Group Title: Staff paper
Title: Steps in the analysis and interpretation of on-farm research-extension data based on adaptability analysis (AA)
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 Material Information
Title: Steps in the analysis and interpretation of on-farm research-extension data based on adaptability analysis (AA) a training guide
Series Title: Staff paper
Physical Description: 21 p. : ill. ; 28 cm.
Language: English
Creator: Hildebrand, Peter E
Bastidas, Elena P
University of Florida -- Food and Resource Economics Dept
Publisher: Food and Resource Economics Dept., Institute of Food and Agricultural Sciences, University of Florida,
Food and Resource Economics Dept., Institute of Food and Agricultural Sciences, University of Florida
Place of Publication: Gainesville Fla
Publication Date: 1995
Copyright Date: 1995
 Subjects
Subject: Agriculture -- Research -- On-farm   ( lcsh )
Field experiments   ( lcsh )
Agricultural innovations -- Research   ( lcsh )
Agricultural systems -- Research   ( lcsh )
Agricultural extension work   ( lcsh )
Alternative agriculture -- Research   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references (p. 21).
Statement of Responsibility: Peter E. Hildebrand and Elena P. Bastidas.
General Note: May 1995.
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Bibliographic ID: UF00082056
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
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Resource Identifier: oclc - 32636643

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STEPS IN THE ANALYSIS AND INTERPRETATION
OF ON-FARM RESEARCH-EXTENSION DATA
BASED ON
ADAPTABILITY ANALYSIS (AA):
A TRAINING GUIDE

Peter E. Hildebrand
and
Elena P. Bastidas'

Modified Stability Analysis or MSA (Hildebrand, 1984) is a procedure for designing, analyzing and
interpreting on-farm trials conducted to assess new technologies and disseminate the resulting
recommendations. This farmer-participatory method now called Adaptability Analysis (Hildebrand
and Russell, draft) can serve as a basis for an entire research-extension program. This guide
demonstrates some of the basic procedures for analyzing and interpreting data taken from an on-farm
research example for which an appropriate design was used (Singh, 1990).


OBJECTIVES:

The use of this guide should provide the user with:

1. The steps to follow to make technology recommendations for specific bio-physical and
socioeconomically-created environments and tailored to the various desires, needs and
constraints of specific farmers.

2. A basic understanding of the design requirements for on-farm research to make it amenable
to analysis by AA.


BASIC CONSIDERATIONS:

1. It is assumed that the user of this guide is basically familiar with AA.

2. While Analysis of Variance (ANOVA) can be used in conjunction with AA (Stroup et al.,
1993), it is not necessary. In this guide, ANOVA is not used.

3. The analyses can be done manually, with a calculator, or on a computer with SAS,
spreadsheet, or other type of analytical program. The degree of sophistication depends on
the user's capabilities and availability of equipment. The processes discussed here, with the
exceptions noted, are independent of the resources used to conduct the analysis.


1 Professor, Food and Resource Economics Department, Institute of Food and Agricultural Sciences, and
Graduate Assistant, Agricultural Education and Communication Department, University of Florida, Gainesville,
Florida 32611-0240, USA.












KEY TERMS

Confidence interval The probability that the selected evaluation criterion (for example, t ha') will
fall within a certain range above and below the mean. It is calculated by the formula:

Y (utx s/Vn)

Diffusion domains Informal and naturally occurring interpersonal communication networks for
diffusion of agricultural technology. Often specific to the commodity or product involved.

Environment The natural biophysical and socioeconomically modified or created conditions existing
for plant or animal growth in the location of the trial.

EnvironmentalIndex, El A convenient measure of the environment at the location of the trial. For
a specific environment, it is the average response of all the treatments for that environment,
usually based on physical yield per hectare.

Evaluation criterion The measure or measures used to compare the treatments in a trial. Can reflect
a researcher's concern (t ha'1), for example, or a farmer's concern (kg/kg seed, among many
others).

Extension recommendations Recommendations specific to a recommendation domain. The
message includes the description of the technology, as well as the specific environment and
specific evaluation criterion for which it is being recommended. Can be designed differently
for use in specifically defined diffusion domains within a single recommendation domain.

Recommendation domains The situations for which specific treatments or technologies will be
recommended. They are defined by a combination of environmental factors and evaluation
criteria.

