Heterogeneous preferences for integrated soil fertility management:

a choice experiment on climbing beans in Burundi

Isabel LAMBRECHT, Miet MAERTENS, Liesbet VRANKEN, Roel MERCKX,

and Bernard VANLAUWE

Bioeconomics Working Paper Series

Working Paper 2013/3

Division of Bioeconomics

Division of Bioeconmics Department of Earth and Environmental Sciences University of Leuven Geo-Institute Celestijnenlaan 200 E – box 2411 3001 Leuven (Heverlee) Belgium http://ees.kuleuven.be/bioecon/

Heterogeneous preferences for integrated soil fertility management: a choice experiment on climbing beans in Burundi

Isabel LAMBRECHT1, Miet MAERTENS1, Liesbet VRANKEN1, Roel MERCKX2

and Bernard VANLAUWE,

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Abstract

Soil nutrient depletion is a fundamental cause in declining per capita food production in Sub-Saharan Africa. Integrated soil fertility management (ISFM) is a promising tool but, despite proven yield and soil fertility benefits, adoption of ISFM techniques among smallholder farmers remains low. In this paper we use a choice experiment to explore how technology traits affect farmers’ adoption decisions of improved climbing bean varieties, a main component in ISFM strategies. We use data from 200 respondents in Burundi and apply multinomial logit, mixed logit and latent class models to estimate the impact of technology traits on farmers’ adoption. Our results show that current and future yields and seed price significantly affect farmers’ stated adoption but that preferences vary – and even reverse in sign – depending on farmers’ wealth, food security status, land ownership and current agricultural practices. The results of ex ante choice experiments focusing on technology traits are highly complementary to insights from more traditional technology adoption studies that focus on farm and farmer characteristics, and can be useful to improve agricultural technology development to better take into account the preferences and needs of local farmers, and improve research and extension programs before they are implemented or out scaled. Key Words: Choice experiment; integrated soil fertility management; preference heterogeneity; agricultural technology adoption; climbing beans; Burundi JEL classification: O33, O13, Q12, Q16, Q24

Corresponding author: Isabel.Lambrecht@ees.kuleuven.be

Acknowledgements

The authors gratefully acknowledge the financial and practical support of the CIALCA project staff for the field work in Burundi. We thank Lotte Willems for guiding the survey team. Special thanks goes to Pieter Pypers and Emily Ouma for extensive discussions on the choice experiment design. The corresponding author is thankful for the research grant provided by FWO Vlaanderen.

1 Division of Bioeconomics, Department of Earth and Environmental Sciences, KULeuven. 2 Division of Soil and Water Management, Department of Earth and Environmental Sciences, KU Leuven 3 TSBF-CIAT, Nairobi

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Heterogeneous preferences for integrated soil fertility management: a choice experiment on climbing beans in Burundi

1. INTRODUCTION

Soil-fertility decline on smallholder fields is seen as the most fundamental biophysical cause of

declining per capita food production in Sub-Saharan Africa (SSA) (Sanchez et al., 1997).

Replenishment of soil nutrients is therefore critical to the process of rural poverty alleviation in SSA

(Gabre-Madhin and Haggblade, 2004; Place et al., 2003). Soil capital – the amount and quality of land

one has access to – is a major asset for smallholders to generate food and cash income (Marenya and

Barrett, 2007). Severe soil nutrient depletion is a main element in the vicious cycle of declining yields,

decreasing rural incomes, deepening poverty, and increased degradation of the natural resource

base (Dasgupta, 2003). An estimated 75% of farmland in SSA is severely depleted of soil nutrients

(The World Bank, 2007).

Soil fertility decline is especially problematic in Burundi, one of the poorest and most densely

populated countries in the world (Beekman and Bulte, 2012). The FAO classifies the country, along

with five other SSA countries, as having very high soil nutrient depletion rates with more than 40 kg

of nitrogen and of potassium and more than 15 kg of phosphor lost per ha per year (FAO, 2001).

During the past decades nearly uninterrupted civil conflict, increasing population pressure, and

severe soil nutrient depletion have steadily lowered land and labour productivity (Beekman and

Bulte, 2012). Adoption of modern agricultural technologies remains low in Burundi (Beekman and

Bulte, 2012).

