Participatory Rural Appraisal: extending the research methods base

Agricultural Systems 62 (1999) 73±85

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Participatory Rural Appraisal: extending the research methods base R. Loader a,*, L. Amartya b a

Department ofAgricultural and Food Economics, The University of Reading, 4 Earley Gate, Reading RG6 6AR, UK b Lumle Agricultural Research Centre, Pokhara, Nepal Received 3 September 1998; received in revised form 16 May 1999; accepted 17 September 1999

Abstract The rapid acceptance of Participatory Rural Appraisal (PRA) approaches to facilitate the understanding of problems among rural people, and the acknowledged priority for such studies to be sensitive to local conditions, has sometimes meant that such approaches have overlooked opportunities for the appropriate application of relevant techniques. PRA and its forebears have for some time incorporated quanti®cation or classi®cation techniques such as matrix ordering or ranking (with considerable success), but with only limited incorporation of more complex analytical tools. This paper suggests that there are methods which, if sensitively incorporated into the PRA framework, can add value to current PRA-based studies, without compromising the ownership of the research or the validity of the outputs. An example is presented from Nepal, where conjoint analysis was used to help farmers to assess their rice variety requirements. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Participation; Research methods; Conjoint analysis; Nepal

1. Introduction This paper reports on a study of farmers' rice variety choice in rural Nepal, and investigates issues relating to the use of Participatory Rural Appraisal (PRA) techniques in similar situations. Lumle Agricultural Research Centre (LARC), near Pokhara, Nepal, has a strong track record in the use of PRA techniques to investigate rural problems (e.g. Pound et al., 1990; Shrestha et al., 1991). The obective of this project was to assess the possibilities for enhancing such techniques by integrating other methodological approaches and, * Corresponding author. Tel.: +44-118-9318966. E-mail address: [email protected] (R. Loader).

in particular, to explore Martin and Sherington's (1997, p. 33) suggestion that, ``Future work [to enhance PRAs] should include: research into assessing the relevance of di€erent existing [statistical] techniques in di€erent participatory research situations.'' The motivation for this work was both research-related (the UK Department for International Development1 were keen to explore extensions to the range of current PRA methods used at LARC with a view to enhancing the practical and policy relevance of their work) and related to the types of information required. The particular objective of this project (conceived by 1 Formerly the Overseas Development Administration (ODA).

0308-521X/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved. PII: S0308-521X(99)00056-6

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Reading and Lumle-based researchers) was to determine the key factors in¯uencing the choice of a particular rice variety among di€erent demographic and economic groups. The paper presents a summary of the ways in which PRA techniques may be used in the ®eld, followed by some suggestions as to how such techniques might be enhanced, and is concluded by a presentation of the results for rice variety choice in Nepal. 1.1. Sample surveys and PRA A major theme of writers who discuss data collection issues in developing countries is that of the limitations imposed by formal survey techniques, and the impossibility of gathering information by certain methods. The success of postal surveys may be limited by low levels of formal education or postal problems in many countries; diaries will often not be possible for similar reasons; interviews based on the recall of events or practices may be time-consuming; the concept of a household may vary between regions and so on. Casley and Lury (1987, p. 2) share the PRA practitioners' intention to examine applicable methods when they observe: [. . .a choice of sophisticated techniques that are inappropriate in the context]. There is often a failure to appreciate logistic and stang diculties; and there is a tendency to follow a design misleadingly termed `optimal'

because it satis®es certain technical criteria, but which is in fact sub-optimal because of the circumstances in which the survey has to be carried out. . . Our basic message can be summed up easily: it is, `keep it simple'. The minimum amount of information required to meet policy needs should be established. PRA methods, with their emphasis on high quality relevant data and on the importance of building information in full collaboration with the information-holders have been discussed fully elsewhere (e.g. Chambers, 1980, 1983, 1994a, b, c; Kumar, 1993). The research reported in this paper does not seek to dispute the epistemological and practical advances in methodology which have resulted. The research does illustrate, however, that there are possibilities for enhancing such methods. In exploring ways m which several (sometimes apparently competing) rural research approaches can be joined, it is important to acknowledge the contributions of each of them to the body of research ®ndings on rural matters. Let us ®rst make a fairly arbitrary split between PRA on the one hand and sample survey work on the other, with the two aligned broadly according to Chambers' table of research approaches (Chambers et al., 1989, p. 182; Table 1). In terms of approaches to research, Table 1 does not imply that sample survey-type work is naturally aligned with the `transfer of technology' approach, nor that PRA is completely `farmer

