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A guide to extensive testing on farms

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Title:
A guide to extensive testing on farms
Added title page title:
Extensive testing on farms
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Hopp, Henry, 1911-
United States -- Foreign Agricultural Service
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Washington
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Foreign Agricultural Service, U.S. Dept. of Agriculture
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English
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4 pts. in 1 v. : illus., tables. ; 26 cm.

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Field experiments ( lcsh )
Agriculture -- Statistics ( lcsh )
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federal government publication ( marcgt )
non-fiction ( marcgt )

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Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.

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Full Text
/6,7qC(
extensive
testing
on far'ns
in four parts
* O iTS laidll. Foreign Agricultural Service April 1954 U.S.DEPARTMENT OF AGRICULTURE




This publication has
been prepared for use in the technical cooperation program of the Foreign Operations Administration.




.74
Actual farms-with their various soil, slope, cropping, and management conditions-are the ultimate test of any new or improved practice.




a guide to
EXTENSIVE TESTING ON FARMS
by Henry Hopp
in 4 parts Part 1: Introduction Part 11: Result Tests
Foreign Agricultural Service
UNITED STATES DEPARTMENT OF AGRICULTURE Washington, D.
April 1954




CONTENTS
Page
Part I. Introduction . . . ....1
When do you make extensive tests on farms? ..3 How long should extensive tests last? .. ... 7 What are the kinds of extensive tests?. o .. ... 7
Part 11. Result tests ................ 8
Step I1. Delimit the regions... 9
Step 2. Decide on the number of farms .... 11 Step 3. Select the farms. .. *... . . . . . 13
Step 4. Decide if you need check plots........ 16 Step S. Put in the plots ................... 18
Step 6. Collect the data ... .. ... .. .... .21
Step?7. Interpret the results .. ... .... 22




PART 1. INTRODUCTION
"Farmers generally would not change their practice from observing what could be done on farms operated at public expense. T here must, therefore, be demonstrations carried on by farmers themselves on their own farms and under ordinary farm conditions."
-A. C. True.
Like the farmer, the technician also demands
demonstration: before he recommends a practice, he
wants proof that it is applicable to the farms in his
area.
Since World War II the concept of technical service to farmers as a tool for agricultural progress has grown rapidly in acceptance throughout the world. Programs to give this type of service are being set up at an unprecedented rate. Naturally these proi ams look to older ones for guidance.
The older technical- service programs generally operate through two well-recognized organizational entities: An experiment station and an extension service. In its simplest form, the experiment station is a place at which experiments are conducted to develop better practices for farms, and the extension service is a group of advisers who help farmers adopt these practices.
But these definitions tend to obscure a vital step in the process of agricultural advancement: the whole 'Complex of testing and proving operations that goes far beyond the intensive research at the experiment station itself and is necessary before an improved agricultural practice is ready for recommendation to farmers. This complex involves outlying substations, experimental farms, experimental plots on farms, farmer cooperators, and often commercial organizations such as seed companies and farm-implement manufacturers. In fact, a considerable part of the work of experiment -stations consists of the extensive testing that is carried on outside the physical confines of a main station.
Each of the two levels of testing serves a distinct purpose: intensive research, at the experiment station, discovers a good practice; exten-




sive testing, on farms, determines the applicability of that practice over a region. Intensive research gives the first clues as to what is a better practice but usually is not a sound basis for a flat recommendation to farmers. Only after an extension agent is armed with satisfactory information on applicability, can he confidently recommend a practice, knowing in advance what results farmers should get.
How extensive testing works in an agriculturals-service program is illustrated by the cooperative activities in California. The foreword to Research Program of the Experiment Station, 1950-1951, published by the University of California's College of Agriculture, states, "The Agricultural Extension Service and the Agricultural Experiment Station have over 7,000 tests, demonstration and research plots scattered over the state each year. They work closely together." And J. Earl Coke, formerly director of agricultural extension in California and now assistant secretary of the United States Department of Agriculture, wrote to the author on September 2, 1952, as follows"Because of the variety of climate, soils and crops grown
in California, it is necessary to conduct test plots in the
field to determine actual adaptability of crops under local conditions. Therefore, for many years the Extension Service has conducted many tests, in most cases with the cooperation of the Experiment Station. For example, grain
variety trials are conducted throughout the State by the
farm advisors. In the majority of the cases the seed for
these variety tests comes from the Experiment Station, which also threshes, weighs, and tabulates results from many of the plots. In addition to the agricultural extension test plots, field tests are conducted by the Experiment Station in various counties, in which in most cases the farm 4.dvisors are involved in some way. The farm
advisor may assist in the selection of the cooperator and the plots; he may also assist in the application of various
treatments used in obtaining the results. This works
very much like the tests run by the Extension Service except that the leadership comes from the Experiment Station."
We see, then, how intensive research at an experiment station is not enough support for an extension program and must be reenforced by adequate extensive testing. Herein lies guidance for organizers of a new agricultural- service program. Although at the beginning the organization will have a much smaller extensive testing program than the State of California, it should nevertheless recognize the place that each of




