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Steps in the analysis and interpretation of on-farm research-extension data based on modified stability analysis

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Steps in the analysis and interpretation of on-farm research-extension data based on modified stability analysis a training guide
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,i, ...~~. . . ........ !il ,
FOOD AND RESOURCE ECONOMICS DEPARTMENT
Institute of Food and Agricultural Sciences University of Florida Gainesville, Florida 32611




STEPS IN THE ANALYSIS AND INTERPRETATION
OF ON-FARM RESEARCH-EXTENSION DATA BASED ON
MODIFIED STABILITY ANALYSIS: A TRAINING GuIDE
BY
PETER E. HILDEBRAND1
STAFF PAPER SP93-11 MAY 1993
Staff papers are circulated without formal review by the Food and Resource Economics Department. Contents are the sole responsibility of the author.
Professor, Food and Resource Economics Department, and Director, International Training Division, Institute of Food and Agricultural Sciences, University of Florida,
Gainesville, Florida 32611-0240, USA.




STEPS IN THE ANALYSIS AND INTERPRETATION OF ON-FARM RESEARCH-EXTENSION
BASED ON
MODIFIED STABILITY ANALYSIS:
A TRAINING GUIDE
ABSTRACT
Modified Stability Analysis or MSA is a procedure for designing, analyzing and interpreting on-farm research-extension activities conducted to assess new technologies or management practices. The guide provides the steps to follow to make technology recommendations for specific bio-physical and socioeconomicallycreated environments and tailored to the desires, needs and resource constraints of specific farmers. It also provides a basic understanding of the design requirements for on-farm research to make it amenable to analysis by MSA. The example used throughout the guide comes from a real on-farm researchextension project conducted near Manaus in the Brazilian Amazon region.
Key words: Environmental index, evaluation criteria, research domain, recommendation domain, participatory methods.




STEPS IN THE ANALYSIS AND INTERPRETATION
OF ON-FARM RESEARCH-EXTENSION DATA BASED ON
MODIFIED STABILITY ANALYSIS:
A TRAINING GUIDE
Peter E. Hildebrand'
Modified Stability Analysis or MSA (Hildebrand, 1984) is a procedure for designing, analyzing and interpreting on-farm research-extension conducted to assess new technologies and disseminate the recommendations. The farmer-participatory method can serve as a basis for an entire research-extension program (Hildebrand and Russell, 1992). 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 desires, needs and resource
constraints of specific farmers.
2. A basic understanding of the design requirements for on-farm
research to make it amenable to analysis by NSA.
BASIC CONSIDERATIONS:
1. It is assumed that the user of this guide is basically
familiar with NSA.
2. While Analysis of Variance (ANOVA) can be used in
conjunction with MSA (Stroup et al., 1991), 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 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 means used to conduct the analysis.
1 Professor, Food and Resource Economics Department, and Director, International Training Division, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, Florida 32611-0240, USA.




2
KEY TERMS
Confidence interval The probability that the selected evaluation
criterion (for example, Mg ha-1) will fall within a certain
range above and below the mean. It is calculated by the
equation: Y (t. 0 s PA)
Diffusion domain 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.
Environmental Index, EX 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 (Mg ha-1), for example, or a farmer's (kg/kg seed,
among many others).
Extension message A recommendation 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.
Extension messages can be designed differently for use in
specifically defined diffusion domains within a single
recommendation domain.
Recommendation domain 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
conditions.
Risk The probability (or percent of time) that the selected
evaluation criterion, such as Mg ha-1, 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.




3
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.
Steps:
1. Calculate the environmental index, EI.
2. Relate treatment response to environment.
2a. 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.
2b. View the observations demonstrating treatment response to EI 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 EI and look for treatment interaction with environment.
4. Compare the results when using alternative evaluation
criteria.
5. Interpret results and define recommendation domains:
5a. Interpret results from the perspective of multiple
evaluation criteria. The persons involved in the on-farm trial process (research and extension workers, 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).
5b. Assess the risk associated with new technologies as compared with the farmers' own technology. Some introduced or new technologies with higher average yields present more risk of lower yields as well.
5c. Characterize the environments. Data for this step are often missing. Trial design and data recording procedures should include adequate opportunity for collecting necessary data.
5d. Make decisions regarding recommendation domains.
5e. Create multiple extension messages for recommendations specific to recommendation domains and diffusion domains and in terms useful to farmers and extension workers.




