HOW TO PERFORM AN
AGRICULTURAL EXPERIMENT
G. STUART PETTYGROVE
VITA
1600 Wilson Boulevard, Suite 500
Arlington, Virginia 22209 USA
Tel: 703/276-1800 . Fax: 703/243-1865
Internet: pr-info@vita.org
July 1971
Revised October 1981
ISBN 0-86619-039-2
Forward
Local technicians in developing countries increasingly are
being called upon to test innovative measures developed by
agricultural researchers at the national or regional level.
Improved plant varieties, new fertilization practices, irrigation,
pesticides, new feed mixtures, and improved harvest
procedures are just a few of the more important innovations
that must be thoroughly tested at the local level before they
are passed on through extension methods to the farmer.
Local research often is not carried out by trained research
personnel, but by extension agents, teachers, training center
workers, community development agents, foreign technicians,
fertilizer and seed distributors, and farmers with large
holdings.
The purpose of this book is to show local farmers and others
the basic steps to design, execute, and measure an agricultural
experiment. This book does not cover statistical anlysis; it is
assumed that trained statisticians are available for this
purpose.
TABLE OF CONTENTS
Foreward
List of Figures
SECTION I. SOME BASIC CONCEPTS
I. The Need for Local Research
II. An Experiment Versus A Demonstration
III. Some Basic Concepts in Statistics
A. The normal distribution
B. The null hypothesis
C. The "significant difference"
SECTION II. HOW TO PERFORM AN AGRICULTURAL EXPERIMENT
I. Preliminary Research
II. Designing the Experiment
A. Replication
B. Random distribution
C. Selection of treatments
D. Selecting the location
E. Plot size and shape
III. Execution of the Experiment
A. How to lay out a right angle
B. Labeling and mapping
C. Uniform application
IV. Measuring and Recording the Results
A. When should measurements be taken?
B. What should be measured?
C. Put all observations in numerical terms
D. A report procedure
Appendix: Table of Random Numbers
Bibliography
LIST OF FIGURES
1. Normal curve
2. Normal curves with and without fertilizer
3. The completely random design
4. Random complete block
5. Random complete block suitable for demonstration
6. How to make random the Latin square design
7. Split-plot design
8. Plot shape
9-A. Laying out a right angle
9-B. Laying out a right angle
9-C. Laying out a right angle
SECTION I
SOME BASIC CONCEPTS
I. THE NEED FOR LOCAL RESEARCH
Many countries today are experiencing what is called "agricultural
development." Basically, this means three things for
agriculture: (1) it has become more complex technically; (2) it
has become less oriented to home consumption and more oriented
to the market; (3) it has become dynamic; that is, it is not
simply moving to a new, more efficient level of operation, but
is in a continuous state of flux.
In many countries, research facilities have been established at
the national and regional level. New plant varieties and innovative
cultural practices are being tested successfully at
these facilities. But before they can have any effect on farm
production, they must be tested thoroughly at research stations,
schools, and farms on the local level.
The basic problem facing local experimenters is whether the use
of a new or different practice will affect the outcome of some
particular segment of agricultural enterprise in their area. If
so, to what extent? If farmers fail to adopt a beneficial practice
because it has not been tested locally, or if they adopt a
harmful practice because it has been tested improperly, local
extension agents and those who have carried out experiments
must share the blame.
Local personnel have a great responsibility to become skilled
in testing and evaluating new practices so that they may avoid
such mistakes. If great care is exercised, untrained personnel
can become sufficiently expert in experimentation to bring many
benefits to the local farmers and, hence, to the entire
community.
II. AN EXPERIMENT VERSUS A DEMONSTRATION
What is an experiment?
An experiment is a test or tentative procedure for the purpose
of discovering something unknown, or for testing a principle or
supposition. It must be carried out in an unbiased manner. No
assumptions are made regarding the outcome; the results must
always be accepted. If we suspect that the results are not
typical, we still must accept them, although we should perform
the experiment again. In an experiment, treatments are replicated,
or repeated, and they are arranged in test plots or as
random units in a procedure.
An observation trial is not used to draw any experimental conclusions,
but may determine if a practice is worth testing.
A result trial on a farm is the testing or demonstration of a
single practice that has been proven elsewhere, but which is
still unproven in the farmer's mind.
What is a demonstration?
A demonstration shows a response that already has been proven
in an experiment. It is not conducted according to the specifications
for an experiment, and therefore cannot be used to draw
conclusions. If it does not demonstrate the expected results,
it is ignored, and may then be plowed over to be run again.
III. SOME BASIC CONCEPTS IN STATISTICS
The statistical analysis of results is beyond the scope of this
paper, but we must understand some basic concepts if we want to
be able to interpret a statistician's analysis of our experiment.
The three concepts described briefly here are the normal
distribution, the null hypothesis, and the significant
difference.
A. The Normal Distribution
Assume that there is a large amount of some crop that is grown
under uniform conditions and harvested in plots of 100 square
feet. The yields recorded for each of these plots probably will
vary from a very low figure to a very high figure. Most of the
plots will yield close to a middle figure. As we move away from
this median to either a higher or lower yield figure, we will
find successively fewer plots. If the yield is plotted against
the number of plots giving a particular yield, the familiar
bell-shaped normal curve will result. (see figure 1)
htp1x3.gif (486x486)
If the same crop is grown under identical conditions with the
addition of a fertilizer treatment, there will still be a wide
range of yields for the 100-square-feet plots. However, the
entire curve will have shifted somewhat to the right. (see figure 2)
htp2x3.gif (486x486)
Note that the two curves overlap in the crosshatched area; some
plots may yield the same with and without fertilizer. If only
a small number of the fertilized plots were measured, it is
possible that all or most of them would fall in this shaded
area. We would not know from our measurements whether the
fertilizer had really increased the yield.
The purpose of proper experimental design is to allow us to
determine whether the treatments have actually shifted the
normal curve, or whether the effect we observe is simply due to
chance. This brings us to the next concept.
B. The Null Hypothesis
The statistician begins the analysis by assuming that the
treatments had no effect, and that any effect observed was due
simply to chance. This assumption is known as the null
hypothesis. If we flip a coin and get "heads" four times in a
row, we assume this to be due to chance and not because of some
special quality of the coin.
Next, the statistician processes the data to determine the
validity of the null hypothesis. He or she may reject the null
hypothesis, deciding that the observed effect of the treatment
was significant, and probably not due to chance. Or, he or she
may accept the null hypothesis, concluding that the observed
effect of the treatment was probably due to chance.