3
three steps has in agricultural development: (1) Intensive research at the experiment station to develop practices, (2) extensive testing of these practices to determine applicability under farmers' conditions, and (3) assistance to farmers in putting the practices into effect. Failure to provide for the second step tends toward research without opportunity for practical outlet, and toward extension without a proved technical basis.
This Guide has been prepared to aid the agricultural technician who is specifically concerned with extensive testing on farms. When a new practice is suggested to him, either by the experiment station or by his own experience, he must still find answers to several questions before he can recommend that practice: How will the practice work in the different parts of my area? How must it be modified for farm use? Will it yield enough benefit to be worthwhile? How much will the benefit vary?
Answering these and similar questions on applicability involves a kind of testing that is different from the intensive research done at the experiment station. For one thing, it requires the cooperation of farmers; for another, it has to be conducted in many locations and under actual farming conditions. Sometimes, to get valid measures of applicability, the technician must go even furtherthan locating the tests on farms: sometimes he must have the farmers themselves do the work, with their own implements and in their own way.
The technician concerned with testing a practice for adaptability
finds himself in the intermediate role of researcher and extension man. In fact, since extensive testing is a step that lies between the intensive research at the experiment station and true extension work on the farm, it is a step that can be taken either by the researcher as a sequel to his station experiments or by the extension man as a preliminary to his blanket recommendation of a practice.
WHEN DO YOU MAKE EXTENSIVE TESTS ON FARMS?
The concept of extensive testing requires that you carefully assess the real reason for your test. If you decide specifically how you want to use the information developed by the test, you will usually be able to decide whether you need an intensive test, an extensive test, or possibly both in sequence. When you intend to use the information to make a final recommendation for farmers, you probably need a test of an extensive nature.
In conventional research, the technician is concerned with a number of practices- sometimes a very large number-and with exact measurement of the responses. For these reasons, he generally lays out his tests in replicated, or repeated, plots on one or more experimental areas. In extensive testing the situation is somewhat different: there the technician usually begins with a practice that is reasonably certain




4
-07 F
Experimental plots at a research station. Practices proved superior here usually need extensive testing on farms before they can be recommended for actual farm conditions.




5
to have a beneficial effect, but he to trying to determine its applicability to farms.
To make clearer the distinction between questions answered by re- search and questions answered by extensive testing, we might point. out broad problems that typically involve applicability and therefore are solved by extensive tests on farms and not by experiments at the. research station.
To determine conditions in an area.-Some tests are undertaken to determine conditions in an area for which the technician is responsible. An example is a test to determine the average response to fertilizer. Problems of this kind might be looked upon as a survey conducted by means of tests. The answer cannot be obtained through research at one or two research stations; the validity of the answer depends on getting an adequate number of tests over the area.
To find responses for different regions. -Another group of problems concerns different responses to a practice in the different regions of the technician's area. A region may be a soil series, a geographic complex, or even a particular class of farmers. You may have a lead on a practice from research at the experiment station, but you may still need to delineate the regions where the practice applies or where variations need to be made in the practice. For example, in one soil region a 10-6-4 fertilizer mixture may be the best; in another, a 5-10-5. Such a problem cannot be solved at one location; the tests will have to be made at a number of places in each region.
To find responses under actual farming conditions. -Research is often conducted under "ideal" conditions, or at least under conditions not usual on actual farms; and the clue to a better practice thus comes out of nonrepresentative conditions. The problem still remains of determining what results the practice will give on the farm. Response of a new crop variety at the research station, on soil that has been managed well, may be far different from the response on farms, where fertilizer practices might be variable and quite different from those at the station.
Or it may be a question not of differences in land conditions but
rather of farmers' operations. Then the testing has to be done not only on the farms but also by the farmers as well-each with his own implements and in his own way. When this latter concept is involved, research tests may indeed be quite inapplicable.
Sukhatmel/ cites an outstanding example in India of a comparison between the results obtained on farms and at experiment stations. His data show the uncertainty of using research- station results as a measure
Y T. V. Sukhatme. "Assessment of Additional Food Production" (report of sample survey in Madhya Pradesh, 1949). Agr. Sit. in India 5:719-724, 1951.




