Farmers Guide to Conducting On Farm Research - Transition to Organic Partnership Program

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FARMERS GUIDE
TO CONDUCTING
ON-FARM RESEARCH

This guide was created by OFRF staff José Pérez Orozco, Mary Hathaway, Thelma Velez,
Heather Estrada, and Elizabeth Tobey.
Graphic design by Brian Geier and Shawn Hatjes.
We are grateful to April Thatcher, Jane Sooby and Catherine Greene for their insights.

FARMERS GUIDE TO
CONDUCTING ON-FARM
RESEARCH
This project is supported through the United States Department of
Agriculture (USDA) Transition to Organic Partnership Program (TOPP).
TOPP is a program of the USDA Organic Transition Initiative and is
administered by the USDA Agricultural Marketing Service (AMS) National
Organic Program (NOP).

www.ofrf.org

Cover image by jcomp on Freepik

Table of Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Steps Of An On-Farm Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
. Step 1: Identify your research question and hypothesis . . . . . . . . . . . . . . . . . . . . . . 4
Table 1. Examples of revising your questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Table 2. Examples of good research questions and their related hypotheses . . . 7
Step 2: Identify what you will measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Table 3. Examples of potential measurements based on what you are testing. . . . 9
Step 3: Choose an experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Step 4: Choose your field and mark location of your plots . . . . . . . . . . . . . . . . . . 17
Step 5: Establish your trial and collect data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Table 4. Examples and resources for sampling strategies . . . . . . . . . . . . . . . . . . 22
Step 6: Analyze your data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Table 5. Yield data per plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Step 7: Draw conclusions and share . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Appendix 1 – Farmer-Researcher Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Farmer-Researcher Profile 1: Gordon’s Grains . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Table 6. Yield data per acre . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
Farmer-Researcher Profile 2: Thelma’s Green Thumb . . . . . . . . . . . . . . . . . . . . . . 34
Table 7. Weed data in grams/square feet at 30 days . . . . . . . . . . . . . . . . . . . . . . 35
Table 8. Weed data in grams/square feet at 60 days . . . . . . . . . . . . . . . . . . . . . . 36
Table 9. Total biomass data in dry weight tons/acre . . . . . . . . . . . . . . . . . . . . . . 36
Farmer-Researcher Profile 3: Brise’s Blooms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Table 10. Disease incidence data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Appendix 2 – Datasheet examples and sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Yield Sample Datasheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Biomass Sample Datasheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Weed Sample Datasheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Stand Establishment Sample Datasheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Appendix 3 – Guidance and resources on statistical analysis . . . . . . . . . . . . . . . . . . . 45
Appendix 4 – Examples of farmer-led on-farm research trials . . . . . . . . . . . . . . . . . 46
Appendix 5 – Other on-farm research resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

INTRODUCTION
About OFRF

FRF is a national non-profit organization founded in 1990 to advance organic agriculture through
scientific research. The organization fosters the improvement and widespread adoption of organic
farming systems by cultivating organic research, education, and federal policies that bring more farmers
and acreage into organic production. For over three decades, OFRF has collaborated with land grant
universities and organic researchers, awarding over $3M in research grants across the United States.
One of OFRF’s core capacities is adapting science-based information for practical and sustainable on-farm
applications. Providing free educational and technical resources to support organic farmers has always been
essential to OFRF’s mission. The organization maintains an extensive research database with hundreds of
organic research articles, and has published many educational guidebooks, factsheets, instructional videos,
and webinars that are available to transitioning and organic farmers.

Who is this guide for?
Organic farmers and ranchers like you are always testing and experimenting with new ideas to improve their
farming operations. You probably have some ideas every day that you would like to try. Incorporating a few
scientific steps in your experiments will generate more reliable results that you can trust.
This guide was specifically created for you, the organic
farmer or rancher who is curious about conducting
some type of trial or experiment on your farm in a more
structured way. Whether you are looking at reducing the
use of off-farm inputs, minimizing disease pressure, trying
out new crop varieties or animal feed, or testing new cover
crop techniques or irrigation sensors, this practical guide
was created to assist you along the way.

If you are a farmer recipient of
OFRF funding to conduct your onfarm research trial, OFRF staff will
provide you in-depth technical support
throughout your entire farm trial.

The benefits of farmer-led research
Research studies have shown that farmers greatly benefit
when they lead on-farm research trials at their farms.
Conducting your own research allows you to address your
farm-specific questions, and has historically supported
the adoption and innovation of sustainable agricultural
practices across the world (Wettasinha, et al. 2014). A
recent study of farmers involved in the farmer-led
research program of the Ecological Farmers Association
of Ontario found that farmers who learned to conduct
their own scientific research were more “knowledgeable,
confident, motivated, and inspired to adopt and/or improve
ecological” farming practices (Nelson et al., 2023, p. 2).
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

“On-farm trials represent
a powerful way for
farmers to gain agency
and support in solving
our most pressing
challenges.”
~Farmer April Thatcher
April Joy Farm, WA
1

How to use this guide
This guide will walk you through seven major
steps needed to conduct a simple on-farm trial,
from the original theory you want to test all the
way through to drawing conclusions from your
results.
For each of these steps, this guide will provide
you with practical information, examples from
other farmers and ranchers, additional ideas
and resources, and the chance for you to put
your own ideas down on paper using farmer
worksheets.
The appendix section provides additional
resources to dive deeper into research design and
implementation, as well as farmer profiles that
walk through each of the research steps.

Time and resources needed

USDA photo

Conducting on-farm trials requires strong commitment. It will take dedication to establish,
maintain, and complete your on-farm trial successfully. An idea that sounds great in mid-winter
might feel overwhelming in August, so thinking through the timeline of the research trial and how
it will fit into the workflow of the farm is a useful step.
The time needed to complete a trial varies depending on what your trial is about. If you are
focusing on seed germination, your trial may take only weeks or a few months and you may take all
your data early in your cropping season. On the other hand, you may want to carry out a multiple
year trial where you collect data throughout multiple seasons, which is likely to create competing
demands with your regular farming activities.
Having stated the above, an on farm-trial also gives you the opportunity to test something new and
feel confident about your results. Undoubtedly, the experience gained from conducting trials on
your farm will help you improve your farming operation in the long run.

ORGANIC CERTIFICATION REMINDER
If you are a certified organic producer and your trial involves using a new input,
variety or practice, make sure that it complies with organic certification standards
and the farm plan you currently follow. It is also important for you to notify your
certifying agency of such changes before you start your trial.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

STEPS OF AN ON-FARM TRIAL

his guide will walk you through seven steps to complete your on-farm trial. For an overview of
these seven steps and how they fit together, see Figure 1.
STEP 1

Identify your research
question and hypothesis
Can mulch help me
increase blueberry yield?

STEP 7

STEP 2

Draw conclusions
and share

Identify what
you will measure
How will I know if
mulch helps or not?

Mulching can help
me increase
blueberry yield.

STEPS OF AN
ON- FARM TRIAL

STEP 6

Analyze your data

STEP 3

Choose an
experimental design
How should I
arrange my trial?

How does yield data
compare across the
treatments in my
trial?

STEP 5

Establish your trial
and collect data
Time to harvest
blueberries from my
trial.

STEP 4

Choose your field and
mark location of your plots
What’s the best
field for my trial?

Figure 1. Steps of an on-farm trial.
“On-farm research has provided me
For examples of farmers going through the
seven steps of this guide, see the Farmer
Researcher Profiles at Appendix 1. Each
farmer profile has a summary of each step
from planning the trial to drawing conclusions.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

with the foundation for improved long
term soil health at my farm. I have
eliminated several off-farm inputs while
fine tuning my cover cropping and
rotation practices.”

~April Thatcher,
Farmer, April Joy Farm, WA

Step 1: Identify your research question and hypothesis
Framing good research questions and hypotheses is critical to the entire on-farm trial process. Your
question and hypothesis are like your vision and mission statements, every action that comes after
should be guided by these. They can provide you with a good roadmap to follow.
Before framing your question and hypothesis, and in case you have not done so already, spend
some time learning about what research has been conducted in the past related to your question.
This may help you see if someone else has tried your ideas in the field, and could help you refine
your own research question. See Sources of research information for a good list of sources you
can search.

SOURCES OF RESEARCH INFORMATION
■ Sustainable Agriculture Research and Education (SARE). This USDA program site
has hundreds of publications and searchable reports of on-farm research conducted by
producers themselves across the country. Type in your keywords in their search box.
■ ATTRA Publication Library. A good compilation of resources for sustainable and
organic agriculture.
■ Organic Farming Research Foundation (OFRF). Publications, online courses and a
searchable organic research grants database.
■ Practical Farmers of Iowa (PFI) runs the Cooperator’s Program, a farmer-led research
initiative since 1987. See examples of farmer-led research protocols and reports in their
searchable database.
■ Ecological Farmers Association of Ontario (EFAO) runs a farmer-led research
program since 2016. Their Research Library database contains farmer-led research
protocols and reports.
■ e-Organic Extension Hundreds of technical resources for organic farming developed
by a coalition of USDA and state Extension programs.
■ Trade Magazines such as Growing for Market and ACRES USA may have some
research articles on organic farming.
■ Look to your state’s university extension system for publications relevant to organic
farming. Additionally, reach out to researchers and extension agents who have organic
farming experience in your region.
■ Google Scholar. Good source for research journal publications. If you can’t access the
full journal articles, your local extension agent may be able to obtain them for you.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Identify your research question
What idea have you wondered about that could
really benefit your farm operation? All farms
have aspects that can be improved, and perhaps
there are one or two challenges that you have
been struggling with for some time and would
really like to find solutions. These issues could
be related to yield, input or water savings,
disease, weed and/or pest management, food
safety, or anything else impacting your farm.

