Originally written February 1, 2006 | Last updated
March 15, 2013
Farmers today have an increasing number of tools for managing crops. New
developments in precision farming technologies, biotechnology, and advancements
in pesticides, equipment, and other ag inputs are converging and arriving at the
farm-gate at an unprecedented rate. Sifting through the overwhelming milieu of
technologies to find the tools that really work is a challenge for farmers and
the consultants and agronomists that serve and support production agriculture.
New treatments may not necessarily be superior in local areas, even
though their average performance over a wider region is superior. Conducting
a performance test on your farm or in cooperation with neighbors using
similar management practices can help you select the best treatments for
Often farmers use technologies with little or no evaluation prior to use.
Industry heavily invests in technology research and development, thus,
ramp-up is fast and products are often marketed and distributed quickly in
an attempt to recover investments in the early phases of technology
adoption. Often farmers, usually at great expense, must learn and re-learn
management of these technologies as new and improved versions are released.
The objective of an on-farm trial is to predict how
different management options will perform compared to each other under your
environment and cropping system. The process of testing a hypothesis and
using the information gained in a cooperative, systematic manner has been
highly successful in providing viable options for making production
decisions on the farm. The process of incremental change and gradual
improvements has evolved into a system of research, development and
production never imagined just decades ago.
In general, there are two major categories of on-farm research
trials. The first is replicated trials that try to account for
field variability with repeated comparisons. Examples include trials
conducted by universities and by public and private plant breeders. The
other type is non-replicated demonstrations such as yield contests, on-farm
yield claims, demonstration trials and farmer observation and experience.
Identifying the source of variability is a primary objective in any
on-farm trial. The use of statistical methods including replication and mean
comparisons improves the reliability and confidence of results. An
overriding strength of on-farm evaluations is the credibility of the results
in the eyes of the end user, the farmer by showing how the practice responds
within his production system. Often the power of these trials can be
enhanced with simple modifications such as replication within locations and
across multiple sites with coordinated effort.
The advent of effective tools for collecting data related to crop
production such as weigh wagons, on farm scales and yield monitors have
removed many of the traditional barriers of on-farm trials. The next phase
in the development of agriculture is necessary coordination of multi-site
trials that will require collaborative specialists for data collection and
What is on-farm research?
What's special about on-farm trials? From a standpoint of actually
doing trials, there is nothing special. The nature of on-farm trials limits
the number of comparisons, doesn't allow some treatments that require
special equipment, and requires timeliness/priority that at times can be an
issue. It is, though, real field conditions. The main advantage for
on-farm trials is that it makes it possible to test over a large number of
sites, thus it can more easily move research from description to
prediction. Knowing what we want to say when the work has been completed
should be a critical precondition for undertaking on-farm, applied research.
If you can't even guess what such a statement might look like, you might
want to hold off. A certain percentage of all on-farm research projects are
wasted effort or even harmful in that they suggest the use of harmful
inputs, suggest the use of useless inputs, and fail to predict (accurately)
the benefit of a useful input.
Check (Control) Plots: Your current practice is
represented in the check or control plot. It does not receive the new
technology being tested; rather it represents your current management style
where your tillage practice, applied fertilizer, variety and/or applied
fungicide is used in the usual manner. The check plot and the treated plot
differ only in the specific treatment comparison being made. Aside from this
treatment, plots are managed exactly the same to avoid biasing results.
In some trials, the new technology incorporates several practices. Avoid
these if possible. For example, consider a trial that compares a farmer's
current planting operation with another planting operation using different
tillage, fertilization and row spacing systems. A fair comparison can only
be made between the two complete systems, not any given part of either
system. This kind of trial is difficult to interpret because of all the
confounding interactions that may occur among the parts.
Replication: Replication is used to determine whether
the difference between plots is due to chance variation or treatment
variation. Chance variation is caused by differences in weather, soil and
other factors. These factors change significantly in space (field to field)
and time (year to year). Through replication in both space and time, average
treatment effect values can be obtained. Replication in space means that
several plots of each treatment are grown in the field (field replication)
or that single plots of each treatment are placed in several fields across
the farm (farm replication). Replication in time is repeating the trial over
several years. Comparisons between average values are 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 and final conclusions about a new practice if it is made only after
being evaluated over several years and/or at several locations.