6
of farm response. He found (1) that the land to which farmers applied new practices was much better than the average farm land of the area, and (2) that, despite this fact, farmers' results were much less than anticipated from research- station results. Some causes of such discrepancies between farmer and r esear ch- station results are that1. Yields from small experimental plots are usually
greater than yields from farmers' fields.
2. Cultural operations (land preparation, seeding, cultivating, and harvesting) in research stations are u sually more timely in application and are more thoroughly and expertly done than on farms. Besides,
some of the farm implements used at the station may
be different from those used by farmers.
3. Research personnel usually apply a practice more
adequately and with more ample consideration of the
factors that produce success than do farmers, who perform the practice as a part of their farming operation.
To measure the profitableness of a practice. -Before a practice can be recommended to farmers, the question of its profitableness often must be answered. For example, you might have this problem: What quantity of fertilizer is the most profitable for farmers to use, considering that there may be many unknown factors, other than nutrient deficiency, that are limiting crop growth under actual farming conditions ? The best quantity of fertilizer for farmers to use, year in and year out, may often be less than the quantity that gives the maximum yield response.
To measure the variability of benefit.-In determining whether farmers in a certain region should adopt a practice, you must be concerned not only with the average benefit in the region but also with the consistency of the benefit to the individual farmers. A practice that gives large benefits on some farms but none on others is not as safe to recommend as a practice that gives consistent results everywhere.
To assess a practice when there is no single. check.-You know that experiments at research stations often include comparisons with present practice. These comparisons are simple to make when there is a single present practice: the experimenter simply includes a check plot in the test. But sometimes there is no single present practice. For example, if each farmer grows his own corn, the experimenter would need as many checks as there are farms. Or, if farmers have different breeds of cows, a large number of checks would be needed in order to test, let us say, a new feeding practice at the research station. It might then be easier to conduct the tests directly on farms, and the checks will be the different actual practices on the several farms.




7
HOW LONG SHOULD EXTENSIVE TESTS LAST?
For some practices the results are influenced as much by weather as by soil. A practice that gives consistent benefit in both good years and bad is a safer practice to recontmend than one that works only in good years. Therefore, if the consistency of a practice from year to year is in question, you will have to repeat the test for several years.
Some practices are likely to be much influenced by yearly variability in weather; others are not. Thus, large applications of fertilizer may be profitable only in seasons with ample rainfall; and an extensive test to determine the benefit year in and year out would have to be repeated several years before final recommendations could be made. On the other hand, feeding an improved ration to calves is likely to have benefits that are fairly independent of weather; test of such a practice could probably be accomplished in one year, or even less.
If the extensive test has to be repeated for several years, no hard
and fast rule can be given as to how many years are required: two years may be enough, but several more may be needed. Your aim should be to run the test for enough years to take in reasonable extremes of weather. Long-term weather records will help you determine when you have struck extremes; but, if such are not available, you can resort to the opinions of extension people and farmers, who have reason to remember the weather.
WHAT ARE THE KINDS OF EXTENSIVE TESTS?
For convenience, it seeps advisable to distinguish between two kinds of extensive tests on farms-the result test and the farm experiment.
The result test is undertaken to find the result of applying a single practice under farm conditions; it is simple, requires little kiTo-wledge of research methods, and has high demonstrational value for farmers. You are already familiar with a related term, "result demonstration." A result demonstration is used to demonstrate a result; a result test is used to test a result. You perform a result demonstration only after the practice has been proved; you perform a result test before it is proved.
Often, however, you may have to test more than one practice. Then you will have to conduct a more complex kind of test-the farm experiment. This kind is undertaken to find which of several practices-for example, several fertilizer formulations-is best un er farm conditions. It requires more application of statistical methods and has less clearcut demonstration value. The designing of it requires the cooperation of technicians with experience in research.




8
PART UI. RESULT TESTS
"It has been said that farmers are a hard class to reach and impress. That is not my experience. They are the most tractable of people if you have anything substantial to offer-but they want proof."
-Seaman A. Knapp.
The result test is closely related to the result demonstration, a technique already in wide use by extension workers. Thereutdmnta tion helps a community of farmers to learn the benefit of a -recommended practice. Its purpose and technique have been summarized as follows:
"It is used to prove the practical application of basic
facts to farm and home problems and is in no sense experImental except possibly in the mind of the demonstrator. With this method the extension worker can uti-'
lize the results secured from the adoption of a farm or
home practice or a combination of practices to prove by
comparison the value of the new method. /
The result demonstration and result test are often quite similar when carried out in the field. The reason is obvious: often the same kind of proof may be required to show the farmers the benefit of a practice as is required to show the technicians. Besides, a result test usually has fine demonstrational value; it is an effective tool for teaching as well as for learning.
But the result demonstration, in its strict sense, io used only with practices of which -the technician already knows the true benefit. His purpose is to give the farmers enough concrete experience with the practice to convince them of its effectiveness. In the result test the same technique is used: trials are made on farms and results are evaluated. But the primary objective is distinctly different. A result test is underLincoln: D. Kelsey and C. C. Hearne. Cooperative Extension Work, p. 345. Ithaca, N. Y., 1949.