Integrated soil fertility management (ISFM) is put forward as a promising tool to alleviate problems

of soil fertility and food insecurity (Vanlauwe et al., 2010). ISFM is a composite technology, including

the use of improved germplasm, judicious mineral fertilizer application and improved organic matter

management (e.g. through crop rotation, incorporation of varieties with more biomass, …). ISFM

emphasizes the complementarities among these agricultural technologies as well as the need for

2

adaptation to local conditions (Marenya and Barrett, 2007; Vanlauwe et al., 2010). Legume crops,

known for their beneficial effects on soil fertility through nitrogen fixation, play an important role in

ISFM strategies (Crews and Peoples, 2004; Odendo et al., 2011; Vanlauwe and Giller, 2006). As some

legume roots remain in the field after harvest, fixed nitrogen remains in the soil and thereby

contributes to the soil fertility for succeeding crops (Odendo et al., 2011).

Climbing beans are important legumes in ISFM. They belong to the common bean species (Phaseolus

vulgaris L.) but their tall growth, long internodes and climbing ability distinguishes them from the

bush bean varieties of common beans (Checa et al., 2006; Graham and Ranalli, 1997). Climbing bean

genotypes have among the highest yield potential of all common bean species (Checa et al., 2006),

and have a higher symbiotic nitrogen fixation capacity and more biomass compared to bush beans

(Graham and Rosas, 1977). Therefore, they are particularly well-suited to be included in ISFM

strategies. It is expected that a focus on climbing bean varieties that are beneficial for soil fertility in

terms of soil nutrient balance and organic matter content can lead to long-term improvements of

yields in SSA (Sanginga et al., 2003). Moreover, beans are an important part of the staple diet in

many poor countries (Crews and Peoples, 2004). In Burundi, beans are both an important food and

cash crop (Vervisch et al., 2012). Introducing improved climbing bean varieties could provide a

promising avenue to improve soil fertility, increase rural incomes, alleviate poverty and reduce food

insecurity in Burundi.

Studies, both in experimental stations and on farmers’ fields, show ample evidence of a positive

impact of ISFM on soil fertility and crop productivity (Kamanga et al., 2010; Place et al., 2003; Pypers

et al., 2011). Yet, adoption of ISFM techniques among smallholder farmers is low and often not

sustained (Dar and Twomlow, 2007; Snapp et al., 2003). Apart from some studies that point to

resource and labour constraints limiting farmers’ adoption of ISFM techniques in Kenya (Marenya

and Barrett, 200; Mugwe et al., 2009), little is known about the adoption of ISFM techniques among

smallholder farmers in poor countries. With this paper we want to contribute to a better

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understanding of the adoption potential for ISFM technologies in poor countries by focusing on the

adoption of climbing beans in Burundi.

There is a rich literature on technology adoption among smallholder farmers (Doss, 2006; Feder an

Umali, 1993) but most studies analyze the adoption decision of farm-households ex post, after new

technologies have been introduced and have spread in a specific region. Studies typically look at

adoption rates and analyze which farm and farmer characteristics explain heterogeneity in

technology adoption across farmers (Feder et al., 1985; Useche et al., 2009). While these studies lead

to interesting insights, they fail to address the explicit role of technology traits in farmers’ adoption

decision.

The importance of technology traits in farmers’ adoption decisions can be revealed through trait-

based models and choice experiments (Useche et al., 2009) but few studies have used choice

experiments in research on agricultural technology adoption in developing countries. Several studies

find significant preferences for livestock traits (Faustin et al., 2010 ; Ouma et al., 2007; Roessler et

al.,2008; Ruto et al., 2008 ; Scarpa et al., 2003 ; Zander and Drucker, 2008) and crop traits (Asrat et

al., 2010 ; Baidu-Forson et al., 1997; Birol et al., 2011; Kikulwe et al., 2011 ; Ndjeunga and Nelson,

2005). Tesfaye and Brouwer (2012) look at the preferences for contracts concerning soil

conservation. These studies also show that trait preferences can be heterogeneous and are

influenced by farm and farmer characteristics.