A GOOD RESEARCH QUESTION SHOULD:
•
•
•
•

Be clear and provide specific information
about what you want to answer
Be focused in its scope and narrow
enough to be addressed in the trial
Be precise and complex enough that it does not
simply answer a closed “yes or no” question
Be testable

Perhaps you have a few ideas that you would like to tackle. Write them all down and think about
them for some time. Which of these ideas is worth taking a good look at? What questions could
be answered by setting up a small, simple experiment at your farm? Do any of these ideas seem
practical and doable?
“Understanding what the question is that you are
trying to answer is important… that’s the bottom line.”

~Farmer Jeremy Barker-Plotkin,
Simple Gifts Farm, Amherst, MA.

Find a few farmer colleagues, an extension agent, or someone with experience who can listen to
you and discuss these ideas. Then prioritize and identify one or two good research questions. Good
research questions are clear, concise and testable statements that guide and focus your research. Do
not be afraid to ask for support to refine your questions. See Table 1 for some examples of “notso-great” research questions, tips and advice to revise them and potential revised questions. See
some examples of good research questions in Table 2.
Table 1. Getting to a good question.
Original question

Comments/advice

Revised question

What’s the best
cover crop I can use,
at what rate and
when to incorporate it
into the soil?

Too many questions asked, and need
to be more specific. Get advice from
other farmers and extension agents.

When is the best time to
incorporate sunn hemp cover
crop to maximize nitrogen
addition to the soil?

Will adding native
vegetation improve
pest suppression in
eggplants?

Not specific enough. Unclear what
and how much native vegetation
will be introduced. Pest suppression
includes all pests, focus on 1 or 2
species only.

Can I increase populations of
thrips’ natural enemies by planting
native wildflower vegetation
rows? And will this translate into
increased pest suppression for my
eggplant crop?

Where can I find
heat-tolerant lettuce
varieties?

This is not really a question you
can answer with an on-farm trial.
Get more advice from farmers and
extension agents. Once you find
potential varieties you may want to
test them in an on-farm trial.

What are the best heattolerant lettuce varieties that
can allow me to extend my
season over the summer?

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Identify your hypothesis
While a research question is the focused question your study hopes to answer, a hypothesis is
the statement the study sets out to prove or disprove. The hypothesis comes directly from your
research question, but it is more specific and testable – that is, with good certainty, you can prove
it true or false. Having a hypothesis is helpful because it frames your trial by making explicitly clear
what you are testing.
For example, let’s say you want to reduce the number of fertilizer applications in the field to reduce
labor demands without impacting yield in a bell pepper crop. You wonder if you can use the same
amount of total nitrogen over the course of the growing season, but reduce the number of times
you apply fertilizer.
Your research question could be:
Can I reduce the number of nitrogen fertilizer
applications in my bell pepper crop from 4 times
per season to 3 times per season without reducing
yield?

And your potential hypotheses could be:
•

Reducing fertilizer applications from 4 to 3
in my summer bell pepper crop will result
in higher yields

•

Reducing fertilizer applications from 4 to 3
in my summer bell pepper crop will result
in similar yields

•

Reducing fertilizer applications from 4 to 3
in my summer bell pepper crop will result
in lower yields

USDA photo

In this example, we have three hypotheses that provides us enough guidance to know:
a) we are comparing two different fertilizer schedules (3 or 4 fertilizer applications, with the total
sum of fertilizer applied remaining equal),
b) what you are going to measure (yield) to compare these two fertilizer schedules, and
c) what you think the outcome will be (higher yields, similar yields or lower yields).
See Table 2 for examples of questions and hypotheses.
For some research inspiration and ideas, see Appendix 4 for a list of 30 farmer-led
on-farm trials conducted through the assistance of the Sustainable Research and
Education Center (SARE), Practical Farmers of Iowa (PFI) and the Ecological Farmers
Association of Ontario (EFAO).
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

USDA photo

Table 2. Examples of good research questions and their related hypotheses
Research questions

Hypotheses

When is the best time to incorporate my sunn hemp
cover crop to maximize nitrogen addition to the soil?

Incorporating sunn hemp cover crop at 12 weeks of growth
provides the maximum nitrogen addition to the soil that can
be taken up by the next crop.

Do trap crops minimize stink bug damage in my
tomatoes and peppers? If trap crops are effective, how
much additional pest suppression do they provide when
compared to my current management?

When planting a combination of sunflower and grain trap
crops, pest incidence is reduced in my pepper and tomato
crops compared with my current management.

Does mulching my garlic with straw affect winter survival,
growth rates, and/or yield?

Mulching with straw will reduce winter kill.
Mulching with straw will improve growth rates. Mulching
with straw will increase yield in my garlic.

How does a pre-plant application of composted manure
affect my spring wheat yield and/or protein content?

A pre-plant application of composted manure will increase
my spring wheat yield and/or protein content.

What is the best planting date to achieve early harvest,
high yield and optimal quality in my sweet corn?

Different planting dates will result in differences in sweet
corn harvest date, yield, and/or quality.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Farmer Worksheet
Step 1 – Identify your research questions and hypothesis
Use the space below to brainstorm 3-5 ideas or questions that you would like to explore to improve
your farming operation. Is there an amendment, cover crop or any other practice that you would
like to try at your farm? See Table 2 for some examples of good questions.
Idea/question 1 ________________________________________________________________
Idea/question 2 ________________________________________________________________
Idea/question 3 ________________________________________________________________
Idea/question 4 ________________________________________________________________
Idea/question 5 ________________________________________________________________
Now take a few minutes or more time to reflect on these ideas. Then choose, from the ideas above,
1 or 2 ideas or questions that you think are the most important for your operation and that can lead
to a simple and doable on-farm trial. Remember, this does not have to be your final question. You
will have time to think about this and very possibly change or refine your ideas.
Refined idea(s), or question(s):
_____________________________________________________________________________
_____________________________________________________________________________
_____________________________________________________________________________
_____________________________________________________________________________
Now, let’s try writing your hypothesis. Remember that the hypothesis is more specific than your
question and states what you believe will be the outcome of your trial. For some examples of
questions and hypotheses, see Table 2. Most questions and hypotheses are refined over time, so
don’t feel pressured to have all this figured out the first time. This worksheet space is just to get you
started. You can always revisit this section and refine your question and hypothesis.
_____________________________________________________________________________
_____________________________________________________________________________
_____________________________________________________________________________
_____________________________________________________________________________

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Step 2: Identify what you will measure
Your question and hypothesis will help you identify what you need to measure. Ask yourself, what
do I need to measure to test my hypothesis? In the previous example, the farmer would need to
measure yield to compare the two fertilizer schedules. It is common for certain types of on-farm
trials to require measuring yield, however you may need to measure other parameters to test your
hypothesis. For example, you may need to measure fruit quality and size if you are comparing
varieties or disease treatments. You may need to measure soil temperature, organic matter or soil
moisture if you are comparing new irrigation systems or cover crop mulches. Other data you may
want to take are fruit sugar levels, weed counts, plant height, leaf number, pest pressure counts, etc.
Regardless of what you are measuring, make sure that it helps you test your hypothesis and answer your
question. See Table 3 for some examples of potential measurements based on what you are testing.
Take into account the time, frequency and resources required to take these measurements. It is
really important to think ahead about the possible tools or equipment you may need, as you may
have to buy or borrow some. There is more practical guidance on data collection on Step 5 below,
but for now, it is essential to identify what measurements are needed to answer your question and
support a hypothesis.
Table 3. Examples of potential measurements based on what you are testing.
Potential measurements based on type of trial (highlighted in green)
If you are
testing:

Yield

Weed
Produce Produce Disease
Pest
Cost count /
quality
size
incidence populations
weight

Germination
rate

Soil
Water
Soil
BioSoil
Soil
organic
saving
nutrients mass Moisture temp
matter

Stem/
root
length
or weight

Pest
treatments
Disease
treatments
Fertilizer /
soil amend.