4
ANALYSIS AND INTERPRETATION OF ON-FARM RESEARCH DATA
INTRODUCTION
On-farm research can have various functions and 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 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 technology conform to varying environments, rather than the contrary, is more in keeping with sustainable agriculture.
Measure of environments: Environmental Index, EI Step 1.
The factors which 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. The first 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 EIs.
To facilitate further analyses, it is convenient to sort the data by descending (or ascending) values of this index. The data in Table 1 are in descending order of the environmental index EI.




5
Table 1. Response of maize (Mg ha-1) to three soil amendments and the farmers' practices from on-farm research results in Amazonas, Brazil (Singh, 1990). FP is farmers' practices, PCW is processed city waste (from Manaus), CM is chicken manure, and TSP is triple super phosphate. For more details see Singh, 1990.
Farm No. FP PCW TSP CM El
- -- -Mg ha ------ -- -- -7 2.5 1.4 4.5 4.0 3.1
6 2.2 1.0 4.2 3.6 2.8
2 0.0 1.1 3.4 4.4 2.2
8 0.2 0.7 3.5 4.0 2.1
5 0.2 0.7 3.4 3.6 2.0
4 0.2 1.1 1.6 2.8 1.4
1 0.2 0.2 1.3 2.8 1.1
3 0.0 0.0 0.2 0.6 0.2
Average 0.7 0.8 2.8 3.2 1.9
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
1.2 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 2a is from linear regression. Figures 2b and 2c 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.
2 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.




6
RESEARCHER'S CRITERION
MAIZE, MANAUS, 1989
4 -~~~.. ... ...... .- .-.-.--.
. . . . .. . .. . . . .. . . .. . .E.. . . .. .
3 0- .5__ 1 .5 2.5 3.5
ENIOMNA NEE
Fiue1.osrvdrspnei 2g a-__ ofte-tetett
o .. ------------------- -- ...... ..... .-. .-.....- -------- .. -----.
0 0.5 1 .5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 1. Observed la response in Mg ha-1 of the TSP etett tram environment (E) for maize in Amazonas, Brazil ig,19)
MAIZE,, MANUS,198




RESEARCHER'S CRITERION MAIZE, MANAUS, 1989 3
M
2 .5 ------ - -- - -- -----------------------. ..... F P
2--- ..... QUAD
1<- -- ---- -------- -------. -----. .... .....I
05 .......---------------------------...... .. ..... .... .
-10 M
0 0 .5 1 1.5 2 '25 3 '35
ENVIRONMENTAL INDEX
Figure 2b. Comparison of linear and quadratic response in Mg ha-1 of the FP treatments to environment (El) for maize in Amazonas, Brazil (Singh, 1990).
RESEARCHER'S CRITERION MAIZE, MANAUS, 1989
4.5
QUA
4 ~~C
3.5 .. .. LIN__ .inQ A
2 0.- .- .3.
ENVIRONMENTAL INDEX
Figure 2c. comparison of linear and quadratic response in Mg ha-1 of the CM treatment to environment (El) for maize in Amazonas, Brazil (Singh, 1990).




8
Interaction of treatments with environment Step 3.
When all treatments have been related to, or regressed on El, 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 Mg ha-1. However, if the lines are not parallel, such as in this case and is most usual 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 assessing risk and characterizing recommendation domains. They also help in deciding upon the confidence that can be placed in the results of the trial based on available data (often data from only one year).
RESEARCHER'S CRITERION MAIZE, MANAUS, 1989
- . . .. . . . . . . . .
--a- PCW
3 ...... ..................... ...... .
* TSP
- -, CM
-..~.~Els
AA A A AA A A
0 0.5 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 3. Estimated responses in Mg ha-1 of the four treatments to environment (El) for maize in Amnazonas, Brazil (Singh, 1990).




9
Assessingr confidence of relative responses to environment
Three criteria help assess the confidence which can be placed in the relative responses of the treatments to environment. 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 of treatment yields used to calculate the Els 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. That is, the environments should not be grouped with only one or two lying outside the grouping.
The data in Table 1 fairly well satisfy the three criteria. The range of Els (3.1 0.2 = 2.9) is greater than the overall mean El (1.9), easily satisfying the first criterion. The range of Els also easily 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, given the number of treatments (see the discussion of trial design in a later section), 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 5) based on only this one year's data.




