How to Estimate Corn Yield Using 2 Methods

Posted September 25, 2018 by Administrator in Agribusiness | 0 comments

Farmers rely on corn yield estimates to make crop management and grain marketing decisions. However, basic methods of calculating crop yield can produce misleading results. The more simple the calculation, the greater the margin of error is. All methods of estimating corn yield require farmers to count the kernels on an ear of corn.

The simplest of methods relies on estimating how many ears per acre a farm produces. While it may be easier to estimate ears per acre and multiply that by the number of kernels per an average size ear of corn, the results will not be accurate. The following outlines two methods for estimating corn yield starting with the least accurate method.

  1. Simple but inaccurate. As mentioned above, the simplest method is the least accurate. To achieve a rough estimate of corn yield, farmers who anticipate around 26,000 ears per acre can select an average size ear of corn, count its kernels, and multiply it by 0.289 (this multiplier implies an average size kernel). Farmers should skip kernels near the top that are less than half the size of regular kernels. To do this, farmers can count the kernels in one row on the ear of corn and multiply it by the number of total rows. For example, 12 rows with 48 kernels is 576 kernels per ear (12*48 = 576). Multiplying this by 0.289 equates to approximately 166.5 bushels per acre (166.464 to be exact). This math makes several assumptions about the ears per acre as well as the average kernel size. It also excludes outliers such as pests, drought, and other stressors that can affect kernel size or overall yield.
  2. Recalculating for population and seed size. The above example assumes a crop production of around 26,000 ears per acre. However, farmers should reduce this number by 1000-2000 to account for pests and other issues that can affect the final yield number. They should also incorporate a multiplier for kernel size ranging from small to large to take weather conditions into consideration. Poor weather will yield smaller kernels and vice versa. For this particular example, farmers should use the following equations to get a better idea of what to expect:
    1. 25,000 ears per acre in a stressful year: 576*0.227=~131 bushels per acre
    2. 25,000 ears per acre in an average year: 576*0.278=~160 bushels per acre
    3. 25,000 ears per acre in a very productive year: 576*0.357=~205.5 bushels per acre

This results in a possible range of 131-205.5 bushels per acre. While the simple method’s number fell in this range, the final result could be much less or much more depending on outside factors.

If the farmer wishes to be even more conservative, he or she can reduce their expected yield by 2000 and use the following equations:

  1. 24,000 ears per acre in a stressful year: 576*0.218=~125.5 bushels per acre
  2. 24,000 ears per acre in an average year: 576*0.267=~154 bushels per acre
  3. 24,000 ears per acre in a very productive year: 576*0.342=~197 bushels per acre

This equation produces a range of 125.5-197 bushels per acre. Both of these equations result in more than 70 bushels per acre in difference and can have significant implications for a farmer’s bottom line and budget. Farmers need to prepare for all possibilities for their corn yield so as not to endanger their farming operation. To learn more ways to protect your livelihood and your farm, contact the experts at Cline Wood.

This document is not intended to be taken as advice regarding any individual situation and should not be relied upon as such. Marsh & McLennan Agency LLC shall have no obligation to update this publication and shall have no liability to you or any other party arising out of this publication or any matter contained herein. Any statements concerning actuarial, tax, accounting or legal matters are based solely on our experience as consultants and are not to be relied upon as actuarial, accounting, tax or legal advice, for which you should consult your own professional advisors. Any modeling analytics or projections are subject to inherent uncertainty and the analysis could be materially affective if any underlying assumptions, conditions, information or factors are inaccurate or incomplete or should change.

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