taken to determine the benefit or effectiveness of an improved practice under the farming conditions of the community. The result test precedes issuance of a recommendation; the result demonstration follows it.
a the result demonstration the number and location of trials are decided by the need for teaching impact on the community; in the result test, by the need for measurement of the practice. Usually, though not always, a larger number of trials will be required for a result test than for a result demonstration.
The proper design of a result test is important to assure its validity. We shall therefore point out in distinct steps the procedures you should follow in setting one up.
STEP 1. DELIMIT THE REGIONS. Begin by deciding on the regions within your area to which the practice should apply. This is a point that is easily overlooked; too often we get into the details of making the test before we ever decide on the regions of application.
Of course, if your particular area is one in which approximately the same conditions-of soil, climate, altitude, and so forthprevail throughout, you will plan to issue only one set of recommendations for the whole area and will therefore have no reason to delimit subdivisions. But if your area is made up of dissimilar regions, each of which may call for a different set of recommendations, you must define these regions.
Generally you will start with a map of the entire area in which you are working. On it you will mark off the regions where the new practice is to be tested, i. e., the subdivisions for which you will issue separate recommendations if results justify. These subdivisions are called test regions. In deciding how many of these to have, you must temper your subdividing with a consideration of practical limitations: The cost of testing in, and the complexity of recommending for, many diverse regions.
Let us see how this is done. The map shows the subdivisions decided upon for a corn-variety test. In this hypothetical example the corn-grow-




10
ing area was first marked off into two main regions-one in the west and another in the east. These were set. up as separate test regions because each had different. growing conditions and would, doubtlessly call for separate recommendations.
4P III (Dry Farming)
Delimitation of area of application for a proposed practice, and subdivision of the area into-test regions (1, 11, 111, IV).
In the western region two further divisions were made according to altitude-the coastal plains (I) and the mountains (II). This was done because research was finding that not all altitudes favor the same varieties.
Region 11 contains three soil types, but these were not designated as separate test regions because they were not extensive and because separate recommendations for each soil would be too complicated to follow.
The eastern region was divided according to farming practice-dry
farming (111) and irrigated farming (IV). This was done because the varieties that will prove the best under one practice will probably not prove the best under. the other and because farmers can easily follow the different recommendations for irrigated and nonirrigated land.
No further subdivisions were made because the number of tests required for these four regions was all that the organization could carry out.
Now, after these four regions were delimited, a complete result test was laid out in each region.




11
STEP Z. DECIDE ON THE NUMBER OF FARMS. Experience has shown that you should have 15 to 30 farms in a result test. Fewer farms will probably give you insufficient information for a sound recommendation to farmers. On the other hand, more than 30 farms will rarely be necessary for a good appraisal of a practice.
You may wonder why the number of farms is important. "'Wouldn't one really good test on a single farm do?" you may ask. A moment's thought will show you how dangerous that idea is.
Look at it this way. The farmers of your area, with your guidance, are trying to determineK what'benefit they will get from the new practice. You want to know this so definitely that you can issue a clear-cut recommendation to everyone. If you conduct a test on just one farm, even if it is a very good test, it will apply only to that one farm. After all, soils vary a great deal over *a region, and farmers vary too. You cannot be sure, from the one farm, what results farmers in general will get. Obviously, you are on dangerous ground in trying to reach a general conclusion from one case.
Then there's another point to bear in mind if you are considering a
test on just one farm. The farm you use for the toot may show a marked benefit from the practice, may even give you an increase of 50 percent or more. But no matter how promising the results on that oefarm may be, they still cannot tell you how much the benefit will vary from farm to farm. The very practice that proved so successful on one farm, may give highly variable results when tried on other farms; and a practice that gives highly variable results is not &a saf* to rcommend to farmers as one that gives consistent results. It is sobering to consider that many a potentially, good practice could be thrown completely "out of the .running" just because it failed in a one-farm test.
Consider an example in which Practice A and Practice B are both
tried on an adequate number of farms. As the following diagram shows. each given the same average yield, 50 bushels per acre. judging from that fact alone, one practice seems just as good as the other. But'when you examine each practice for consistency of result, we see that Practice