Weed
suppression
Pruning

Varieties
Seeding /
germination
Irrigation
systems
Cover crops

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Farmer Worksheet
Step 2 – Identify what you will measure
Take a look at the questions and hypotheses you came up with in Step 1. In your opinion, what do
you need to measure in order to test your hypothesis? What measurements are needed to know
what treatment is better for your farm? Yield data is commonly measured, but measuring other
parameters could be useful as long as it helps you answer your question and hypothesis. Remember
that it will take time and resources to take these measurements. This exercise will get you started
thinking of your idea and help you refine your process.
Use the space below to brainstorm on possible measurements for your trial. Make a short list if
needed.
Possible measurement 1
_____________________________________________________________________________
_____________________________________________________________________________
Possible measurement 2
_____________________________________________________________________________
_____________________________________________________________________________
More measurements
_____________________________________________________________________________
_____________________________________________________________________________

Step 3: Choose an experimental design
Based on the guidance from your question and hypothesis, you will need to choose from a few
simple experimental designs to set up your on-farm trial. An experimental design provides guidance
on how to set up your trial based on the number of treatments with the goal of minimizing
variation and bias. Using an experimental design will make your farm testing more structured and
its results more reliable.
The three most common types of designs used for on-farm trials are:
a) Paired Comparison: When you only need to compare one different practice at your
farm (treatment) with what you generally do (control). You only have two treatments you
are comparing in this design, your control (business as usual) and the new treatment you
are testing. Examples: two varieties, two fertilizers, two pesticides, two pest management
strategies, or two types of irrigation, etc.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

b) Comparison of 3 or more treatments (Commonly known as Randomized Complete
Block Design): Very similar to the paired comparison, except that you have more than 2
treatments. You have 3 or more treatments that you are comparing and one of them is
your control.
c) Split Plot: This design is more complex, and it is used when you want to compare two
different things at your farm, such as two new varieties of carrots (1st Level of treatments)
with three types of fertilizers (2nd Level of treatments). Instead of conducting two
separate trials, -one trial to test the carrot varieties and another one to test the fertilizersyou put them together to see how they interact. For example, it is possible that one of the
varieties of carrots respond better to one of the three types of fertilizer used. Another
example could be that you want to test grafting tomatoes (1st Level of treatments) and
3 new tomato varieties (2nd Level of treatments) to minimize disease and improve yield
and flavor. This type of design will require significant space for you to set up enough
replications for each treatment.
See Figure 2 for a graphical representation of these three types of experimental designs.

USDA photo by Lance Cheung.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Paired Comparison:
Comparing fertilizers A and B on 4
blocks.

3 or more Treatments
Comparison:
Comparing 3 fertilizers,
A, B, and C, on 6 blocks.

Split Plot Design:
Comparing 3 fertilizers, A, B, and C plus the use of Mulch and No mulch control on 4
blocks. Mulch and no mulch are used as the main plots, while the fertilizer treatments
are the subplots.

Figure 2. Comparison of the three most common experimental designs.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Elements of experimental design
Following are definitions of a few key elements of experimental design. See Figure 3 for a
graphical representation of these concepts.
a) Treatment is the name of the farm practice you are comparing. For example, if you are
comparing a cover crop mulch with plastic mulch for weed control, you have two treatments:
Treatment A: plastic mulch, Treatment B: cover crop mulch.
b) Control is the treatment that you currently use at your farm and the one you are
comparing with a new way of doing things. In the example above, if you routinely use plastic
mulch, that is your control. Treatment A (Control): plastic mulch, Treatment B: cover crop
mulch. In order to know if cover crop mulch is better or the same for weed control than
plastic mulch, you must include your control in your trial.
c) Plot is the smallest unit of area on your on-farm trial that contains one of your
treatments. For example, Treatment A is assigned to one plot, while Treatment B is assigned
to another plot.
d) Replication is the number of repetitions or times you will compare your treatments. It is
recommended to have a minimum of 4 replications. Having 6 or more replications is ideal.
The more replications you have, the more successful you will be in minimizing variation and
bias in your trial. This is usually accomplished by arranging your treatments in Blocks or
groups.
e) Randomization is used to minimize variation within a replication or block so that they
are not always placed in the same order. This allows for the existing variation to affect all
treatments more equally.

The three experimental designs recommended here are
classified as block experimental designs due to the use of
blocks. Following the mulch example from above, the farmer
had 6 replications or blocks. Each block has both Treatment A
and Treatment B. So in total, we have 6 Treatment A plots and
6 Treatment B plots.
See Figure 3 for a representation of all these elements.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Figure 3. Elements of an experimental design

WATCH OUT FOR BIAS

Bias is the unintentional tendency of favoring one treatment over another. In
this case, you may be rooting for one treatment over another, and so you may
subconsciously decide to put the treatment you favored on the most fertile land.
Your results may reflect this difference in soil fertility, rather than the actual
treatment difference.

Photo by PavloBaliukh on Istock.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Figure 4. Advantages of using experimental design principles in farm trials
Comparison of a typical farm trial with a farm trial using an experimental design. By using randomization
and blocks or replications, the experimental design minimizes sources of variation such as the fertility
gradient in the field and forest shade on the east side. In this case the typical farm comparison would
favor treatment A due to the higher fertility in that side of the field and unfavor treatment C due to shade.
In the experimental design these two factors are affecting all three treatments more equally.

Managing variation
Variation is the difference of naturally occurring conditions in various areas of your field. That is
to say, conditions change naturally depending on what section of your field you are located in. For
example, there could be a moisture, slope, or fertility gradient in your field and that will affect the
outcome of your trial. Other sources of variation include field history, other fields nearby, shade,
etc. Your goal as a farmer-researcher is to try to minimize variation by taking these factors into
account so that they affect your treatments equally.
For example, let’s say that you are testing 2 different varieties of kale to the one you currently
grow, so in total you have 3 varieties of kale in your trial, or 3 treatments. However, the field you
have available for this trial is known for having a fertility gradient and some shade from the forest
nearby. By following an experimental design with replications and randomization of each block,
you minimize the chance of favoring any of the 3 varieties. You give the 3 varieties the same
chances to show how well they can grow.
See Figure 4 for a comparison between a farm trial that uses an experimental design and a farm
trial that does not use it. This comparison is based on the kale example above.
For examples of farmers going through the seven steps of this guide, see the
3 Farmer Profiles located on Appendix 1. Each farmer profile has a summary
of each step from planning the trial to drawing conclusions.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Photo from NRCS Oregon.

Farmer Worksheet
Step 3 – Choose an experimental design
Remember that the experimental design is chosen based on the number of treatments you
have and if you are testing more than one practice at your farm. See Figure 3 for a graphical
representation of these three designs.
• Paired Comparison: Comparing 2 treatments of one practice. Examples, 2 fertilizers, 2
varieties, 2 mulches.
• Comparison of 3 or more treatments: Similar to the paired comparison, except that it
has 3 or more treatments.
• Split plot: In this case you are testing two different things at your farm. For example, you
may want to test 2 fertilizers and 3 varieties at the same time.
Try your best to choose an experimental design. Given your number of treatments and what you
are comparing, what is the most likely design for your trial? Write out your different treatments.
__________________________________________________________________________
__________________________________________________________________________
__________________________________________________________________________
__________________________________________________________________________
Remember to ask for help from a local extension agent, non profit farming organization or
researcher.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Step 4: Choose your field and mark location of your plots
Choosing a field for your trial is an important step. For example, the field you choose should
be easily accessible or in a location that you visit frequently, so that you can keep an eye on it
throughout the season. Another critical factor is the natural variation found within your field.
Remember that differences in things like soil slope, texture, moisture, fertility, or field history will
impact the results of your trial.
So, try your best to choose a field that is as uniform as possible so as to minimize variation. At
the same time, pay attention to any external factors that may influence your trial, such as your
neighbor’s field, prevailing winds, typical insect migrations, or a nearby patch of forest, etc.
Depending on the type of trial you have, you may need to use buffer zones around and within your
trial, especially where the possibility of pesticide or input drift or runoff could occur.
Using an experimental design with replications and randomization will help you minimize any field
variation. As a farmer, you have an intimate knowledge of your fields and can possibly point out
each of your field’s “temperament,” or their strengths and challenges. Use this knowledge when
deciding what field to use for your trial and how to lay out your treatments to minimize variation.
For example, if you have a field where fertility goes from low to high, make sure that your blocks
fall across this gradient. See Figure 5 for more examples.

Figure 5a

Figure 5b

Figure 5c

Figure 5. Addressing field variability with blocking
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 so that conditions are as
uniform as possible within each block.
•
•
•

Figure 5a: 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 5b: Place whole blocks within different soil types.
Figure 5c: 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.

Figure credit: Sustainable Agriculture Research and Education (SARE). Used with permission.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Draw your map
Once you have decided what field you will
use for your trial and your experimental
design, you are ready to draw your plot map
on paper. Given the number of treatments
you have and the number of replications
(minimum 4), decide how big your plots and
whole trial will be. The size of an on-farm
trial varies, and can be determined using
practical criteria, such as the length of the
field and the width of your tractor pass.
Ideally, you should use the same bed and field
dimensions as you generally do at your farm,
using the same implements. Decide what size
your plots will be and start drawing a plot
map, starting with drawing each block, which
will contain all your treatments.

IDEAS FOR RANDOMIZATION
•

If you only have two treatments, you
can flip a coin.

•

If you have between 3 and 6
treatments, roll a dice, assigning
each treatment to one dice number.

•

Use a random number calculator or
generator online. You write what letter
or numbers to draw from and the
calculator provides one random letter
or number every time you want.

When you label each of your plots on paper and in your field, they need to be placed randomly
rather than in order. This can be easily done by pulling papers from a hat. For example, if you have
3 treatments: A, B, and C; you write down each of these letters and fold them in small pieces of
paper. Then you put them inside a hat and pull them out one at a time. This assures that they will
be placed randomly for each of your blocks. So block 1 may look like this: C, A, and B. Block 2
may look like B, C, A.; block 3 may look like A, C, B., and so forth. See Ideas for randomization.
Finish drawing a map of your on-farm trial plots, clearly labeling all your treatments and
repetitions as well as all the measurements. Make sure you specify what each treatment letter
represents, and any other details you may need later on, such as important dates for data collection
or application rates per treatment.
Create copies of this map for future reference and keep at least one copy in a safe place. See
Figure 6 for a plot map example.