In this paper, we use a choice experiment to analyze the adoption of improved climbing bean

varieties in Burundi. Improved climbing bean varieties are introduced in the country as a component

of ISFM practices to increase soil fertility and crop productivity. Our case-study focuses specifically on

an area that is targeted for further expansion of the ISFM program but where at the time of the

experiment no extension activities had taken place. We carried out an unlabeled choice experiment

with 200 respondents and complemented the experimental data with a small survey. We apply

multinomial logit, mixed logit and latent class models to analyze preference heterogeneity for

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specific attributes of improved climbing bean varieties. Our results demonstrate that there is

significant heterogeneity in the valuation of the attributes, and that this heterogeneity is at least

partly correlated with household characteristics.

In the next section, we outline the empirical design. In section three we describe the socio-economic

and agronomic characteristics of our sample, followed by a section on the econometric approach. In

section five we describe and discuss our results. We conclude with the implications of our findings in

a final section.

2. EMPIRICAL DESIGN

2.1. Study area and sampling method

Our study area covers two communes, Mutaho and Makebuko, in Gitega province in the centre of

Burundi. The Consortium for Improving Agriculture-based Livelihoods in Central-Africa (CIALCA) has

been introducing ISFM techniques, including improved climbing bean varieties, in several collines in

these communes since 2006. For our purpose we focus on those collines that were not part of the

program yet but where activities for outscaling are planned. In a three stage sampling design we

randomly selected four such collines in each commune, one or two sous-collines in each selected

colline, and a total of 200 households in these sous-collines. Because climbing beans are almost

exclusively grown by women in our case-study area, we focused on female respondents, either the

female household head in monoparental households or the spouse of the male household head in

biparental households.

2.2. Choice experiment design

Choice experiments build on the theory of Lancaster (1966), which suggests that consumers do not

derive utility from goods or services as such, but from the properties of the goods or services. For

the design of the choice experiment we first need to identify the relevant properties or attributes of

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the technology and the levels over which these attributes can vary. We then need to combine these

attribute levels in choice cards.

2.2.1 Attributes and attribute levels

To identify the attributes for our choice experiment, we conducted interviews with ISFM experts4

[ Table 1]

and gender-separated group discussions with farmers in four non-sampled sous-collines. This

resulted in the identification of five relevant attributes of improved climbing bean varieties (table 1):

1/ maturation period, 2/ increase in bean yield with fertilizer application, 3/ increase in bean yield

without fertilizer application, 4/ increased soil fertility for the following season, and 5/ seed price.

Many other characteristics of climbing bean varieties, such as seed color, palatability, or cooking time

were mentioned to play a role in farmers’ seed valuation during the group discussions. However, to

keep the choice experiment comprehensive for the respondents, we limited the number of attributes

to five (Caussade et al. 2005; Hensher et al., 2005).

For each attribute we need to determine the number of attribute levels and identify the levels itself

(Hensher et al., 2005). A larger number of attribute levels leads to more detailed insights in attribute

preferences, but necessitates a higher number of choice sets (Hensher et al., 2005; Hoyos, 2010). We

chose for three attribute levels for each attribute as it is the minimum number of levels that enables

to detect non-linear relationships (Hensher et al., 2005).

The first attribute is the maturation period, expressed as the number of days from planting to crop

maturity (table 1). Increasing the amount of biomass in the form of crop residue can have a beneficial

impact on soil fertility but might entail a longer maturation period. We expect farmers to dislike a

longer maturation period as it postpones access to cash and food and jeopardizes timely sowing for

4 These experts included CIAT project managers, local project agronomists and ISABU agronomists

6

the next season. Three levels – 95 days, 110 days and 125 days – have been chosen based on the

minimum and maximum time to maturity. Together with climbing bean experts we determined the

range in which climbing bean maturation is biophysically feasible in our research area. During focus

group discussions respondents agreed that varieties with a maturation period over 125 days would

never be accepted by farmers.

The second and third attribute are climbing bean yields, respectively with and without fertilizer use

(table 1). The group discussions revealed that farmers have difficulties to accurately estimate crop

yields. It was agreed that the best strategy would be to express yields as output per kg seed sown

and the yield benefit as an additional output in comparison with farmers’ current average yield.