12
B is obviously the safer one to, recommend. The lowest yield we get with B is 40 bushels, but with A we get. a yield as low as 10:
Practice A
Lowest Average Highest
yield yield yield
10 bu. -00 50 5bu. 9 0 9bu.
Practice B
Lowest Average Highest
yield yield yield
40 bu. *-O 50 bu. 4...o 60 bu.
in other words, if you had made just a one-farm test, both practices might have given the same increase in yield. Here is where testing on a single farm really falls down: it shows absolutely nothing about the Variability of the benefit over the region. The only way to determine the variability is to try the practice on a sufficient number of farms.
Although we know from past experience that 15 to 30 farms are required for most tests. these numbers are, after all, a rather offhand specification. To be exact, the number of farms you will require in order to have confidence in the answer from your test will depend on two considerations: .(I) How great abenefit you expect from the new practice on the average and (2) how much -variability you expect the results to show. If you expect a large benefit from the practice and fairly consistent results throughout the region, you can cut down on the number of farms.
.The little table on the next page will help you decide how many farms to have. Suppose, for example, that you want to test a new variety of corn for your region. Let us say that you expect it to give so large a benefit that it doubles the yield, that is, gives an average increase of 100 percent. Let us say, too, that you expect~the increase to be quite variable in the region. Then, referring to the table, you will find that 15 farms or so should participate. Of course it is well to put the test on a few extra farms besides, since on some farms the test may not be carried through to the end.
This is about as close as we can come at this point to determining
the number of farms that are required. As you. might suspect, there are more precise methods for making this determination. But they are rather complex.




13
If you expect an And, if you expect Then you should
average increase the increase in have this numof- the region to be- ber of farms:
.. -Quite varablo, 10
LFairly consistent 7
fQuite variable 15
100% 1-Fair ly consistent 10
50% JQuite variable 25
0 Fairly consistent 15
5JQuite variable,. 30
LFairly consistent 20
If you ever get into a costly result test, in which it is important to determine the best number of farms quite accurately,:ryou might want to use a more precise method. The procedures are given in Parts III and IV of this Guide. But for most tests the foregoing table is adequate.
STEP 3. SELECT THE FARMS. The purpose of your test is to obtain an answer that applies to all farms in the region. Yet it is obviously irnp-ossible to have all farms participate in a result test. The criterion whereby the participating farms are chosen is important: the participating farms must be representative of all the farms. If they are not, you will never get results that are correctly applicable to the region, no matter how carefully you conduct the test.
One method of selecting the
farms-a method that is no longer considered valid-is to choose farms that are thought to be "typical" of the region. Sometimes you will hear a technician say, "I selected this farm for the test because it is typical." Get away from that idea, "typical." It is fraught with danger.
First of all, the concept of a "typical" area is fallacious. There is no such place A region




14
__~ -w~
Modern and primitive cultivating methods practiced in the same community point up the need for tests of applicability. To make sure that results of a test apply to a whole community, the technician must put the test on
a representative cross section of all farms.




is certain to include many variations-in topography, soil, previous cropping practice, farm size, method of cultivation, farmerA' abilities, and so forth. There is, in truth, no way to define what is typical. No single plot of ground can represent an entire area, with its many diversities.
There is another reason for getting away from the idea, "typical." Even if you could select typical farms, you would not'be doing anything worthwhile. For then the results of the test would apply only to these typical, or average, farms. But in a result test, not only do you need to know the average benefit of the practice; you need to know also how consistent the benefit is over the range of conditions in the region. To find this out, you will have to set up the test on a range of farms. Be careful not to overlook this point: Variability of the response is just as important as the average of the response.
In view of the nonexistence of typical sites and the need to test a practice on a range of sites, the only valid procedure is to select farms for the test that are a fair cross section of all thefarms,. Only then will you have farms participating that are a true representation of the farms in the region.
Now, how can you get a fair cross section? The ideal way would be for you to put all the farmers' names in a hat and, blindfolded, to draw out the required number. In practice, however, it is usually impossible to select the farms in so random a manner. For one thing, it may be inconvenient to obtain a list of all the farmers in the area. Besides, it may not be feasible to include certain farms.
In selecting the test farms, then, you will have to compromise on the principle of randomization. You may have to confine them to farms located along accessible roads, or to farms with which cooperative relations can be easily established. The less you confine your choice to a particular class of farms, however, and the more you take the farms "as they come," the closer you will be to having a true representation. Remember this: Every departure from randomization imposes the danger of bias in the applicability of the results. But this 1s a risk you have to take. It will be up to you to decide how far you can wander from random in selecting the farms without seriously detracting from the applicability of the results to the region.