Remember that your crops will grow and may cover the flags and
labels you put before planting.
Keep a copy of your plot map – with all your treatments and plots
clearly identified – in a safe place for future reference.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

BLOCK 1

BLOCK 2

BLOCK 3

FIELD 2

BLOCK 4

BLOCK 5

FIELD 3

BLOCK 6

FIELD 1
FIELD 4

Figure 6. Plot map example with an experiment in Field 2. The experiment shown here is
comparing three treatments (A, B, and C) and the treatments are repeated in 6 blocks.

Mark your plots
Next, it is time to lay out your plots in the field based on the map you have created. Using
measuring tape, carefully identify and mark where each plot and repetition will be located. You may
need at least 2 people to do this. Use irrigation flags or tall stakes and weather-resistant labels to
clearly mark and identify each repetition and each treatment.
Remember that your crops will grow and may cover the flags and labels you put before planting.
Train all your staff about what you are testing in your trial and to be careful around the trial plots,
especially when using heavy machinery that may knock down the flags and treatment signs. Monitor
the trial on a regular, scheduled basis to make sure that all your flags and labels remain in place.
Remember that all your plots need to be managed in the same exact way (irrigation,
fertilization, weeding, pruning, planting date, etc.), except for the treatment you are testing.
For example, if you are comparing the use of a new cultivation implement versus an old one,
make sure that all other crop activities remain exactly the same for all of your plots.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Farmer Worksheet
Step 4 – Choose your field and mark location of your plots
Think about the best possible field you can place your trial. Remember that it should be as
uniform as possible and keep in mind any sources of variation within and outside the field.
Based on your available land and equipment, think about the size of your plots and entire trial.
Think about the possible number of replications (minimum 4, ideally 6. The more replications,
the better, but it is also more work). Then below, draw your field and surrounding areas,
indicating any source of variation (fertility, soil, slope, moisture, etc.). You can also print out
a Google satellite map of your farm and start your plot map there. Then draw the potential
location of each block or replication of your trial. For examples of these, see Figure 6.

In addition to your map, it is time to identify your treatments using letters or numbers.
Treatment A _____________________ Treatment B ________________________
Treatment C _____________________ Treatment D ________________________
Treatment E _____________________ Treatment F _________________________
Then, use one of the randomization ideas to start labeling each of your plots per block.
Remember that you can always change or refine these ideas.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Step 5: Establish your trial and collect data
It is time to start your trial in the field. Remember that all your farm activities need to remain
exactly the same for all your treatments, except for the one or two things that you are testing. In
Step 2 you identified what you would measure to answer your hypothesis and question. It is time to
make decisions about how you would take these measurements.
Depending on your experiment, you may need to
collect data from your trial several times during the
season. For example, if you are interested in weed, pest
or disease pressure, it will be important to monitor
the field over time and collect data 2-3 times before
harvest. It is recommended to create a calendar for
your trial where you keep track of the important dates.
For example, if you are testing input application rates,
it is important to spell out in detail the dates, treatments
and application rates. This way, you don’t have to
remember what you have to do with each treatment,
since you have planned and specified this in advance.

Remember to have a data
collection plan in place
before starting your trial
in the field. This will help
you to make sure that you
have all the equipment
and tools you need when
you start collecting data.

Yield data is commonly collected for on-farm trials by measuring harvest totals. In addition to,
or instead of, yield, you may be interested in other traits, such as germination rate, fruit quality
and flavor, weed pressure, or soil improvements, etc. In order to take these measurements, make
decisions on what tools, equipment, and standard procedures you will use. That is, you have to
make a plan for how you will take these measurements. You may need to borrow some tools or
equipment from your local extension agent or researcher to collect your data. See some advice, tips
and resources for data collection on Table 4.

Collecting data
It is usually not practical to measure the entirety of your plots; instead,
sampling some sections of your plots will be enough. Collecting soil samples
in your plots in a zig-zag pattern, or placing a sample frame or a hula hoop
randomly in your plot a few times and measuring the weeds that fall inside the
frame are examples of random sampling. Even for yield data, you may want
to harvest some samples only, which can be accomplished by hand if it is
difficult to harvest an exact area when harvesting mechanically.

Photo by deyangeorgiev on Istock.

For yield data, make sure that you document the plot area you harvested so
that you can calculate pounds per acre. If your trial has the potential to be
influenced by different factors at field edges like drift, prevailing winds, or
runoff, it is recommended to conduct the sampling in the middle rows of your
beds, to minimize such influence. Finally, have sampling bags or containers
labeled in advance by repetition and treatment to avoid any mistakes. See a few
examples of strategies to collect sample data randomly in Table 4.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Table 4. Examples and resources for sampling strategies
Measure stand establishment by counting the number of emerged seedlings in several
rows or use a square sample frame. For germination tests off the field, check out this test
guide from Southern Exposure Seed Exchange.
Measure soil organic matter or other soil properties by sampling soil randomly in
your beds in a zigzag pattern. The overall goal of soil sampling is to collect a sample that
is representative of the conditions you would like to measure. Follow your local soil lab’s
recommendations for sample collection as some tests require more soil than others. The Haney
soil test is often recommended to estimate food available for the soil biological community. This
NCAT/ATTRA publication provides an in-depth discussion of soil tests and labs recommended to
measure soil health.
Measure yield by harvesting from a carefully measured area. In some cases, farmers who
usually harvest mechanically may need to consider harvesting measured treatment areas by
hand. Make sure that the scale you use measures down to the weight units that are helpful
to you. Conversions can always be made to make your measurements more meaningful, for
example, going from lbs/plot to bu/ac.
Measure biomass by placing a sample frame or hula hoop randomly in your beds. Then
cut all the cover crops or forage growing in the area at soil level and place it on a labeled cloth
bag to measure weight. For an example of biomass sampling and dry weight calculation, see
this video and publication from the University of Georgia Extension. Another example is this
guide for biomass sampling from USDA-NRCS.
Measure weed suppression by doing weed counts based on a sample frame or hula hoop
placed randomly in your beds once or twice per season. You can also cut the weeds at soil
level and place them in a paper bag for fresh weight measurement. If you cut the weeds, make
sure you mark this location so as not to sample in this area twice. Calculate dry weight based on
the microwave method included in this University of Georgia publication.
Measure pest or disease presence by sampling for pest numbers in your beds using
traps, or visual observation of randomly chosen plants. You can also use a frame to sample
multiple plants found in the frame area. This should be done at least twice during the season.
See this video from Penn State for pest and disease scouting techniques, which can be used for
sampling. See another video on scouting in vegetable crops created by eOrganic. For more indepth information on disease sampling, see this video lecture by Alison Robertson from Iowa State
University.
Measure fruit quality by weighing, measuring diameter, disease and pest presence, or
send to a laboratory for fruit sugar content, dry matter content, firmness, color, and
acidity. A hand-held and relatively inexpensive brix refractometer can also be used to measure sugar content.
Measure flavor by doing a blind produce test to rate size, shape, color, flavor, aroma, texture
and overall quality. More information on how to conduct consumer tasting panels and a good example
of flavor evaluation datasheets, consult The Grower’s Guide to Conducting On-farm Variety Trials.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

USDA photo by Peggy Greb.

Data sheets
It is important to collect research data using data sheet templates to minimize the risk of data entry
errors. Make sure to collect data per plot, do not lump together the yield or data from all the plots
of one treatment to find their average. See Appendix 2 for a few recommended data sheets and
other sources of data sheets you can use. When possible, get help when collecting and entering
data, as it is easy to make mistakes with these tasks. Finally, store your data sheets in a safe place
and take pictures of these as a back up.

Field observations
Make sure to write observations about your trial throughout the season. This should include
observations on weather, pest, disease, crop growth, and any other observations you find
interesting. These notes can be very helpful when it is time to interpret your trial results.
Additionally, take pictures of your on-farm trial as frequently as possible, especially if you can easily
observe differences among treatments.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Farmer Worksheet

Step 5 – Build out your data collection plan

See what you wrote on Step 2 and explore how you will go about taking the measurements you identified using the following
questions. It is ok if you don’t have all the details on your data collection plan at this point. These questions are just to get you
started. You can always revisit these questions later on when you are ready to make decisions about your trial. Remember that
you will need to use a datasheet template to enter your data. See examples of datasheets in Appendix 2.
Measurement 1 ____________________________
Explain how you will measure this, what are the steps?
_______________________________________________________________________________________
_______________________________________________________________________________________
List what you will need to measure it (people, tools, bags, equipment, time)?
_______________________________________________________________________________________
_______________________________________________________________________________________

How many times and when will you collect this data? ______________________________________________
What procedure will you use to collect samples randomly?
_______________________________________________________________________________________
_______________________________________________________________________________________
If needed, who can you contact to help you develop the sampling procedure? _________________
Measurement 2 ____________________________
Explain how you will measure this, what are the steps?
_______________________________________________________________________________________
_______________________________________________________________________________________
List what you will need to measure it (people, tools, bags, equipment, time)?
_______________________________________________________________________________________
_______________________________________________________________________________________

How many times and when will you collect this data? ______________________________________________
What procedure will you use to collect samples randomly?
_______________________________________________________________________________________
_______________________________________________________________________________________
If needed, who can you contact to help you develop the sampling procedure? _________________
Measurement 3 ____________________________
Explain how you will measure this, what are the steps?
_______________________________________________________________________________________
_______________________________________________________________________________________
List what you will need to measure it (people, tools, bags, equipment, time)?
_______________________________________________________________________________________
_______________________________________________________________________________________

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Step 6: Analyze your data
You have done all this hard work and collected data from your trial. It is time to use statistics to
help you find your results. Rather than just simply using an average and drawing conclusions from
any difference, statistical tests help us have more certainty that the difference we see is the actual
difference among treatments, rather than due to chance or any other external factor.

