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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:. 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.
First is a review of some basic experimental design terminology. Treatments: A treatment is the production practice that you are evaluating. Examples of treatments include choice of variety, different fertilizer rates, different fertilizer timing, choice of cover crops, different cover crop management strategies, timing of planting, type of tillage, different pest control methods or different irrigation strategies.
The choices are limitless given the complexity of farming. On-farm research usually compares just two or three practices.
Small-scale intensive onion production on plastic in Interlaken, NY. Cornell extension vegetable specialist Christine Hoepting found growers could improve yields and reduce bacteria incidence by using alternatives to black plastic mulch, and by increasing planting density. Courtesy Cornell University Cooperative Extension. Variable: In statistics, a variable is any property or characteristic that can be manipulated, measured or counted.
In on-farm research, the independent variable is the different treatments practices you are applying, and the dependent variable is the effect or outcome you are measuring. What you measure in your particular experiment depends on what treatments you apply. Examples include crop yield, weed density, milk production or animal weight gain. Plot: Plots are the basic units of a field research project—the specific-sized areas in which each treatment is applied.
Replication: Replication means repeating individual treatment plots within the field research area. If you set up an experiment comparing two treatments, instead of setting out just one plot of Treatment A and one plot of Treatment B, you repeat the plots within the field multiple times. Replications reduce experimental error and increase the power of the statistics used to analyze data.
Block: It is usually not possible to find a perfectly uniform field in which to conduct the experiment, and some sources of variation simply cannot be controlled e. In order to address the problem of field variability, divide your field of interest into sections that have common slope and soil characteristics.
Within each section—typically known as blocks—field conditions should be as uniform as possible. Taken together, however, all of your blocks should encompass the variability that exists across the research area. After delineating the areas for your blocks, make sure you include each treatment inside each block; that way, your blocks can serve as replications.
In most on-farm research studies, four to six blocks are sufficient to provide a good level of confidence in the results. Figure 2 provides examples of how to use blocking to address field variability due to slope or soil type. Agricultural research should usually be blocked because of field variability. If your field has a known gradient, such as a fertility or moisture gradient, it is best to place blocks to that conditions are as uniform as possible within each block.
Figure 2a: On a slope, for example, each whole block should occupy about the same elevation. Treatments are randomized and run across the slope within each block. Figure 2b: Place whole blocks within different soil types.
Figure 2c: If blocks cannot be used to account for variability, then each treatment should run across the whole gradient, as in all the way down the slope or all the way across the field. This arrangement can also be used for a completely randomized design see Figure 3. Randomization: In addition to replication, randomization is also important for addressing the problem of field variability, reducing experimental error and determining the true effect of the treatments you are comparing.
Replications should be arranged randomly within the field. Or in the case of a blocked experimental design, treatment plots must be arranged randomly within each block. If you have three treatments, for example, you cannot place those treatments in the same left-to right sequence within each block.
They must be arranged in a random order. This can be done using the flip of a coin, drawing numbers from a hat or using a random number generator for each block. Skip to content The previous section summarized the 10 steps for developing and implementing an on-farm research project. 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. 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?
The goal of this book is to make some underutilized but potentially very useful methods in experimental design and analysis available to.
Design and Analysis of Experiments
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Experimental Design Scenarios Worksheet. DESIGN: A randomized crossover trial with advanced life support performed in two different conditions, with and without exposure to socioemotional stress. Scenario 1: A student is studying how far room temperature water would squirt out of a plastic milk carton when 4mm holes are punched at different heights from the bottom of the container.
Many microbial ecology experiments use sequencing data to measure a community's response to an experimental treatment. In a common experimental design, two units, one control and one experimental, are sampled before and after the treatment is applied to the experimental unit. The four resulting samples contain information about the dynamics of organisms that respond to the treatment, but there are no analytical methods designed to extract exactly this type of information from this configuration of samples. Here we present an analytical method specifically designed to visualize and generate hypotheses about microbial community dynamics in experiments that have paired samples and few or no replicates. The method is based on the Poisson lognormal distribution, long studied in macroecology, which we found accurately models the abundance distribution of taxa counts from 16S rRNA surveys. To demonstrate the method's validity and potential, we analyzed an experiment that measured the effect of crude oil on ocean microbial communities in microcosm. Our method identified known oil degraders as well as two clades, Maricurvus and Rhodobacteraceae, that responded to amendment with oil but do not include known oil degraders.
In statistics , a full factorial experiment is an experiment whose design consists of two or more factors, each with discrete possible values or "levels", and whose experimental units take on all possible combinations of these levels across all such factors. A full factorial design may also be called a fully crossed design. Such an experiment allows the investigator to study the effect of each factor on the response variable , as well as the effects of interactions between factors on the response variable. For the vast majority of factorial experiments, each factor has only two levels. If the number of combinations in a full factorial design is too high to be logistically feasible, a fractional factorial design may be done, in which some of the possible combinations usually at least half are omitted. Ronald Fisher argued in that "complex" designs such as factorial designs were more efficient than studying one factor at a time.
Faculty Profile. Stuart H. Hurlbert B. Ecology of lacustrine plankton, with emphasis on the study of prey-predator, competitive and grazing interactions using experimental microcosms. Ecology and restoration of the Salton Sea, California.
Ecology Lab Pdf. Unit 3 Oceans. Set up the pots in the trays. The required text for the lecture is the second edition of Plant Physiological Ecology by H. Ecology, or ecological science, is the scientific study of the distribution and abundance of living organisms and how the distribution and abundance are affected by interactions between the.
Systems Analysis And Design Pdf It provides a comprehensive coverage of the fundamental aspects of system analysis and design and supports them with industry-oriented researches and examples.