C. The "Significant Difference"
The term significant will be found in the results of many
experiments. This may also be indicated by an asterisk (*) or
by the phrase "significant at the 5% level." These all indicate
that the statistician has determined that there is only a five
percent chance that the observed difference was due to chance.
If the results are found "highly significant," indicated by a
double asterisk (**) or by the phrase "significant at the 1%
level," this indicates that there is only a one percent probability
that the observed effect of the treatment was due to
chance.
This discussion indicates that a single experiment, no matter
how carefully designed and executed, cannot conclusively
"prove" that a treatment has a significant effect. This is why
experiments should be repeated.
SECTION II
HOW TO PERFORM AN AGRICULTURAL EXPERIMENT
I. PRELIMINARY RESEARCH
Good preliminary research, including a search of the available
literature and interviews of experienced persons, will save a
great deal of trouble later. The experimenter should not be
afraid to ask for help now; help may be of no use once the
experiment has been laid out. The preliminary research should
cover the following points:
(1) A careful study of the crop should be made. The local soil
should be studied in fertilizer and irrigation experiments.
For pest control experiments, information on the
life cycle of the pest should be obtained.
(2) Economic factors should be studied, especially if a new
crop is being introduced. Will treatments affect the
market for this crop? What is the cost of treatments?
(3) Has this experiment been performed already? Quite likely,
a similar experiment has been carried out. Were the
results clear, and do they affect the planned experiment?
Have similar experiments been carried out in other
districts?
The preliminary research should be recorded so that it may be
included in the final report.
II. DESIGNING THE EXPERIMENT
In any experiment, errors are introduced by factors beyond the
control of the experimenter: soil heterogeneity, plant variability
(due to genetic variability), plant competition within
and between plots, variation in the moisture content of harvested
plants, climate variations (when experiments are run for
more than one year) , and the size and shape of plots. Such
errors cannot be eliminated, but they can be reduced, primarily
by the replication of treatments and use of random distribution,
careful selection of treatments and location, and the
proper design of plots,
A. Replication
Replication is the repetition of a treatment several times to
obtain a mean value or yield. In field experiments, a single
replicate generally is planned to contain one plot of each
treatment in a rather compact block. Replication is accomplished
by repeating blocks. A nonreplicated trial is not an
experiment.
The number of replications depends upon the degree of precision
desired and the degree of soil heterogeneity. Generally, two
replications is not enough. The American Society of Agronomy
suggests 3-6 replications for field plots. The small number
suffices where average rather than annual results are desired.
For corn yields, 4-6 replications are often used. For small
nursery plots, 5-10 replications are recommended.
B. Random Distribution
Random distribution means that treatments are assigned to plots
in a random fashion, or are placed randomly within a block. The
reason for doing this is to eliminate any bias that might occur
if we assigned treatments to plots with some kind of order or
system.
The random distribution procedure should be completely objective.
It may be accomplished by flipping a coin, drawing cards
from a deck, or by using a specially prepared table of random
numbers, such as the one found in the appendix of this paper.
1. The completely random design
htp3x9.gif (486x486)
This design results when treatments are assigned to a previously
determined number of plots. It is useful for some types of
treatments on animals, but is not an efficient design for field
trials with plants. Its main advantage is its simplicity and
flexibility. Treatments are assigned to plots by drawing cards
from a deck, slips of paper from a container, or by using the
table of random numbers in the appendix.
Example: A, B, and C represent three different levels of
nitrogen tested on wheat. Four samples for each
level X three levels = 12 plots.
2. The random complete block
In this design, treatments are assigned at random within a
block, and the entire block is replicated (see Figure 4). The
htp4x10.gif (486x486)
blocks should be kept as compact as possible, and the number of
treatments as low as possible consistent with the objectives of
the trial.
The main advantage of the random complete block design is the
high reliability of the data obtained from it, and its suitability
for demonstration (as seen in Figure 5).
htp5x10.gif (437x437)
Example: A-F are six different fertilizer treatments for
sugar beets. Note that each treatment occurs
once in each block. Six treatments X five
replications = 30 plots.
3. The Latin square design
In this design, treatments occur once in each column and once
in each row, and treatments are random in both directions (see
Figure 6). Thus, the Latin square removes variability in two
htp6x10.gif (540x540)
directions while the random complete block removes it in only
one direction. The number of replications always equals the
number of treatments in a Latin square design. It is more precise
than the random complete block, but it becomes cumbersome
for more than eight treatments.
In Figure 6, columns and rows are first numbered from 1 to 5,
and treatments are assigned to the plots in regular alphabetical
order, simply rotating the order one place in each row or
column.
In the middle square, we have the same square after the columns
have been rearranged by choosing at random the numbers at the
heads of the columns.
In Step 3, we have now chosen the rows at random by the same
method. The procedure is completed. Note that in the righthand
square, treatments appear only once in each row and column.
4. The split-plot design
htp7x12.gif (540x540)
This design is used to test two factors in combination. It is
not the most precise design for this purpose, but is often used
to facilitate physical operations. For example, some field
treatments, such as irrigation, are more conveniently applied
to relatively large strips through the experimental area. If
different dates of harvest are one of the factors being tested,
it may be easier to harvest in strips through the experimental
area rather than to harvest a few feet of one row and then skip
across rows for another small harvest area.
There are many split-plot designs. They vary in precision. If
possible, an experienced person should be contacted for advice
before one uses this design. The basic design involves assigning
one factor to main plots that are arranged in random complete
blocks or in a Latin square. Assign to the main plots
those treatments for which you are willing to sacrifice precision.
The treatments of the second factor are assigned at
random to sub-plots within each main plot.
Example: Planting dates and fertilizer treatments on
tomatoes. Three planting dates (main plots) X
four fertilizer treatments (subplots) X three
replications = 36 plots.
C. Selection of Treatments
Many factors that influence the farmer's profit can be applied
as contrasting practices in an experiment. Rate of seeding,
date of planting, spraying and dusting treatments, fall vs.
spring plowing, method of seed bed preparation, surface vs.
furrow application of irrigation water, weed control by herbicides
vs. cultivation, fertilizer treatments, pasture grass-legume
mixtures, and crop rotations are only a few of the more
important ones.
In selecting fertilizer treatment rates, it is desirable to use
rates that differ by equal intervals, such as 20, 40, 60, 80,
and 100 pounds of nitrogen per acre. We may have an idea of
what rate would be inadequate and what rate would be well in
excess of optimum. We should test the entire range, including
two or three levels between the minimum and maximum. An
untreated control plot is not necessary in a fertilizer plot
where it is understood that the crop needs some minimum level
of fertilizer to grow well. However, the demonstration value of
any experiment will be enhanced if we designate a control plot
that represents the local practice.