Before you conduct your statistical test,
you need to make sure that your data
follows a normal distribution. If you
sample the weight of 100 wheat plants in
your field, it is very likely that some will
be in the lower values, some in the upper
values and the majority will be in the
middle range. That would be a normal
distribution, often called a bell-shaped
curve or bell-shaped distribution. Before
running statistical analysis, ’normality
tests’ are used to make sure that your
data follows a normal distribution. The
statistical tests recommended in this
guide are appropriate for data that
follows a normal distribution. If that is not
the case, and your data does not follow a
normal distribution then you will have to
rely on other statistical tests called Nonparametric, which we will not discuss
here, but you can find more information
in Appendix 3.

The statistical tests recommended here use
a value called ‘Levels of Confidence’ which
help determine trust if a difference among
treatments is valid or not. Levels of confidence
of 90% or 95% are common. You get to choose
the level of confidence you want, but most
farm trials use a 95%. A level of confidence of
95% means that there is still a 5% chance that
our results may be wrong, but you can be 95%
certain of your results. In science, you can’t be
100% certain of anything, but by having a 95%
level of confidence, you can safely view your
results as trustworthy.
The statistical test you choose for your trial
depends directly on the experimental design you
have.
• For a Paired Comparison, use a t-Test.
• When comparing 3 or more treatments,
use an Anova test
• For Split Plot design, use an advanced
type of Anova test.
All three of these tests make similar calculations
with your data. We won’t get into the details of
how these tests work here, for more information
on these statistical tests and how they work see
Appendix 3.

Don’t worry if you feel overwhelmed in this section, there are free online tools that will do
the statistical analysis for you. You just have to enter your data. See below for guidance
on how to use a few recommended online tools.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Each of these tests calculates the Least Significant Difference value or LSD, which is a value
calculated based on the mean and variation of your data plus the level of confidence. The LSD
value tells you the smallest significant difference between treatment averages. When the average
differences among your treatments are higher than the calculated LSD, then you can safely trust
that your results are indicating a statistically valid difference. This means that one or more of your
treatments indeed produced higher values than the other treatments. On the other hand, if the
average differences among treatments are lower than the LSD value, then there are no statistically
significant differences among your treatments. See an Example of data analysis below.

Example of data analysis
Farmer-Researcher David conducted an on-farm trial to compare two types of postharvest pruning
or hedging treatments in blueberries. Some of the farmer’s peers recently recommended a more
aggressive pruning than the farmer currently does, removing about 50% more plant material. The
farmer implements an on-farm trial to test this idea and determine what is best for the farm. The
trial had 2 treatments: A: Control (or the current pruning program), B: More aggressive pruning.
The farmer’s hypothesis is: Pruning an additional 50% plant material during post-harvest will
negatively affect yield in blueberry crop.
Given that the study is comparing 2 variations of one practice (pruning), the farmer uses a
Paired Comparison design with 6 replications in randomized blocks. See Figure 7 for a graphic
representation of the trial design.
The farmer conducts the on-farm trial and then harvests and weighs total pounds per plot yield for
the entire experiment. The yield data (lbs/plot) is entered into a datasheet as follows in Table 5.

Figure 7. Paired comparison with 6 replications in randomized blocks

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Based on the averages alone, it would appear that
there were small differences among treatments.
This may indicate that the more aggressive pruning
treatment increased yield, rather than lowered it,
as the farmer suspected. But before jumping to
conclusions, the farmer used a statistical test to
analyze their data and feel more confident about the
results.
The farmer chose the standard 95% of confidence
interval and used an T-Test on FarmStat online
tool from the University of Nebraska-Lincoln to
analyze the data. The Least Significant Difference
value (LSD), or sometimes called the Fisher’s LSD,
was calculated as 6.6. Then the farmer compared
the LSD value to the differences among averages
for the two treatments.
Treatment B (149.83) – Treatment A (147.67) =
2.16. This value is lower than the LSD value of 6.6,
therefore we can say that pruning 50% more plant
material post harvest in the blueberry crop did not
affect yield either positively or negatively. Yield
remained the same for both pruning treatments.

Table 5. Yield data per plot
Treatment A
(control)
lb/plot

Treatment B
lb/plot

Block 1

150

145

Block 2

145

155

Block 3

140

150

Block 4

151

149

Block 5

153

152

Block 6

147

148

Average

147.67

149.83

Range

140-153

145-155

The farmer-researcher can draw good information from this trial. There was no yield difference
between the two pruning treatments. The recommendation from other farmers for more
aggressive pruning did not help to increase yield. On the other hand, pruning this additional plant
material didn’t lower yield, as the farmer suspected. When it comes to blueberry yield, the farmer
can use any of the two treatments going forward. Other considerations, such as pruning costs
come into play when deciding what to do. The farmer is considering conducting the trial again
using a different blueberry variety.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

USDA photo by Preston Keres.

How do you go about conducting these tests?
There are various free statistical analysis tools on the internet that you can use to analyze your data.
You have to enter all your data but the online tools will do the analysis for you. If you have access
to Microsoft Excel or Google Sheets you can also conduct the analysis yourself. See Free online
tools for statistical analysis for a list of recommended statistical sites that can help you run the
statistical test for your trial.
Finally, don’t be discouraged if you struggle with statistics. Most of us do. Ask for help from your
local extension agent or researcher. If you are a recipient of OFRF funding for your research trials,
OFRF staff will assist you with this.
Free online tools for statistical analysis
FarmStat from the University of Nebraska-Lincoln. It’s a simple tool for paired comparison
and 3 or more treatments comparison designs. There is a tutorial video you can view for
reference.
Jamovi. This is a free open source software tool that you can use online. You may need to
create a free account so that you can use the tool for 45 minutes per session. You can run a
T-test for paired comparison designs, Anova tests for 3 or more treatment comparison; or Anova
test for a split plot design. See a Jamovi video tutorial from Idaho State University.
Organic Seed Variety Trial Tool – This tool, developed by the Organic Seed Alliance, can help
you plan and view results for your on-farm trial. It was designed specifically to help farmers do
trial varieties for organic farming. Using this tool you can conduct t-test for paired comparison
designs and Anova tests for a 3 or more treatment comparison. See a tutorial video on how to
use this tool.
Microsoft Excel or Google Sheets. You can conduct t-test and Anova analysis in these tools.
Anova tests in these tools will only tell you if there were significant differences among treatments
Note: See Appendix 3 for more specific guidance on how to use these online tools.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Step 7: Draw conclusions and share
Once you have statistically analyzed your results, it is time to ask, do these results appear to agree
with your hypothesis? What about your original question? Take some time to reflect on your trial
and your results and think about what these mean for the rest of your farm.
Depending on your results, is it time to expand
the practice you tested to the rest of your farm?
Or do you need to conduct more testing or find
additional information to make this decision?
Perhaps you may consider running the same
experiment next year, as weather conditions
change. Discuss this with a trusted farmer
colleague or extension agent and use your field
observation notes to help you interpret your
results. Often, trial results lead to even more
refined or helpful ways to ask questions to
improve your practices.

When drawing conclusions from your
trial, remember to take into account the
differences in cost among your treatments.
An increase in yield may not look so good
after all if you had to double the amount of
labor used for the new agricultural practice.
If your treatments will vary in cost, it is
recommended that you keep track of them
so that you can use this information to
make informed decisions.

It is important to keep your results in context. Whatever the outcome, it does not mean that these
results should apply to all farms in the country where the same crops are grown. Your results are
more likely to apply to your local farm and your local farming community. Feel free to share your
experience conducting an on-farm trial and your results with your peers and friends. It is likely you
learned a lot from the entire process and that you may want to continue tinkering with on-farm
testing to improve your operation. Share your learning and excitement with others. They can learn a
lot from you; farmers learn best from other farmers!
Don’t be discouraged if your results are not statistically significant, as that is a valid result.
Conducting on-farm trials takes hard work and sometimes you don’t get the outcomes you
expected. Keep in mind that even not statistically significant results are good information for your
farm. For example, your results may let you know that a specific practice you were very excited
about does not really work and it is not worth investing more of your energy and resources. This is
good guidance for your farm management.

“As farmers, we are generally people who wonder what’s going
on… we have some thoughts, we observe things. Diving a little
bit more on some questions is something that a lot of farmers
would benefit from.”
~Farmer Jeremy Barker-Plotkin

Amherst, MA

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

USDA photo by Preston Keres.

Finally, stay curious! Use the information you obtained from your on-farm trial and keep asking
questions that have the potential to improve your operation. In science, as in a farming operation,
new questions keep coming up. We encourage you to use these guidelines to continue conducting
on-farm trials at your farm. The most relevant and impactful research for your farm may well be
the one you design and lead.