Low ------------------------------------ High
Soil Fertility Gradient (or yield potential, organic matter, pH,
Fig. 1. An example of a plot design, with a check plot
and treated plots arranged in three replications. The entire
trial area should be kept within a uniform soil condition. Other
plot arrangements are possible.
Randomization: Randomization prevents bias of any one
treatment in any way (intended or unintended). To randomize a trial,
randomly assign replicated check plots and treatments (Fig. 1) by drawing
numbers out of a hat or flipping a coin as you assign treatments to plots.
How can on-farm research be conducted so that it means something?
The key to an on-farm test is that it must make repeated and unbiased
side-by-side comparisons of the practices in question. These repeated
comparisons are called replications. With replication, we can use simple
statistical formulas to decide if one practice or â€œtreatmentâ€ differs enough
from the other to be sure the difference was not due to chance.
A well conducted performance test on your farm or in cooperation with
neighbors using similar management practices is an important part of
deciding upon new agronomic practices for efficient and profitable crop
production. Key ingredients of an on-farm testing program include:
- Establish goals and objectives.
- Which treatments will be evaluated and what is the check
- Select a site.
- Lay out plots on the selected site.
- Decide who, what where, when and how measurements will be
- Determine how data will be analyzed.
- Extending the results.
On-farm trials 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 hypothesize that a strip-tillage system will leave more surface
residue that the tillage system you now use. The trial you establish would
involve a comparison between the two systems. One objective will be to
determine if the reside levels are different between the two tillage
systems. Residue levels would be one type of data collected as part of the
Selection of treatments to be tested: Keep treatments simple. Limit the
number of treatments to no more than four (10 for a hybrid/variety trial), including one well-known
treatment as a check (control) plot. As the number of treatments increase so
does the complexity of the on-farm trial. Choose treatments that you expect
to be significantly different. With experience you will gain confidence in
your on-farm testing abilities and you can move on to testing treatments
involving minor impact, difficult to test production practices. It is very
important that production inputs remain constant, except for the tested
In field planning
Field variation can mask treatment differences, so chose as uniform a
field site as possible. Organize the field plot layout so that that all
treatments have an equal opportunity to perform. 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 as 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. Use 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?
Selecting treatments. When selecting treatments, limit the number of
treatments to no
more than 10 and include two or three well-known check treatments. Select hybrids
of similar maturity.
Replication and Randomization: Choose a uniform area in
the field. Each practice should be repeated (replicated) two or more times
in the trial. Two different layouts or designs are commonly used. Completely
random designs are used on uniform sites. In this design, all treatments
have an equal chance of being assigned to any given plot including identical
treatments side by side. Use a randomized complete block design when it is
not possible to obtain a uniform test site. Arrange the plots so that each
treatment is used once in each replication. Treatments should be arranged in
a different order in each replication (randomized). See Figure 1. 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 within the replication. The effects of
replications can be removed or blocked out when analyzing the data.
Plot size: Optimum plot size for on-farm tests may vary
greatly with the size of uniform land area, number of treatments and size of
equipment. 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. Plots should be similar in size. Avoid using field edges in
plots. Field edges should be left as borders. 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.
Management: Each plot should be managed exactly the same
and as close as possible to the conditions which normally occur on your
Data collection and record keeping
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. Detailed written records are often required
to interpret data. Written documentation preserves the details of your
on-farm trial so you can share information with others. Remember, the more
you plan and document, the greater confidence you will have in your results.
The following information includes the baseline data needed to document
and interpret a valid, unbiased test.
Trial description: State the goals, objectives,
treatments and experimental design of the trial.
Field history: Record obvious variations within the test
site and the previous cropping history including crop rotation, tillage
practices, previous crop and variety, and previous fertilizer and pesticides
applied. Make a diagram showing the layout of the field trial.
Soil test and fertility: Sample soil using university
guidelines and send samples to a laboratory for analysis. Apply appropriate
fertilizer uniformly to the entire trial area.
Field notes and observations: Keep accurate and
up-to-date records. Walk the area every few weeks during the season. Note
obvious strengths and weaknesses of the treatments plus any general problems
with the test. Record any conditions that might influence stand
establishment including variety, seeding, planting date, soil temperature,
type of planter, seeding depth, row spacing, and residue levels. Take notes
of other field operations, such as the type of equipment, depth of tillage
operations and materials applied.
