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A Grower's Guide

Baird Miller, Ed Adams, Paul Peterson, and Russ Karow

(also in pdf format)


This guide provides growers already doing on-farm testing (OFT) with a tool to refine their testing system. It also provides growers considering on-farm testing for the first time a basic background in testing theory and procedures. The guide is narrowly focused on one type of test--replicated strips. This type of test is similar to testing done by university researchers and is the easiest to statistically analyze and interpret.


Before we discuss on-farm testing, you must understand the following terms and concepts used in field testing:

Check Plots:

A check plot or control represents your current practice. It does not receive the new technology being tested. This might be your conventional tillage practice, fertilizer applied in the usual manner, the variety you currently grow, or a crop not receiving a fungicide application. The check plot and the treated plot differ only in the specific treatment comparison being made. Aside from this, plots must be managed exactly the same to avoid biasing the results.

In a tillage experiment, the check plot could be your normal plow operation and the new technology might be a sweep chisel. Aside from the two tillage treatments, all other production practices must be the same: planting date, fertilizer rate, variety, weed and disease management.

In some situations, the new technology incorporates several practices. For example, if a conventional till-plant operation is compared to no-till seeding, different tillage, fertilization and seeding systems are being compared. A fair comparison can only be made between the two complete systems, not any given part of either system.


Replication, meaning repitition, is used to determine whether the difference between plots is due to chance variation always present in fields or caused by the treatment(s) being evaluated. Through replication, average treatment effect values can be obtained. Comparisons between average values are always more accurate than those between single plots. Replicating your check and treatment plots at least three or more times will give you much greater confidence in your results.

Trials are generally replicated in both space and time. Replication in space means that several strips of each treatment are placed in a field (replication on-site) or that single strips of each treatment are placed in several fields across the farm (replication over sites). Replication in time is repeating the trial over several years. Climatic conditions, soils, and other factors can change significantly from location to location and year to year. It is critical that final conclusions about a new practice be made only after being evaluated over several years and/or at several locations.


Randomization assures that any one treatment is not biased or favored in any way. To randomize a trial, randomly mix the order and placement of replicated check plots and treatments (Fig. 1). You may draw treatment numbers out of a hat or flip a coin as you assign treatments to plots. If treatments are assigned to plots in a nonrandom fashion, you may unknowingly introduce bias.

Fig 1

Figure 1. An example of a completely random plot design, with a check plot (C), a treated plot (T), and 3 replications. Notice the entire trial area is kept within a uniform soil condition. Other plot arrangements are possible.

The following example shows how these basic ideas are used in an on-farm testing situation:


The steps involved in laying out an on-farm test are:

  1. Establish your goal and objectives.
  2. Determine what treatments you will use and what your check plot or control will be.
  3. Select a site.
  4. Determine how best to lay out your plots on the selected site.
  5. Determine what data you will collect and how you will collect it.
  6. Determine how the data will be evaluated.
  7. Determine how the data will be shared with others.


Every OFT project must have a goal and specific objectives. Goals are statements of the overall theme of your experiment. A goal, for example, may be to reduce soil erosion on your farm.

Objectives are statements of the problem you wish to evaluate in your project. These are the ideas you want to test or questions you want to answer. Objectives are measurable and relate to your overall goals. The objectives will determine what is measured and the type of data you will collect during the project. For example, you might postulate that a no-till seeding system will leave more surface residue than the seeding system you now use. The trial you establish would involve a comparison between the two systems. One objective will be to determine if the residual levels are different between the two seeding systems. Residue levels would be one type of data collected as part of the trial.


When selecting treatments for a trial, keep them simple and few, no more than 3, including the check plot. As treatments increase in number, so do the number of plots, and the complexity of the OFT project.

Choose treatment comparisons that represent significantly different production practices. Until you have a significant amount of experience in on-farm testing, avoid making treatment comparisons of minor production practices.

Always include an appropriate check or control plot. For instance, you might wish to compare deep placement of fertilizer versus broadcast application. Knowing that surface applied fertilizer is often less efficient than deep placed fertilizer, it may be tempting to increase the surface applied rate in order to try to equalize treatments; however, this would confound the study. If placement is the main objective, different fertilizer rates should not be included as treatments. If is very important that production inputs other than the treatments being tested remain constant. If management inputs are changed between treatments, the results may be biased due to the input differences.


The most common problem with on-farm trials is lack of recognition that field variation can mask or conceal treatment differences. Take special care to plan and organize the field plot layout to assure that all treatments have an equal opportunity to perform. Choose a field site with the greatest possible uniformity. Regardless of whether you have a 40-ft by 100-ft plot or a 100-foot by quarter-mile plot, a uniform field location is critical. When choosing a site consider previous crop history (fertilizer rates, herbicides, tillage, etc.), drainage, soil texture, soil depth, topography, pest infestations, and bordering influences such as trees, runoff from neighboring fields, lack of fencing from animals, and other factors. Avoid placing trials in runoff areas, near fence lines or in field corners. These areas are often subject to multiple or irregular applications of fertilizer and herbicides.