In a factorial experiment, the effect of more than one factor
is studied. For example, we may study the effects of four
levels of nitrogen and three levels of phosphorus. This would
give 3X4 or treatment combinations. You should try to keep the
experiment simple, not studying too many factors at once.
D. Selecting the Location
This is a highly critical step in the performance of an experiment.
The most important consideration in selecting a location
is soil heterogeneity. It was formerly believed that "the
experimental field should contain many different soil types to
be representative." This is a misconception. The soil should be
representative of that generally found in the area. However,
the land within the experimental area should be as uniform as
possible with respect to topography, fertility, the subsoil,
and previous management.
The causes of soil heterogeneity are the following:
(1) Topography: hillsides may cause gullies and the washing
down of nutrients. Low spots or variation in the texture
of the subsoil will cause plant variation.
(2) Variation in the moisture content.
(3) Variation in the penetration of irrigation water.
(4) Wide variation in available soil nutrients.
(5) Competition and shading from trees and hedgerows.
(6) Past use of the soil, including previous varietal and
cultural trials, and previous applications of organic
matter, fertilizer, and crop refuse.
What steps can we take to reduce the soil heterogeneity?
(1) Select land with a slight (1-2%), uniform slope. Avoid the
use of draws, lowlands, and other irregularly shaped
pieces of land.
(2) Where previous trials have been run that might affect soil
uniformity, grow one or more "blank trials" before experimenting.
A blank trial is a single crop--preferably a
small grain--that is grown as uniformly as possible over
the entire field to "smooth out" soil variations.
(3) Place new plots at a right angle to previous plots.
(4) Select land at least 20-30 yards from trees, hedgerows,
and roads.
(5) Record all information concerning the past history and
present condition of the land and included it in the final
report. This will assist others in interpreting the
results.
E. Plot Size and Shape
1. Plot size
In most local experiment stations or schools where land is
limited, the size and shape of the plot is a matter of convenience.
However, there are several considerations to take into
account.
There are two basic plot sizes: (a) nursery plots, cared for by
hand, which often have multiple short rows 10-22 feet long; and
(b) field plots, adapted to the use of standard farm machinery.
Larger plots commonly are used for corn, sugar beets, and hay
rather than for small grains. Small plots may be necessary
where many varieties or strains are being tested, where the
amount of seed of a new variety is limited, or where funds are
short.
Researchers generally agree that an increase in plot size will
reduce the error for plots up to about 1/40 acre (100 square
meters). Above that size, the decrease in error is less than
would be provided by an increase in the number of replications.
Small plots are more variable due to (a) fewer plants, b)
losses in harvest or errors in measurement, and (c) competition
and greater border effects.
2. Plot shape
htp8x15.gif (437x437)
Plot shape generally makes no difference. Relatively long,
narrow plots should have their long dimension facing in the
direction of the greatest soil variation so as to overcome soil
heterogeneity.
There are two other practical considerations in plot shape.
First, plots should be sufficiently wide to allow border strips
to be removed or to minimize the importance of borders that
remain. Second, field plots should be of a shape and size to
accommodate farm machinery.
3. Suggested plot sizes and shapes for various crops
(from Field Plot Technique by E. L. Leclerg, et al.
* Small grain: 3-4 rows X 10-20 feet (center rows harvested).
* Corn: 4-6 rows X 10-12 hills.
* Soybeans: 1-4 rows (2-3 feet apart) X 16 feet.
* Sorghum: 2-4 rows X 30 feet (center rows harvested in 3 and
4 row plots).
* Alfalfa: 7 feet X 60 feet (center five feet harvested with
a mower); 5-8 drilled rows 7" apart with a 12-14" alley
between border rows; 3-5 drilled rows 12" apart with an 18"
alley, and the entire plot harvested.
* Sugar beets: four rows (20-24" apart) X 30-60 feet (plants
thinned to 12" apart in row)
4. Border rows and guard areas
When there is competition between adjacent rows of different
varieties, especially where they differ in growth habits,
serious error may be introduced. In semi-arid or sub-humid
areas where plants compete for water, small grain yields are
greatly affected by plant competition. For this reason, single
row plots are not used. With many crops, 3-5 row plots are
grown, but the two outside rows are not harvested for yield.
Where alfalfa rows are spaced 7" apart, interplot competition
is a serious factor. If alleys between plots are widened to
14", border rows should still be removed because the alley
itself may allow border rows to grow more vigorously than the
plants on the inside rows.
Fertilizer application often requires the use of machinery, but
the flow of such fertilizer may not be precisely controlled on
the ends of the field. Therefore "guard areas" 1-2 feet wide at
the ends of the plot are thrown out.
III. EXECUTION OF THE EXPERIMENT
A. How to Lay out a Right Angle
If the corners of the plots are not laid out at exactly 90
degrees, plots will cover a different area than we imagine they
do. The following procedure is based on the fact that a
triangle with sides in a 3:4:5 ratio forms a perfect right
angle.
Equipment
* 50-foot cloth tape measure, heavy string, or wire marked at
30, 40, and 50 feet.
* Stakes
* String
Procedure
(1) Lay out a baseline with stakes and string. The length of
this line will equal the desired width of the total plot.
Place two stakes (A and A') as corner posts, as shown in
Figure 9-A. Connect A and A' with string.
htp9ax17.gif (437x437)
(2) Place a third stake (B) next to the string exactly 40 feet
from A.
(3) Have a co-worker hold the end of the tape on corner stake
A while you draw an arc with a 30-foot radius. You should
swing the extended tape to draw this arc across the
approximate place the side boundary will pass.
(4) Have the coworker hold the end of the tape at stake B.
Following the same procedure as in step (3), draw an arc
with a radius of 50 feet, as shown in Figure 9-B. Place a
htp9bx17.gif (437x437)
stake (C) where the two arcs cross.
(5) Tie a string from stake A to stake C. This forms a right
angle at A (see Figure 9-C). Now repeat the process at A'.
htp9cx18.gif (437x437)
B. Labeling and Mapping
Accurate mapping and labeling is a simple procedure that is
crucial for a successful experiment. For example, if someone
pulls up your marker stakes before the experiment is completed,
and you have made no map for your records, the experiment may
be ruined.
You must draw a map because field markers often are obliterated
by weather or tractor drivers. The map should refer to permanent
structures, such as fence posts, standpipes, building
corners, etc. You should be able to locate each separate treatment
exactly, even if all the stakes, strings, and labels are
removed from the field. Also at this stage, the planned treatments
should be listed and described. The map should indicate
which treatment each plot receives.
Field markers should be written in grease pencil, which will
not wash off in the rain or by irrigation water. Stakes may be
used to label plots; cardboard tags often are used in orchards.