For examples of farmers going through the seven steps of this guide, see the
Farmer-Researcher Profiles located in Appendix 1. Each farmer profile has
a summary of each step from planning the trial to drawing conclusions.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Appendix 1 – Farmer-Researcher
Farmer-Researcher Profile 1: Gordon’s Grains
Background
Gordon’s Grains is a 100-acre organic grain farm located in the state of Michigan, USA. The
farmers specialize in small grain production such as barley, oats, and wheat. Their main buyers are
the high-quality flour and the artisanal brewing industries. One of their main buyers, a brewery,
recently approached the farmers to try a new variety of barley. The farmers are unsure if this new
variety will grow well at their farm, as it was bred specifically for another region of the country.
Step 1- Identify your research question and hypothesis
Questions: Can this new variety of barley adapt and grow well in my fields? Will it produce good
yields when compared to the variety currently grown?
Hypothesis: The new barley variety will not produce the same or more yield per acre as the variety
grown at the farm for years.
Step 2 – Identify what you will measure
The farmers will measure grain yield per acre during harvest time.
Step 3 – Choose an experimental design
Based on the number of new varieties compared, the farmers will have 2 treatments. Treatment
A: control (the variety currently grown). Treatment B: new barley variety. The experimental design
used will be a Paired Comparison.
Step 4 – Choose your field and mark location of your plots
The farmers have chosen to use a 0.5 acre field located on the west side of their property. This
field has been used to grow barley and wheat in the past few years. The field is flat and uniform
and borders the neighbor’s conventional corn and soybean production. To protect from possible
drift, the farmer will establish a 25’ buffer to protect the trial. The farmers have decided to use
6 replications or blocks. Plot size is 2 tractor passes of the entire field length. Placement of the
treatments in each repetition is decided randomly by drawing pieces of paper from a hat. The
farmers use measuring tape, flags, waterproof labels and stakes to mark the field. Figure 8 shows
the plot map for this trial.
Step 5 – Establish your trial and collect data
The farmers plant the 2 varieties on the same day. Yield data will be measured by harvesting each
plot individually and stopping to weight harvest totals for each plot. The farmers ensure that the
harvest area on each plot is the same in all treatments by trimming the plots perpendicular to the
beds. The harvest data is entered in a datasheet for further analysis. See Appendix 2 for a yield datasheet sample.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Figure 8. Plot map example for Gordon’s Grains with two treatments (A and B) repeated in 6
blocks.

Step 6 – Analyze your data
The farmers use a T-test in FarmStat, a free online statistical tool from the University of
Nebraska-Lincoln, to enter and analyze their data. See more information on available online
free tools for statistical analysis on Step 6. See Table 6 for a summary of the data collected. The
farmers run the statistical T-test, which provided the Least Significant Difference value (LSD).
They compare the LSD value to the average differences among treatments to see if there is a
statistically valid difference among treatments. The farmers found that the LSD value is lower
than the average differences between treatments.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Table 6. Yield data per acre
Blocks

Treatment A (Control)
bushels/acre

Treatment B (new barley variety)
bushels/acre

Block 1

52.52

55.02

Block 2

53.01

55.45

Block 3

53.21

55.33

Block 4

52.11

54.95

Block 5

55.03

55.52

Block 6

52.86

56.87

Average

53.12

55.52

52.11 – 55.03

54.95 – 56.87

Range

Calculated LSD value: 1.198
Average difference between treatments: Treatment B (55.52) minus Treatment A (53.12) = 2.4

Step 7 – Draw conclusions and share
Given that the LSD test value is lower than the average difference between treatments, the farmers conclude that this new barley variety produces a higher yield than the currently grown variety.
There is a significant statistical difference between yield of the two varieties. The farmers compare their results with their original research questions and hypotheses. Their suspicion that this
new barley variety will not perform well at their farm did not come to pass. The farmers decide to
slowly increase acreage of the new barley variety to sell to their client, while continuing to monitor
yields. They share with their farmer peers about their trial and feel more confident about the barley
varieties grown at their farm. The farmers at Gordon’s Grain decide their next test should be a taste
test of the new beer!

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Farmer-Researcher Profile 2: Thelma’s Green
Background
Thelma’s Green Thumb is a 20-acre organic vegetable farm located in the state of Georgia, USA. The farmers
grow a wide variety of vegetables, including greens, roots, tomatoes, peppers, strawberries, blueberries and some
peaches. They are located near two urban centers where they sell most of their produce through farmers markets,
a large CSA program and wholesaling to local restaurants and community grocery stores. For years they have used
sunn hemp as their default cover crop, but they are now looking for alternatives due to high seed prices. They would
like to test sorghum sudan, pearl millet, lab lab, and also try a mix of sorghum sudan and cowpeas. When using
cover crops, the farmers’ primary objectives are to obtain sufficient weed suppression and produce as much organic
matter as possible to benefit the soil. These concerns are critical to the success of the following fall and winter
crops.
Step 1- Identify your research question and hypothesis
Questions: Can other cover crop alternatives provide better weed suppression and add more organic matter than
sunn hemp currently does? Given increasing seed prices, what is the most affordable way to keep weeds down and
add as much organic matter to the soil as possible?
Hypothesis: The mix of sorghum sudan and cowpea cover crop will be the most affordable, provide the
best weed suppression, and add more organic matter than all the other cover crops.
Step 2 – Identify what you will measure
The farmers will measure weed suppression and the amount of organic matter (biomass) produced by each
treatment. The farmers are also concerned with the cost of cover crop seeds.
Step3 – Choose an experimental design
Based on the number of cover crops compared, the farmers will have 5 treatments. Treatment A is sunn hemp
(control), Treatment B is sorghum sudan, Treatment C is pearl millet, Treatment D is lab lab, and Treatment E is a
mix of sorghum sudan and cowpeas. The experimental design used will be Comparison of 3 or more treatments
(also known as Randomized Complete Block Design).
Step 4 – Choose your field and mark location of your plots
The farmers have chosen to use 1/4 of an acre field located on the north side of their property. This field has
been used intensely to grow tomatoes and green vegetables within the last 5 years. The soil in this field is relatively
uniform but it tends to keep more moisture towards the north edge. The farmers have decided to use 4 replications
or blocks, which will be laid out across the moisture gradient. The farmers decided on the size of their plots
by dividing the field in 4 replications and each replication by 5 treatments. Placement of the treatments in each
repetition is decided randomly by drawing pieces of paper from a hat. The farmers use measuring tape, flags,
waterproof labels and stakes to mark the field. See Figure 9 for the trial plot map.
Step 5 – Establish your trial and collect data
The farmers decide to plant the cover crops by hand given the small plot size. Planting for all plots occurs on the same
day. The farmers decide to collect weed pressure data twice, at 30 and 60 days after planting, using a hula hoop as a sample
frame. The sample will be taken twice from each plot and the frame will be placed randomly. Weeds will be counted,
placed in labeled paper bags and fresh weighted. Total biomass produced will be measured at the same time as the 2nd
hula hoop weed sampling. All the cover crop grown above the soil will be cut and freshly weighted. The farmers will use
a measuring scale, labeled paper bags (for weeds) and labeled cloth bags (for cover crops) to make these samplings. Dry
weight will be calculated using the microwave method explained in this University of Georgia publication.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Figure 9. Plot map example for Thelma’s Green Thumb showing five treatments (A
through E) repeated in four different blocks that are arranged across a moisture
gradient.
Step 6 – Analyze your data
The farmers use an Anova test in FarmStat, a free online statistical tool from the University of Nebraska-Lincoln.
See more information on available online free tools for statistical analysis on Step 6. They compare the Least
Significant Difference (LSD) value to the average differences among treatments to see if there is a statistically valid
difference among treatments. See Tables 7, 8 and 9 for a summary of weed pressure data and total biomass yield.

Table 7. Weed data in grams/square feet at 30 days

Blocks
Block 1
Block 2
Block 3
Block 4
Average
Range

Treatment A
(Control)
Sunnhemp

Treatment B
Sorghum sudan

Treatment C
Pearl Millet

Treatment D
Lab lab

Treatment E

10.2
8.2
7.9
10

9.2
10.1
8.5
9.9

8.8
10.2
12.3
9.5

7.2
9.5
5.6
10.1

9.5
8.6
10.4
8.8

9.07

9.42

10.2

8.1

9.32

7.9 – 10.2

8.5 – 10.1

8.8 – 12.3

5.6 – 10.1

8.6 – 10.4

Sorghum sudan +
cowpeas

Calculated LSD value: No LSD value generated
No significant differences among treatments.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Table 8. Weed data in grams/square feet at 60 days
Treatment A
(Control)
Sunnhemp

Treatment B
Sorghum sudan

Treatment C
Pearl Millet

Treatment D
Lab lab

19.5

25.1

23.5

20.1

17.5

Block 2

18.2

24.8

22.5

21.3

Block 3
Block 4

17.8
20.1

20.1
22.3

21.6
24.7

22.6
19.5

16.5
15.9

Average

18.9

23.07

21.2

16.6

Blocks
Block 1

Treatment E

Sorghum sudan +
cowpeas

Calculated LSD value: 2.38
Average difference between treatments: Treatment B (23.07) minus Treatment A (18.9) = 4.98, higher
than the LSD value
Average difference between treatments: Treatment B (23.07) minus Treatment E (16.6) = 6.47, higher than the LSD value
Average difference between treatments: Treatment D (21.2) minus Treatment E (16.6) = 4.6, higher
than the LSD value
Average difference between treatments: Treatment C (23.07) minus Treatment A (18.9) = 4.98, higher
than the LSD value

Table 9. Total biomass data in dry weight tons/acre
Treatment A
(Control)
Sunnhemp