The characteristics of a uniform field site depend on the type of test being conducted. Pay particular attention to things that strongly influence your treatments. For example, when testing a soil-applied herbicide, soil organic matter content pH, and texture consistency are important. In a fertility trial, the soil must have uniform drainage, soil depth, organic matter content, and soil nutrient levels. For variety or tillage comparisons, the overall soil productivity level should be constant within the field site. Use your county soil survey maps of the fields being considered to help you select the site.

Consider site access when selecting a plot location. Is the site easily accessible for mid-season treatment applications and data collection? If early or differential harvest is likely (such as with an alternate crop), can you get at the site with harvest equipment without destroying other crops? Will you hold a tour of your site? If so, is there ready access for visitors and their vehicles?


After selecting a site, actual plot layout must be considered. Two different layouts or designs, the completely random design and randomized complete block design, are commonly used. Completely random designs are used if the test site is known to be very uniform, without differences in soil characteristics, fertility levels, slope, and previous crop. Layout of the plots might look like those in Figure 1. In this design, all treatments have an equal chance of being assigned to any given plot. It is possible to have identical treatments side by side.

Use a randomized complete block design when it is not possible to obtain a uniform test site. For example, the test site may have different slopes, previous crops, soil depths, etc. In this case, treatments are grouped into sets called replications. Each replication contains a complete set of treatments. Each replication is placed in a uniform area. Using such an arrangement allows all treatments to have equal potential to perform. Through this design, the effects of replications can be removed or "blocked out" when analyzing the data. Plot layout might look like that in Figure 2.

Fig 2

Figure 2. An example of randomized complete block design with a check plot (C), treatment plot (T), and 3 replications. Each block of treatments (replication) is kept within a uniform soil condition.

An alternative approach is to include variation equally across all the treatments in a test. As an example, field strips can include the field variation by running the strips perpendicular to the variation. A layout of this type is shown in Figure 3. You must be extremely careful to include the same variation equally across all treatments to have a valid test. Establishing treatments within a uniform area is still the best method.

Fig 3

Figure 3. An example of a replicated strip trial, using a completely random design. Strips have been laid out so that each treatment has the same amount of soil variability.


Plot size is determined by field size, uniformity of the field, equipment used and area needed to carry out a particular treatment. Adjust plot lengths so that each treatment is within a reasonably uniform area or so that each uniformly covers the field variation as discussed above. Strip plot width is determined by the width of equipment used to apply treatments (e.g., planter, sprayer, etc.) and/or harvest plots. The width of the established treatment should be larger than the harvest width. This way there will be a uniform harvest width and errors in harvesting will not affect side by side treatments. Typical treatment plots are between 1/10 and 1/2 acre.


Written records of steps taken in conducting on-farm trials are important for two reasons. First, detailed records are often required to interpret data. Sometimes the results are unclear. Thinking about them further and looking over documentation usually brings explanations to light. Second, written documentation preserves the details of your OFT project so you can share information with others.

Five record sheets are included here in pdf format to show the type of records you should keep: 'On-Farm Research Field Record Log', 'On-Farm Research Management Summary', 'On-Farm Research Costs Summary', 'On-Farm Research Income Summary', and 'Rainfall Record Sheet.' Also, keep required record sheets on any pesticide applications.

The following information includes the baseline data needed to document and interpret a valid, unbiased test. This information is easily entered on the record sheets.

OFT Trial Description. Clearly state the goals, objectives, treatments and experimental design of the trial.

Field History. Record differences in soil type and other obvious variations within the test site and the previous cropping history. Include crop rotation, tillage practices, previous crop and variety, fertilizer and pesticides applied. Make a diagram showing the layout of the field trial.

Soil Test and Fertility Program. Sample soil from the intended harvest areas using university guidelines. Send samples to a reputable laboratory for analysis. Make fertilizer applications based on soil test results. Record the quantity and form of fertilizer used.

Soil Moisture at Seeding. If your soil samples are taken near the time of seeding, record the depth to moisture and depth of moisture. Have your soil samples evaluated for available soil moisture.

Planting Conditions. Record the crop, variety, seeding rate (pounds per acre and seeds per pound), planting date, soil temperature, type of planter, seeding depth, row spacing, residue levels and any other conditions that might influence the stand establishment and crop production.