Weather: Record significant weather events such as high
winds, frost and hail. Precipitation is more variable than temperature
between sites. If practical, place a rain gauge at the test site with a
little oil in it to prevent water from evaporating and after each storm,
record rainfall and empty the rain gauge.
Insects, Weeds and Disease: Record the presence and
density of pest (diseases, insects and weeds) and the extent or severity of
crop damage. Note differences between treatments, if any.
Crop Growth and Development: During the growing season
record the crop stage at the time of treatment and management operation
applications, such as pesticide spraying. When abnormal conditions occur,
such as drought, note the differences in plant growth response among
treatments. It is just as important to record no differences among
treatments at a certain growth stage as it is to record obvious differences.
Measurements: Depending on the test, take stand counts
of the crop, ratings of weed control, disease or insects. Weigh the yield of
each plot, take a moisture sample, and adjust yields to the same moisture
content. Yield estimates are needed to make production and economic
comparisons between treatments. Measure the size of the harvest area using a
measuring tape or before or immediately after you harvest each plot. These
distances are then multiplied by the width of the combine head to arrive at
the harvested area and yield per acre.
Harvest the middle area of each treatment plot so that border effects do
not bias the results. Yields can be measured with a local truck scale, a
weigh wagon, or a properly calibrated yield monitor. 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, you must be
corrected to constant moisture.
Data analysis and Statistics
Data analysis largely depends on how the project was designed and
conducted. Simple statistical software packages are available. Microsoft
Excel has a very good analysis of variance procedure.
Economics: Use a partial budget analysis where
Grower return = (Yield*Price) - [Yield * (Handling+ Hauling+ Storage+
- Price = Weighted Price per Bushel = 50% November 15 Average Cash
price + 25% March CBOT Futures price ($0.15 basis) + 25% July CBOT
Futures price ($0.10 basis). November 15 Average Cash price derived from
Wisconsin Ag Statistics; CBOT Futures prices derived from closing price
on first business day in December.
- Handling costs = $0.02 per bushel
- Hauling costs = $0.04 per bushel
- Storage costs = $0.02 per bushel for 30 days
- Drying costs = $0.02 per bushel per point of moisture
- Trucking costs = $0.11 per bushel for 100 miles
Analysis: Use averages over replicates to compare
treatments. A well-conducted test will have small differences among plots of
the same treatment and some large difference between treatment averages.
Consider all-important traits and not just yield.
Variations in yield and other measurements because of variations in soil
and other growing conditions lower the precision of the results. Statistical
analysis makes it possible to determine, with known probabilities of error,
whether a difference is real or whether it might have occurred by chance.
Means are often separated using a number labeled "LSD" which stands for
least significant difference. LSD's at an appropriate level of confidence
(usually 10%) are used. Where the difference between two selected treatments
within a column is equal to or greater than the LSD value at the bottom of
the column, you can be sure that in nine out of ten chances that there is a
real difference between the two treatment averages. If the difference is
less than the LSD value, the difference may still be real, but the
experiment has produced no evidence of real differences.
Statistics are only a tool to help prevent us from deceiving ourselves
and others. Growing conditions in any particular year can have large effects
on certain practices. Two years of replicated data are a minimum for
supporting most practices. Statistics, as commonly used, often describe
better than they predict. But, stats used over a lot of site-years can
provide a measure of the usefulness of a prediction based on data. And yet,
statistical statements always involve probability, and this is not always
easy to â€œapplyâ€ when it comes to inputs. Statistics do NOT substitute for
the large amount of data (site-years) that good on-farm testing always
Time: Data from one field in one year may be misleading.
About two to three years of your own tests in conjunction with other
reliable information should be adequate to select treatments to be practiced
on larger acreage. One year's data may be adequate to discard poor
treatments from the test. Replace discarded treatments with new treatments
in any tests conducted next year.
Adjustments in the number site-years should be considered if expected
variability due to soil type is high (then you need more soil types), if
expected variability due to years (weather) is high (then you need more
years), and/or if variability is expected to be high over both soils and
years, (then you will need a lot of sites and years). If variability is
expected to be high over varieties/hybrids, you have a problem.
Summary of results
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.
On-farm testing is not a quick cure for anything, but it should greatly
accelerate innovation and adoption of new practices by providing reliable,
quantitative answers that apply directly to a producers situation.