10
Multiple evaluation criteria Step 4.
The evaluation criterion used to calculate the environmental index EI, above, is Mg ha"1, the most common criterion used by agronomists in crop trials and appropriate in most cases as the basis for calculating the EI. However, few farmers use this criterion when making production decisions. If seed, labor or cash are most scarce, more appropriate evaluation criteria are kg/kg seed, kg/day of labor in a critical period, or kg/dollar of cash cost, respectively. MSA easily lends itself to analysis using multiple criteria. The fourth step is to compare alternative evaluation criteria. Figure 4 is based on analysis of the usual farmers' criterion of kg/$ cash cost, Table 2.
Notice that the same EI is used regardless of the criterion being evaluated. The EI 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 researchers' criterion, Mg 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 2. Response of maize (kg $-1 cash cost) to three soil amendments and the farmers' practices from on-farm research results in Amazonas, Brazil (Singh, 1990). FP is farmers' practices, PCW is processed city waste (from Manaus), CM is chicken manure, and TSP is triple super phosphate. For more details see Singh, 1990.
Farm No. FP PCW TSP CM EI
------------kg $-I-- ------7 208.3 6.8 45.9 31.5 3.1
6 183.3 4.8 42.9 28.4 2.8
2 0.0 5.3 34.7 34.7 2.2
8 16.7 3.4 35.7 31.5 2.1
5 12.5 3.4 34.7 28.4 2.0
4 20.8 5.3 16.3 22.0 1.4
1 12.5 0.8 13.3 22.4 1.1
3 0.0 0.0 1.5 5.1 0.2




FARMER'S CRITERION 1
MAIZE, MANAUS, 1989
250
200--F
PCW
V 1 5 0 ....-------- --------------- .... ..... .........TSP
C.)M
1 0..... ......... CM ---------------- ----------- -- .. .......... ....
~ 50 ... -.~.~..Els
.............. ................. .......... .....
0 .......................................
AA A A AA A A
0 0.5 1 1. 5 2 2.5 3 3.5 ENVIRONMENTAL INDEX, El
Figure 4. Estimated responses in kg $-1 of the four treatments to
environment (El) for maize in Amazonas, Brazil (Singh, 1990).
DEFINING RECOMMENDATION DOMAINS FOR DIFFUSION Step 5.
The fifth step is to interpret the results, define recommendation
domains and create extension messages. This involves five substeps: 1) interpret multiple evaluation criteria, 2) assess
risk, 3) characterize the environments, 4) define the
recommendation domains, and 5) create the extension messages.
Recommendation domains, the situations for which specific
treatments or technologies will be recommended, depend upon both
the characteristics of the environments and the choice of
evaluation criteria.
Interpret alternative evaluation criteria Se a
Many evaluation criteria may apply to the same set of on-farm
research data. In the example, two evaluation criteria have been
demonstrated: Mg ha-1 in Figure 3 and kg $-1 in Figure 4. In the case of the former, using the researchers' criterion, TSP would
be recommended for the two highest Els and CM would be
recommended for the remaining environments. The two top Els are
* PF,. But the f ourth highest is also PF,. The dif ference is that
the top two have a pH higher than 5.0 and phosphorus levels above
7.0 ppm. But farmers will not have this kind of information, so
* it may be necessary to group all PF, in one recommendation domain
and all other classes in the other. The persons involved in the trial, including the farmers, should make this judgement to help
facilitate dissemination of the results.




12
For the farmers' criterion, kg none of the amendments are superior to the farmers' practices for land cleared from either secondary or primary forest in the first year of crop production. Thus, based on this criterion, none of the amendments would be recommended for farmers producing maize on land being used the first year after clearing. Beyond the first year of production, either CM or TSP would be recommended, the choice, perhaps depending on risk considerations.
Risk considerations Step 5b.
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 specific recommendation domains. As an example, consider the choice between CM and TSP in the second or third year of use, and based on the farmers' criterion, kg
The equation:
Y (t. 0 s /-/n)
gives the confidence interval for the a level of probability from a two-tailed 'It" table for n-1 degrees of freedom and where s = sample standard deviation for the observations in the potential recommendation domain. In the two-tailed 'It" table, a probability level a = 0.4 means that 40% of the values lie outside the interval and 60% lie inside the interval defined by the equation. The lower value of this equation:
Y (t. a s /-,/n) (2)
provides information on the level of risk associated with the technology in this recommendation domain. In this case, the a level of probability from a one-tailed 'It" table is the probability that yield or other evaluation criterion values would fall below the value represented by the second equation. Table 3 shows the calculations and Figure 5 graphically shows the risk levels for CM and TSP for the farmers' criterion, kg $-1, 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 so would be recommended for the relevant environment.




