Citation
Steps in the analysis and interpretation of on-farm research-extension data based on adaptability analysis (AA)

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
Creator:
Hildebrand, Peter E
Bastidas, Elena P
University of Florida -- Food and Resource Economics Dept
Place of Publication:
Gainesville Fla
Publisher:
Food and Resource Economics Dept., Institute of Food and Agricultural Sciences, University of Florida
Publication Date:
Language:
English
Physical Description:
iii, 25, [18] p. : ill. ; 28 cm.

Subjects

Subjects / Keywords:
Agriculture -- Research -- On-farm ( lcsh )
Field experiments ( lcsh )
Agricultural innovations -- Research ( lcsh )
Agricultural systems -- Research ( lcsh )
Agricultural extension work ( lcsh )
City of Gainesville ( local )
Farmers ( jstor )
Recommendations ( jstor )
Corn ( jstor )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )

Notes

Bibliography:
Includes bibliographical references (p. 24).
General Note:
"May 1995."
Funding:
Staff paper (University of Florida. Food and Resource Economics Dept.) ;
Statement of Responsibility:
Peter E. Hildebrand and Elena Bastidas.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
The University of Florida George A. Smathers Libraries respect the intellectual property rights of others and do not claim any copyright interest in this item. This item may be protected by copyright but is made available here under a claim of fair use (17 U.S.C. §107) for non-profit research and educational purposes. Users of this work have responsibility for determining copyright status prior to reusing, publishing or reproducing this item for purposes other than what is allowed by fair use or other copyright exemptions. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. The Smathers Libraries would like to learn more about this item and invite individuals or organizations to contact Digital Services (UFDC@uflib.ufl.edu) with any additional information they can provide.
Resource Identifier:
020992279 ( ALEPH )
33056549 ( OCLC )
AKL3969 ( NOTIS )

Downloads

This item has the following downloads:


Full Text
Staff Paper Series
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
ADAPTABILITY ANALYSIS (AA): A TRAINING GUIDE
PETER E. HILDEBRAND1 AND
ELENA BASTIDAS
STAFF PAPER SP95-4R MAY 1995
(Revised)
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, Institute of Food and Agricultural Sciences, and Graduate Assistant, Agricultural Education and Communication Department, University of Florida, Gainesville, Florida
32611-0240, USA.




TABLE OF CONTENTS
OB JECTIVE S .............................................................. 1
BASIC CONSIDERATIONS .................................................. 1
K EY TERM S ............................................................... 2
SUMMARY OF THE STEPS IN ANALYSIS AND INTERPRETATION
OF ON-FARM RESEARCH-EXTENSION DATA ............................ 3
IN TR ODUCTION .......................................................... 4
RESPONSE OF TREATMENTS TO qREIrr ENVIRONMENTS .................. 4
STEPS IN THE ANALYSIS AND INTERPRETATION OF ON-FARM RESEARCHEXTENSION DATA ................................................... 4
Calculate a measure of environments: Environmental Index, EI ................... 4
Relate treatment response to environment, ................................... 6
Assess interaction of treatments with environment ............................. 9
Characterize the environments ........................................... 10
Define recommendation domains ......................................... 11
Compare results repeating steps 2 through 5 using alternative evaluation criteria ..... 15 Create extension recommendations for each of the recommendation domains ........ 18
DESIGN OF ON-FARM TRIALS .............................................. 20
Nature of on-station and on-farm trials ..................................... 20
O n-farm trials ........................................................ 20
Designing on-farm trials for Adaptability Analysis ............................ 21
REFEREN CES ............................................................ 24
APPEN D IX .............................................................. 25




List of Tables
Table 1. Response of maize (t/ha) to three soil amendments and
the farmers' practices from on-farm research results .................................... 5
Table 2. Response of maize (t/ha) to three soil amendments and
the farmers' practices from on-farm research results (sorted data) ......................... 5
Table 3. Environmental characteristics of the sites for the on-farm maize trials .................... 10
Table 4. Risk calculations comparing CM and TSP for land class PF ........................... 13
Table 5. Response of maize (kg/$ cash cost) to three soil amendments
and the farm ers' practices ........................................................ 15
Table 6. Risk calculations comparing CM and TSP for land classes: PF2, SF2 and WL ............... 17
ii