Research domain The range of environments over which a trial is conducted. Ideally it represents
a wide set of biophysical and socioeconomic conditions.

Risk The probability (or percent of time) that the selected evaluation criterion, such as t ha-, will fall
below a certain level.

Trial Usually refers to a set of treatments being evaluated over a range of environments. Can refer
to the set of treatments at each environment. This double definition seldom confuses in
context.











SUMMARY OF THE STEPS IN ANALYSIS AND INTERPRETATION OF ON-FARM
RESEARCH-EXTENSION DATA

A prerequisite to being able to thoroughly analyze and interpret on-farm research data is that the
design of the trial is adequate and amenable to this type of analysis. The design of on-farm research
will be discussed later in the guide. The steps are:

1. Calculate the environmental index, EI.

2. Relate treatment response to environment.

2 a. Plot all observations (data points) for each treatment against the environmental index
on a graph. This important step is often overlooked but should not be. Ignorance
of the nature of the relationship can lead to inappropriate conclusions.

2 b. View the observations demonstrating treatment response to El and make an estimate
of the relationship of each treatment to environment. This can be done with linear or
curvilinear regression or by drawing a line.

3. Compare the responses of all treatments to El and look for treatment interaction with
environment.

4. Characterize the environments. Data for this step are often missing. Trial design and data
recording procedures should include adequate opportunity for collecting necessary data such
as: soil type, pH, planting date, etc.

5. Interpret results and define recommendation domains:

5 a. Define tentative recommendation domains.

5 b. Assess the risk associated with new technologies within the tentative recommendation
domains as compared with the farmers' own technology using the distribution of
confidence intervals.2

5 c. Define final recommendation domains. The persons involved in the on-farm trial
process (researchers, extension workers and farmers) are in the best position to use
their imagination, knowledge and judgement to interpret the results and to convert
them into useful recommendations (Andrew and Hildebrand, 1993).

6. Compare the results repeating steps 2 through 5 using alternative evaluation criteria.

7. Create extension recommendations for each of the recommendation domains and formulate
messages appropriate to each of the diffusion domains.



2 ANOVA can also be used within tentative recommendation domains to determine the significance of the
differences among or between the treatments. This analysis is not included in this guide.











ANALYSIS AND INTERPRETATION OF ON-FARM RESEARCH DATA


INTRODUCTION

On-farm research can have various functions and the trials can be managed by researchers, extension
workers and/or farmers (Hildebrand and Poey, 1985). The most appropriate for incorporating farmer
participation is a simple (few treatments), non-replicated design which has one to three treatments
to be compared with the farmers' own technologies. For purposes of demonstration, a trial conducted
in the Amazon basin of Brazil (Singh, 1990) with four treatments and on eight environments, without
replication, will be used. Additional information on design will follow discussion of the steps in the
analysis.


RESPONSE OF TREATMENTS TO DIFFERENT ENVIRONMENTS

The term "environment" is used here rather than "farm" or "location" because on a single farm, or
even in a single field, more than one environment can exist for the production of the livestock or
crops grown. Similar farmers do manage different environments differently just as different farmers
may manage similar environments differently. Furthermore, making technologies conform to varying
environments, rather than making the environments conform to the technology, is more in keeping
with sustainable agriculture.


STEPS IN THE ANALYSIS AND INTERPRETATION OF ON-FARM RESEARCH-
EXTENSION DATA

Calculate a measure of environments: Environmental Index. El Step 1

The factors that influence the environment for raising crops or livestock are many and complex and
are very difficult to assess. A convenient substitute measure of the quality of each environment where
a trial has been conducted is the average "yield" of all the treatments included when, and only when
the same treatments are included in all the sampled environments. Thefirst step is to calculate this
index, EI, which provides an effective measure of the environmental differences in the research
domain represented by the range of Els.

The data that will be used to demonstrate this analysis are summarized in Table 1. To facilitate
further analyses, it is convenient to sort the data by descending (or ascending) values of this index.
The data in Table 2 are sorted in descending order with respect to the environmental index EI.