Table 1 Research approaches compareda Transfer technology (sample survey)

Farmer ®rst (PRA)

Decision (user)-driven

Main objective

Transfer technology

Empower farmers

Provide policy/management information

Analysis of needs and priorities by

Outsider

Farmers assisted by outsiders

Policy makers and managers

Primary R&D location

Experiment station, laboratory, greenhouse

Farmers' ®elds and conditions

Both

Main R&D practices

Precepts, messages, packages of practices

Principles, methods, basket of choices

Knowledge and ownership of knowledge (not only to farmers)

The `menu'

Fixed

A la carte

Blended

a

Source: adapted from Chambers et al. (1989), and reviewer comments. PRA, Paticipatory Rural Appraisal.

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®rst', but the PRA approach to agricultural and social research emerged from some dissatisfaction with the apparent wholesale transfer of certain research technologies into situations where they may be inappropriate. As Janssen and Ashby (1993) note, technology transfer activities have to be sensitive to the local situation. They refer to technology transfer including the transfer of appropriate research technology or research methodology, and this section re¯ects this thinking. In addition, column three of Table 1 suggests that apart from researchers and farmers, policy makers should also be considered in the selection of methods. As Mearns (1992, pp. 29±38) found in Mongolia: . . .wealth ranking was useful in this context . . . by giving `every appearance of being the kind of `hard' statistical method that Mongolian researchers and bureaucrats, like their counterparts in many parts of the world, have been professionally socialised to expect'. Chambers (1994c, p. 1438) also adds that ``. . .matrix scoring for varieties of a crop provides not only fascinating and useful information and insights . . . but also good-looking tables with ®gures''. Indeed Chambers remarks that such quantitative presentations invest the PRA techniques with enough credibility to make them amenable to use by researchers in other disciplines.

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One area of considerable strength in PRA studies is the use of these ranking and rating techniques (often for issues related to wealth or social strati®cation). The methods used in these situations have similarities to those used in more complex statistical approaches and seem to be well accepted by PRA practitioners and participants alike. Participants seem able to rank data e€ectively (McCracken et al., 1988), and to produce useful groupings of individuals into wealth or other categories (according to the objectives of the process). The step from ranking or matricising exercises such as these, to more complex interpretations is not large, indeed some of the input/stimuli generation procedures are very similar. Fig. 1 illustrates the nature of the PRA input/ output process, stressing the fact that both usable inputs and outputs which are `negotiable' (Chambers, 1983) are crucial to the success of any such method. Fig. 1 suggests that PRA researchers should develop their research approach with the inputs to the process involving stakeholders at all stagesÐ in the de®nition of the research problem(s)Ðperhaps through focus groups or village-level discussions, in the drawing up of the necessary stimuliÐ perhaps matrices or cards, and in the conduct of the main research work. Any such technique needs to be ¯exible enough to ensure that the participatory nature of the design process does

Fig. 1. Participatory Rural Appraisal (PRA) inputs±PRA outputs.

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not compromise the processes involved (be they diagrammatic, discursive, or statistical) in producing the outputs of the exercise. Depending on the technique, it is also important that the numbers (summaries) from the output are easily interpretable. A simple technique such as direct matrix ranking can produce a variety of quantitative output and it is important that, if it is used, discussion of its outputs retains a participatory feel. 1.2. Incorporating other research approaches In seeking to enhance PRA methods it is important to note that modern agricultural market research tends to make extensive use of quantitative methods, as evidenced by recent issues of, for example, the Journal of Marketing Research, or the Journal of Consumer Research, and this may seem to con¯ict with typical PRA goals. However, Martin and Sherington (1997) note that more use can be made of statistical methods in these contexts given that they are used sensitively and applied correctly, highlighting the need for either closer supervision of trials (with the associated expense) and/or re®nement of the (statistical) methods used. There is perhaps a danger in the nomenclature at this point. Marketing research perhaps carries some connotation of pro®t-oriented businesses researching their customers. However, `marketing research' here is used as a broad term to describe an information process, often with the objective of providing information for some further research or development activityÐsuch as planning development priorities or organising marketing channels. As illustrated above, Janssen and van Tilburg (1996) use marketing research as a generic term to cover the evaluation of a number of key issues, such as the improvement of (marketing) technologiesÐstorage, transportation, packaging, grading and sorting; agro-industrial development; institutional design and strategy for farmer interest groups and co-operatives; market access problems of small farmers; food acquisition costs; and other policy interventions. Exploring these issues is also often an objective of PRA activities and methods.