List of Figures
Figure 1. Observed response in t/ha of the TSP treatments to environment (El) ................... 6
Figure 2 a. Estimated linear response in t/ha of the TSP treatment to environment (El) ................. 7
Figure 2 b. Comparison of linear and quadratic response in t/ha
of the FP treatments to environment (El) ........................................... 7
Figure 2 c. Comparison of linear and quadratic response in t/ha of the CM treatment
to environm ent (EI) ........................................................... 8
Figure 3. Estimated responses in t/ha of the four treatments to environment (EL) ................... 9
Figure 4. Relationship of soil pH to EI of the sites for the on-farm maize trial .................... 11
Figure 5. Tentative recommendation domains and the technology recommended for maize,
based on environmental factors (land class) and the evaluation criterion (t/ha) ............ 12
Figure 6. Risk levels for treatments CM and TSP for Land class PF,. Evaluation criterion t/ha ...... 14 Figure 7. Estimated responses in kg/$ cash cost of the four treatments to environment (El) ......... 16 Figure 8. Risk levels for the criterion kg/$ cash cost and the treatments CM and TSP ............. 17
Figure 9. Summary of the recommendation domains and the technology recommended ............. 18
Figure 10 Summary of the recommendation domains and the recommended technology based on
environmental characteristics (land type) and two evaluation criteria, t/ha and kg/$ ........ 19
i




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 (AA) can serve as a basis for an entire research-extension program (Hildebrand and Russell, draft). 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 (ANO VA) 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.
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.




2
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 2
Diffusion domains Informal and naturally occurring interpersonal communication networks for
diffusion of information on 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. Includes influences of any differences
in management practices not part of treatments.
Environmental Index, 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), for example, or a farmer's concerns and/or needs (kg/kg seed,
among many others).
Extension recommendations An extension message that 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 percentage 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 also
refer to the set of treatments at each environment. This double definition is seldom confusing
in context.
2Y= mean yield (or other measure of the criterion) for the treatment t. the value from a "t" table for probability level alpha, s =sample standard deviation;, n = number of observations.




3
SUMMARY OF THE STEPS IN ANALYSIS AND INTERPRETATION OF ON-FARM RESEARCH-EXTENSION DATA To thoroughly analyze and interpret on-farm research data it is necessary that the design of the trial is appropriate 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, El.
2. Relate treatment response to environment.
2 a. Plot all observations (data points) for each treatment against the environmental index on
a graph This is an important step that 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.
2 c. Evaluate data quality.
3. Assess treatment interaction with environment comparing the responses of all treatments to EL.
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.'
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 H1ildebrand, 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.
3 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.




4
ANALYSIS AND ]INTERPRETATION
OF ON-FARM RESEARCH EXTENSION DATA
INTRODUCTION
On-farm research extension trials can have various functions and can be managed by researchers, extension workers and/or farmers (Hildebrand and Poey, 1985). The most appropriate design for incorporating farmer participation is a simple (few treatments), non-replicated trial 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 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 cultivation of crops. 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, reduces the need for off-farm resources and is more in keeping with sustainable agriculture.
STEPS IN THE ANALYSIS AND INTERPRETATION OF ON-FARM RESEARCHEXTENSION DATA
Calculate a measure of environments: Environmental Index. E Step..I
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 wihen, the same treatments are included in all the sampled environments. Thefirvt step is to calculate this index, El, which provides an effective measure of the environmental differences in the resarh 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 El.




5
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/ha
Farm No. FP PCW S CM El
1 0.2 0.2 1.3 2.8 1.1
2 0.0 1.1 3.4 4.4 2.2
3 0.0 0.0 0.2 0.6 0.2 "Ml'
4 0.2 1.1 1.6 2.8 1.4 O
5 0.2 0.7 3.4 3.6 2.0
6 2.2 1.0 4.2 3.6 2.8
7 2.5 1.4 4.5 4.0 3.1
8 0.2 0.7 3.5 4.0 2.1
Average 0.7 0.8 2.8 3.2 1.9
EP = 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 EL.
t/ha
Farm No. EP PCW TSP CM EI
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
FP = farmers' practices, PCW = processed city waste (from Manaus), TSP = triple super phosphate and CM = chicken manure.




