Jenn Sabourin and Aaron Breimer on field-scale data collection – RealAgriculture
Whether it’s just leaving a check strip or repeated trials, collecting and analyzing data in the field can be overwhelming.
For this episode of The Agronomists, host Lyndsey Smith is joined by Jenn Sabourin, from Antara Agronomy, Man., And Aaron Breimer, from Deveron, near Chatham, Ont., To talk about setting up a ‘field testing, data collection and all important to do SOMETHING with the data.
SUMMARY
- Disclaimer: We may not have access to clips tonight as our guests for this episode did a great job of getting us ready!
- Bremer’s preferred data concerns soil analyzes
- Sabourin’s preferred starting point is to ask questions about management practices
- A slide that includes Nicholas Cage? Stay tuned!
- Confirmation bias: when setting up trials, unconsciously look for the answer we were hoping for
- Correlation does not equate to causation (ice cream sales versus shark attacks, number of people who drowned falling into a swimming pool correlated with the movies Nicholas Cage appeared in)
- Setting up a test in the spring… the seeds get stuck a little too quickly in the soil? It is important to set expectations and explain why the test is being performed.
- REPLICATIONS. That’s all. But in reality, replications are extremely important, especially when the final dataset can be so affected by environmental conditions. (Editor’s note, can speak from personal experience in making the mistake of not reproducing enough)
- How far away can these replications be? It all depends on the question or the objective. 20 miles could be a huge radius for some matters; the weather could affect testing too much with such a large range
- Are the numbers blowing your head? Leave it to the experts…?
- Random control strips.
- Also, replicate the trials over a few years, and not just the treatments
- What about using a field length strip of each treatment? Breimer says you can’t run stats on it.
- And if you’re a beginner, yield is a great number to collect first, but what’s next? The next most important piece of information for Breimer? Past management practices. Can learn a lot from the farmer who can explain a lot at times. Sabourin says the equipment widths are really handy information for setting up trials. One-pass treatments, etc.
- What is the minimum farm size to justify the cost of the technology needed to collect the data?
- What’s a good approach for outliers? Strange numbers or really obvious data points ‘cleaning’ data that are really there, they affect the data and need to be treated in some way
- Data analysis is not easy
- Equipment calibration is really important.
- With a drought year like 2021, or when there is excess humidity, where do you draw the line to keep this data or put it in the boat?
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