13
Table 3. Risk calculations comparing CM and TSP using the
equation Y (t, 0 st/n) 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 classes PF2, SF2 and WL); and degrees of freedom = 3.
Probability of
a lower value Kq $- cash cost
( % ) tdf=3 CM TSP
0.25 25 0.765 15.7 11.2
0.20 20 0.978 14.6 9.7
0.15 15 1.250 13.2 7.9
0.10 10 1.638 11.3 5.2
0.05 5 2.353 7.7 0.3
0.025 2.5 3.182 3.6 -5.4
0.01 1 4.541 -3.2 -14.7
0.005 0.5 5.841 -9.7 -23.6
0.0005 0.05 12.941 -45.3 -72.4
RISK ESTIMATION
WL, SF2, PF2
20
18 _CM
16 ---
0o 12 ...... ......... ......... ...... -..-.' .. .................. ... ...... ...................
14 ......................... ........................TSP
0 8
o 2 .....
C0) 10 _ .........._
8 . ...... ...... . ... .....
S. ..... ..... ......... .. ..... .................
01
0 5 10 15 20 25
ESTIMATED PERCENT OF TIME BELOW VALUE
Figure 5. Risk levels for the criterion kg $- and the treatments CM and TSP from the maize trials in Amazonas, Brazil (Singh, 1990).




14
Characterizingr the environments Step Sc.
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 class", Table 4. The soils characteristics are self explanatory. Land class 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 4. Environmental characteristics of the sites for the onfarm maize trials in Amazonas, Brazil (Singh, 1990).
El Land Type pHi ECEC Al sat 23.1 PFl 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 PFl 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 4 have been sorted by El, it is easy to assess the relationship between El 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 El being the dependent variable as was done with pH in Figure 6.
Perhaps the most useful for farmers and extension agents is the land class characteristic, because farmers in these conditions seldom, if ever, have detailed soil information on 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.




15
ENVIRONMENTAL CHARACTERIZATION
MAIZE, MANAUS, BRAZIL (SINGH, 1990)
3.
OBNS
R2 .94
o 6 ............. ..----- ------------------ -----------.---.---J
< 2 -__------------------__~Z 1. ---01........
38' 4 4.2 4.4 4.6 4.8 66 pH
Figure 6. Relationship of soil pH to El of the sites for the onfarm maize trials in Amnazonas, Brazil (Singh, 1990).
Recommendation domains Steps 5d and e.
Based on the above analyses, interpretations and judgements, several recommendation domains can be specified, based on this single on-farm trial. The treatment to be recommended depends on the environment and on the evaluation criterion of the farmer. The former (environment) requires information about the kind of field the farmer is going to plant, when it will be planted, and/or other environmental factors, not included as treatments, which may have appeared in the analysis to be important. The latter (evaluation criteria) depend on the scarcity of resources available to the specific farmer and what that farmer would like to maximize for the specific crop or livestock in question. For the example used in this guide, Table 5 and Figure 7 summarize the recommendation domains and messages available to an extension agent for different farmers. If Mg ha- is a relevant criterion, TSP would be recommended for land taken from primary forest and in first year of production. CM would be recommended for all other land. If kg $-1 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.




16
Table 5. 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
PFI SF1 PF2 SF2 WL
Criterion
Mg ha' TSP CM CM CM CM
kg $- FP FP CM CM None
Source: Singh, 1990
On-Farm
Research-Extension
Population Multiple in
of Farms Criteria Multiple Environments
.P .Mg/ha
PF, PF,
FP kg/$
Mo/ha
SF ... .. .. SF,
S FP kga/$
................ M /ha
- PF PF
_ kg/$
WL Ma/ha WL
Not recommended kg/$
Level of Adoption High SNone
Figure 7. Extension messages for multiple environments and several evaluation criteria with MSA.




17
DESIGN OF ON-FARM TRIALS
It is now time to return to consideration of the design of onfarm trials appropriate to generate the kind of data used in this example and amenable to interpretation using Modified Stability 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. Note that differences in farmer management become factors affecting environment and have a positive rather than a negative effect on trial design. The farmers' practices, as treatments, must be included and may differ from farm to farm to reflect each farmer's individual management practices. 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 MSA. 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. Thus, for 8
treatments (a large number for this type of trial, but not
excessive), 6 environments would be sufficient. For 6
treatments, 8 environments; for 4 treatments, 12
environments; for 3 treatments, 16 environments; and for 2
treatments, 24 environments.
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.