16
STEP 4. DECIDE IF YOU NEED CHECK PLOTS. You must now decide whether your test requires one or two plots per farm.
If you intend that farmers should substitute the new practice for some practice they are already using, you should have two plots on every farm: one for the new practice and one for the old. The following are examples: (1) Substituting a new variety for an old;
(2) using fertilizer instead of none;
(3) using an insecticide instead of not; (4) trying a new ration for cows instead of the old one. 114-A The old practice is called the
check, or control. It is the condition against which the new practice is to be compared.
You may note that the term plot" does not seem to be the one to use for animal tests or tests in the farm family. You can still use the term, however, except that you should realize that in such a circumstance the plots are groups of animals or people, one receiving a new practice and the other receiving the check practice.
Some kinds of comparisons do not permit you to have the check on
the same. farm as the new practice; for these you will have, only one plot per farm. For example, if you are testing the effect of pest control on cattle, you might have to treat a whole herd. Then the new practice is applied to one farm, and another farm serves as the check. Each farm becomes a plot; and two farms serve as a single comparison, just as in the foregoing example, where the two plots were on the same farm.
Sometimes the test is not comparative; the practice under test is not intended to replace what farmers had previously been doing. Possible examples are1. Finding out how a new vegetable does in a region.
2. Finding out if rabbit culture pays.
3. Making a survey of yield of wheat.
In such cases you have just one plot per farm. The information obtained from each farm will be the production of the plot rather than the advantage it has over a check plot. Here, again, if the test is with animals or human beings, the word "plot" means the group being tested.




17
A farmer and his family join the technician in making plans. Extensive testing requires farmer cooperation and, often, farmer participation.




18
STEP S. PUT IN THE PLOTS. So far you have decided on the number of farms, selected them, and fixed the number of plots per farm. Now you are ready to start the actual field work of putting in the plots.
Choosing the location of the one or two plots on a farm will be influenced considerably by the desires of the cooperating farmer. He may want to have the plots in a certain field, for example, close to a road. This is all right. There is just one point to remember: Do not select the piece of ground solely because it is the best piece of ground on the farm. Rather, select it without any judgment one way or the other as to its relative fertility. Your aim should be to locate the test plots at random, without any purpose for your choice as far as soil conditions are concerned. Follow this thought in locating the plots on every farm; then the test as a whole will be a true cross section of the conditions in the region. Some plots will be on good ground, some on poor. All in all, you will have a fair representation of the soils in the region.
When your test requires two plots on a farm-a check plot and a newpractice plot-you must be careful to guard against bias.. Do not pick the place for the new-practice plot so that it is on more fertile land than the check. Doing so would be an error; it would invalidate the honesty of thetest, for the results might show very much in favor of the new practice. The test would be biased and would not show the true benefit of the practice. You will use the results of the test as a guide in making recommendations to-farmers, and you do not want the difference in yield between the new and old practices to be biased by differences in soil fertility.
You would like, of course, to have the two plots fall on land that is exactly alike in fertility and other respects. But this coincidence would be quite a difficult thing to accomplish, if not impossible.
Have the plots as close together as possible and choose them according to convenience rather than relative fertility. Rely on repetition of the plots from farm to farm to equalize whatever fertility differences may exist between the plots on each farm individually.
The size of the plots is a further consideration. You know that at




19
research stations most experimental plots, re -rath*t$'*'Wll and all of the same size. But in most result tept you wiU :'mAv 1he plots large; what is more, you need not have therti the same 4ise on -all farms. Observe this rule: Make the plots large enough to permi# $ollowing the usual farming methods and to mak, the results enlyble. Anything larger than this is unnecessary, and will not make r t. more precise.
Moreover, the check plot on a farm need not be thesanme size as the new-practice plot. As the diagram shows, the new-practice plot can be just a segment of a field, with the remainder serving as the check.
Check
plot
Possible sample
harvested in the I
check plot L J New-practiceo#plot
In result tests, the check plot may be much larger'than the new-practice plot. Sometimes just a
sample of the check plot may be harvested for the
comparison.
Then, when harvesting, determine the areas of the two plots and convert the yields to an equal-area basis.
In getting data, it may be more convenient to harvest just a sample out of the check plot. Try to locate the sample next to. Onew-practice plot and have it about the same size.
If you harvest just a sample out of the check plot, be careful again to guard against bias. Do not deliberately select a poorer spot. The way to avoid bias is to decide on the location of the sample at the very start of the test, before you see the results. For example, you might decide on having the sample of the same dimensions as the new-practice plot and adjoining it on the west.
To be safe, stipulate the position of the sample when you write the test plan-even before you start the field work.