Make sure your application, the field markers, and the map all
agree at the time treatments are applied.
C. Uniform Application
Failure to apply treatments uniformly is a very common mistake
that decreases the value of the experiment. Great care should
be taken to insure that fertilizer, pesticides, seed treatments,
etc., are applied uniformly over the plot, as specified.
Application equipment should be cleaned between trials. Seeds
must be swept out when different varieties are being planted.
If more than one worker is applying treatments, do not have the
same worker apply the same treatment over more than one
replication.
Do not inadvertently add factors. For example, when fertilizer
is side-dressed on a row crop, the shoes on the applicator may
prune some of the roots, and this will affect plant growth. The
proper untreated check would consist of a plot through which
the fertilizer rig had been pulled without the material. Seed
soaked in a chemical should be compared with seed soaked in
water, not with dry seed.
Carefully weigh all the materials used, if so required. Calibrate
application equipment to make sure you are putting on the
amount you think you are. Fertilizer elements should be mixed
several weeks before the application to allow time for any
chemical reactions to take place.
Obtain a uniform stand. Small grains will tiller--or put forth
new shoots--where adjacent plants are missing, but corn and
many row crops will not "fill in" empty areas. One solution is
to plant thick, then thin down to the desired stand.
Uniform care of plots is important. Weeds greatly influence
crop yields and should be removed early in the trial.
IV. MEASURING AND RECORDING THE RESULTS
Considerable time and expense has been spent thus far, yet many
experimenters fail in the end because they measure and record
the results improperly. The experimenter may take measurements
at the wrong time. Or he or she may take measurements at the
right time, but fail to put all results in numerical terms. He
or she may measure at the right time, and do so in numerical
terms, but fail to measure all the affected attributes. Or the
experimenter may do all these things correctly, but not record
the results in a simple, complete form.
A. When Should Measurements be Taken?
Different varieties mature at different times, and therefore
should not all be harvested at the same time. The experimenter
must watch closely and harvest each variety as it matures. He
or she must record the total days to maturity for each variety.
The rate at which results are reached is sometimes important.
For seed germination, both the rate of emergence and the percentage
of seeds germinating should be recorded.
B. What Should be Measured?
This is an extremely important question, one not adequately
considered by inexperienced experimenters. In some experiments,
workers may simply harvest and weigh the crop with no consideration
for other factors that are important on the market, and
which may have been affected. The market and nutritional value
of the product must always be kept in mind. Even at a local
experiment station or school where there is no sophisticated
measuring equipment, there are many attributes that can be
measured. For example, fertilizer treatments on tomatoes may
affect not only the total yield, but also the time to maturity,
the color, the size and shape, and the susceptibility to
diseases. For corn, the number of ears should be counted,
and--if facilities are available--the moisture percentage
measured for a sample of ears that represent all sizes, with
kernels from one or two rows on each ear.
The following are other attributes of field and horticultural
crops that might be measured:
* Sugar content of sugar beets
* Specific gravity of potatoes
* Grade of peaches
* Oil and protein content of soybeans
* Coumarin content of sweetclover
* Hulling percentage and milling quality of oats
* Ginning and fiber properties of cotton
* Pithiness of carrots
In short, when deciding what to measure, always keep in mind
the value of the product on the market.
C. Put All Observations in Numerical Terms
Many attributes of quality do not readily lend themselves to
measurement in numerical terms. For example, we may want to
measure the amount of insect damage on crop leaves after pesticide
treatments. It may seem easiest to judge damage as
"light," "moderate," and "heavy." But unless we put everything
in numerical terms, a statistician cannot make use of our
results.
In the case of disease or insect damage, a convenient numerical
scale should be set up. For example, to measure potato scab,
set a scale ranging from 0 to 10. Zero represents a potato
completely free of scab, and 10 represents a potato entirely
covered with scab. In some places, standard scales have been
established--1-5 or 1-7--and photographs representing each step
are used as a method of standardization. In general, the
following recommendations may be made.
(1) Try to design the scale so that observations are normally
distributed, that is, the middle number is the most frequently
observed.
(2) There should be as many steps in the scale as an experienced
observer can distinguish.
(3) Where any individual judgment is involved in making
observations, try to avoid having more than one person
make the observations.
D. A Report Procedure
Research is a continuous process, even at the local level.
Single experiments seldom determine new farming practices; the
results of several experiments have a cumulative effect. For
this reason and others, the written report of our experiment
must receive some attention. It must be complete, but not overly
complex. It must convey clearly and concisely what the
experimenter tested, under what conditions the test took place,
and the results. If the report is to be placed in a file with
similar reports, there may already be a standard format. if
there is no sample format, the following is generally
acceptable:
(1) Title page. This should indicate clearly the nature of the
experiment. The experimenter's name, the date, and location
must be included.
(2) Introduction. This must include a review of the literature
and basic background information, including all similar
experiments carried out previously. The problem must be
defined.
(3) Procedure. This must include pertinent soil and climatic
conditions, a careful description of the treatments, and
an explanation of how the treatments were applied.
(4) Results. These should be given in both tabular and graphic
form, with the results of the statistical analysis shown
clearly.
(5) Conclusion and recommendations. As a minimum, any further
experiments called for by the results should be mentioned.
(6) Appendix. This may include a plot map and the statistician's
calculations.
APPENDIX: TABLE OF RANDOM NUMBERS(1)
htpx23.gif (540x540)
To make random any set of ten items or less, begin at a random
point on the table and follow either rows, columns, or
diagonals in either direction. Write down the numbers in the
order they appear, disregarding those that are higher than the
numbers being made random and those which have appeared before
in the series. If you wish to make random more than ten
numbers, pairs of columns or rows can be combined to form two
digit numbers and the above process followed.
_____________
(1) Thomas M. Little, and F. J. Hills. Experimental Methods for
Extension Workers. (Davis, California: University of California
Agricultural Extension Service, 1966), p. 55.
BIBLIOGRAPHY
Hopp, Henry. A Guide to Extensive Testing on Farms. Washington,
D.C.: USDA Foreign Agricultural Service, 1951.
Leclerg, E. L. , Leonard, W. H. , and Clark, A. G. Field Plot
Technique. Minneapolis: Burgess Publishing Co., 1962.
Little, Thomas M., and Hills, F. J. Experimental Methods for
Extension Workers. Davis, California: University of
California Agricultural Extension Service, 1966.
