Video on data analsis of agricultural field with out replication
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Basics of Experimental Design
The previous section summarized the 10 steps for developing and implementing an on-farm research project. In steps 1 through 3, you wrote out your research question and objective, developed a hypothesis, and figured out what you will observe and measure in the field. Now you are ready to actually design the experiment. This section provides more detail on step 4 in the process.
Recall from the introduction that on-farm research provides a way of dealing with the problem of field and environmental variability. In comparing the effects of different practices (treatments), you need to know if the effects that you observe in the crop or in the field are simply a product of the natural variation that occurs in every ecological system, or whether those changes are truly a result of the new practices that you have implemented.
Take the simple example of comparing two varieties of tomatoes: a standard variety and a new one that you have just heard about. You could plant half of a field in the standard variety and the other half of the field in the new variety. You plant the tomatoes on exactly the same day, and you manage both halves of the field exactly the same throughout the growing season. Throughout the harvest period, you keep separate records of the yield from each half of the field so that at the end of the season you have the total yield for each variety. Suppose that under this scenario, the new variety had a 15 percent higher yield than your standard variety. Can you say for sure that the new variety outperforms your standard variety? The answer is no, because there may be other factors that led to the difference in yield, including:
The new variety was planted in a part of the field that had better soil.
One end of the field was wetter than the other and some of the tomatoes were infected with powdery mildew.
Soil texture differences resulted in increased soil moisture from one end of the field to the other.
Part of the field with the standard variety receives afternoon shade from an adjacent line of trees.
Weed pressure is greater in one part of the field with the standard variety.
Adjacent forest or wildlands are a source of pests that affect one end of the field more than the other.
Because the experiment was not set up to account for field variability, you cannot conclude whether one variety’s superior performance was due to the variety itself or due to differences in growing conditions. You did not replicate the treatments. Therefore you have no way to apply a statistical test of your data. As you think about your own farm, what other sources of variation might have an impact on your
research question?
With the right experimental design and statistical analysis, you can identify and isolate the effects of natural variation and determine whether the differences between treatments are “real,” within certain levels of probability. This section looks at three basic experimental design methods: the paired comparison, the randomized complete block and the split-plot design. Which one you choose depends largely on the research question that you are asking and the number of treatments in your experiment (Table 2).
The number of treatments in your experiment should be apparent from your research question and hypothesis. If that is not the case, then you will need to go back and refine your research question so that you have more clarity as to what you are testing. As previously noted, when identifying your research question (step 1), remember to keep things simple. Avoid over-complicating your experiment by trying to do too much at once. And, keep in mind that although the randomized complete block and split-plot designs provide more information than the paired comparison, they also require a larger field area, more management and more sophisticated statistics to analyze the data. Table 2 also lists the type of statistical analysis associated with each experimental design method. These statistical techniques are covered in the next section, Basic Statistical Analysis for On-Farm Research. First is a review of some basic experimental design terminology.