Treatment B
Sorghum sudan

Treatment C
Pearl Millet

Treatment D
Lab lab

Block 1
Block 2
Block 3
Block 4

2.57
2.55
2.49
2.53

2.58
2.63
2.57
2.59

2.53
2.46
2.51
2.31

2.4
2.36
2.39
2.34

2.51
2.41
2.39
2.61

Average

2.53

2.593

2.45

2.37

2.48

2.49 – 2.57

2.57 – 2.63

2.31 – 2.53

2.34 – 2.40

2.39 – 2.61

Blocks

Range

Treatment E

Sorghum sudan +
cowpeas

Calculated LSD value: 0.11
Average difference between treatments: Treatment A – (2.53) minus Treatment D (2.37) = 0.16, higher than the LSD value.
Average difference between treatments: Treatment B – (2.593) minus Treatment C (2.45) = 0.14,
higher than the LSD value.
Average difference between treatments: Treatment B – (2.593) minus Treatment D (2.37) = 0.22,
higher than the LSD value.
Average difference between treatments: Treatment B – (2.593) minus Treatment E (2.48) = 0.113,
higher than the LSD value.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Step 7 – Draw conclusions and share
The farmers use the statistical results to make conclusions about their trial. At first sight, Treatment D (Lab lab)
appears to provide more weed suppression than the other treatments. However, the statistical test found no
statistical significant differences among the cover crop treatments. On the other hand, sunnhemp and the mix of
sorghum sudan+cowpeas showed better weed suppression at 60 days. There were statistical significant differences
between sunnhemp and sorghum sudan+cowpeas mix and the other treatments. In terms of biomass production,
Sorghum sudan monocrop (Treatment B), showed higher biomass production than the rest of the treatments,
except for Sunn hemp (Treatment A). Sunnhemp showed higher biomass production than Lab lab (Treatment D),
but no statistical differences were found with the other treatments.
The farmers obtained a lot of good information from this trial. They compared their results with their original
research questions and hypotheses. They also use seed costs to calculate costs per lbs of biomass produced. They
decided to incorporate sorghum sudan into their regular cover crop use, alternating it with sunn hemp every other
season. Whenever sunn hemp seed was expensive or hard to find, they relied on sorghum sudan. They sometimes
use the mix of sorghum sudan and cowpeas whenever weed suppression is the main objective. Finally, the farmers
shared their experience with their peers and felt more confident about what cover crops work well for their farm
system.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Farmer-Researcher Profile 3: Brise’s Blooms
Background
Brise’s Blooms is a small organic urban farm located in the state of Massachusetts, USA. The farmers grow highend edible flowers, greens and tomatoes for restaurants and some catering companies. They grow all their crops
under greenhouse conditions. They have recently been struggling with fungal diseases among the violas, a type of
popular edible flower. They are looking for solutions to this problem by trying 3 new varieties and 2 foliar fungal
sprays. The farmers suspect that some of the new varieties will respond better to at least one of the fungal sprays,
so instead of conducting two separate experiments, they put them together.
Step 1- Identify your research question and hypothesis
Questions: Do any of these foliar fungicidal sprays provide enough disease suppression in violas? Do any of these
other viola varieties present any natural resistance to fungal disease?
Hypothesis: Among the three new varieties and 2 foliar sprays, at least one of the new viola varieties will show
better resistance to disease but the fungal foliar sprays will not be more effective than no spray at suppressing
disease.
Step 2 – Identify what you will measure
The farmers will measure disease incidence twice during the season to capture how the varieties and foliar sprays
perform in early and late growth stages.
Step3 – Choose an experimental design
The farmers are testing two different things: 3 new varieties and 2 fungicidal foliar sprays. This is a Split Plot
Design. There will be 4 variety treatments: Varieties 1, 2, 3, and 4; in this case, variety 4 is the control (the variety
they normally grow). There will be 3 foliar spray treatments: A, B, and C; in this case, treatment C will be the
control, or no spray.
Step 4 – Choose your field and mark location of your plots
The farmers have chosen to use a small section of the greenhouse to conduct the trial. There will be 4 blocks of
replications. The variety level will be used as the larger plots, while the foliar spray will be considered the subplots.
See Figure 10 for the plot map of this trial. Each plot will be a small flower bed. Placement of the treatments in
each repetition is decided randomly by drawing pieces of paper from a hat. The farmers use measuring tape, flags,
waterproof labels and stakes to mark the plots.
Step 5 – Establish your trial and collect data
Planting for all the treatments takes place on the same day. Disease incidence is measured twice (at 30 and 60
days after planting) by using a small circular frame (hula hoop) placed randomly twice in the center of the plot to
minimize drift effects. The plants located in the middle rows will be examined for the presence of disease. Severity
of disease will be measured for each plant in the sample using a rating scale from symptomless (1) to extremely
diseased (5). Then an average for the whole sample will be calculated by adding all the numbers and dividing by the
number of plants in the sample.
Step 6 – Analyze your data
The farmers use an Anova test in Jamovi online tool to enter their data and analyze their trial. See Table 10 for a
summary of this data. For their analysis, they use two “fixed factors,” as the variety and fungicidal spray are the two
different levels of treatments they compared. The statistical analysis showed significant differences among both
treatments, varieties and fungicidal sprays, but no interaction between these two. For more information on Jamovi,
see Step 6 – Free online tools for statistical analysis.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Figure 10. Trial plot map for Brise’s Blooms showing three foliar spray treatments (A, B, and
C) and four varieties (1, 2, 3, and 4) in four blocks.

Table 10. Disease incidence data at 60 days
Var 1

Block 1
Block 2
Block 3
Block 4

Var 2

Var 3

Var 4 (control)

1.84

1.8

1.78

1.59

1.6

1.98

1.78

2.08

2.05

1.75

1.73

1.98

1.66

1.92

1.84

1.88

1.78

1.88

2.1

2.01

1.65

1.96

1.66

1.74

1.8

1.69

1.62

1.94

1.91

2.02

1.99

1.7

1.94

1.77

1.5

1.91

1.8

1.85

1.66

2.02

2.1

2.01

1.89

1.91

1.8

1.91

Mean values for each treatment
Var 1 = 1.77

Var 2 = 1.82

Var 3 = 1.98

A = 1.77

B = 1.88

C (control) = 1.89

Var 4 (control) = 1.82

The statistical results showed that:
• Variety 1 (1.77) is significantly different from Variety 3 (1.98)
• Variety 2 (1.82) is significantly different from Variety 3 (1.98)
• Variety 4 (control) (1.82) is significantly different from variety 3 (1.98)
• Fungicidal treatment A (1.77) is significantly different from fungicidal treatments B (1.88) and C (1.89, control).

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Step 7 – Draw conclusions and share
The farmers use the statistical results to make conclusions about their trial, taking into account seed and input
prices. The farmers compare their results with their original research questions and hypotheses. The farmers
discovered that Varieties 1, 2 and 4 (control) had a significantly lower disease severity than variety 3. On the other
hand, the results show that fungicidal spray A had a significantly lower disease severity than fungicidal spray B and
C (no spray control). The farmers decided to continue the use of fungicidal spray A, and continue the use of the
new varieties (except for Variety 3), as they showed good aesthetic qualities. They share information about their trial
with their peers, and feel more confident about what strategies to use to minimize fungal disease incidence for viola
production. They are hoping to do another trial the following season with additional varieties.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Appendix 2 – Datasheet examples and sources
Yield Sample Datasheet
Farmer Name: ________________________ Crop(s):_____________________________
Planting date: _____________________
Harvesting Date: ________________________
Area harvested per sample __________
Area of sample frame (if applicable): __________
Scale units: ___________________ Container/bag weight (if applicable): ______________
Samples collected by: ________________________________________________________
Note taker: __________________________________________________

Block
Treatment
Number
ID

Weight
Calculate
units
yield
(lb
or
tons/acre)
_________

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Observations

Biomass Sample Datasheet
Farmer Name: ________________________ Crop(s):_____________________________
Planting date: _____________________
Sampling Date: ________________________
Area of sample frame: ________________ Scale units: ___________________
Container/bag weight (if applicable): ______________
Samples collected by: ________________________________________________________
Note taker: __________________________________________________
Block
Number

Treatment ID

Sample Number
(if applicable)

Fresh weight
(units) _________

Estimated Dry
Weight (units)
_________

Calculate total
biomass
(lb or tons/acre)

Observations

Notes: A column for “Sample Number” is included here in case you collect more than one sample per plot.
Guidance on estimating dry weight samples is found on Table 4.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Weed Sample Datasheet
Farmer Name: ________________________ Crop(s):_____________________________
Planting date: _____________________
Sampling Date: ________________________
Area of sample frame: ________________ Scale units: ___________________
Container/bag weight (if applicable): ______________
Samples collected by: ________________________________________________________
Note taker: __________________________________________________

Block
Number

Treatment
ID

Sample
Number

(if applicable)

Weed
count

Fresh
weight
(units)
_________

Observations

Notes: A column for Sample Number is included here in case you collect more than one sample
per plot.
FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Stand Establishment Sample Datasheet
Farmer Name: ________________________ Crop(s):_____________________________
Planting date: _____________________
Sampling Date: ________________________
Area or length of sample: ________________ Samples collected by: ____________
Note taker: ___________________________

Average
Block
Number

Treatment
ID

(Add plants

Plants
germinated and divide
germinated by number of samples
taken in the same
plot)