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Tel: 703/276-1800 * Fax: 703/243-1865
Internet: pr-info[at]vita.org
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AGRICULTURAL EXPERIMENT
G. STUART PETTYGROVE
VITA
1600 Wilson Boulevard, Suite 500
Arlington, Virginia 22209 USA
Tel: 703/276-1800 . Fax: 703/243-1865
Internet: pr-info@vita.org
July 1971
Revised October 1981
ISBN 0-86619-039-2
Forward
Local technicians in developing countries increasingly are
being called upon to test innovative measures developed by
agricultural researchers at the national or regional level.
Improved plant varieties, new fertilization practices, irrigation,
pesticides, new feed mixtures, and improved harvest
procedures are just a few of the more important innovations
that must be thoroughly tested at the local level before they
are passed on through extension methods to the farmer.
Local research often is not carried out by trained research
personnel, but by extension agents, teachers, training center
workers, community development agents, foreign technicians,
fertilizer and seed distributors, and farmers with large
holdings.
The purpose of this book is to show local farmers and others
the basic steps to design, execute, and measure an agricultural
experiment. This book does not cover statistical anlysis; it is
assumed that trained statisticians are available for this
purpose.
TABLE OF CONTENTS
Foreward
List of Figures
SECTION I. SOME BASIC CONCEPTS
I. The Need for Local Research
II. An Experiment Versus A Demonstration
III. Some Basic Concepts in Statistics
A. The normal distribution
B. The null hypothesis
C. The "significant difference"
SECTION II. HOW TO PERFORM AN AGRICULTURAL EXPERIMENT
I. Preliminary Research
II. Designing the Experiment
A. Replication
B. Random distribution
C. Selection of treatments
D. Selecting the location
E. Plot size and shape
III. Execution of the Experiment
A. How to lay out a right angle
B. Labeling and mapping
C. Uniform application
IV. Measuring and Recording the Results
A. When should measurements be taken?
B. What should be measured?
C. Put all observations in numerical terms
D. A report procedure
Appendix: Table of Random Numbers
Bibliography
LIST OF FIGURES
1. Normal curve
2. Normal curves with and without fertilizer
3. The completely random design
4. Random complete block
5. Random complete block suitable for demonstration
6. How to make random the Latin square design
7. Split-plot design
8. Plot shape
9-A. Laying out a right angle
9-B. Laying out a right angle
9-C. Laying out a right angle
SECTION I
SOME BASIC CONCEPTS
I. THE NEED FOR LOCAL RESEARCH
Many countries today are experiencing what is called "agricultural
development." Basically, this means three things for
agriculture: (1) it has become more complex technically; (2) it
has become less oriented to home consumption and more oriented
to the market; (3) it has become dynamic; that is, it is not
simply moving to a new, more efficient level of operation, but
is in a continuous state of flux.
In many countries, research facilities have been established at
the national and regional level. New plant varieties and innovative
cultural practices are being tested successfully at
these facilities. But before they can have any effect on farm
production, they must be tested thoroughly at research stations,
schools, and farms on the local level.
The basic problem facing local experimenters is whether the use
of a new or different practice will affect the outcome of some
particular segment of agricultural enterprise in their area. If
so, to what extent? If farmers fail to adopt a beneficial practice
because it has not been tested locally, or if they adopt a
harmful practice because it has been tested improperly, local
extension agents and those who have carried out experiments
must share the blame.
Local personnel have a great responsibility to become skilled
in testing and evaluating new practices so that they may avoid
such mistakes. If great care is exercised, untrained personnel
can become sufficiently expert in experimentation to bring many
benefits to the local farmers and, hence, to the entire
community.
II. AN EXPERIMENT VERSUS A DEMONSTRATION
What is an experiment?
An experiment is a test or tentative procedure for the purpose
of discovering something unknown, or for testing a principle or
supposition. It must be carried out in an unbiased manner. No
assumptions are made regarding the outcome; the results must
always be accepted. If we suspect that the results are not
typical, we still must accept them, although we should perform
the experiment again. In an experiment, treatments are replicated,
or repeated, and they are arranged in test plots or as
random units in a procedure.
An observation trial is not used to draw any experimental conclusions,
but may determine if a practice is worth testing.
A result trial on a farm is the testing or demonstration of a
single practice that has been proven elsewhere, but which is
still unproven in the farmer's mind.
What is a demonstration?
A demonstration shows a response that already has been proven
in an experiment. It is not conducted according to the specifications
for an experiment, and therefore cannot be used to draw
conclusions. If it does not demonstrate the expected results,
it is ignored, and may then be plowed over to be run again.
III. SOME BASIC CONCEPTS IN STATISTICS
The statistical analysis of results is beyond the scope of this
paper, but we must understand some basic concepts if we want to
be able to interpret a statistician's analysis of our experiment.
The three concepts described briefly here are the normal
distribution, the null hypothesis, and the significant
difference.
A. The Normal Distribution
Assume that there is a large amount of some crop that is grown
under uniform conditions and harvested in plots of 100 square
feet. The yields recorded for each of these plots probably will
vary from a very low figure to a very high figure. Most of the
plots will yield close to a middle figure. As we move away from
this median to either a higher or lower yield figure, we will
find successively fewer plots. If the yield is plotted against
the number of plots giving a particular yield, the familiar
bell-shaped normal curve will result. (see figure 1)
htp1x3.gif (486x486)
If the same crop is grown under identical conditions with the
addition of a fertilizer treatment, there will still be a wide
range of yields for the 100-square-feet plots. However, the
entire curve will have shifted somewhat to the right. (see figure 2)
htp2x3.gif (486x486)
Note that the two curves overlap in the crosshatched area; some
plots may yield the same with and without fertilizer. If only
a small number of the fertilized plots were measured, it is
possible that all or most of them would fall in this shaded
area. We would not know from our measurements whether the
fertilizer had really increased the yield.
The purpose of proper experimental design is to allow us to
determine whether the treatments have actually shifted the
normal curve, or whether the effect we observe is simply due to
chance. This brings us to the next concept.
B. The Null Hypothesis
The statistician begins the analysis by assuming that the
treatments had no effect, and that any effect observed was due
simply to chance. This assumption is known as the null
hypothesis. If we flip a coin and get "heads" four times in a
row, we assume this to be due to chance and not because of some
special quality of the coin.
Next, the statistician processes the data to determine the
validity of the null hypothesis. He or she may reject the null
hypothesis, deciding that the observed effect of the treatment
was significant, and probably not due to chance. Or, he or she
may accept the null hypothesis, concluding that the observed
effect of the treatment was probably due to chance.
C. The "Significant Difference"
The term significant will be found in the results of many
experiments. This may also be indicated by an asterisk (*) or
by the phrase "significant at the 5% level." These all indicate
that the statistician has determined that there is only a five
percent chance that the observed difference was due to chance.