18
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. 1992 (Book draft). Modified stability 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. (Forthcoming)
B02/FSSC (TEXT)




19
Table 1. Response of maize (Mg ha"1) to three soil amendments and the farmers' practices from on-farm research results in Amazonas, Brazil (Singh, 1990). FP is farmers' practices, PCW is processed city waste (from Manaus), CM is chicken manure, and TSP is triple super phosphate. For more details see Singh, 1990.
Farm No. FP PCW TSP CM El
-- ------------ Mg ha'-- ----- -7 2.5 1.4 4.5 4.0 3.1
6 2.2 1.0 4.2 3.6 2.8
2 0.0 1.1 3.4 4.4 2.2
8 0.2 0.7 3.5 4.0 2.1
5 0.2 0.7 3.4 3.6 2.0
4 0.2 1.1 1.6 2.8 1.4
1 0.2 0.2 1.3 2.8 1.1
3 0.0 0.0 0.2 0.6 0.2
Average 0.7 0.8 2.8 3.2 1.9




20
Table 2. Response of maize (kg $~cash cost) to three soil amendments and the farmers' practices from on-farm research results in Amiazonas, Brazil (Singh, 1990). FP is farmers' practices, PCW is processed city waste (from Manaus), CM is chicken manure, and TSP is triple super phosphate. For more details see Singh, 1990.
Farm No. FP PCW TSP CM El
--- ------------kgS1---I---- -- -- -7 208.3 6.8 45.9 31.5 3.1
6 183.3 4.8 42.9 28.4 2.8
2 0.0 5.3 34.7 34.7 2.2
8 16.7 3.4 35.7 31.5 2.1
5 12.5 3.4 34.7 28.4 2.0
4 20.8 5.3 16.3 22.0 1.4
1 12.5 0.8 13.3 22.4 1.1
3'0.0 0.0 1.5 5.1 0.2




21
Table 3. Risk calculations comparing CM and TSP using the equation Y (t, 0 st/n) with a one-tailed "t" table, when V = 19.5 and s = 10.0 for CM; Y = 16.5 and s = 13.7 for TSP; n = 4 environments (land classes PF2, SF2 and WL); and degrees of freedom = 3.
Probability of
a lower value Kg $-I cash cost
a %) tdf=3 CM TSP
0.25 25 0.765 15.7 11.2
0.20 20 0.978 14.6 9.7
0.15 15 1.250 13.2 7.9
0.10 10 1.638 11.3 5.2
0.05 5 2.353 7.7 0.3
0.025 2.5 3.182 3.6 -5.4
0.01 1 4.541 -3.2 -14.7
0.005 0.5 5.841 -9.7 -23.6
0.0005 0.05 12.941 -45.3 -72.4




22
Table 4. Environmental characteristics of the sites for the onfarm maize trials in Amazonas, Brazil (Singh, 1990).
EI Land Type _H ECEC Al sat P205
3.1 PFl 5.2 4.21 58.3 7.4
2.8 PFl 5.1 3.45 69.1 7.1
2.2 SF1 4.6 2.29 91.7 4.5
2.1 PFI 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




23
Table 5. 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
PF, SF1 PF2 SF2 WL
Criterion
Mg ha-' TSP CM CM CM CM
kg $-I FP FP CM CM None
Source: Singh, 1990




24
RESEARCHER'S CRITERION
MAIZE, MANAUS, 1989 5
4 ..................................................................................................................................
0 TSP
4 ....................................................................................................................
3
2I ...................................................................................................................
0U
0 0.5 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 1. Observed response in Mg ha-1 of the TSP treatment to environment (El) for maize in Amazonas, Brazil (Singh, 1990).
B02/Singh5 .WQ1
(RES CRTN, TSP)




25
RESEARCHER'S CRITERION
MAIZE, MANAUS, 1989 5
4 ................................................................................................. ...............................
NTSP
4 .................................................................. .................................................
3 ............................................. ........ .............................................................
0 0.51 1.5 2 1253 3.5
ENVIRONMENTAL INDEX, El
Figure 2a. Estimated linear response in Mg ha-1 of the TSP treatment to environment (El) for maize in Amazonas, Brazil (Singh, 1990).
B02/Singhs .WQ1
(RESCRTNTSPREG)