6
Relate 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.' 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.
Plot observations Step2a
RESEARCHER'S CRITERION MAIZE, MANAUS, BRAZIL, 1989
5
4 .. . .. . .. . .... .. .... .. . .. .. .. ... .. .. .. .. . .. . .. . .. . .. . .. ... .. .. . .. . ... .
N W 0
cc ......
3 ----------------------------------------------------------------------------------------2 ... ...... ...... .............. .......... ....... ....... .................................
3...........
. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .
0 M ID
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 1. Observed response in t/ha of the TSP treatment to environment (E0) for maize in Amazonas, Brazil (Singh, 1990).
4 thris 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.




7
View Observations Step 2 b
RESEARCHER'S CRITERION MAIZE, MANAUS, 1989 5,
34 ........................................... ....................................................
3 .............. .................................................................................... m
01
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 2 a. Estimated linear response in t/ha of the TSP treatment to environment (El) for maize in Amazonas, Brazil (Singh, 1990).
RESEARCHER'S CRITERION MAIZE, MANAUS, 1989 5
4 ............................................................................................
FP
2(U .................................................... ..........
QUAD
LIN
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 2 b. Comparison of linear and quadratic response in t/ha of the FP treatments to environment
(El) for maize in Amazonas, Brazil (Singh, 1990).'
5In the case of the fanner's practice the estimated linear response to Els below EI= 1 and the estimated
curvilinear response to Els below E1= 1.6 present negative yield values. These negative values will be ignored in step 3 when comparing the responses of all treatments to El because they do not represent true values.




8
RESEARCHER'S CRITERION
MAIZE, MANAUS, 1989
5.
3 ............................ ..... ..................................................................... C
2 QUAD
2. X..........---- ... .....**-.. .. .
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 2 c. Comparison of linear and quadratic response in t/ha of the CM treatment to environment
(EL) for maize in Amazonas, Brazil (Singh, 1990). Evaluate data quality. Step 2 c
Three criteria help evaluate 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.




9
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 El (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 Es, 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.
Assess interaction of treatments with environment Step 3
When all treatments have been related to, or regressed on El, the third step is to assess 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
MAIZE, MANAUS, 1989
5.
4........................................ ....................F
3.........................-.. ... .. ........
TSP
............................ . ........................... I
IEls
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 3. Estimated responses in t/ha of the four treatments to environment (E0) for maize in the Amazonas, Brazil (Singh, 1990).




10
Characterize 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 class", Table 3. 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 3. Environmental characteristics of the sites for the on-farm maize trials in Amazonas, Brazil (Singh, 1990).
El Land class pH ECEC ALsat F2-Q5
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 Es 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 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 EI (Table 3).




ENVIRONMENTAL CHARACTERIZATION MAIZE, MANAUS, 1989
3.5
u.......................
Z OBS
0~ F=0.95
.z ............. .... ......................................................
w
0
3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4
pH
Figure 4. Relationship of soil pH to EI of the sites for the on-farm maize trial in Amazonas, Brazil (Singh, 1990).
Define recommendation domains Se
Define tentative recommendation domains Step 5 a
The recommendation domains depend on the environmental characteristics and selected evaluation criteria, in this case, t/ha. Using this researcher criterion, TSP would be recommended for the highest Els and CM would be recommended for all the other land (Figure 3). Notice that the two highest Els are PF,, but the fourth highest is also PF, (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 may be necessary to group all the PF, in a single recommendation domain and all the other classes in another.