Calculate plants
per square foot

Observations

Other datasheet resources
For great examples of variety trial and flavor datasheets, consult Appendix G of The Grower’s Guide to
Conducting On-farm Variety Trials developed by the Seed to Kitchen Collaborative.
A great example and discussion of insect sampling datasheet is found in this eOrganic article titled Overview of
Monitoring and Identification Techniques for Insect Pests by Geoff Zehnder, Clemson University.
Great biomass calculation instructions and an example for biomass sampling datasheet developed by the USDA
NRCS in Iowa in this Estimating Cover Crop Biomass publication.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Appendix 3 – Guidance and resources on statistical
analysis
Guidance to run statistical analysis for free online tools.
FarmStat from the University of Nebraska-Lincoln.
Use the “Randomized Complete Block Design” option. Enter your data directly or copy from Excel. For the analysis, use the Fisher’s LSD option. Then, identify the LSD critical value to compare with the differences in average
among your treatments. The tool also provides you with concluding statements from the analysis.There is a tutorial
video you can view as well.
Jamovi
You can run a T-test for paired comparison designs, Anova tests for 3 or more treatment comparison; or Anova
test for a split plot design. Enter your data by rows, so that each row is one observation. For example, 1st. Column
= Treatment A, 2nd. Column = Block 1, and 3rd. Column = the data for this treatment. To run the analysis, enter
the “Dependent variable,” which will be the data you are comparing (yield, germination rates, etc.), and the “Fixed
Factors” (names of the treatments you are comparing, represented by treatment letters. In a split plot design you
will have 2 fixed factors. The test will tell you if there are statistical significant differences among treatments. Use
the “Tukey Post Hoc Test” to figure out where exactly these differences are among treatments. Any result below
0.05 can be considered statistically significant. See a Jamovi video tutorial from Idaho State University.
Organic Seed Variety Trial Tool
Using this tool you can conduct a T-test for a paired comparison design and Anova tests for a 3 or more treatment comparison. By entering the number of treatments and repetitions, this tool can help you design your trial by
automatically placing your treatments randomly. The tool asks you to upload your data in a “.csv” file type. If you
have your data on Google Sheets or Microsoft Excel, you can “Save As” and choose .csv as the format. Make sure
that your file matches the number of columns and column headers as the template for RCBD example found in the
“upload your data tab.” Analyze your data by running a “Means Table” analysis. It will provide you the mean results,
identifying statistically significant differences among treatments by the letters next to your results (a, b, c,… where
each letter is statistically different from the other letters). You can also visualize your data in plots. See a tutorial
video on how to use this tool.
Microsoft Excel or Google Sheets.
You may have to download statistical add-ons (XL Miner Analysis Tool Pack in Google sheets and Add ins – Analysis Tool Pack in Microsoft Excel). For a t-test, enter the code “=Ttest” in any cell and it will then ask you for the
data for each treatment (called range), the type of test (choose type 2, two sample equal variance), and tails (choose
2 tailed distribution). The resulting number will tell you whether the difference you see is statistically significant or
not. If the number is equal or lower than 0.05, then the difference shown in your treatments is statistically significant. See a T-test tutorial using Microsoft Excel here and an Anova test tutorial here using the same software.
More In-depth resources on Statistical Analysis
How to Conduct Research on Your Farm or Ranch is a Sustainable Agriculture Research and Education (SARE)
publication that contains in-depth information on on-farm research experimental design, statistical analysis and
practical advice to conduct an on-farm trial.
OFRF’s On-Farm Research Guide for more tips and another example of statistical analysis.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Appendix 4 – Examples of farmer-led on-farm research
trials
Sustainable Agriculture Research and Education (SARE) producer projects summaries (chosen randomly).
Find more farmer-led reports here.
•

Effects of using ducks as biological control to manage weeds and pests within an orchard crop system. Golden Sun Farm & Nursery LLC. Oregon. See summary here.

•

Optimizing No-Till Methods for a Direct-to-Market Organic Vegetable Farm. Whitewater Gardens Farm.
Minnesota. See summary here.

•

Interseeding cover crops and grazing cattle to improve soil health, water infiltration, and profitability within an
Organic transition. Getting Farms LTD. Iowa. See summary here.

•

Sustainable Biofungicide for Organic Farms. Cannivera. Wisconsin. See summary here.

•

Investigating best practices for efficient minimal heating of high tunnels with modular heaters and row covers.
MIllsap Farms LLC. Missouri. See summary here.

•

Effects of Vermicast Extract and Cover Cropping on the Soil Food Web and Crop Health as Compared to
Beds Treated with Conventionally Applied Compost. Samaritan Community Center. Arkansas. See summary
here.

•

Breeding and Evaluation of Butternut Squash Varieties for Southeast Organic Farms. Common Wealth Seed
Growers / Twin Oaks Seed Farm. Virginia. See summary here.

•

Reduction of Water Use On Peony Crops By Using Shade Cloth. Cherry Petals Flower Farm. Utah. See summary here.

•

Efficacy of insect-exclusion nettings and shade cloth combinations on diversified vegetable production in the
Southwest. Highwater Farm. Colorado. See summary here.

•

Ginger Spacing in High Tunnels for Maximum Yields. Rustic Roots Farm. Maine. See summary here.

Practical Farmers of Iowa – Cooperators Program. Summary reports of farmer-led on-farm trials (chosen randomly). Find more farmer-led reports here.
•

Fine-Tuning Fertility for Better Broccoli. Humble Hands Harvest, Wild Woods Farm, and Scattergood
Friends Farm. Iowa. See summary here.

•

Flame Weeding Organic Soybeans. Iowa. See summary here.

•

Living Mulch for Pathway Weed Management in Bell Peppers. Iowa. See summary here.

•

Clover Cover Crop Termination Date for a Rye-Corn System. Iowa. See summary here.

•

Annual Flowers as Pollinator Resource for Cucurbits. Iowa. See summary here.

•

Economic and Soil Health Impact of Grazing Different Cover Crop Mixes. Iowa. See summary here.

•

Potting Soil Comparison for Vegetable Seedling Quality. Iowa. See summary here.

•

Planting Corn in 60-in. Row-Widths for Interseeding Cover Crops. Iowa. See summary here.

•

Replacing Corn with Hybrid Rye in Feeder Pig Rations. Iowa. See summary here.

•

Organic Control of Squash Vine Borer in Winter Squash. Iowa. See summary here.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Ecological Farmers Association of Ontario – Farmer-Led Research Program. Summary reports of farmer-led
on-farm trials (chosen randomly). Find more farmer-led reports here.
•

Iceberg Lettuce variety trial. Ontario. See summary report here.

•

Alternative to traditional peat moss starter mixes. Paper Kite Farm. Ontario. See summary report here.

•

Efficacy of mycorrhizal inoculants on vegetable transplants. Grenville Farms. Ontario. See summary report
here.

•

Effects of liquid and biological amendments on emergence and yield of no-till planted spring cereals. Orchard
Hill Farm. Ontario. See summary report here.

•

Assessing methods for nutrient application to prevent chlorosis in chestnuts. Summergreen Tree Crops &
Mushrooms. Ontario. See summary report here.

•

No-till tomatoes 3-ways. Jones Family Greens. Ontario. See summary report here.

•

Pasture-raised chicken breed comparison. Burdock Grove. Ontario. See summary report here.

•

Does planting timing of green mulches affect yield of garlic and labor? Eva Mae Farm – East. Ontario. See
summary report here.

•

Okra variety trial for southern Ontario and southern Quebec. Ontario. See summary report here.

•

Performance of Chantecler chickens on a reduced protein grower ration. D&H Newman Farm. Ontario. See
summary report here.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

Appendix 5 – Other on-farm research resources
•

The USDA Sustainable Agriculture Research and Education (SARE) has many resources related to onfarm research. How to Conduct Research on Your Farm or Ranch is a SARE publication that contains
in-depth information on on-farm research experimental design, statistical analysis and practical advice to
conduct an on-farm trial. This publication also covers on-farm research on pasture and livestock systems.
SARE also runs a Producer Grant program where farmers can apply for grants to conduct research on their
farms. These grants are administered by regions, so each region will have their own application deadlines and
application priorities.
Finally, SARE keeps a searchable database of all their grants conducted by farmers, researchers and graduate
students.

•

Practical Farmers of Iowa runs the Cooperator’s Program, a farmer-led research initiative, since 1987. Farmers
conduct on-farm trials and share their results through reports and through an annual conference. See examples of farmer-led research protocols and reports in their searchable database.

•

Ecological Farmers Association of Ontario (EFAO) runs a farmer-led research program since 2016. Their
Research Library database contains farmer-led research protocols and reports.

•

For more in-depth information and practical tips check out our very own OFRF’s On-Farm Research Guide.

•

The B. C. Forage Council produced a Guide to On-Farm Demonstration Research specifically created for
forage producers. The guide provides guidance and worksheets to assist you in your on-farm forage trial.

•

Organic Seed Alliance publication: The Grower’s Guide to Conducting On-farm Variety Trials.

•

The University of Georgia Extension publication Designing Research and Demonstration Tests for Farmers’
Fields.

•

Oregon State University publication Experimenting on the Farm: Introduction to Experimental Design.

FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

References
Nelson, E., Hargreaves, S., & Muldoon, D. (2023). Farmer knowledge as formal knowledge: A case study of
farmer-led research in Ontario, Canada. Journal of Agriculture, Food Systems; and Community Development.
Advance online publication. https://doi.org/10.5304/jafscd.2023.124.010
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FARMERS GUIDE TO CONDUCTING ON-FARM RESEARCH

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