Fig. 2 extends Fig. 1 to illustrate some of the similarities in PRA input types and the possible quantitative outputs which may result. The diagram is based on Checkland's (1993) notion of `input/output matchings' and transformation processes changing inputs to outputs. Table 2 outlines four commonly used quantitative research techniques often used by marketing researchers. The techniques discussed (and the example presented from Nepal) all share the characteristic that they can be generalised, they can be used with relatively small samples (or to explore the nature of individual or small group responses), and they can be applied at a relatively simple level. They all also have the vital characteristic of being based on simple datagathering procedures. The aim of the discussion which follows is to identify and assess techniques which might be adapted to suit and enhance PRA exercises. 2. Case study 2.1. Background The research described below was carried out for representatives of the UK Department for International Development at LARC to explore extensions to the range of current PRA methods used, with a view to enhancing the practical and policy relevance of the Centre's work. The particular policy question addressed was the determination of which of the various underlying characteristics of rice variety choice were most important to farmers in the mid-hill region of the country (in the area of the Keware Banjyang Village Development Committee (VDC)2, south of Pokhara). With some knowledge of the relative importance of such characteristics, farmers and policymakers can more accurately de®ne their priorities for variety choice, development and replacement. This study represents the ®rst attempt (for this region) to identify the reasons why farmers choose to plant particular varieties of 2 A VDC is the smallest autonomous political unit and consists of nine wards.

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Fig. 2. Participatory Rural Appraisal inputs±usable quantitative outputs. Table 2 Classi®cation of selected statistical research techniques Technique

Main objective

Type of data input

Main output

Factor analysis

To summarise (large number of factors/ Quantitative or qualitative/categorical. variables into smaller number of key Wide variety of data types can be factors accounting for as much of the interpreted and summarised variation as possible. To isolate patterns in respondents' data

Three or four factors, with an indication of which of the original variables are associated with each factor. Factors can be used in subsequent analyses

Cluster analysis

Quantitative or qualitative/categorical. To group data together, with the subobjective of assessing common reasons Wide variety of data types can be for clusters, age, sex, region, etc. Usually interpreted and summarised used to group respondents

Diagrams/data showing (two or three) clusters of information, labelled by age, sex, region, etc. Tree diagrams and dendograms. Visual summaries

Multi-dimensional To summarise data, usually in the form scaling of a two-dimensional map

Usually a number of rating scales on Map showing how close di€erent aspects of the issue, sometimes (or far) variables or expressed in the form of distance data individuals are from each other

Conjoint analysis

Stimuli are pre-designed, with speci®c levels of the individual factors

To assess the importance of individual factor levels on the overall rating of a product or issue

Set of comparative ®gures showing contribution of each level of each factor to overall evaluation/preference

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of them grow rice on the khetlandÐthe main rice-growing land type. Essential to the social and economic fabric of the area are two key variables, namely ethnic group, and degree of food self-suciency. It was hypothesised that these two variables would have a strong in¯uence on the choices made by farmers. Families were identi®ed by ethnic group (either Brahmin/Chetri, Gurung/Magar, or Kami/Damal/Sarki) and by `food suciency' group, which relates to the proportion of the year a family can subsist on its own rice production. Farmers are de®ned as `food surplus' if they are food sucient for more than 12 months, `food balanced' if they are food sucient for 7±12 months, and `food de®cit' if they are food sucient for less than 7 months. Although the farmers were all members of the same VDC, they were selected randomly within this frame. Table 3 illustrates the composition of the sample. A range of preparatory work was undertaken to prepare for the main data collation exercise reported below. This work was undertaken according to the Samuhik bhraman methods outlined by Mathema and Galt (1983). Key informant interviews were used, and three farmer group interviews were held (split according to ethnic group), to isolate a series of key demographic and variety choice data. This qualitative phase revealed that ®ve key factors seemed to determine why farmers chose particular varieties. These factors were the main inputs to the conjoint analysis process, which is described below.