12
The persons involved in the trial, including the farmers, should make this type of judgement to facilitate the dissemination of results6. By grouping all PF1 into one domain, two tentative recommendation domains have been defined for the criterion t/ha (Figure 5). If t/ha is a relevant criterion, it would apply that either TSP or CM could be recommended for a field taken from primary forest and in the first year of production and that CM should be recommended for all the other fields. A final recommendation for PF1 will depend on risk analysis.
Recommendations
Land Class Evaluation Criterion:
t/ha
PF1 TSP or CM
.. ................ ................ ......i::::i!:i::: i: iiii: ::::::: :l ii i : :
.. .. .. .. ..s F .. .. ... . . . . . ..iiiiiiii ~ii ili~ii ii i~iiiiii S l ~ iIi~ii iii
SF2
Source: Singh, 1990
Figure 5. Tentative recommendation domains and the technology recommended for maize, based on environmental factors (land class) and the evaluation criterion (t/ha ), Rio Preto da Eva, Amazonas, Brazil.
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.
6 If for some reason research-extension personnel feel that they can include such characteristics as pH, ECEC, and/or Al saturation, perhaps these recommendation domains could be redefined. Alternatively, it may be necessary to confirm if farm 8 was really PF1 or that it is, perhaps SF1 and misclassified.




13
The formula:
y- (to s/Vn) (1)
gives the confidence interval for the level of probability from a two-tailed "t" table for n-I 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 of 0.4 means that 40% of the values lie outside the interval (either above or below) and 60% lie inside the interval defined by the formula.
The lower value of this formula:
Y (t s/v/n) (2)
provides information on the probability that values lie below the confidence interval and is a measure of the level of risk associated with the technology in this tentative recommendation domain. In this step we consider the choice between CM and TSP for PF,. 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 class PF1 using the formula Y (t. s//n) with Y = 3.9 and s = 0.2 for CM; =4.1 and s =0.5 for TSP; n =3 and df= 2, and using a from a one-tailed "t" table.7
probability
a 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
7 If only a two-tailed "t" table is available, the a value for a one-tailed "t" test is half the a value for the twotailed test. That is, an a value of 0.4 in a two-tailed "t" table, indicating that 40% of the values lie outside the interval corresponds to an a value of 0.2 in a one-tailed table indicating that 20 % of the values lie below the interval.




14
RISK ESTIMATION
LAND CLASS PF,
CuM
0 5 10 15 20 25
RISK (0/ times below Y-axis value)
Figure 6. Risk levels for treatments CM and TSP for Land class PF1. Evaluation criterion tfha, Amazonas, Brazil (Singh, 1990).
Values on the X-axis represent the percentage of times that yields of the indicated treatments fall below the values represented on the Y-axis. In this case there is no significant difference associated with the risk of TSP and CM for maize planted in PF,, either could be recommended .
Define final recommendation domains Step 5 c
Based on the risk analysis of results in PP1 we can confidently group all the PP1 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.




15
Compare results repeating steps 2 through 5 using alternative evaluation criteria S
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 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 (Table 5). Figure 7 is based on the analysis using this criterion. Note that the same EI has been 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 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/$ cash cost
FarmNo. EP PCW TSP CM EI O7 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 37.7 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
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 and analyses should be made for each criterion. In this example, two evaluation criteria have been demonstrated: t/ha in Figure 3 and kg/$ cash cost in Figure 4. Once again, note that the EL remains constant and does not change for different evaluation criteria.




16
FARMER'S CRITERION
MAIZE, MANAUS, 1989
200 ....................................................................................... ............
FP
6150 ................................................................................ ......................
0 Pcw
C0
<10............................. ........TS
50..................................................C
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 7. Estimated responses in kg/$ cash cost of the four treatments to environment (El) for maize in Amazonas, Brazil (Singh, 1990).
Define tentative recommendation domains Step 6 a
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/$ cash cost), none of the amendments are better than the farmers' practices for PF1 and SF1. 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 is in the first year of use. After the first year, if maize must be produce, either CM or TSP could be recommended. The option would depend on risk analysis.
Determine risk Step 6 b
Table 6 shows the calculations and Figure 8 graphically shows the risk levels for CM and TSP for the farmers' criterion, kg/$ cash cost, and for maize cropping on land 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.




17
Table 6. Risk calculations comparing CM and TSP using the equation Y (t,- s/Vn) with a onetailed "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 a lower value td=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
POOR ENVIRONMENTS: PF, SF AND WL
18
14 ........................................................................................................ ........................................
14
12 -1
-- 12 .................................................. .......... ......................................................... :- "..................
:1 0
CO c
C-) 8 ................ ........................... ----------------------------4 ......... ..... ..........
2 ......... ......................... ................................-----.......................................................................
0*
0 5 10 15 20 25
RISK (% times below Y-axes value)
Figure 8. Risk levels for the criterion kg/$ cash cost and the treatments CM and TSP from the maize trials in the Amazonas, Brazil (Singh, 1990).