Treatments frequently differ in performance and these differences may vary
with management practices, weather patterns, soil conditions, and other
environmental and management practices. Replicated trials which take into
account field variability are the most reliable and need to be part of
informed selection of new practices for profitable crop production.
Lessons from experience
- If possible, include the cooperator's current practice
- You can't over document a trial: maps, plot layout, field
markers, distances, etc. A field at planting looks a lot different five
- Communicate with the cooperator early and often - planting and
harvest are especially important
- Plan multiple locations......you'll probably lose a couple
- Be nice to people with scales and weigh wagons. You need them.
- Have an efficient data collection system.
- Visit the trial during the growing season. Measure distances, take
stand counts, look for environmental and manmade problems. Document
location of potential problems
- Send a letter of thanks and the results to the cooperator
- Purchase, borrow, make, or steal equipment when you can wheel, 300
ft. tape, generator, large shop vacuum, PVC stand count stick, PDA,
moisture tester, sprayer, etc.
- THE COOPERATOR can make or break a project. It helps if
they're interested in the result. Be willing to accommodate the
- Commitment to the project......a poor trial is worse than no trial
Evaluating Corn Hybrid Demonstration Plots
This is the time of year when many farmers visit and evaluate hybrid
demonstration plots planted by seed companies and county Extension
personnel, among others. When checking out these plots, it's important to
keep in mind their relative value and limitations. Demonstration plots may be
useful in providing information on certain hybrid traits, especially those
that are usually not reported in state corn performance summaries. The
following are some hybrid characteristics to consider while checking out
hybrid demo plots.
PLANT/EAR HEIGHT. Corn
reaches it maximum plant height soon after tasseling occurs. Remember that
although a big tall hybrid may have a lot of eye appeal, it may also be
more prone to stalk lodging in the fall. Unless your interest is primarily
silage production, increasing plant height should not be a major concern.
Generally later maturity hybrids are taller than earlier maturity hybrids.
Big ears placed head high on a plant translate to a high center of gravity,
predisposing a plant to potential lodging. The negative effects of stalk rot
on stalk lodging in the fall may be worsened by high ear placement. Plots
that have been subjected to early season (V7 or earlier) defoliation caused
by hail or frost often have lower than normal ear height.
STALK SIZE. Generally
speaking, a thicker stalk is preferable to a thinner one in terms of overall
stalk strength and resistance to stalk lodging. As you inspect a test plot,
you will see distinct differences among hybrids for stalk diameter. However,
also check that the hybrids are planted at similar populations. As
population increases stalk diameter generally decreases. Keep in mind
that uneven emergence and development may make such comparisons difficult because late emerging plants are
DISEASES. During the grain
fill period, leaf diseases can cause serious yield reductions and predispose
corn to stalk rot and lodging problems at maturity. Ear rots can also impact
yield and grain quality. The onset of leaf death shortly after pollination
can be devastating to potential yield, since maximum photosynthetic leaf
surface is needed to optimize grain yield. Hybrids can vary considerably in
their ability to resist infection by these diseases. Demonstration plots
provide an excellent opportunity to compare differences among hybrids to
disease problems that have only occurred on a localized basis. Look for
differences in resistance to northern corn leaf blight, gray leaf spot, and diplodia
Check to see if foliar
fungicides have been applied and what crop rotation has been followed.
Typically you'll encounter more severe foliar disease problems in no-till,
STALK ROTS. Hybrids will
likely differ widely when faced with strong stalk rot pressure. Begin
checking plants in late August or about 6 weeks after pollination by
pinching lower stalk internodes with your thumb and forefinger. Stalks that
collapse easily are a sure indicator of stalk rot. Remember that hybrids
with thicker stalks may be in plots having thin stands.
LODGING. Perhaps as important
as stalk rot resistance is the stalk strength characteristics of a hybrid.
Sometimes, superior stalk strength will limit the adverse effects of stalk
rot. If your variety plot is affected by stalk rot in late August and early
September, evaluate stalk lodging of the different hybrids. Most agronomists
characterize plants with stalks broken below the ear as "stalk
lodged" plants. In contrast, corn stalks leaning 30 degrees or more
from the center are generally described as "root lodged" plants; broken
stalks are usually not involved. Root lodging can occur as early as the
mid-to- late vegetative stages (as it did this year) and as late as harvest
maturity. Both stalk and root lodging can be affected by hybrid
susceptibility, environmental stress (drought), insect and disease injury.