rice, and the ®rst to take account of a wide variety of di€erent factors. Jaiswal (1994) observes that farmers tend to use a di€erent set of criteria for the evaluation of new varieties to the scientists who develop them. In particular, farmers tend to use a wider range of criteria (including, for example, consumption and cooking criteria) than those prioritised by plant developers (such as yield and resistance to pests). As Vaidya and Gibbon (1991, p. 8) note: ``. . .near the time of crop maturity, farmers are asked to rank the treatments of FFVTs in the ®eld. . ., [and] the high yielding variety is not necessarily the farmer's choice.'' LARC is an autonomous, multi-disciplinary agricultural research, extension and training centre, funded by the Government of the United Kingdom. The Centre is responsible for generating and implementing suitable technologies for the 11 hill districts of the Western Development Region of Nepal. This region covers 18,600 km2, and comprises about 350,000 farming households (Gurung and Floyd, 1991). The region represents one of the Centre's o€-station research sites, selected to be representative of a particular agroecological zone. The site is representative of the mid-hill region covering an altitude range of 900± 1500 m above sea level. The Keware-Banjyang VDC consists of 497 households. Initial investigation revealed that 294 of these households had access to khetland and therefore grew rice. This formed the sampling frame for the study. One-hundred and thirty-one farm families were selected for this work during 1995. All

Table 3 Distribution of sample according to ethnic and food status Ethnic group

Food suciency group Food surplus

Total Food balanced

Food de®cit

Number in sampling frame

Number in sampling frame

Number in sample

Number in sample

Number in sampling frame

Number in sample

Number in sampling frame

Number in sample

Brahmin/Chhetri Gurung/Magar Kami/Sarki/Damai

29 10 7

20 10 7

26 82 22

15 15 18

9 88 21

9 20 17

64 180 50

44 45 42

Total

46

37

130

48

118

46

294

131

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2.2. Conjoint analysis Conjoint analysis and some more general scaling procedures were used to assess the key factors in variety choice and the relative importance of ethnic group and food suciency in making such choices. The basis of conjoint analysis is explained brie¯y in this section. The objective of a conjoint experiment is to assess the importance of certain factors in a€ecting a respondent's decision making or preferences, and is to answer questions such as ``How important is the price of the fertiliser compared to the yield potential of the fertiliser?'' in the determination of overall preference. The answer to this question relies on the researcher being able to assess how important price is as a variable in this decision process, and also the relative e€ects of di€erent levels of price (say 15, 25 or 35 rupees per kg). In this way, researchers can assess answers to questions such as what would happen to the preference for this product, if we increased price by 10 rupees. The drawback with many statistical techniques used for answering such questions is that the individual factors are usually considered in isolation (e.g. respondents are often asked to rate or evaluate colour, taste, size, price, yield potential, etc., individually). This is contrary to the basis for making decisions in realityÐwhere decisions are made on the basis of the whole product or good. A farmer, in choosing which rice variety to plant, is assumed not to think independently of the factors which in¯uence his decision, but to consider all aspects of the variety together, e€ectively confounding many of the interactions between the factors. In conventional PRA studies, wealth ranking, for example, assesses a wide variety of aspects that contribute to wealth together, to allow participants and discussants to express an overall impression. Conjoint analysis attempts to simulate this practical consideration of all aspects of a problem jointly, and then decomposes the results to provide insights on the individual levels of the aspects. 2.3. Conjoint design A conjoint analysis essentially involves local analysts, participants and policymakers in designing