Table 1. Response of maize (t ha') to three soil amendments and the farmers' practices from on-farm
research results in Amazonas, Brazil (Singh, 1990).

t ha1


TSP


Average


CM

2.8
4.4
0.6
2.8
3.6
3.6
4.0
4.0

3.2


FP = farmers' practices, PCW processed city waste (from Manaus), TSP triple super phosphate and CM = chicken manure.


Table 2. Response of maize (t ha') to three soil amendments and the farmers' practices from on-farm
research results in Amazonas, Brazil (Singh, 1990). Data sorted in descending order with respect to
the environmental index EI.


t ha1
TSP
4.5
4.2
3.4
3.5
3.4
1.6
1.3
0.2

2.8


CM El
4.0 3.1
3.6 2.8
4.4 2.2
4.0 2.1
3.6 2.0
2.8 1.4
2.8 1.1
0.6 0.2

3.2 1.9


FP farmers' practices, PCW processed city waste (from Manaus), TSP triple super phosphate and CM chicken manure.


Farm No.
7
6
2
8
5
4
1
3

Average


1.4
1.0
1.1
0.7
0.7
1.1
0.2
0.0

0.8











Relation of treatment response to environment


Step 2.


The yield data for each treatment should be related to the environmental index. The second step is
to view the observations by graphing the results of one treatment against El as in Figure 1.3 It is
necessary to decide if the relationship is linear or curvilinear, and some estimate of the relationship
must be made. It is satisfactory simply to draw a line or a curve through the data. With practice, this
can become fairly precise. Linear regression can be accomplished easily with many inexpensive
calculators, and linear or curvilinear regression can be estimated with a computer. The estimated
relationship shown in Figure 2 a is from linear regression. Figures 2 b and 2 c show comparisons of
linear and curvilinear regression for FP and CM. For treatments FP and CM it is fairly evident that
curves represent the nature of the data better than straight lines. Therefore, for the remainder of this
analysis, curves will be used for these two treatments. For PCW and TSP straight lines are adequate.


RESEARCHER'S CRITERION
MAIZ, MANAUS, BRAZIL, 1989


2

1


0 0.5 1 1.5 2 2.5
ENVIRONMENTAL INDEX, El


r
TSP
A
L


3 3.5


Figure 1. Observed response in t ha1" of the TSP treatment to environment (EI) for maize in
Amazonas, Brazil (Singh, 1990).



3 For this step all graphs should have identical axes so they can be compared easily by placing one graph over
the other. This will also facilitate the comparison of treatment responses to environment in the next step.


. .A. . . . .
0
. . . . . . . . . . . . . . . . .



















5


4-


3


I2

1


0

-.


RESEARCHER'S CRITERION
MAIZ, MANAUS, 1989









---------------------- ----------------------- ------------






A A A AAA A A


~TSP

Al


0 0.5 1 1.5 2 2.5
ENVIRONMENTAL INDEX. El


Figure 2 a. Estimated linear response in t ha"1 of the TSP treatment to environment (EI) for maize
in Amazonas, Brazil (Singh, 1990).



RESEARCHER'S CRITERION
MAIZ, MANAUS, 1989


5


4


3


2


1


0

.-


a
FP

CUAD

UN
A
El


0 0.5 1 1.5 2 2.5
ENVIRONMENTAL INDEX, El


Figure 2 b. Comparison of linear and quadratic response in t ha'1 of the FP treatments to
environment (EI) for maize in Amazonas, Brazil (Singh, 1990).


w|


-


-----------------------------------------------------------


-----------------------------------------------------------


---------------------------------------------- !!---/ ----------


------------------------------------- -----------------




A A A A A A A


"1














RESEARCHER'S CRITERION
MAIZ, MANAUS, 1989














A A A A A A A
I I I I I
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El


CM

CUAD

UN
A
El


Figure 2 c. Comparison of linear and quadratic response in t ha'1 of the CM treatment to
environment (EI) for maize in Amazonas, Brazil (Singh, 1990).




Interaction of treatments with environment

When all treatments have been related to, or regressed on EI, the third step is to compare the
response of the treatments to environment, as in Figure 3. No interaction exists if all the lines are
parallel. If no treatment by environment interaction exists (in practice this seldom occurs), the
treatment which is greatest over all environments is the best for the criterion used, here t ha''.
However, if the lines are not parallel, such as in this case, and is most common in practice, treatment
by environment interaction exists and different treatments may be best for different environments.
Notice that the values of the El are shown in the lower part of Figure 3. This will be useful in
characterizing recommendation domains. They also help evaluate data quality.










