By Rick Beltz  rickb@agpartners.net

The corn is at that stage where you can start predicting the yields.   With the Pro Farmer tour kicking off this week, I thought this would be very fitting.  This is the formula that I use and it seems to work pretty well most of the time.   It is not the one they will use on the tour.  Get out in your field and grab some cobs and see how close you can get.

Yield Component Method

Other pre-harvest yield prediction methods exist (Lauer, 2002; Lee & Herbek, 2005; Thomison, 2015), but the Yield Component Method is probably the most popular because it can be used well ahead of harvest; as early as the so-called “roasting ear” or milk (R3) stage of kernel development. Under “normal” conditions, the kernel milk stage occurs about 18 to 22 days after pollination is complete (Nielsen, 2016). Estimates made earlier in the kernel development period risk being overly optimistic if subsequent severe stresses cause unforeseen kernel abortion (Nielsen, 2013).

The Yield Component Method was originally described by the University of Illinois many years ago and is based on the premise that one can estimate grain yield from estimates of the yield components that constitute grain yield. These yield components include number of ears per acre, number of kernel rows per ear, number of kernels per row, and weight per kernel. The first three yield components (ear number, kernel rows, kernels/row) are easily measured in the field.

Final weight per kernel obviously cannot be measured until the grain is mature (kernel black layer) and, technically, at a grain moisture of 15% since that is the typical moisture value used to determine a 56-lb market bushel. Consequently, an average value for kernel weight is used as a proverbial “fudge factor” in the yield estimation equation. As first described many years ago, the equation originally used a “fudge factor” of 90, which represented 90,000 kernels per 56-lb bushel. In terms of how kernel weight is usually measured in research, this would be equal to about 282 grams per 1000 kernels.

Be aware that actual kernel numbers per 56-lb bushel among years or fields within years can vary significantly and is influenced by both growing conditions and hybrid genetics. Kernel weight among hybrids can easily vary from less than 65,000 kernels per 56-lb bushel to more than 100,000 kernels per 56-lb bushel. Kernel weight from year to year for the same hybrid can easily vary by 20,000 kernels per bushel or more simply due to variability in growing conditions during the grain filling period.

Crop uniformity also influences the accuracy of any yield estimation technique. The less uniform the field, the greater the number of samples that should be taken to estimate yield for the field. There is a fine line between fairly sampling disparate areas of the field and sampling randomly within a field so as not to unfairly bias the yield estimates up or down.

1.  At each estimation site, measure off a length of a single row equal to 1/1000th acre. For 30-inch (2.5 feet) rows, this equals 17.4 linear feet.  TIP: For other row spacings, divide 43,560 by the row spacing (in feet) and then divide that result by 1000 (e.g., [43,560 / 2.5] / 1000 = 17.4 ft).
2. At each estimation site, measure off a length of a single row equal to 1/1000th acre. For 30-inch (2.5 feet) rows, this equals 17.4 linear feet.  TIP: Do not count dropped ears or those on severely lodged plants unless you are confident that the combine header will be able to retrieve them.
3. For every fifth ear in the sample row, record the number of complete kernel rows per ear and average number of kernels per row. Then multiply each ear’s row number by its number of kernels per row to calculate the total number of kernels for each ear.  TIPS: Do not sample nubbins or obviously odd ears, unless they fairly represent the sample area. If row number changes from butt to tip (e.g., pinched ears due to stress), estimate an average row number for the ear. Don’t count the extreme butt or tip kernels, but rather begin and end where you perceive there are complete “rings” of kernels around the cob. Do not count aborted kernels. If kernel numbers per row are uneven among the rows of an ear, estimate an average value for kernel number per row.
4. Calculate the average number of kernels per ear by summing the values for all the sampled ears and dividing by the number of ears.  EXAMPLE:  For five sample ears with 480, 500, 450, 600, and 525 kernels per ear, the average number of kernels per ear would equal:  (480 + 500 + 450 + 600 + 525) divided by 5 = 511
5. Estimate the yield for each site by multiplying the ear number (Step 2) by the average number of kernels per ear (Step 4) and then dividing that result by a kernel weight “fudge factor”. Unless your seed company can provide some insight into kernel weight values for their hybrids, I suggest simply performing separate calculations using “fudge factor” kernel weight values equal to 75, 85, and 95. This range of values probably represents that most commonly experienced in the central Corn Belt.  Example:  Let’s say you counted 30 harvestable ears at the first thousandth-acre sampling site. Let’s also assume that the average number of kernels per ear, based on sampling every 5th ear in the sampling row, was 511. Using “fudge factor” values of 75, 85, and 95; the estimated range in yield for that sampled site would (30 x 511) divided by 75 = 204, or divided by 85 = 180, or divided by 95 = 161 bushels per acre.  Repeat the procedure throughout field as many times as you deem representative. Tally and average the results separately for each “fudge factor” used for the calculations.

Random Sample of Ears

Poor tip fill due to N deficiency

Kernel size differences due to N deficiency

Remember that this method for estimating pre-harvest grain yield in corn indeed provides only an estimate. Since kernel size and weight will vary depending on hybrid and environment, this yield estimator should only be used to determine “ballpark” grain yields. Yield can easily be overestimated in a year with poor grain fill conditions (e.g., low kernel size and weight from a drought year) and underestimated in a year with excellent grain fill conditions (e.g., larger kernel size and weight from non-stress grain fill periods). The closer to kernel black layer stage you sample, the more accurately you can “guesstimate” whether kernel weight will be above or below average for this year.