18
Define final recommendation domains Step 6 c
If kg/$ cash cost is relevant, as it would most likely be to these farmers, what they normally 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 criterion.
Recommendations:
Land Class Evaluation Criterion:
kp$ of cash cost
PP1
SF1
PP2
SF2 CM
WL
Source: Singh, 1990
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.
Create extension recommendations for each of the recommendation domains Step 7
and formulate messages appropriate to each of the diffusion domains
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.




19
Having specific recommendations facilitates making messages for use by extension. Extension messages are oral (used, for example, in radio programs or with person-to person contact) or written (extension bulletins) communications that can be generated based on each recommendation for different clients (commercial producers, small farmers, different language groups, etc.).
Recommendations: Land Class Evaluation Criterion: I Evaluation Criterion:
I
t/ha kp-/ of cash cost
or ., ....
. . . . . ... .... S ::: ::: ......... ........: ...................
SF1
..-"~~~~~~~~~~~~~~~ .. .' ./ ... ..- .. .' ::::: : ::;: : :::'::
..................... .. .... .: / :// ""
V/ V
PF2
SF2 /, ,,//.CM
..................... / / -"". / './ / I
Wf ~ ///o/
////".// //,/
Source: Singh, 1990
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.




20
DESIGN OF ON-FARM TRIALS
Designing on-farm trials is very different from creating experimental designs for a trial conducted on an experiment station. The reasons for undertaking the trials also are usually quite different. This means that the nature of the results will also be different and the uses to which the results are put will also vary. In turn, this means that the analysis of the data will need to be different.
Nature of on-station and on-farm trials
Traditional experiment station trials are set up to assess the effect of a set of treatments. In a complete trial the treatments are replicated (repeated in different blocks) to facilitate a statistical analysis to help determine if any differences among treatments can be considered real or just a matter of chance. Controls in an on-station experiment often are zero-level treatments. Such traditional experiment station trials are enumerative in nature and their purpose is descriptive. Most of the common statistical procedures such as ANOVA are enumerative in nature as well. When a sufficient number of replications have been used in the experimental design, ANOVA will help estimate differences among treatments-were these differences from the control or from each other real or did they result from chance? When significant differences exist, the researcher can describe them and relate them to the conditions at the trial site on the station and for the year when the experiment was conducted. The results apply to those specific conditions and are not meant to be extrapolated to other locations, conditions or years. The same kinds of conclusions would be relevant to trials conducted on an individual farm using the same kind of replicated design.
Experiment station trials are not usually meant to be predictive. From the results of the experiment described above, it would not be reasonable to predict that the same results would be achieved at other locations, conditions or years. In order to make a statement about what might happen in the future (prediction) the same experiment would have to be repeated at the same location over a number of years. Then it would be possible to describe what happened at that location and over that period of years. Prediction would be based on the premise that the same results could be expected in future years.
On-farm research is usually conducted for the purpose of making recommendations to a broader group of farmers than just those who participated in the trial. The recommendation is, in fact, a prediction that the same results will occur on specified kinds of farms or fields if the practice is followed by farmers with those conditions. Statistical procedures that involve prediction are analytic in nature. "Ad hoc statistical procedures are common in analytic studies ... [because] traditional methods [such as the most common forms ofANOVA] simply cannot accommodate the complexity of on-farm trials." (Stroup et al., p. 160)
On-farm trials
The design of on-farm trials for the purpose of making recommendations to a much larger number of farmers must take into account this difference in the nature of the two research approaches. In