RESEARCHER'S CRITERION
MAIZ, MANAUS, 1989


0 0.5 1 1.5 2 2.5
ENVIRONMENTAL INDEX, El


FP

PCW

TSP

CM
E
Els


3 3.5


Figure 3. Estimated responses in t ha"1 of the four treatments to environment (EI) for maize in
the Amazonas, Brazil (Singh, 1990).


Assessing data quality.

Three criteria help assess data quality. The first relates to the range of environments sampled, the
second to the general conditions of the year in the research domain, and the third to distribution of
the environments in the research domain.

1) The range of the environmental index, El, should be at least as large as the overall
mean EL If this criterion is violated, it usually means the research domain included only
the best environments (perhaps only "progressive farmers" were involved), or else that the
year was exceptional and resulted in high yields throughout the research domain.

2) The range and distribution of yields of the farmers' practices should be similar to
expected yields over a series of years. If the year was particularly good or bad overall,
or only very good or very poor sites were chosen, this criterion could be violated.

3) The distribution of the Els should be reasonable across the range of environments
sampled.











The data in Table 2 fairly well satisfy the three criteria. The range of Els (3.1 0.2 = 2.9) is greater
than the overall mean EI (1.9), easily satisfying the first criterion. The range of yields of the farmers'
practices (FP) represents the normally expected range of yields under these conditions, satisfying the
second criterion. The distribution of Els, shown in Figures 1 to 3, is also quite reasonable, satisfying
the third criterion. Therefore, even though the number of environments is quite low (8), it should be
expected that the relationships among the treatments over various environments represented in Figure
3 will be stable over time, should the trial be repeated in this research domain (not necessarily the
same farms or sites). It also means that the persons involved in the trial can have confidence in
making recommendations to farmers in the specified recommendation domains (see step 6) based on
only this one year's data.


Characterizing the environments Step 4,

Environments can be characterized using both biophysical and socioeconomic factors that may, at the
same time, be both quantitative and qualitative in nature. Data obtained for the environments in the
Amazon example include soils characteristics and a category called "land type", Table 3. The soils
characteristics are self explanatory. Land type refers to the kind of forest that was cleared (P =
primary, S = secondary) and the number of years it has been cropped (1 = first year, etc.). The term
WL refers to land that had been cleared by bulldozer at the time of colonization and is, essentially,
waste land.


Table 3. Environmental characteristics of the sites for the on-farm maize trials in Amazonas, Brazil
(Singh, 1990).


El Land Lpe pH ECEC Alsat P2s

3.1 PF1 5.2 4.21 58.3 7.4
2.8 PF1 5.1 3.45 69.1 7.1
2.2 SF1 4.6 2.29 91.7 4.5
2.1 PF1 4.5 2.26 79.2 6.8
2.0 PF2 4.6 2.45 80.0 5.0
1.4 SF2 4.1 3.12 94.8 2.8
1.1 SF2 4.2 1.99 90.7 2.0
0.2 WL 3.9 1.35 94.8 0.1


Because the data in Table 3 have been sorted by EI, it is easy to assess the relationship between EI
and these characteristics. Lower Els are associated with lower pHs, lower phosphorus levels, lower
ECECs and higher aluminum saturation. If desired, these relationships can also be graphed and/or
estimated by regression with EI being the dependent variable as was done with pH in Figure 4.












Perhaps the most useful for farmers and extension agents is the land type characteristic, because
farmers in these conditions seldom, if ever, have detailed soil information on0 their fields. It can be
seen that both the nature of the forest that was cleared for the field and the number of years in use
are closely associated with El (Table 3).


ENVIRONMENTAL CHARACTERIZATION
MAIZ, MANAUS, 1989


S2.5
z
IJ
2
z
U1
21.5
z
Z
0
S1

0.5


E
OBS

R2=.95


4 4.2 4.4 4.6 4.8 5 5.2
pH


Figure 4. Relationship of soil pH to El of the sites for the on-farm maize trial in Amazonas, Brazil
(Singh, 1990).