Field Operations and Observations. Record all field operations in diary format. Take notes on the methods of your field operations, such as the type of equipment, depth of tillage operations and materials applied to either the whole field or to just one treatment.

Weather. General observation of growing season weather conditions is all that is required. If practical, place a rain gauge at the test site. After each storm, record rainfall in the rainfall record sheet and empty the rain gauge. A little oil in the rain gauge will prevent the water from evaporating before you can get out to the field to measure it.

Insects, Weeds and Disease. Make notes on the presence and density of insects and diseases, date of infestation, and extent or severity of damage. Record similar observations for persistent weeds. Note differences between treatments, if any, due to pests. If pesticide treatments are being compared, take more detailed data to evaluate crop injury and level of control of different pest species.

Crop Growth and Development. During the growing season make and record observations of plant growth and development. Record the date each treatment reaches a critical growth stage. It is just as important to record that you see no differences among treatments at a certain growth stage as it is to record obvious differences. For example, critical stages in cereal crop development include emergence, tillering, stem elongation, booting, and heading. Record crop stage at the time of treatment applications, such as spraying or top dressing. When abnormal conditions occur, such as drought, note the differences in plant growth or response among treatments.


It is important to plan ahead and identify what should be measured, and when and how to take measurements. What you will measure depends on the project's objectives.

If the purpose is to increase yield, then a measure of yield is required. If the objective of a new practice is to increase soil moisture, then soil water tests are needed. If the purpose is to increase net farm profit, then you must analyze costs and returns (including yield).

If you need help in deciding what to measure and how to measure it, consult your county extension agent. Without appropriate data and a method to measure treatment differences, your trial will have little value or could lead to inaccurate conclusions. Remember, the more you plan and document, the greater confidence you can have in your results.


Yield estimates are needed to make production and economic comparisons between treatments. To be valid, yield measurements must be taken from comparable areas in each treatment plot. You must measure the size of the harvest area. Measure plot lengths with a measuring tape or other reliable measuring device before or immediately after you harvest each plot. These distances are then multiplied by the width of the combine header to arrive at the harvested area. Harvested area is used to calculate yield per acre. An example of yield calculation is shown in the data analysis section of this manual.

Harvest the middle portion of each treatment plot. This assures that the yields are not affected by a condition bordering the treatment. Yields can be measured with a local truck scale, a weigh wagon, or using the barrel method. Harvest equipment must be completely empty and clean before each treatment is harvested.

Save a sample from each treatment to determine moisture content at harvest and any other quality factors that may be important such as test weight and protein content. If moisture contents differ between the treatments, yield must be corrected to a constant moisture.

The barrel method is a slow and time-consuming method to determine yield, but may be useful if other methods are not available. It involves measuring the volume of grain yield using a standard 55-gallon barrel. Treatment yields can be compared using the barrel method as long as the test weights do not differ between the treatments. For example, with wheat you can use the following protocol:


Data collected in the trial must first be converted to commonly used units prior to analysis and summary. The following is an example, converting wheat yields to common units.

Combine header width: 20 ft
Length harvested: 400 ft
Harvested area: 20 ft x 400 ft = 8000 sq ft
Acres harvested: 8000 sq ft/43,560 sq ft per acre = 0.18 acre
Measured grain weight: 1000 lbs
Wheat in bushels: 1000 lbs/60 lbs per bushel = 16.7 bushels
Yields in bushels per acre: 16.7 bushels/0.18 acre = 92.8 bushels/acre

Data analysis largely depends on how the project was designed and conducted. Using a uniform field site, simple treatments, and a randomized complete block design, a research test can be statistically analyzed and the results quickly evaluated and interpreted. Simple statistical software packages are available through your county extension agent to do data analysis.


To interpret results from your on-farm trial, carefully summarize management history, data collected, and observations made. Summary forms are provided in the back of this guide for this purpose. The summarized results should address your goals and objectives. If your objective was to reduce costs, equal or even lower yields may be an acceptable result as long as costs are reduced and the net return has improved.
Take the time to share the results with your neighbors and county extension agents. This flow of information and experience is necessary for the progress of agricultural production and management.

Baird Miller is an extension agronomist with Washington State University. Ed Adams is a regional Water Quality Coordinator with Washington State University. Paul Peterson is an area extension agent with Washington State University. Russ Karow is an extension agronomist with Oregon State University.


Contact us: Hans Kok, (208)885-5971 | Accessibility | Copyright | Policies | WebStats | STEEP Acknowledgement
Hans Kok, WSU/UI Extension Conservation Tillage Specialist, UI Ag Science 231, PO Box 442339, Moscow, ID 83844 USA
Redesigned by Leila Styer, CAHE Computer Resource Unit; Maintained by Debbie Marsh, Dept. of Crop & Soil Sciences, WSU