Improved varieties are expected to increase yields, which we express as an additional output per kg

seeds sown. The yield response of improved climbing bean varieties likely improves with mineral

fertilizer application (Vanlauwe et al., 2010). We therefore include the yield response with and

without mineral fertilizer as two separate attributes. We expect farmers to have high preferences for

yield increases. About half of the sampled farmers use mineral fertilizer on climbing beans. We

expect those farmers to value yield increases with mineral fertilizer application more. The exact

levels of the yield increases – 0, 2.5 or 5 kg per kg seed sown without mineral fertilizer and 0, 10 or

20 kg per kg seed sown with mineral fertilizer – were based on the characteristics of the improved

climbing bean varieties that were candidates for dissemination in the research area5

The fourth attribute relates to soil fertility improvement (table 1). As suggested by the farmers

themselves during focus group discussions, we define this as a yield increase for the subsequent

maize crop. Most farmers in the area are rotating beans with maize, such that nutrient

.

5 On-farm trials with climbing bean varieties revealed a high yield variability both with and without mineral

fertilizer addition. For fields that had not been targeted by extreme weather or other deteriorating conditions,

yield ranged between 10kg to 35kg of beans per kg of climbing beans sown.

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replenishment associated with improved climbing varieties would materialize in better maize yields

in the following season. In a study on climbing bean-maize rotation in South Kivu, Lunze and Ngongo

(2011) found that climbing bean cultivation indeed had a significant positive effect on the yield of the

subsequent maize crop. Again, the exact levels – 0, 7.5 or 15 kg per kg sown maize – have been

chosen based on the advice of the program agronomists and results of climbing bean field

experiments in the area6

The fifth attribute is the seed price, expressed as the price paid for one kg of seeds (table 1). This

represents the cost of using improved climbing bean varieties – along with the cost of a longer

maturation period – and hence, we would expect farmers to prefer a lower seed price. The price

levels – 1000, 1250 or 1500 FBU – were chosen based on the market price at the time of the survey,

and the maximum and minimum price focus group respondents found acceptable.

. We expect that farmers have lower preferences for soil fertility

improvements or future maize yield increases than for more immediate increases in bean yields.

People in our research area are poor and likely have short time horizons. Under these conditions,

there might be a large trade-off between current investments and future returns, even if the future is

a relatively short time span of two agricultural seasons.

2.2.2 Choice cards

We developed choice cards consisting of two generic alternatives and a status-quo option. This last

option was added to allow respondents not to adopt improved climbing bean varieties (Hensher et

al., 2005). Given that we selected five attributes and three different levels for each attribute, the

number of possible choices are very high. We used a fractional factorial design, a subsample of the

full factorial design, to select choices and combine them in choice cards. We used the D-efficiency

6 Maize yields vary widely from about 25kg to per kg of seed sown without mineral fertilizer addition and in

poor soils, to an exceptional 60kg per kg of seed sown with mineral fertilizer addition and in fertile soils. Lunze

and Ngongo show yield increases of 15%-46% of maize grain yields in rotation with climbing bean.

8

criterion to optimize the efficiency of the design and allow the main effects to be estimated as

accurately as possible (Hensher et al., 2005; Kuhfeld, 2010). The choice sets were carefully checked

to rule out dominant choices in which one alternative is strictly better than another. With this

procedure we arrived at a total of 27 different choice cards, which were divided in three different

blocks to be proposed to the respondents.

2.3. Data collection

The choice experiment was carried out with a sample of 200 respondents in the period September-

October 2011, and was accompanied by a small survey. The respondents were randomly assigned to

one of the three choice blocks, each comprising nine choice cards. The choice task was first

comprehensively introduced to the respondents to make sure the task of hypothetically buying

improved climbing bean varieties or not was properly understood. A pilot test before the actual

choice experiment confirmed the respondents’ understanding of the choice task. A small household

survey accompanied the choice experiment to gather information on household and respondent

specific factors that could influence farmers’ preferences.

3. HOUSEHOLD AND RESPONDENT CHARACTERISTICS

Before turning to the actual analysis of the choice experiment, we shortly describe the main socio-

economic characteristics of the respondents and their households (table 2). One out of ten

households in our sample are female headed. Households consist generally of two to three adults,

and two to three children. They own on average 4 fields and less than one livestock unit (which

equals one cow). The nearest tarmac road takes on average half an hour to reach on foot. Education

levels are low, with respondents having on average 2.9 years of schooling and household heads 3.4.

There is a huge problem of food insecurity in our study area: almost all households (91%) are food

insecure and almost 40% are even severely food insecure.