Define recommendation domains Step 5


Define tentative recommendation domains


Step 5 a


The recommendation domains depend on the environmental characteristics and selected evaluation
criteria, in this case, t ha'1. Using this researcher criterion, TSP would bel recommended for the
highest Els and CM would be recommended for all the other land. Notice that the two highest Els
are PF1, but the fourth highest is also PFi (see Table 3). The difference between these environments
is that the higher ones have a pH greater than 5.0 and a phosphorus level above 7.0 ppm. However,
farmers usually will not have this detailed information about their soils, so it is considered necessary
to group all the PFi in a single recommendation domain and all the other classes in another. The
persons involved in the trial, including the farmers, should make this type of judgement to facilitate











the dissemination of results4. In this way, two tentative recommendation domains have been defined
for the criterion t ha"' (Figure 5). Ift ha' is a relevant criterion, TSP or CM would be recommended
for a field taken from primary forest and in the first year of production and CM would be
recommended for all the other fields.

Figure 5. Tentative recommendation domains and the technology recommended for maize, based
on environmental factors (land type) and the evaluation criterion (t ha'), Rio Preto da Eva,
Amazonas, Brazil.


Record
Evalua


Land Type


nmendation
tion Criteria
t/ha
.................i~iii~ iiiil


Determine risk associated with the new technology Step 5 b.

The probability of low values (a measure of risk) of the selected criterion for any of the technologies
being assessed in the trial can be estimated by means of a distribution of confidence intervals based
on the treatment results in the tentative recommendation domain.


The formula:


gives the confidence interval for the level of probability from a two-tailed "t" table for n-1 degrees
of freedom and where s = sample standard deviation for the observations in the tentative
recommendation domain. In the two-tailed "t" table, a probability level = 0.4 means that 40% of


4Iffor some reason research-extension personnel feel that they can include such characteristics as pH, ECEC,
and/or Al saturation, perhaps this recommendation domains could be redefined. Alternatively, it may be necessary to
confirm if farm 8 was really PF1 or that has been perhaps SF1 misclassified.


V(ttx slVh)















the values lie outside the interval and 60% lie inside the interval defined by the formula.
The lower value of this formula:


provides information on the level of risk associated with the technology in this tentative
recommendation domain. In this case, the level of probability from a one-tailed "t" table is the
probability that yield or other evaluation criteria would fall below the value represented by the second
equation. In this step we consider the choice between CM and TSP for PF1. Table 4 is a summary
of the calculations and Figure 6 shows graphically the risk levels for the two treatments.



Table 4. Risk calculations for t ha-' comparing CM and TSP for land type PF1 using the formula Y -
(t.x s//-f) with Y= 3.9 and s = 0.2 for CM; = 4.1 and s= 0.5 for TSP; n= 3 and df= 2.


probability
C of a lower value tdf=2 CM TSP

0.2500 25.00 0.816 3.76 3.83
0.2000 20.00 1.061 3.73 3.75
0.1500 15.00 1.386 3.68 3.66
0.1000 10.00 1.886 3.62 3.51
0.0500 5.00 2.920 3.48 3.20
0.0250 2.50 4.303 3.23 2.79
0.0100 1.00 6.965 2.94 2.00
0.0050 0.50 9.925 2.54 1.13
0.0005 0.05 31.598


Y (to.x s/IVii)















RISK ESTIMATION
LAND TYPE PF1


4.5
4
3.5
3
2.5
S2
1.5
1
0.5
0


CM

TSP


5 10 15 20 25
RISK


Figure 6. Risk levels for treatments CM and TSP for land type PFi. Evaluation criterion t ha",
Amazonas, Brazil (Singh, 1990).


Values on the X-axis represent the percent of times that yields of the indicated treatments fall below
the values represented on the Y-axis.

In this case there is not a significant difference associated with the risk of TSP and CM for maize
planted in PFi, either could be recommended .


Defining final recommendation domains


Step 5 c


Based on the risk analysis of results in PF1 we can confidently group all the PF, in a single
recommendation domain. In this way, the tentative recommendation domains indicated in Figure 5
become definitive or final recommendation domains for the criterion t ha'.