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a series of inputs (as discussed above). These inputs can be cards, descriptions, pictures or actual products, and there are various reports of such stimuli being used in practice in developing country situations (e.g. Malhotra, 1988; Janssen et al., 1991/2). As far as possible, potential participants should largely be able to design their own stimuli for such work through participatory activities such as group meetings and discussions. Each of the separate stimuli (from a total of say 15) is designed to carry certain levels of the variables of interestÐwith each stimulus carrying a di€erent combination of variable levels. In this way the participants have `pre-designed' the experiment, by specifying in advance a sensible and practically valid set of combinations. Respondents are then asked to evaluate the 15 `pro®les'Ðeither by scoring them individuallyÐ marking the extent to which they like the product o€ered, or indicating in some way their feeling. This can also be achieved by ranking the pro®lesÐor even by `binary scaling' (Malhotra, 1988)Ðwhere respondents simply sort the cards into two piles, representing `like' or `dislike'. For each respondent, there is then a rank order (or set of ratings) for the pro®les presented. As the stimuli were pre-designed, the conjoint procedure also has a series of independent variables (the individual levels of the variables in the experiment) which are the same for each respondent. What the statistical procedure then attempts to do is to derive (from this raw data of 15 rank numbers and the pre-designed variable levels) a set of weights (or coecients) for the levels of the variables which, when added (or multiplied) together most closely reproduce the respondent's original rank order. If the procedure produces a set of ranks the same as, or close to, the original set (as they usually do), then the part-worths/weights on the individual levels represent the contribution of that level of the variable to the overall ranking/ rating of the variable `preference'. These partworths are then directly comparable to each otherÐso that the relative importance of these various variables can be assessed. Conjoint analysis is simple for people to interpret, and it is simple to do. Indeed, Malhotra (1988) encouraged researchers to use it in marketing

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research studies for developing countries. It contrasts starkly with the compositional methods of some other techniques, where respondents are asked to evaluate separately a variety of key components of a problem. In the same spirit as direct matrix ranking, however, conjoint analysis o€ers full pro®les of an issue or complex situation, asking participants to evaluate `real things' which have equivalence in their daily activities. In this respect, the information from conjoint analysis is relevant, timely, and useful, allied to a substantial measure of statistical validity. Conjoint analysis also enables researchers to assess issues at the level of the individual or small group (as opposed to large samples). At its least aggregated, conjoint analysis considers responses (to a number of stimuli) of one person or group, and contrasts can quickly be drawn between that person or others in similar, or di€erent demographic or economic conditions. This evaluation process could include groups meeting to reconcile di€erences and groups could de®ne their own levels for the variables, and make up their own pro®lesÐthis would further enhance the participatory nature of the exerciseÐchecking whether the groups elicited by the conjoint experiment are appropriate and sensible. 2.4. Designing the stimuli As discussed above, the realism of the conjoint analysis task presented to respondents was considered to be of prime importance in this study. This approach entailed the construction of 18 cards (see Appendix), each showing a hypothetical rice variety, described according to the ®ve factors under study. The 18 di€erent descriptions (designed according to an orthogonal statistical design) represented the inputs to the conjoint analysis. Four `hold-out' descriptions were also prepared, making a total of 22 cards. These four cards were not used in the computations of farmer's preferences, but were used to test the results obtained from the 18 pro®les presented.3 Five factors were eventually isolated from the various qualitative phases of the study as representative of the key features upon which farmers make decisions on variety selection. The ®ve fac-

tors, with their associated levels, are shown in Table 4. The factor levels were de®ned as follows (with the details being explained to participants where necessary): 1. Grain yieldÐin comparison to the farmer's local variety, with the level `high' representing a yield around 0.5 muri/hal4 higher than the local, and `less' being interpreted as a yield 0.5 muri lower than the local variety. 2. Straw lengthÐbased on the International Rice Research Institute's standards, the levels were de®ned as `tall', representing 140 cm or more, `medium', representing a mean of around 120 cm, and `short' representing a mean of around 100 cm. A stick with visual indicators of height was used to aid in the discussions. 3. TasteÐparticipants felt that the best way to describe taste related to taste and mouth-feel relative to local varieties. In this way, `soft' meant a taste like the local Pakhe Jarneli rice, with `rough' representing a taste similar to the Khole Marsi variety. 4. MaturityÐis measured on two levels. The ®rst level, `early' represents a crop duration similar to Pakhe Jarneli, with `late' inferring a

Table 4 Variety factors and levels Factor

Levels

Grain yield Straw height Taste Maturity Threshing

High - Same - Less Tall - Same - Short Soft - Medium - Rough Early - Late Easy - Moderate - Hard

3

This is a standard procedure in conjoint studies. A proportion of the stimuli are not used in the computations, but are required to test the validity of the results. If the respondents' evaluation of the hold-out stimuli can be reproduced by the same results of the conjoint experiment conducted on the main data, it is said to be validated. 4 1 muri=91 l (approx. 48.8 kg of unmilled rice); 1 hal =762 m2.