If the results are found "highly significant," indicated by a
double asterisk (**) or by the phrase "significant at the 1%
level," this indicates that there is only a one percent probability
that the observed effect of the treatment was due to
chance.
This discussion indicates that a single experiment, no matter
how carefully designed and executed, cannot conclusively
"prove" that a treatment has a significant effect. This is why
experiments should be repeated.
SECTION II
HOW TO PERFORM AN AGRICULTURAL EXPERIMENT
I. PRELIMINARY RESEARCH
Good preliminary research, including a search of the available
literature and interviews of experienced persons, will save a
great deal of trouble later. The experimenter should not be
afraid to ask for help now; help may be of no use once the
experiment has been laid out. The preliminary research should
cover the following points:
(1) A careful study of the crop should be made. The local soil
should be studied in fertilizer and irrigation experiments.
For pest control experiments, information on the
life cycle of the pest should be obtained.
(2) Economic factors should be studied, especially if a new
crop is being introduced. Will treatments affect the
market for this crop? What is the cost of treatments?
(3) Has this experiment been performed already? Quite likely,
a similar experiment has been carried out. Were the
results clear, and do they affect the planned experiment?
Have similar experiments been carried out in other
districts?
The preliminary research should be recorded so that it may be
included in the final report.
II. DESIGNING THE EXPERIMENT
In any experiment, errors are introduced by factors beyond the
control of the experimenter: soil heterogeneity, plant variability
(due to genetic variability), plant competition within
and between plots, variation in the moisture content of harvested
plants, climate variations (when experiments are run for
more than one year) , and the size and shape of plots. Such
errors cannot be eliminated, but they can be reduced, primarily
by the replication of treatments and use of random distribution,
careful selection of treatments and location, and the
proper design of plots,
A. Replication
Replication is the repetition of a treatment several times to
obtain a mean value or yield. In field experiments, a single
replicate generally is planned to contain one plot of each
treatment in a rather compact block. Replication is accomplished
by repeating blocks. A nonreplicated trial is not an
experiment.
The number of replications depends upon the degree of precision
desired and the degree of soil heterogeneity. Generally, two
replications is not enough. The American Society of Agronomy
suggests 3-6 replications for field plots. The small number
suffices where average rather than annual results are desired.
For corn yields, 4-6 replications are often used. For small
nursery plots, 5-10 replications are recommended.
B. Random Distribution
Random distribution means that treatments are assigned to plots
in a random fashion, or are placed randomly within a block. The
reason for doing this is to eliminate any bias that might occur
if we assigned treatments to plots with some kind of order or
system.
The random distribution procedure should be completely objective.
It may be accomplished by flipping a coin, drawing cards
from a deck, or by using a specially prepared table of random
numbers, such as the one found in the appendix of this paper.
1. The completely random design
htp3x9.gif (486x486)
This design results when treatments are assigned to a previously
determined number of plots. It is useful for some types of
treatments on animals, but is not an efficient design for field
trials with plants. Its main advantage is its simplicity and
flexibility. Treatments are assigned to plots by drawing cards
from a deck, slips of paper from a container, or by using the
table of random numbers in the appendix.
Example: A, B, and C represent three different levels of
nitrogen tested on wheat. Four samples for each
level X three levels = 12 plots.
2. The random complete block
In this design, treatments are assigned at random within a
block, and the entire block is replicated (see Figure 4). The
htp4x10.gif (486x486)
blocks should be kept as compact as possible, and the number of
treatments as low as possible consistent with the objectives of
the trial.
The main advantage of the random complete block design is the
high reliability of the data obtained from it, and its suitability
for demonstration (as seen in Figure 5).
htp5x10.gif (437x437)
Example: A-F are six different fertilizer treatments for
sugar beets. Note that each treatment occurs
once in each block. Six treatments X five
replications = 30 plots.
3. The Latin square design
In this design, treatments occur once in each column and once
in each row, and treatments are random in both directions (see
Figure 6). Thus, the Latin square removes variability in two
htp6x10.gif (540x540)
directions while the random complete block removes it in only
one direction. The number of replications always equals the
number of treatments in a Latin square design. It is more precise
than the random complete block, but it becomes cumbersome
for more than eight treatments.
In Figure 6, columns and rows are first numbered from 1 to 5,
and treatments are assigned to the plots in regular alphabetical
order, simply rotating the order one place in each row or
column.
In the middle square, we have the same square after the columns
have been rearranged by choosing at random the numbers at the
heads of the columns.
In Step 3, we have now chosen the rows at random by the same
method. The procedure is completed. Note that in the righthand
square, treatments appear only once in each row and column.
4. The split-plot design
htp7x12.gif (540x540)
This design is used to test two factors in combination. It is
not the most precise design for this purpose, but is often used
to facilitate physical operations. For example, some field
treatments, such as irrigation, are more conveniently applied
to relatively large strips through the experimental area. If
different dates of harvest are one of the factors being tested,
it may be easier to harvest in strips through the experimental
area rather than to harvest a few feet of one row and then skip
across rows for another small harvest area.
There are many split-plot designs. They vary in precision. If
possible, an experienced person should be contacted for advice
before one uses this design. The basic design involves assigning
one factor to main plots that are arranged in random complete
blocks or in a Latin square. Assign to the main plots
those treatments for which you are willing to sacrifice precision.
The treatments of the second factor are assigned at
random to sub-plots within each main plot.
Example: Planting dates and fertilizer treatments on
tomatoes. Three planting dates (main plots) X
four fertilizer treatments (subplots) X three
replications = 36 plots.
C. Selection of Treatments
Many factors that influence the farmer's profit can be applied
as contrasting practices in an experiment. Rate of seeding,
date of planting, spraying and dusting treatments, fall vs.
spring plowing, method of seed bed preparation, surface vs.
furrow application of irrigation water, weed control by herbicides
vs. cultivation, fertilizer treatments, pasture grass-legume
mixtures, and crop rotations are only a few of the more
important ones.
In selecting fertilizer treatment rates, it is desirable to use
rates that differ by equal intervals, such as 20, 40, 60, 80,
and 100 pounds of nitrogen per acre. We may have an idea of
what rate would be inadequate and what rate would be well in
excess of optimum. We should test the entire range, including
two or three levels between the minimum and maximum. An
untreated control plot is not necessary in a fertilizer plot
where it is understood that the crop needs some minimum level
of fertilizer to grow well. However, the demonstration value of
any experiment will be enhanced if we designate a control plot
that represents the local practice.
In a factorial experiment, the effect of more than one factor
is studied. For example, we may study the effects of four
levels of nitrogen and three levels of phosphorus. This would
give 3X4 or treatment combinations. You should try to keep the
experiment simple, not studying too many factors at once.