21
most settings where on-farm research is being conducted, farmers want and need information and recommendations rapidly. On the other hand, research resources, whether from governmental or non-governmental organizations or from the farmers themselves, are usually very scarce. Therefore, research methods and designs that are efficient yet effective and permit broad extrapolation beyond just the farms or environments involved in the trials are a necessity. Designs must be flexible and permit farmer and researcher innovation yet maintain the power to achieve confidence in the recommendations resulting from the trials. Above all, the designs used for on-farm research must be adaptable to many conditions and uses.
Designing on-farm trials for Adaptability Analysis
Adaptability Analysis provides the basis for the design of on-farm trials and meets the criteria set forth above. Because farmers should be active participants in the trials and involved in all aspects including selection of treatments and the nature of the environments used for the trial, it is necessary to be very clear on the differences between treatments and environments.
Treatments and replications versus environments
In a few words, in an on-farm trial using Adaptability Analysis, anything that is not a treatment becomes a factor affecting the environment for each of the locations. Consider a 2 x 2 factorial crop trial with a randomized complete block arrangement but with only one block at each location. Treatments are 1) each farmer's own local variety with no synthetic fertilizer, 2) an improved variety with no synthetic fertilizer, 3) the farmer's local variety with synthetic fertilizer, and 4) the improved variety with synthetic fertilizer. Because farmers in this research domain seldom use synthetic fertilizer with their local varieties, the local variety with no fertilizer can be considered as the check or control and should be exactly the same as what each participating farmer does on the rest of his or her field.
Except for these four treatments, there is no reason why each fanner participating in the trial must follow the same cultural practices so long as the treatments are the same. Early or late planting, one or two weedings, use of manure, irrigated or rainfed, low or high densities, etc., or even additional fertilizer applications, are all factors that influence the nature of the environment in which the crops or livestock are being grown. These factors need to be documented for environmental characterization, but they are not different treatments, and actually have a positive effect on the analysis by AA rather than a negative effect. Because these cultural practices represent those of different farmers and are not controlled by the researcher, it is difficult for a researcher trained only in experiment station research to accept. This is because in on-station research, the researcher is interested in knowing the effect of only those variables being studied, i.e. the treatments. If other factors are allowed to vary, their effect on the production environment 'can confound the effect of the treatments. In on-farm research, however, the purpose of the trial is to find out how the treatments
SIt is acceptable for the farmers' own varieties to differ if they normally use different varieties.




22
What would happen to the above design if the farmers planted the two varieties correctly, but each farmer used a different level or mix of synthetic fertilizer (which originally were thought of as treatments)? The remaining factors that are still common to all locations (the varieties) are the only remaining treatments and the different fertilizer applications become factors affecting the environments. This, then, would leave a simple two treatment design. The different fertilizer applications of the individual farmers would be recorded and used to characterize environments. A similar design would have resulted if the farmers decided not to use the improved variety but rather just used the same fertilizer application on part of their fields and no fertilizer on the rest. Fertilizer becomes the treatment (if the level applied was the same in all cases).
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 treatments, or at least a common sub set of treatments. Note that differences in farmer management practices become factors affecting environment and have a positive rather than a negative effect on trial design. The farmers' practices 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. Furthermore, in most cases, one plot which contains only the individual farmers usual practices will serve as the control treatment in an on-farm trial. This is because the farmer (as well as the research and extension personnel) must judge any new technology against what the farmer already is doing to determine if the new technologies are better. It is not adequate to determine only which of the new treatments is better than the other new treatments.
Replications
Only one block at each location is needed for analysis by AA. If more than one block (multiple 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. Usually the blocks will be averaged for analysis by AA. More than two blocks per location is not an efficient use of resources unless each block is considered a different environment.
Environments
The number of environments is more important than the number of replications in each environment. Based on Stroup et al. (p. 172) a simple rule can be devised:




23
The rule of 48 Yhe 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; 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.
9 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 environmental characterization.