20
MONSOON
The farmer measures the benefit of a new practice: "Good program building provides for evaluation of results. "-Kelsey and Hearne.




STEP 6. COLLECT THE DATA. You should make every effort to get numerical data on the results of the test; only with such data will you be able to tell the farmers what amount of benefit they can expect to receive from the new practice on their own farms. Try to get actual measurements. Do not be content to take merely your im-. pression of the results.
Although you may make a
number of intermediate observations that are valuable, the ultimate information you want is the actual yield or other benefit of the practice. When you harvest the test plots, be sure that you also make a record of the size of each plot. Then you can convert the yields from plots of different sizes to yields from plots of the same size.
Here are some examples of data that have been collected in result tests:
Example 1. Data from part of a corn test.
New variety Old variety Yield r acre
Farm Yield Plot size Yield Plot size New var e Control
(Bushels) (Acres) (Bushels) (Acres) (Bushels) (Bushels)
Jones 9 1/4 100 5 36 20
Smith a8 1 25 1 28 25
Doe 16 1/2 1100 50 32 22
Example 2. Data from part of a feeding test on milk cows.
New feed Old feed Production per cow
Farm Production Number of cows Production Number of cows New ad Old feed
(Pounds) (Pounds) (Pounds) (Pounds)
Jones 89 4 653 36 2Z 18
Smith 627 20 576 20 31 29
Doe 148 10 211 19 15 II
Example 3. Data from part of a test of kudzu production.
Farm Yield Plot size Yield per acre
(Tons)
Jones 1.79 tons 10,000 sq. ft. 7.8
Smith 598 lbs. 1/10 acre 3.0
2oe 8584 lbs. 0.8 acre 5.4




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STEP 7. INTERPRET THE RESULTS. The ultimate outcome of a result test is the issuance of a recommendation, a statement on how well the new practice works in the region. The data you have collected from the several test plots will now have to be interpreted for regional application. How valid this interpretation will be depends partly on how valid was the cross section of farms that was selected in the first place.
The interpretation of the results involves a number of statistical calculations, but fortunately none of these are difficult or complicated. Consider that much of the effort and planning you have expended thus far will have been wasted if you do not now bring the test to its proper conclusion with a proper statistical analysis of the data. In fact, much of the validity of the recommendations that ensue from the test will depend on your making this analysis.
Table I shows how to make an analysis for a test that is comparative. The data are from a test of a corn variety. First list the yields from the new and check practices for each location. The benefit is obtained by subtracting the check yield from the now-practice yield (note that farm 13 gave a minus benefit). Enter the squares of the benefits in the last column. Add all columns. The means, or averages, are obtained by dividing the sums by the number of farms.




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Table I.-Data from a corn variety test on 25 farms
Yield
Farm Y Benefit Square of
New practice Check benefit
Bushels per acre Bushels per acre Bushels per acre
I .... 23 16 7 49
2 .... 37 26 11 121
3 .... 24 17 7 49
4 .... 20 14 6 36
5 .... 28 20 8 64
6 .... 39 28 11 121
7 .... 8 6 2 4
8 .... 17 12 5 25
9 .... 28 20 8 64
10 . 25 18 7 49
11 .. 22 16 6 36
12 . 22 16 6 36
13 . 11 13 -2 4
14 . 21 15 6 36
15 . 18 14 4 16
16 .. 17 12 5 25
17 . 37 26 11 121
18 . 31 22 9 81
19 . 14 10 4 16
20 ... 21 15 6 36
21 . 28 20 8 64
22 . 24 17 7 49
23... 22 16 6 36
24... 28 21 7 49
25... 26 19 7 49
Sum. 591 429 162 1,236
Mean 23.6 17.2 6.5