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A rating scale was used by participants to indicate their preference scores for each variety description, ranging along ®ve points from `very good' through to `very bad'Ðagain cards (with visual cues-lines representing strength of feeling) were used to facilitate participants' understanding of the meaning of the scale.

they are presented in Table 6, and show that yield is paramount in farmers' choice of variety (it has the highest part-worth or utility value), followed by early maturity. The data of Table 6 can be used as the inputs to a simple regression model exploring the in¯uence of the individual variables on preference. This model is presented below with the associated conjoint analysis statistics. The interpretation is conventional, with, for example, a one unit increase in yield (that is from `lower' to `same' or from `same' to `higher', preference (on a scale of 1±5) increases by 0.9052 units. This is a rather crude measure in this form, and makes assumptions about the linearity and relativity of the scale values, but it illustrates how these conjoint utility values might be used in further analysis.

3. Results

Preference ˆ ÿ0:4224 ‡ 0:9052  grain yield ‡ 0:2653  straw length ‡ 0:2354

1 month-later maturationÐsimilar to Khole Marsi or Thulo Jarneli. 5. ThreshingÐthe extent of threshing is normally represented by the number of beatings required to extract the grain from the whole plant. The level `easy' is de®ned as requiring only ®ve such beatings (similar to Pakhe Jarneli), with `medium' requiring 10, and `hard' requiring 15.

Table 5 illustrates the di€erences in varietal practice with implications for both ethnic and food suciency groups; in most cases farmers grow Pakhe Jarneli, followed by Thulo Jarneli and Khole Marsi, respectively. Conjoint analysis results can be presented in a number of di€erent ways. The most fundamental of these is a set of `part-worths' for each level of each factor. These part-worths represent the relative importance of each factor level to the determination of overall utility. For the whole sample Table 5 Number of varieties grown per household (by ethnic and suciency group) Groups

Number of households growing:

Total

1 variety

2 varieties

24 (54.5%) 33 (73.3) 38 (90.5)

20 (45.5) 12 (26.7) 4 (9.5)

44 45 42

Food suciency groups Food surplus 22 (59.5) Food balance 34 (70.8) Food de®cit 39 (84.8)

15 (40.5) 14 (29.2) 7 (15.2)

37 48 46

Total households

36 (27.5)

131

Ethnic groups Brahmin/Chhetri Gurung/Magar Kami/Dami/Sarki

95 (72.5)

 Taste ‡ 0:4968  time of maturity ÿ 0:0967  threshing This relationship is found to be signi®cant according to the measures given below, which indicate a relatively good correlation between observed and predicted values. Table 6 Individual part-worths for whole sample Variable/factor

Level

Part-worth or utility value

Yield

Higher Same Lower

2.7 1.8 0.9

Taste

Soft Medium Rough

0.7 0.5 0.2

Straw length

Tall Semi-dwarf Dwarf

0.8 0.5 0.3

Maturity

Early Late

1.0 0.5

Threshing

Easy Moderate Hard

ÿ0.1 ÿ0.2 ÿ0.3

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Test statistics: Pearson0 s R ˆ 0:983 … p ˆ 0:000† Kendall0 s Tau ˆ 0:895 … p ˆ 0:000† As was mentioned above, four hold-out cards were prepared to test the validity of the experiment. This procedure is carried out to overcome criticisms by Hair et al. (1992) regarding the relationship between the number of cards tested and the correlation between observed and expected results. The hold-outs are not used in the estimation of the part-worths, but are used to test the predictive validity of the conjoint model. Full details of the cards and hold-outs are shown in the Appendix. To assess the impact of this relationship between numbers of cards and overall correlation, the correlation between observed and expected scores for the four hold-outs was assessed, to check on the validity of the utilities. These correlations again were signi®cant, indicating the study was internally valid: Pearson0 s R ˆ 0:994 for four hold-outs … p ˆ 0:0032† Kendall0 s Tau ˆ 1:000 for four hold-outs … p ˆ 0:0208† A second important (and related) output from a conjoint analysis study is a simple appraisal of the importance of each of the factors in determining overall preference. These results are derived by taking the ranges of utility values (part-worths) for each level of each factor and dividing them by the sum of all the utility values. The factors can then be compared since the utility values are expressed on a common scale. Fig. 3 shows the relative importance of each of the ®ve factors in determining farmers' rice variety preference (for the whole sample). The data were then analysed according to some of the demographic details which were collected. Table 7 illustrates the fact that for grain yield there is a signi®cant di€erence in the importance of grain yield as a factor among ethnic groups (although all