D. Selecting the Location
This is a highly critical step in the performance of an experiment.
The most important consideration in selecting a location
is soil heterogeneity. It was formerly believed that "the
experimental field should contain many different soil types to
be representative." This is a misconception. The soil should be
representative of that generally found in the area. However,
the land within the experimental area should be as uniform as
possible with respect to topography, fertility, the subsoil,
and previous management.
The causes of soil heterogeneity are the following:
(1) Topography: hillsides may cause gullies and the washing
down of nutrients. Low spots or variation in the texture
of the subsoil will cause plant variation.
(2) Variation in the moisture content.
(3) Variation in the penetration of irrigation water.
(4) Wide variation in available soil nutrients.
(5) Competition and shading from trees and hedgerows.
(6) Past use of the soil, including previous varietal and
cultural trials, and previous applications of organic
matter, fertilizer, and crop refuse.
What steps can we take to reduce the soil heterogeneity?
(1) Select land with a slight (1-2%), uniform slope. Avoid the
use of draws, lowlands, and other irregularly shaped
pieces of land.
(2) Where previous trials have been run that might affect soil
uniformity, grow one or more "blank trials" before experimenting.
A blank trial is a single crop--preferably a
small grain--that is grown as uniformly as possible over
the entire field to "smooth out" soil variations.
(3) Place new plots at a right angle to previous plots.
(4) Select land at least 20-30 yards from trees, hedgerows,
and roads.
(5) Record all information concerning the past history and
present condition of the land and included it in the final
report. This will assist others in interpreting the
results.
E. Plot Size and Shape
1. Plot size
In most local experiment stations or schools where land is
limited, the size and shape of the plot is a matter of convenience.
However, there are several considerations to take into
account.
There are two basic plot sizes: (a) nursery plots, cared for by
hand, which often have multiple short rows 10-22 feet long; and
(b) field plots, adapted to the use of standard farm machinery.
Larger plots commonly are used for corn, sugar beets, and hay
rather than for small grains. Small plots may be necessary
where many varieties or strains are being tested, where the
amount of seed of a new variety is limited, or where funds are
short.
Researchers generally agree that an increase in plot size will
reduce the error for plots up to about 1/40 acre (100 square
meters). Above that size, the decrease in error is less than
would be provided by an increase in the number of replications.
Small plots are more variable due to (a) fewer plants, b)
losses in harvest or errors in measurement, and (c) competition
and greater border effects.
2. Plot shape
htp8x15.gif (437x437)
Plot shape generally makes no difference. Relatively long,
narrow plots should have their long dimension facing in the
direction of the greatest soil variation so as to overcome soil
heterogeneity.
There are two other practical considerations in plot shape.
First, plots should be sufficiently wide to allow border strips
to be removed or to minimize the importance of borders that
remain. Second, field plots should be of a shape and size to
accommodate farm machinery.
3. Suggested plot sizes and shapes for various crops
(from Field Plot Technique by E. L. Leclerg, et al.
* Small grain: 3-4 rows X 10-20 feet (center rows harvested).
* Corn: 4-6 rows X 10-12 hills.
* Soybeans: 1-4 rows (2-3 feet apart) X 16 feet.
* Sorghum: 2-4 rows X 30 feet (center rows harvested in 3 and
4 row plots).
* Alfalfa: 7 feet X 60 feet (center five feet harvested with
a mower); 5-8 drilled rows 7" apart with a 12-14" alley
between border rows; 3-5 drilled rows 12" apart with an 18"
alley, and the entire plot harvested.
* Sugar beets: four rows (20-24" apart) X 30-60 feet (plants
thinned to 12" apart in row)
4. Border rows and guard areas
When there is competition between adjacent rows of different
varieties, especially where they differ in growth habits,
serious error may be introduced. In semi-arid or sub-humid
areas where plants compete for water, small grain yields are
greatly affected by plant competition. For this reason, single
row plots are not used. With many crops, 3-5 row plots are
grown, but the two outside rows are not harvested for yield.
Where alfalfa rows are spaced 7" apart, interplot competition
is a serious factor. If alleys between plots are widened to
14", border rows should still be removed because the alley
itself may allow border rows to grow more vigorously than the
plants on the inside rows.
Fertilizer application often requires the use of machinery, but
the flow of such fertilizer may not be precisely controlled on
the ends of the field. Therefore "guard areas" 1-2 feet wide at
the ends of the plot are thrown out.
III. EXECUTION OF THE EXPERIMENT
A. How to Lay out a Right Angle
If the corners of the plots are not laid out at exactly 90
degrees, plots will cover a different area than we imagine they
do. The following procedure is based on the fact that a
triangle with sides in a 3:4:5 ratio forms a perfect right
angle.
Equipment
* 50-foot cloth tape measure, heavy string, or wire marked at
30, 40, and 50 feet.
* Stakes
* String
Procedure
(1) Lay out a baseline with stakes and string. The length of
this line will equal the desired width of the total plot.
Place two stakes (A and A') as corner posts, as shown in
Figure 9-A. Connect A and A' with string.
htp9ax17.gif (437x437)
(2) Place a third stake (B) next to the string exactly 40 feet
from A.
(3) Have a co-worker hold the end of the tape on corner stake
A while you draw an arc with a 30-foot radius. You should
swing the extended tape to draw this arc across the
approximate place the side boundary will pass.
(4) Have the coworker hold the end of the tape at stake B.
Following the same procedure as in step (3), draw an arc
with a radius of 50 feet, as shown in Figure 9-B. Place a
htp9bx17.gif (437x437)
stake (C) where the two arcs cross.
(5) Tie a string from stake A to stake C. This forms a right
angle at A (see Figure 9-C). Now repeat the process at A'.
htp9cx18.gif (437x437)
B. Labeling and Mapping
Accurate mapping and labeling is a simple procedure that is
crucial for a successful experiment. For example, if someone
pulls up your marker stakes before the experiment is completed,
and you have made no map for your records, the experiment may
be ruined.
You must draw a map because field markers often are obliterated
by weather or tractor drivers. The map should refer to permanent
structures, such as fence posts, standpipes, building
corners, etc. You should be able to locate each separate treatment
exactly, even if all the stakes, strings, and labels are
removed from the field. Also at this stage, the planned treatments
should be listed and described. The map should indicate
which treatment each plot receives.
Field markers should be written in grease pencil, which will
not wash off in the rain or by irrigation water. Stakes may be
used to label plots; cardboard tags often are used in orchards.
Make sure your application, the field markers, and the map all
agree at the time treatments are applied.