Root lodging may be
associated with western corn rootworm injury. However, much root lodging occurs as the result of other factors, i.e. when a hybrid susceptible
to root lodging is hit by a severe windstorm. A hybrid may be particularly
sensitive to root lodging yet very resistant to stalk lodging. A cornfield
may exhibit extensive root lodging in July but show little or no evidence of
root lodging at harvest maturity in September (except for a slight â€œgoose
neckingâ€ at the base of the plant).
TRANSGENIC TRAITS: Because
damage from European corn borer (ECB) and western corn rootworm (RW) can be
very localized, strip plot demonstrations may be one of the best ways to
assess the advantages of ECB Bt and RW Bt corns. The potential benefit of
the ECB Bt trait is likely to be most evident in plots planted very early or
very late; the potential benefit of the RW Bt trait is likely to be most
evident in plots planted following corn or in a field where the first year
western corn rootworm variant is present.
HUSK COVERAGE/EAR ANGLE.
Hybrids will vary for completeness of husk coverage on the ear as well as
tightness of the husk leaves around the ear. Ears protrude from the husk
leaves are susceptible to insect and bird feeding. Husks that remain tight
around the ear delay field drydown of the grain. Hybrids with upright ears
are often associated with short shanks that may be more prone to ear and
kernel rots than those ears that point down after maturity. Differences in ear â€œorientationâ€ among hybrids can be strongly
influenced by growing season and plant density. Also, under certain
environmental conditions, some hybrids are more prone to drop ears, a major
problem if harvesting is delayed.
The following are some
additional points to consider during your plot evaluations:
- Field variability alone
can easily account for differences of 10 to 50 bushels per acre. Be
extremely wary of strip plots that are not replicated, or only have check
or tester hybrids inserted between every 5 to 10 hybrids. The best test
plots are replicated (with all hybrids replicated at least three times).
- Don't put much stock in
results from ONE LOCATION AND ONE YEAR, even if the trial is well run and
reliable. This is especially important in years with tremendous
variability in growing conditions (e.g. planting dates) across the state. Don't overemphasize results from ONE TYPE OF TRIAL. Use
data and observations from university trials, local demonstration plots, and
then your own on-farm trials to look for consistent trends.
- Initial appearances can be
deceiving, especially visual assessments! Use field days to make careful
observations and ask questions, but reserve decisions concerning hybrid
selection until you've seen performance results.
- Walk into plots and check
plant populations. Hybrids with large ears or two ears/plant may have thin
- Break ears in two to check
relative kernel development of different hybrids. Use kernel milk line
development to compare relative maturity of hybrids if hybrids have not yet
reached black layer. Hybrids that look most healthy and green may be more
immature than others. Don't confuse good late season plant health (stay
green) with late maturity.
- Differences in standability will not show up until later in the season and/or until after a
windstorm. Pinch or split the lower stalk to see whether the stalk pith is
beginning to rot.
- Visual observations of
kernel set, ear-tip fill, ear length, number of kernel rows and kernel
depth, etc. may provide some approximate basis for comparisons among hybrids
but may not indicate much about actual yield potential. Hybrid differences
are common for tip kernel abortion (â€œtip diebackâ€ or â€œtip-backâ€) and â€œzipper earsâ€ (missing kernel rows). Even if
corn ear tips are not filled completely, due to poor pollination or kernel
abortion, yield potential may not be affected significantly, if at all,
because the numbers of kernels per row may still be above normal.
- Find out if the seed
treatments (seed applied fungicides and insecticides) applied varied among
hybrids planted, e.g. were the hybrids treated with the same seed applied
insecticide at the same rate? Differences in treatments may affect final
stand and injury caused by insects and diseases.
What are the implications? How far can the research take you?
On-farm testing is not a quick cure for anything, but it should greatly
accelerate innovation and adoption of new practices by providing reliable,
quantitative answers that apply directly to a producer's situation.
Treatments frequently differ in performance and these differences may vary
with management practices, weather patterns, soil conditions, and other
environmental and management practices. Replicated trials that take into
account field variability are more reliable than non-replicated trials and
improve the confidence of implementing of new practices for profitable crop