On-Farm Research With Crops

	Rey Torres, a Tao County, N.M., extension specialist, helped farm families who recently formed a new wheat cooperative determine whether to diversify into greenhouse vegetables for the fresh market. – Photo by Jeff Caven

Once you’ve identified an objective, you can design an experiment to collect the desired information. The best way to have faith in your results is to design research plots that you can compare against each other – again and again.

Each research experiment involves “treatments,” or practices on different field plots designed to test your hypotheses. Replicating your treatments – or repeating the same treatment in the same field – will allow you to distinguish between random variation in the system and the real effects of your work. Analyzing data in a valid statistical manner is virtually impossible without replicated treatments. Most scientists would advise at least three replications.

Researchers also randomize treatments to eliminate any potential bias that might exist in the system. For example, if organic matter gradually increases from west to east across a field and a two-treatment experiment is laid out in that field in a simple alternating pattern from west to east – such as Plot A-Plot B-Plot A-Plot B – each “B” treatment will have a built-in bias of more organic matter compared to its corresponding “A” treatment. Randomizing the pattern of replicated treatments will help eliminate that bias. Randomize your treatments even if you do not see any indication of differences in your fields.

While researchers use several different experimental designs for field trials, on-farm researchers studying cropping systems typically use either of the two shown below.

Randomized Complete Block Design
The most popular experimental design used for crop research, the randomized complete block design, groups treatment plots together and randomizes them within replicated blocks. The following example shows how a trial testing three treatments of varying nitrogen rates (0, 80 and 160 lbs/acre), each replicated three times, might be laid out in a randomized complete block design.

For example, a farmer might apply commercial fertilizer at 80 pounds per acre in one plot, 160 pounds in another and none in a third. The layout of plots in the field should be random.

Split-Plot Design
Another popular and useful design for on-farm researchers is the split-plot design. This design allows you to test two different factors and how they interact. For example, to determine how much you can reduce nitrogen in corn following a hairy vetch cover crop, the split-plot design could be used as follows: Set up the main plots, each split into two treatments (vetch versus no vetch). Then overlay each main plot with a second treatment (varying nitrogen rates).

Such experimental designs are particularly well suited to farmers. Treatments can be laid out in strips, with length of the plots determined by the length of the field and the width by the equipment you use.

Applying Treatments and Collecting Data
It is important to treat every plot exactly the same except for that part that is intentionally varied – the treatments. Unintended variation within your plots can occur from many sources. Moreover, some variation can result from how treatments are applied and data is collected.

In Illinois, for example, a crop farmer set up an on-farm research project testing reduced rates of a herbicide mix on ridge-tilled soybeans. He tested four application rates – full, three-quarters, half and zero. He then used a standard randomized complete block design, properly replicating each treatment. But he did one thing wrong: He rotary-hoed all the zero-rate plots, but not any of the others.

After the farmer introduced an element of variation to one treatment, comparing the zero-rate plots to the other treatments was like comparing the proverbial apples to oranges.

Data collection is another potential source for mistakes. Take all measurements under the same conditions, using the same methods. Be as uniform as possible when applying treatments and collecting data. To analyze an experiment properly, you must have data from each individual treatment plot. Averaging all the treatment “A” plots and averaging all the treatment “B” plots will not be usable for analysis.

Tips for crop researchers:

Keep it simple, especially at first. Limit your project to a comparison of two or three treatments. As you gain confidence, try something a little more challenging.
Seek help. Key times for professional assistance are at the design stage and then again when analyzing your data.
Replicate and randomize. Plan on enough field space to do more than one strip of each treatment being tested. Mix treatments within blocks.
Stay uniform. Treat all the plots exactly the same except for the differing treatments. If possible, locate your experiment in a field of uniform soil type.
Harvest individual plots. Record data from each individual plot. Don’t lump all treatment types together or you’ll lose the value of replication.
Remain objective. The results may not turn out as you hoped or planned. Be prepared to accept and learn from negative results.
Repeat the same research project multiple years. Climate is never the same from year to year. Repeat your experiment until you are comfortable with the results under varying conditions.
Don’t ignore unexpected results. Sometimes, an experiment will generate useful information outside your project parameters. Maybe you’ve introduced a new legume to test animal weight gain after grazing, but then find that your soil organic matter has increased. Unintended findings like those could prove quite useful.
Manage your time wisely. Expect to devote extra time to your research during busy harvest seasons. Make sure you can carry out your experiment or get extra help.

-- Dan Anderson, University of Illinois

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