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Now you use the sum, of the squares oftthe-benefits, 1,236, to obtain a term known as the standard deviation (s.d.). The following calculations show how this is done:
Sum of squares of benefit .-.-----.- 1,236
(Sum of benefit)Z (162)2 26,244 0
- = - -1,050
Number of farms 25 25
Difference = 1236- 1050 --- --------------186
Difference 186 7.75
Number of farms minus 1 24
Standard deviation = square root of 7.75 ----- 2.8 bu.
Sd. x 100 2.8 x 100
Standard deviation in percent = check mean 17.2 = 16%
You are now prepared to consider three pertinent questions in issuing recommendations to farmers.
Question 1. What was the average increase in yield from the practice?
Solution: Divide the mean benefit by the mean yield of the check and multiply by 100; i.e.,
6.5
1 x 100 = 38 percent
Answer: Farmers on the average can expect an increase of 38 percent or 6.5 bushels per acre. Question 2. What is the minimum increase in yield that farmers can expect 3 out of 4 times ?
Solution: Multiply the standard deviation in percent by 0.7/ and subtract the product from the average increase in percent; i.e.,
16 x 0.7 r 11.2 percent 38 11.2 = 27 percent Answer: Three out of four times, the increase in yield will be at least 27 percent.
/ The number 0.7 here is a mathematical constant used in statistics.




as
Question 3. What percent of the fariners are likely to get no increase in
yield from the new practice?
Solution: Divide the mean benefit by the standard deviation to obtain a ratio, i.e.,
6.5
2.3
Look up answer for this ratio in the following table, interpolating if necessary: Ratio Answer
-(Percent)
2.6 Fewer than *
2.3 1
2.0 2
1.6 5
1.3 10
1.0 15
.8 20
.7 25
Answer: About I percent of the farmers can normally expect to get no increase in yield from the new practice.
The statement you can make for farmers as a result of the test might be something like this:
"What we consider a good cross section of
farmers have participated in our test, and their
results indicate that the new variety will increase
yield in our region by 6.5 bushels per acre, or
38 percent, on the average; three-quarters of the
farmers can expect at least a 27 percent increase;
and fewer than 1 farmer out of 100 should fail to
get an increase."
We might now illustrate the procedure for interpreting results when the test is not comparative-when you have data on a practice that does not compare with an old or check practice. In table 2 we have some data on the yield of kudzu. Interpreting these yield results is not so very different from interpreting comparative benefit results. Enter the squares of the yields in the last column and add both columns. The mean yield, 5.5 tons per acre, is the sum of the yields, 99.1, divided by the number of farms, 18.




26
Table 2.-Data for a kofsu..test on 18 farms
Farm Yield Square of yields
TM per acre
1 --------------7.8 60.84
2 -- --------------3.0 9.00
3 ----------------5.4 29.16
4 ---------------5.9 34.81
5 ---------------.--.- 4.2 17.64
6 -- -------------- -5.6 31.36
7 -- --------------6.5 42.25
8 -- -------------4.9 24.01
9 -- --------------5.4 29.16
10 - ----------- 4,4 19.36
11- ------- - 5.1 26.01
12 - ------ 6.8 46.24
13 --- -------------5.9 34.81
14 -- -------------4.8 23.04
15 --------- 6.1 37.21
16 --- --------5.3 28.09
17 -.-.----- 6.2 38.44
18 ----- - - --------- - 5.8 33.64
Sum ------------99.1 565.07
Mean -- -----------5.5
The standard deviation is found in very much the same way as in the
previous example:
Sum of squares of yield - - - - 565.07
(Sum of yield)2 (99.1)2 9820.81
Number of farms 18- 18- -------- 545.60
Difference 565.07 545.60-- 0- .......... 19.47
Difference 19.47
Number of farms minus 1 = r - 17-1.1453
Standard deviation = square root of 1. 1453 -----1.07
Standard deviation in percent = s.d. x 100 1.07 x 100 =19 S mean 5.5




With data that are not comparative, we might also consider three pertinent questions about the practice.
Question 1.- What was the average yield of kudzu in the region?
Answer: 5.5 tons per acre.
Question 2. What is the least yield that three out of four farmers can
expect?
Solution: Multiply the standard deviation by 0.7 and subtract the product from the mean yield; i.e.,
1.07 x 0.7 = 0.749 tons per acre
5.5 0.749 = 4.8 tons per acre Answer: Three times out of four, the yield will be at least
4.8 tons per acre.
Question 3. What is the minimum yield farmers can expe ct7
Solution: Multiply the standard deviation by 4 / and subtract the product from the mean yield; i.e.,
1. 07 x 4 a=4. 28 tons per acre
5.5 4.28 = 1.2 tons per acre Answer: It would be very unusual for the yield to be less than 1.2 tons per acre.
From these results, you would be justified in issuing a recoinmendation to the community along these lines:
"Kudzu, when established on a representative
cross section of farms in the region, yielded an
average of 5.5 tons of forage per acre. While
there is some variability over the region, three out of four farmers can expect at least 4.8 tons per acre; and practically all farmers should get
.1.2 tons per acre as a minimum."
Te4here is another mathematical constant.