24
REFERENCES
Andrew, C.O. and P.E. Hildebrand. 1993. Applied agricultural research: Foundations and
methodology. Westview Press. Boulder, Colorado.
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. 1995 (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 oxisoils of the
Amazon Basin of Brazil through use of organic amendments. Unpublished Ph.D.
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.
Guidc/Jumel7




25
APPENDIX




Table 1. Response of maize (t/ha) to three soil amendments and the farmers' practices from onfarm research results in Amazonas, Brazil (Singh, 1990).
t/ha
Farm No. _P EM ISP CM E
1 0.2 0.2 1.3 2.8 1.1
2 0.0 1.1 3.4 4.4 2.2
3 0.0 0.0 0.2 0.6 0.2
4 0.2 1.1 1.6 2.8 1.4
5 0.2 0.7 3.4 3.6 2.0
6 2.2 1.0 4.2 3.6 2.8
7 2.5 1.4 4.5 4.0 3.1
8 0.2 0.7 3.5 4.0 2.1
Average 0.7 0.8 2.8 3.2 1.9
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 onfarm research results in Amazonas, Brazil (Singh, 1990). Data sorted in descending order with respect to the environmental index EL.
t/ha
Farm No.CW TSP CM E
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
FP = farmers! practices, PCW processed city waste (from Manaus), TSP triple super phosphate and CM = chicken manure.




Table 3. Environmental characteristics of the sites for the on-farm maize trials in Amazonas, Brazil (Singh, 1990).
El Land class pH ECEC AL=t 2-Q5
3.1 PF1 5.2 4.21 58.3 7.4
2.8 PF, 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




Table 4. Risk calculations for t/ha comparing CM and TSP for land class PF,, using the formula Vy (t." s/Vn) with' = 3.9 and s = 0.2 for CM; Y = 4.1 and s 0.5 for TSP; n= 3; and degrees of freedom = 3; using a from a one-tailed "t" table.
probability
a of a lower value td-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




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/$ cash cost
Farm No. P PCW TSP CM 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 37.7 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
FP = farmers' practices, PCW = processed city waste (from Manaus), TSP = triple super phosphate and CM = chicken manure.




Table 6. Risk calculations comparing CM and TSP using the equation y (t." s/Vn) with a onetailed "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, SF, and WL); and degrees of freedom = 3.
Probability of
a a lower value tdf=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




RESEARCHER'S CRITERION
MAIZE, MANAUS, BRAZIL, 1989
5
4 ....................................................................................................................................................................................
0 E N
3 .....................................................................................................................................................................................
cu
2 ....................................................................................................................................................................................
n
N
. ....................................................................................................................................................................................
0
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 1. Observed response in t/ha of the TSP treatment to environment (0) for maize in Amazonas, Brazil (Singh, 1990).




RESEARCHER'S CRITERION MAIZE, MANAUS, 1989
5
2 TSP
00 0. 5 1, 1.5 22.5 33.5
ENVIRONMENTAL INDEX, El
Figure 2 a. Estimated linear response in tfha of the TSP treatment to environment (0) for maize in Amazonas, Brazil (Singh, 1990).




RESEARCHER'S CRITERION
MAIZE, MANAUS, 1989
5
4 ..........................................................................................................................................................................
3 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------FP
ca
QUAD
. ............................................................................................................ --- ---------------------------------------------------LIN
0 ....... w -------- ............. ........................ ............. ............................................................
H
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 2 b. Comparison of linear and quadratic response in t/ha of the FP treatments to environment (EI) for maize in Amazonas, Brazil (Singh, 1990).




RESEARCHER'S CRITERION MAIZE, MANAUS, 1989
5.
3 CM
2 QUAD
LIN
0 1 1
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 2 c. Comparison of linear and quadratic response in t/ha of the CM treatment to environment (0l) for maize in Amazonas, Brazil (Singh, 1990).




RESEARCHER'S CRITERION
MAIZE, MANAUS, 1989
45 ................................................................. ................
FP
TSP
Els
0~
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 3. Estimated responses in t/ha of the four treatments to environment (0L) for maize in the Amazonas, Brazil (Singh, 1990).




ENVIRONMENTAL CHARACTERIZATION .MAIZE, MANAUS, 1989
3.5
3 .............................................................................................................................................
xw ........... *"* ......
0Z 2.5 ........................................................................................................ ...........................................................
2 -------------------------------------------------------------------- ... ....... .................................................................................
Z OBS
2 1 .5 .................................................. ..................................................................................................................
Z 2
0 R =.95
> .............................. ......................................................................................................................................
Z
w .........
w
0 .5 ............ .......................................................................................................................................................
............... ............. ...... ....
-------------....................
0
3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4
pH
Figure 4. Relationship of soil pH to EI of the sites for the on-farm maize trial in Amazonas, Brazil (Singh, 1990).




