Barchart National Corn Price Index complete a bearish key reversal on its long-term monthly chart. Note this was the second consecutive month the NCI decreased, also indicating a possible slowdown in demand. Recall the last three Cattle of Feed reports have shown a seasonal decrease in on feed numbers though ethanol demand remains strong. As for exports, the end of May showed US corn with an export shipment pace projection of 2.5 bb while the latest weekly update (for the week ending June 24) showed a pace projection of 2.47 bb. Still, the 2021-2022 available stocks-to-use situation is the third highest on record behind 2011-2012 (7.3%) and 2010-2011 (7.7%).

SOYBEANS: The DTN National Soybean Price Index (NsI, unweighted national average cash price) was calculated at $15.86 on June 30, 2022. This correlated to an available stocks-to-use calculation of 3.0%. The end of May had the NSI at $16.51 and 2.4% while the previous June saw the NSI come in at $14.25 and 4.6%. We did see the US available stocks-to-use situation loosen slightly during June with most of the activity happening toward the end of the month. Given it is unlikely merchandisers found bushels hidden somewhere, the most likely reason for the drop is a decrease in demand. Recall I’ve been talking about for a number of weeks (months?) the possibility of old-crop sales being cancelled or rolled to new-crop, and idea that seemed to be turning to reality as June ended and July began. Still, long-term fundamentals remain bullish, as indicated by the continued inverse in the new-crop November22-to-July23 forward curve. Meanwhile, the Barchart National Soybean Price Index completed a bearish spike reversal on its monthly chart during June. All this sets the stage for what should be an interesting end of the 2021-2022 marketing year.

[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.