Field Studies: Setting up a trial

© 2018-Business Farmer

Editor’s Note: This is the second part of a four-part series on agricultural research and interpretation by University Extension Educators Josh Coltrain, Kansas State University; Sara Berg, South Dakota State University: Lizabeth Stahl, University of Minnesota; John Thomas, University of
Nebraska Lincoln.
Part 1 discussed the importance of arranging research plots in replicated patterns instead of simple side-by-side comparisons.

SCOTTSBLUFF, Neb. – Higher yields, greater efficiency, reduced environmental impact! This may sound like a used-car dealership sales pitch, but it could represent the objectives that make an operation sustainable.
Increasingly, farmers are generating on-farm research data that encompass a wide-range of practical topics. However, setting up those experiments so that the data is statistically valid is not necessarily common knowledge.
The first step in setting up an on-farm trial is to choose a topic of interest. While this may seem simple, one important factor that must be considered is that the topic cannot be too complex. For example, a producer may be interested in how different corn hybrids react with increasing rates of fertilizer at different planting populations and planting dates.
While this sounds like an interesting experiment, the complexity is simply too great for an on-farm trial. With three different options for each factor (three hybrids, three rates, etc.) there would 81 different treatment combinations in a single replication. In this case choosing one of the factors to study (for example, plant population) would be recommended.
The next step is to choose an area of a field with limited variability. To successfully do this, prior knowledge of the field is a must. Laying out an experiment in an area of a field with pre-existing variability weakens the data generated from the experiment.
The underlying variability could make it almost impossible to detect treatment differences if they exist. If variability in the field is not accounted for, after conducting a study you might but not be able to tell if any yield differences were due to differences in soil type, drainage, etc., or the treatment.  However, if a field has a uniform pattern (i.e. increasing productivity north to south) laying out the plots so that the treatments follow this pattern is acceptable. (see top illustration in accompanying graphic).
An earlier article explained the importance of replicated comparisons vs. side-by-side comparisons, illustrating that replication and randomization of treatments within a replication is vital. Replication and randomization help you determine if any differences you see might be due to chance, error, or variability you can’t account for. The actual experimental design will depend on the variables to be studied. Extension personnel will likely be able to help set up the experiment.
The bottom photo in the accompanying graphic shows a 36-acre field example taken from the Web Soil Survey. This field has two different soil types, although many fields have much more variability than this.
Soil type A is a fairly productive silt loam, while soil type B is a much less productive silty clay loam. If the field was simply split vertically down the middle into two different treatments, the results would be very misleading. However, if treatments were laid out in replicated blocks running from left to right, the variability would be nearly equally distributed across treatments, making for a valid comparison.
On-farm research can be a valuable tool for farmers. As new products and technology emerge in this ever-changing field, new questions and methods arise. Considering the current economics of production agriculture, producers are finding more value in answering questions using on-farm research methods in their own fields.
Choosing a topic of interest, setting up the test on a uniform field area, and using proper experimental design and replication are key parts of a successful on-farm experiment. Following these steps can greatly assist in generating broadly applicable data.
Anybody who is interested in doing research on their own farm should contact the local extension office.


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