Alternative evaluation criteria


The evaluation criterion used so far has been t ha', commonly used by agronomists in crop trials and
appropriate in the majority of cases to calculate El. Nevertheless, few farmers use this criterion when
making production decisions. Farmers' evaluation criteria depend on resource scarcity and the
product that the farmers want to maximize from their crop or livestock in question. If seed, labor or
cash are scarce, the most appropriate criteria are: kg/kg seed, kg/days labor, or kg/$ cash cost,
respectively. With AA it is easy to analyze data using alternative criteria.

The sixth step is to compare treatment results using alternative evaluation criteria. In this case we
will use kg/$ cash cost. Figure 7 is based on the analysis using this criterion. Note that the same El
has been used regardless of the criterion being evaluated. The El values used to form the X-axis do
not change. The criteria used on the Y-axis do change. The same procedures were used to obtain
these relationships as were used to obtain the relationships based on the researcher's criterion, t ha'.
Cash costs of the treatments were FP = $12, PCW = $208, TSP = $98 and CM = $127. Notice that
very different conclusions result when the evaluation criteria change. This is important because it
relates to the recommendations that will be made.


Table 5. Response of maize (kg $-' cash cost) to three soil amendments and the farmers' practices
from on-farm research results in Amazonas, Brazil (Singh, 1990).

kg $-1


Farm No.


208.3
183.3
0.0
16.7
12.5
20.8
12.5
0.0


TSP

45.9
42.9
34.7
35.7
34.7
16.3
13.3
1.5


31.5
28.4
37.7
31.5
28.4
22.0
22.4
5.1


FP = farmers' practices, PCW = processed city waste (from Manaus), TSP = triple super phosphate and CM = chicken manure.


Many evaluation criteria may apply to the same set of on-farm research data. In this example, two
evaluation criteria have been demonstrated: t ha'1 in Figure 3 and kg $'1 in Figure 4.


Step 6












FARMER'S CRITERION
MAIZ, MANAUS, 1989


250

200

150
0
" 100

S50

0

-50


FP

PCW

TSP

CM
A
Els


0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El


Figure 7. Estimated responses in kg $-' of the four treatments to environment (EI) for maize in
Amazonas, Brazil (Singh, 1990).


STEP 5 USING ALTERNATIVE EVALUATION CRITERIA

a. Defining tentative recommendation domains

Based on analysis of the data of Table 5 and Figure 7 and the characterization of the environments
we can say that for the farmers' criterion (kg/$), none of the amendments are better than the farmers'
practices for PF1 and SF,. Thus none of the amendments would be recommended for the production
of maize when the land has been taken out of primary or secondary forest and in the first year of use.
After the first year, either CM or TSP could be recommended. The option would depend on risk
analysis.

b. Determining risk

Table 6 shows the calculations and Figure 8 graphically shows the risk levels for CM and TSP for the
farmers' criterion, kg $', and for maize cropping after the first year of use, using equation (2). In this
case, CM is less risky (has a lower probability of low values) than TSP and would be recommended
for maize being planted on land in the second year of use.










Table 6. Risk calculations comparing CM and TSP using the equation Y (tax s/Jii) with a one-
tailed "t" table, when Y = 19.5 and s = 10.0 for CM; Y= 16.5 and s = 13.7 for TSP; n = 4
environments (land types: PF2, SF2 and WL); and degrees of freedom = 3.

Probability of
c, a lower value t=3 CM TSP

0.2500 25.00 0.765 15.7 11.2
0.2000 20.00 0.978 14.6 9.7
0.1500 15.00 1.250 13.2 7.9
0.1000 10.00 1.638 11.3 5.2
0.0500 5.00 2.353 7.7 0.3
0.0250 2.50 3.182 3.6
0.0100 1.00 4.541
0.0050 0.50 5.841
0.0005 0.05 12.941


RISK ESTIMATION
LAND TYPE: TI, BS2, BP2


CM
TSP


Figure 8. Risk levels for the criterion kg $' and the treatments CM and TSP from the maize trials
in the Amazonas, Brazil (Singh, 1990).


. . . . . . . . . . . . . . .



.. ... ......








18




c. Define final recommendation domains

If kg $s1 is relevant, as it would most likely be to these farmers, what they already do (FP) is the best
on all land in first year of use. If farmers want or need to produce maize a second year, CM would
be recommended. Figure 9 gives a summary of the recommendations for this evaluation criteria.