26
RESEARCHER'S CRITERION
MAIZE, MANAUS, 1989
3
2.5-................................................................................................................ ................... FP
2-..................................................................................................................................... Q UA D
1 .5 ............................................................L....................... ..... ...N...............
1 ..... ...... .... ... ...... ..... ...................... ....... .... ..... .. ... ........... ......... .... .. ........
-1 11 11 --0 0.5 1.5 2 2.5 33.5
ENVIRONMENTAL INDEX
Figure 2b. comparison of linear and quadratic response in Mg ha-' of the FP treatments to environment (El) for maize in Amazonas, Brazil (Singh, 1990).
B02 /Mzgrph .WQ1
RES CRTNI FP




27
RESEARCHER'S CRITERION
MAIZE, MANAUS, 1989
4.5
LIN
1 .5 ................................ .... ........................................................................
1
ENVIRONMENTAL INDEX
Figure 2c. comparison of linear and quadratic response in Mg ha'1 of the CM treatment to environment (El) for maize in Amnazonas, Brazil (Singh, 1990).
B02/Mzgph.WQ1
RES CRTN, CM




28
RESEARCHER'S CRITERION
MAIZE, MANAUS, 1989
5
.....FP
4 ................................................................................................................................................. .. ., ,, . ................................ .
... ... ... PCW
3 .t .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . .. . ..... ..... .. ........... . . .
.... TSP
-1,1, .....' CM
. .................... ;".......................; ............................................................................................ ..... ..................................................................... ~
Els
O0 ............ ........................................... ..... ....................
A A A A AA A A
-1 I
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 3. Estimated responses in Mg ha-' of the four treatments
to environment (EI) for maize in Amazonas, Brazil (Singh, 1990).
B02/Singh5.WQ1
(RES CRTN, 4TRTS)




29
FARMER'S CRITERION
MAIZE, MANAUS, 1989
250
FP
2 0 0 ................................................................................................................................................................................................ ............
/
," PCW
..10-........... .. ... .. ... .. .....7/ IP....................w. .......
"t 1 5 0 ....................................................................................................................................................................... --..................... .......
0 /
TSP
cd 1 0 0 ............................................................................................................................................................ ...............................
7 CM
0 A
5 0 ......................................................................................................................................... .......................... .. .
----- ----- ---- ----- ----.............. ..... ....
...............................
................
........-.. .
A A A A AA A A
-50 1 1 1 1 ,1,
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 4. Estimated responses in kg $- of the four treatments to
environment (EI) for maize in Amazonas, Brazil (Singh, 1990).
B02/Singh5 .WQI
(FMR CRTN, 4TRTS)




30
RISK ESTIMATION WL, SF2, PF2
20
1 8 ...................................................................................................................................................................................................................................... C M
1 6 ......................................................................................................................................................................................................................................
......... TSP
r 1 2 ............................................................................................. ....... ; ........................................................................................................................
1 0 ....................................................................... ;- . ............. ........................................................................ ..................................
........................................................
1 2
6 ................ ...........................................i.... ................................................................................. .........................................
0 5 10 15 20 i5
ESTIMATED PERCENT OF TIME BELOW VALUE
Figure 5. Risk levels for the criterion kg $-1 and the treatments
CM and TSP from the maize trials in Amazonas, Brazil (Singh,
1990).
B02/MZGPH. WQI
( CONFINT, CM, TSP )




31
ENVIRONMENTAL CHARACTERIZATION
MAIZE, MANAUS, BRAZIL (SINGH, 1990)
35-w
OBSNS
3-................................................................
3 ............................................................................................................................................................................................
R2 .94
...................................................................................................................................................... ...................................
o 2.-.........................................
2 ............................................................................................. 1. .......... i .............................................................................
wU
2- ......................... ..............................................................................................
z
w
10
3. 4 U .
op
Figure.....la...nhi...f.sil.....o...o.....si....fr...e.. n
fammietilznAaons rzl(ig,19)
w0/NP.g
0.5...................................................




32
On-Farm
Research-Extension Population Multiple in
of Farms Criteria Multiple Environments
Mg/ha
.-.. ..... ..... *. .. X* .. ............. .
kP/sPF
' FP kg/$ '
Mq/ha
SF SF
FP kg/$
FP
IMMg/ha
CO 2 2
PF CM !ii~~~i~iiiiiiiiiii !iiiiiii~iii~i~i i~iiii =kg/$ F
CM Mg/ha
SF SF
CM kg/S
WL .WL
Not recommended kq/S
Level of Adoption High !i None
Figure 7. Extension messages for multiple environments and several evaluation criteria with MSA.
B02/Fig-7.DRW