Fig. 3. Overall importance of the factors derived from conjoint analysis. Table 7 Means of the utilities of di€erent factors by ethnic group Factors

Ethnic group Brahmin/ Gurung/ Kami/Dami/ Chhetri Magar Saki

Grain yield 1.00 Straw 0.29 Height Taste 0.28 Maturity 0.36 Threshing ÿ0.09

0.77 0.30

0.95 0.20

0.24 0.57 ÿ0.15

0.18 0.56 ÿ0.05

Signi®cance ( p)

0.012 ns ns ns ns

groups evaluated grain yield as their most preferred factor in choosing between varieties). It can be seen from the table that for the Gurung Magar group, maturity is also of importance. Table 8 reveals signi®cant di€erences between the food groups. Not surprisingly, the food de®cit group place a signi®cant premium on grain yield, with factors such as taste and maturity rating lower. 4. Conclusions The methods used in this study seem to have been successful in assessing farmers' priorities and preferences in variety choice, and have yielded a hierarchy of the factors determining such choices.

R. Loader, L. Amartya / Agricultural Systems 62 (1999) 73±85 Table 8 Means of the utilities of the factors by food suciency group Factors

Grain yield Straw Height Taste Maturity Threshing

Food group Food surplus

Food balanced

Food de®cit

0.88 0.32

0.87 0.26

0.96 0.22

0.30 0.36 ÿ0.16

0.18 0.60 ÿ0.02

0.23 0.49 ÿ0.13

Signi®cance ( p)

0.161 ns ns 0.19 0.04

Objective, observable, physical factors prove to be the most important in making decisions, although there are some di€erences in prioritisation depending on farmers' ethnic backgrounds and state of food suciency. This simple adaptation of conjoint analysis has overcome some of the diculties experienced with more complex methods by proving participatory and realistic. In particular it may have helped overcome some of the diculties reported by Buadaeng and Eckert (1993) and Malhotra (1988), by o€ering participants a range of choices which is less likely to be conditioned by expectation of interviewer bias, due mainly to the realism of the task. This sort of study could therefore be applied in a range of non-standard situations, and across di€erent agro-ecological zones, to compare requirements between di€erent areas. Extending the conjoint component a little could enable choice simulations to be run, which would enable researchers to begin to predict uptake and assess the quantities and shares of particular varieties in speci®c areas. The technique lends itself to a number of variations, e.g. the adding of a `wish' variety, and if properly applied conjoint analysis could accomplish thisÐin fact it could isolate wish varietiesÐwith these forms of simulation. In recent years, the focus of social research techniques in rural areas has shifted from a position where the accent was formerly on relatively large amounts of quantitative information (often survey-based) to one where there is a greater focus on the quality and appropriateness of the data collected, and on the nature of the process by

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which data is gathered (Chambers, 1980, 1983a; Casley and Lury, 1987). Naturally, results from this sort of work need to be treated with some caution, and throughout it has been stressed that the quality of the input exercise, and the data obtained therefrom, will condition the quality of the output. In order to extend the methods further, there is a challenge to researchers to make the methods more amenable to true participatory activity, i.e. the tools need to provide outputs which can be readily discussed and amended as necessary in consultation with participants. As far as future developments are concerned, Martin and Sherington (1997, p. 33) note that: Future work [in statistics] should include: . Research into assessing the relevance of different existing techniques in di€erent participatory research situations. The most useful foci of such work are likely to be the analysis of ranked observations and the analysis of hierarchical multi-level models; . Provision of relevant reference material. One possible format would be a set of case studies of detailed analyses, using a range of statistical ideas and techniques, as well as data from a range of participatory studies; . The market research literature has many sophisticated methods for handling consumer preference studies, and the applicability of these to FPR needs to be investigated. This paper has addressed some of these issues. Further reading Cattin and Wittink, 1982; Longhurst, 1998; Mearns, 1988; Riley and Alexander, 1996. Acknowledgements The authors are grateful to an anonymous reviewer who made helpful comments on an earlier draft of this paper.

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R. Loader, L. Amartya / Agricultural Systems 62 (1999) 73±85

Appendix Speci®ed cards and hold-outs

R. Loader, L. Amartya / Agricultural Systems 62 (1999) 73±85

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