Figure 9. Summary of the recommendation domains and the technology recommended for maize,
Rio Preto da Eva, Amazonas, Brazil, based on environmental factors and evaluation criteria.


Land Type


SF2

Source: Singh, 1990.


Source: Singh, 1990.


Recommendation
Evaluation Criteria


Create extension recommendations for each of the recommendation domains
and formulate messages appropriate to each of the diffusion domains


Step 7


The results of using AA in on-farm research facilitate the creation of multiple recommendations
tailored to specific environments and for different farmers' criteria. Many different farmers included
in the research domain can benefit from this type of research-extension program. Figure 10
summarizes the recommendation domains and the resulting recommendations based on the results,
analysis and interpretation of the example used in this guide.


1 I








19






Having specific recommendations facilitates making messages for use by extension. Extension
messages are oral (used, for example, in radio programs) or written (extension bulletins)
communications that can be generated based on each recommendation for different clients
(commercial producers, small farmers, different language groups, etc.).

Figure 10. Summary of the recommendation domains and the recommended technology for maize
based on environmental characteristics (land type) and two evaluation criteria, t ha"' and kg/$, Rio
Preto da Eva, Amazonas, Brazil.












DESIGN OF ON-FARM TRIALS

It is now time to return to consideration of the design of on-farm trials appropriate to generate the
kind of data used in this example and amenable to interpretation using Adaptability Analysis.

Treatments. Treatments should be few in number to facilitate participation of farmers in the trial.
This enhances diffusion of acceptable results but also helps research and extension workers
understand the farmers' evaluation criteria, needed for analysis of the data. All environments should
have the same treatment, or at least a common sub set of treatments. The farmers' practices may
differ from farm to farm to reflect each farmer's individual management practices. Note that
differences in farmer management become factors affecting environment and have a positive rather
than a negative effect on trial design. For this reason, it is not necessary to control these practices
when conducting on-farm trials. These differences, of course, must be carefully documented to serve
in characterizing the environments of each farm.

Replications. No replications are needed for analysis by AA. If replications are desired by the
research or extension worker to help assure that a trial will not be lost at a specific location, two
blocks is a sufficient number.

Environments. The number of environments is more important than the number of replications in
each environment. Based on Stroup et al. (pp.13-14) a simple rule can be devised:

The rule of 48 The number of treatments times the number of environments should equal
approximately 48 so long as the number of environments does notfall below about 12.
Thus, for 4 treatments, 12 environments would be sufficient. For 3 treatments, 16
environments; for 2 treatments, 24 environments.'


Data quality

Finally, in order to increase the probability that the first of the three criteria for confidence will be
met, the design should include a wide range of environments, including different kinds of farmers and
physical settings, and these should be distributed as well as possible to help satisfy the third of these
criteria. The second criterion depends largely on natural conditions beyond the control of the persons
doing the on-farm research.




1 It is recognized that in the example used in this guide, the number of environments is less than 12. It was
used because it facilitates calculations, but it does result in a limited number of observations upon which to base the
environmental characterization.











REFERENCES

Andrew, C.O. and P.E. Hildebrand. 1993. Applied agricultural research: foundations and
methodology. Westview Press. Boulder, Colorado (Forthcoming).

Hildebrand, P.E. 1984. Modified stability analysis of farmer managed, on-farm trials. Agronomy
Journal, 76:271-274.

Hildebrand, P.E. and F. Poey. 1985. On-farm agronomic trials in farming systems research and
extension. Lynne Rienner Publ. Inc., Boulder, Colorado.

Hildebrand, P.E. and J.T. Russell. 1994 (Book draft). Adaptability analysis: A method for the
design, analysis and interpretation of on-farm research-extension.

Singh, B.K. 1990. Sustaining crop phosphorus nutrition of highly leached oxisols of the Amazon
Basin of Brazil through use of organic amendments. Unpublished PhD Dissertation,
University of Florida, Gainesville.

Stroup, W.W., P.E. Hildebrand and C.A. Francis. 1993. Farmer participation for more effective
research in sustainable agriculture. Chapter 12 In: Technologies for sustainable agriculture
in the tropics. American Society of Agronomy, Madison, Wisconsin.


Guide/Mar-24




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