Recommendations
Land Class Evaluation Criterion:
t/ha
.. ... .. ...... ......... .: ,...... ,.... : .:.... :.............. ........:.
.. .... .. .. ........ .. ........: .. ..: : ..: ...... :..... :............
....,....:....: ...:,...: .: ... :: .. ... ..: ..:..:....... :....:...... ....: .,.
.............................. .:"':.: "i:"":.: :. :"":.:" :" .............: ." I"-..".."'" .............
...................... I .".".'.'.' .
.. : : ............:...... :......
:. / .' ./ / ............. / .' / :" .
... ... .. ... .. ... ... .. ... .. ...|- ... .. ..... y .. .. . ..... ....y ".. .. ..
/ .: ................... ........-......
.- .. : / ... / : ... .. .. .. ..... .. .. ..
...................................... ": "/ ... Y ./ ./ ..."".. "..
S F .":.":."::. ::.:.. .. .. .":....:: ::.- ..:i..-:" :":.. .... .'".. .".. ... .".:... .. ..
'/ .. ..:" : ". "y.. .. .'. ."Y ". .
S . .. .. .. ..... ". . . . / ..
.. . .. . . .. . . .. . ..:: ::.j ..:? :. :: ::< :...:.. .j...:: :: ::.. . ::.. .. . .
. .y / / .".. ." ." ." .:" :" . ." . "
. .. . . / / / : . . . / .. ...
Sour... .Si.,.....
Figure ~ ~~ .. ... .. Tettv.ecm edto.dmisadth.ehoog .eo mnedfrmie
based ~ ~ ~ ~ ~ ~ ... enio.nal..os(a..ls)ad.h vlaio rtro (/aRoPrt aEa
A.......s,........




RISK ESTIMATION FOR LAND CLASS PF
4
, CM
TSP
1
0
0 5 10 15 20 25
RISK (% times below Y-axis value)
Figure 6. Risk levels for treatments CM and TSP for Land class PF,. Evaluation criterion t/ha, Amazonas, Brazil (Singh, 1990).




FARMER'S CRITERION MAIZE, MANAUS, 1989
2 0 0 ...... ..... ...................................................................................... .............
FP
(1)50.................................. .......
50. . ............... ........................... . . C
..... ... .. ....... ........- .
. . . . .-El
0 0.5 1 1.5 2 2.5 3 3.5
ENVIRONMENTAL INDEX, El
Figure 7. Estimated responses in kg/$ cash cost of the four treatments to environment (E0) for maize in Amazonas, Brazil (Singh, 1990).




RISK ESTIMATION POOR ENVIRONMENTS:PF2,SF AND WL
18
o16
I
0 --"1-10 --- -CM
0
1 0 ............................. ..............................................~ . . . . . . ... . T SP.. IV
< 8 ....................................
4 .................. .....................................7 .. .................................... .........................................................................
2 ....................................................................
0 -1
0 5 10 15 20 25
RISK (% times below Y-axes value)
Figure 8. Risk levels for the criterion kg/$ cash cost and the treatments CM and TSP from the maize trials in the Amazonas, Brazil (Singh, 1990).




Recommendations: Land Class Evaluation Criterion:
kp of cash cost PF1
.................. ... .................
SFI
PF2
......................................... C
SF2 CM
................................. ........
WL
Source: Singh, 1990
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.




Recommendations: Land Class Evaluation Criterion: i Evaluation Criterion:
I
t/ha kg/$ of cash cost
PF, .. ) or ZM" .." / .." .-- .....SF..................:
PF21
.........,....,.......S F2 CM
....................../ / / // / / '
L / Z///'/
________/77iI/ /
Source: Singh, 1990
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




Full Text
xml version 1.0 encoding UTF-8
REPORT xmlns http:www.fcla.edudlsmddaitss xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.fcla.edudlsmddaitssdaitssReport.xsd
INGEST IEID E6E17VRZL_BH29WZ INGEST_TIME 2018-12-17T21:16:58Z PACKAGE UF00054817_00001
AGREEMENT_INFO ACCOUNT UF PROJECT UFDC
FILES