Want to run check strips to see if a new product or technique will pay off on your farm? Here’s how:

1. Keep it simple. Start with a question you want answered, such as: Will 50% more nitrogen increase my canola yield AND profitability? Does a second in-crop herbicide application pay? Does boron applied at flowering reduce flower abortion and increase overall yield? One variable makes for an easier trial set up and more straightforward statistical analysis.

2. The only difference between the two strips should be the treatment in question — otherwise the result will be confounded. An example of a confounded study would be comparing the effect of boron added to fungicide versus a check strip with neither one. In that situation, you can’t tell if the effect is due to fungicide or boron. If you want to test boron at flowering, use fungicide in both treatments. If you want to test fungicide, leave boron out or have it in both treatments.

3. Have four replicates. If treatment strips are 100 feet wide, this allows for two windrows within each strip. Use two treatment strips in this case, providing for four windrows. Leave an untreated gap between each treatment strip.

4. Choose an area of the field where treated and untreated strips cover similar slopes and soil quality. Multi-year yield maps can help identify good locations. Have at least 500 feet of length per strip and make them wide enough for a windrow to fit well within the boundaries.

5. Weigh each windrow separately within the treated/untreated test area. An accurate scale increases confidence in the results. A weigh wagon or cart with a scale is ideal, but keep it parked in one level location and don’t move it between strips.

6. Do statistical analysis of the results. Enter weights for each strip into the Paired T-test calculator. Another option is the Indian Head Agricultural Research Foundation (IHARF) data analysis tool. Terms to know:

—Mean. This is the average of the five yields for each treatment. The mean difference between Treatments A and B is 1.7 bu./ac.

—Probability of this result. Due to the fairly consistent yield results, the calculator shows a low probability — 5.6% — that this result could have occurred by chance alone. The lower the better. “When analyzing field data, we generally have more confidence in a result when the probability is 5% or lower,” Hartman says.

—Least significant difference (LSD). As the IHARF tool says: “A p-value of 0.05 is chosen most frequently in scientific experiments, however 0.10 is sometimes recommended for large-scale field trials to account for increased overall variability relative to small-plot studies. At p=0.05, there is a 5% probability of either (1) concluding there is a difference between two treatments when, in actuality, there is no difference, or (2) concluding there is no difference between the two treatments when a difference actually existed. At p=0.10, the probability that one of the previous two errors will occur is 10%.

7. Repeat over a few years to see how the treatment performs under different growing conditions.