C. Uniform Application
Failure to apply treatments uniformly is a very common mistake
that decreases the value of the experiment. Great care should
be taken to insure that fertilizer, pesticides, seed treatments,
etc., are applied uniformly over the plot, as specified.
Application equipment should be cleaned between trials. Seeds
must be swept out when different varieties are being planted.
If more than one worker is applying treatments, do not have the
same worker apply the same treatment over more than one
replication.
Do not inadvertently add factors. For example, when fertilizer
is side-dressed on a row crop, the shoes on the applicator may
prune some of the roots, and this will affect plant growth. The
proper untreated check would consist of a plot through which
the fertilizer rig had been pulled without the material. Seed
soaked in a chemical should be compared with seed soaked in
water, not with dry seed.
Carefully weigh all the materials used, if so required. Calibrate
application equipment to make sure you are putting on the
amount you think you are. Fertilizer elements should be mixed
several weeks before the application to allow time for any
chemical reactions to take place.
Obtain a uniform stand. Small grains will tiller--or put forth
new shoots--where adjacent plants are missing, but corn and
many row crops will not "fill in" empty areas. One solution is
to plant thick, then thin down to the desired stand.
Uniform care of plots is important. Weeds greatly influence
crop yields and should be removed early in the trial.
IV. MEASURING AND RECORDING THE RESULTS
Considerable time and expense has been spent thus far, yet many
experimenters fail in the end because they measure and record
the results improperly. The experimenter may take measurements
at the wrong time. Or he or she may take measurements at the
right time, but fail to put all results in numerical terms. He
or she may measure at the right time, and do so in numerical
terms, but fail to measure all the affected attributes. Or the
experimenter may do all these things correctly, but not record
the results in a simple, complete form.
A. When Should Measurements be Taken?
Different varieties mature at different times, and therefore
should not all be harvested at the same time. The experimenter
must watch closely and harvest each variety as it matures. He
or she must record the total days to maturity for each variety.
The rate at which results are reached is sometimes important.
For seed germination, both the rate of emergence and the percentage
of seeds germinating should be recorded.
B. What Should be Measured?
This is an extremely important question, one not adequately
considered by inexperienced experimenters. In some experiments,
workers may simply harvest and weigh the crop with no consideration
for other factors that are important on the market, and
which may have been affected. The market and nutritional value
of the product must always be kept in mind. Even at a local
experiment station or school where there is no sophisticated
measuring equipment, there are many attributes that can be
measured. For example, fertilizer treatments on tomatoes may
affect not only the total yield, but also the time to maturity,
the color, the size and shape, and the susceptibility to
diseases. For corn, the number of ears should be counted,
and--if facilities are available--the moisture percentage
measured for a sample of ears that represent all sizes, with
kernels from one or two rows on each ear.
The following are other attributes of field and horticultural
crops that might be measured:
* Sugar content of sugar beets
* Specific gravity of potatoes
* Grade of peaches
* Oil and protein content of soybeans
* Coumarin content of sweetclover
* Hulling percentage and milling quality of oats
* Ginning and fiber properties of cotton
* Pithiness of carrots
In short, when deciding what to measure, always keep in mind
the value of the product on the market.
C. Put All Observations in Numerical Terms
Many attributes of quality do not readily lend themselves to
measurement in numerical terms. For example, we may want to
measure the amount of insect damage on crop leaves after pesticide
treatments. It may seem easiest to judge damage as
"light," "moderate," and "heavy." But unless we put everything
in numerical terms, a statistician cannot make use of our
results.
In the case of disease or insect damage, a convenient numerical
scale should be set up. For example, to measure potato scab,
set a scale ranging from 0 to 10. Zero represents a potato
completely free of scab, and 10 represents a potato entirely
covered with scab. In some places, standard scales have been
established--1-5 or 1-7--and photographs representing each step
are used as a method of standardization. In general, the
following recommendations may be made.
(1) Try to design the scale so that observations are normally
distributed, that is, the middle number is the most frequently
observed.
(2) There should be as many steps in the scale as an experienced
observer can distinguish.
(3) Where any individual judgment is involved in making
observations, try to avoid having more than one person
make the observations.
D. A Report Procedure
Research is a continuous process, even at the local level.
Single experiments seldom determine new farming practices; the
results of several experiments have a cumulative effect. For
this reason and others, the written report of our experiment
must receive some attention. It must be complete, but not overly
complex. It must convey clearly and concisely what the
experimenter tested, under what conditions the test took place,
and the results. If the report is to be placed in a file with
similar reports, there may already be a standard format. if
there is no sample format, the following is generally
acceptable:
(1) Title page. This should indicate clearly the nature of the
experiment. The experimenter's name, the date, and location
must be included.
(2) Introduction. This must include a review of the literature
and basic background information, including all similar
experiments carried out previously. The problem must be
defined.
(3) Procedure. This must include pertinent soil and climatic
conditions, a careful description of the treatments, and
an explanation of how the treatments were applied.
(4) Results. These should be given in both tabular and graphic
form, with the results of the statistical analysis shown
clearly.
(5) Conclusion and recommendations. As a minimum, any further
experiments called for by the results should be mentioned.
(6) Appendix. This may include a plot map and the statistician's
calculations.
APPENDIX: TABLE OF RANDOM NUMBERS(1)
htpx23.gif (540x540)
To make random any set of ten items or less, begin at a random
point on the table and follow either rows, columns, or
diagonals in either direction. Write down the numbers in the
order they appear, disregarding those that are higher than the
numbers being made random and those which have appeared before
in the series. If you wish to make random more than ten
numbers, pairs of columns or rows can be combined to form two
digit numbers and the above process followed.
_____________
(1) Thomas M. Little, and F. J. Hills. Experimental Methods for
Extension Workers. (Davis, California: University of California
Agricultural Extension Service, 1966), p. 55.
BIBLIOGRAPHY
Hopp, Henry. A Guide to Extensive Testing on Farms. Washington,
D.C.: USDA Foreign Agricultural Service, 1951.
Leclerg, E. L. , Leonard, W. H. , and Clark, A. G. Field Plot
Technique. Minneapolis: Burgess Publishing Co., 1962.
Little, Thomas M., and Hills, F. J. Experimental Methods for
Extension Workers. Davis, California: University of
California Agricultural Extension Service, 1966.
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assessment and program development
support; by-mail and on-site consulting
services; information systems training. It
also publishes a quarterly newsletter and a
variety of technical manuals and bulletins.
VITA
1600 Wilson Boulevard, Suite 500
Arlington, Virginia 22209 USA
Tel: 703/276-1800 * Fax: 703/243-1865
Internet: pr-info[at]vita.org
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