Adam Gurr wanted to know if he could cut his canola-seeding rate and not sacrifice yield or extend maturity. So the Brandon, Man. farmer set up a three-year trial to answer the question.
Gurr compared his usual seeding rate to one that was 20 per cent less and one that was 50 per cent less. He ran the trial on four different sites — two in year one and a single site in each of the next two years. These sites included four different fields and three different soil types.
“We concluded that indeed we could cut our rates by 20 to 50 per cent depending on TKW (thousand kernel weight), and in the process we developed a different way of determining our seeding rate,” he says. “We now take our TKW in grams and subtract 20 to 30 per cent to come up with a seeding rate in pounds per acre.”
If canola seed has a TKW of six grams, subtracting 30 per cent gives 4.2. That becomes his seeding rate — 4.2 lbs./ac.
“In the trial, we also noticed that our seed mortality rates are really low,” Gurr says. “We are confident that our seed survival will be 80 per cent or more, and ultimately that low mortality rate allows us to cut our seeding rates.”
Gurr’s whole canola-seeding operation is based on achieving a final stand of six to eight plants per square foot. “After our on-farm trial, now we target seven to eight plants per square foot for our seeding rate as we are confident the final plant stand will meet our target.”
This is one example of the value a trial-driven decision can provide. The key is to create an effective trial and keep the results in perspective. Confidence in on-farm trials depends on the following 10 steps.
10 steps to useful on-farm trials
These few steps will take on-farm experiments to another level, making them fair to all treatments and putting results in proper perspective. Canola Council of Canada (CCC) agronomy specialist Nicole Philp and Alberta Agriculture and Forestry oilseed specialist Murray Hartman provide the following 10 steps:
1. Commit to finishing the trials. If the trial is worth doing and will provide information that will benefit the farm’s profitability, then set a goal to take it to harvest.
“Stuff happens at seeding and harvest and it’s easy to say, ‘Nope, I’m not doing it,’” Philp says. “Committing to the trials is the most important step.”
2. Keep it simple. “It doesn’t have to be complicated,” Philp says. Start with a question you want answered, such as: Will 50 per cent 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 setup and more straightforward statistical analysis. Simplicity also makes it easier to accomplish Step 1.
3. Avoid “confounding.” 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.
4. Arrange the strips fairly. Place two strips beside each other in an area of the field where they 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. Replicate the strips. More strips mean more data points, which will increase statistical relevance of the result. Include three or four treatment/no-treatment pairs through the trial area. Also, if treatment strips are 100 feet wide, this allows for two windrows within each strip. Each windrow could be combined and weighed separately, providing more information for statistical analysis.
“If you are taking the time to set up an on-farm trial, take the time to replicate,” Gurr says. “Replication adds very little time to trial setup. It does add a little bit more time at harvest, but this is worth it as it facilitates the use of statistical analysis.”
Hartman adds that replication in one field one year really just tells you how the treatment worked in those specific conditions. “For results that can be applied to most fields, most years, I like to see 15 to 20 site years of data gathered from multiple fields over multiple years,” he says. That is where Step 10, below, can help.
6. Weigh the strips. An accurate scale increases confidence in the results. A weigh wagon is ideal, but keep it parked in one level location and don’t move it between strips.
Yield monitors can be accurate if calibrated against the specific crop condition. “The risk here is that if treatments show clear differences, the combine yield monitor should be calibrated between each strip,” Hartman says. “For this reason, a weigh wagon remains the simpler and more accurate option.”
7. Take time to make in-season observations. Keep a list of all field and treatment information, including rainfall. Try to walk the full length of plots a few times during the growing season to check for differences in drown-out spots, plant densities and calendar dates for bolting and first flower, for example. “Identify and record conditions that may have influenced the final result,” Philp says.
Hartman adds that these observations are important to spot differences that may sway the trial result.
“If the treatment is going on after these differences were already observed, it may be difficult to determine whether any yield difference between strips was the result of the treatment or the pre-existing conditions.”
8. Go beyond averages. In addition to calculating the average yields of two strip treatments over many sites, estimate the probability that the difference is due to chance. See the example in the sidebar.
9. Do statistical analysis of the results. “This allows one to sort out whether the differences in yield are a result of the treatment applied or simply a result of natural variability,” Gurr says. “The danger without statistical analysis is that you make changes to your system based on differences that are not ‘real.’” The sidebar also expands on this idea.
10. Share your results. Collaborate with other growers to learn from others and share your results. That way, you don’t have to make all the discoveries and mistakes yourself. Communicate results with research and extension personnel.
“Sharing results from the same trial repeated on other fields with a mix of soil types and weather conditions provides a result that you can more confidently extrapolate to most situations,” Hartman says, adding to his comments for Step 5.
A quick statistics test
Adam Gurr took part in the Canola Council of Canada’s Ultimate Canola Challenge in 2015. He ran a boron test with five paired strips on his farm site at Rapid City, Manitoba. His trial produced the results shown in the table beloiw.
When these results are plugged into the Paired T-test calculator that Murray Hartman recommends, it spits out the results in yellow at the bottom of the table. (You can find the calculator here)
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 per cent — 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 five per cent or lower,” Hartman says.
The Indian Head Agricultural Research Foundation (IHARF) data analysis tool, which Gurr uses, provides an instant analysis of least significant difference (LSD).
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 five per cent 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 per cent.
For complete results from the CCC Ultimate Canola Challenge boron trials, go to canolacouncil.org and look for the “2014-15 results summary.” The overall conclusion is that three years of small-plot boron trials from Gurr’s and all other sites did not show any consistent, significant or statistical differences in canola yield or quality.
Growers who want to participate in on-farm Ultimate Canola Challenge trials in 2016 can contact Nicole Philp at email@example.com.