[Table 2]

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We observe important and significant differences between the two communes (table 2). Households

in Mutaho have less human and physical capital, with lower levels of education and less land,

livestock and other assets. In addition, they have a lower probability of being female-headed and of

being a member of an association but a higher probability of being food insecure or severely food

insecure. Moreover, they have more children but a smaller number of adults in the households, and

are located further away from tarmac roads.

In table 3 we show some agronomic characteristics of households in the sample. We find a very high

proportion of households that ever used mineral fertilizer (96%). A much lower proportion ever used

improved varieties of any type of crop (20%) and a only a very low proportion used improved

climbing bean varieties during the last year (5%). The use of improved varieties is lower in Mutaho

than in Makebuko. Climbing beans are the main subsistence crop for the large majority of household

in Mutaho (70%) and for almost half of the household (45%) in Makebuko. Only one fifth of the

households mix climbing bean varieties in the field, and the average number of local varieties sown is

only two in Mutaho, and 1.5 in Makebuko. About half of the households sold a part of their climbing

bean harvest. The majority applied mineral fertilizer on their climbing beans.

[Table 3]

4. ECONOMETRIC APPROACH

4.1 The models

We use three different estimation techniques: 1/ a multinomial logit model (MNL), 2/ a mixed logit

model (MXL), and 3/ a latent class model (LC). So far both theoretical and empirical evidence is

inconclusive on which model is superior to explain stated choice data (Greene and Hensher, 2003).

4.1.1 Multinomial logit model

We start with estimating MNL models, in which it is assumed that the error terms of the utility

function are independently and identically distributed. We estimate a simple MNL model in which

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only the technology attributes are included. Yet, as is the case with consumer preferences for goods

and services, farmers preferences for technologies may be characterized by heterogeneity.

Accounting for preference heterogeneity likely results in more accurate estimates of individual

preferences and enables to extract more information (Boxall and Adamowics, 2002).

We introduce preference heterogeneity in the MNL model by including interaction terms of the

technology attributes with a set of observed relevant socio-economic characteristics. Many

candidate variables exist as possible interaction terms with our attributes. As it is not feasible to

include all of them in one model we proceeded as follows. First, an extensive list of socio-economic

indicators was drawn and interaction terms were created with each attribute. Then, interaction

terms were tested for each indicator. If a Wald test showed a significant improvement of the model

fit, the candidate interaction terms were listed. After that, all listed terms were included in one

model. Non-significant terms (confirmed by the Wald test) were then removed.

The MNL model is simple but has some important disadvantages. Even with interaction terms, the

MNL model can only shed light on observable heterogeneity and ignores unobserved sources of

heterogeneity or random taste variation (Boxall and Adamowics, 2002; Hoyos, 2010; Train, 2009). In

addition, the property of independence of irrelevant alternatives restricts substitution patterns in the

MNL model. We can overcome these limitations with mixed logit models.

4.1.2 Mixed logit model

In MXL coefficients are estimated over a distribution rather than as point estimates (Hoyos, 2010;

Train, 2009). Any distribution is allowed for the unobserved factors but the distribution of each

random coefficient must be specified. Commonly, these are normal or lognormal distributions, but

misspecification can lead to serious bias in parameter estimates (Carlsson et al., 2003). Therefore, we

first explore which parameters should be considered as random, what their distribution should be,

and which numbers of draws are required to secure a stable set of parameter estimates.

11

We use the procedure of Hensher et al. (2005) to select the random parameters and we apply the

kernel density estimation procedure proposed by Greene and Hensher (2003) to decide on their

distribution. We estimate a simple MXL model with only technology attributes and an MXL-

interaction model with interaction terms between the attributes and socio-economic characteristics.

For both models, the density plots show us that the normal distribution is a good approximation for

the empirical distribution of the parameters. As preferences for yield increase and soil fertility

improvement are expected to be positive, we could specify the distributions of these attributes as

lognormal. However, we find no significant improvement in model fit if we impose log-normality for

these parameters. We prefer therefore to keep the model comprehensive and report the output for

the model with a normal distribution of all parameters. We use 250 Halton draws, which provides a

sufficient balance between stability of the estimates and estimation time. A Wald test rejects the

hypothesis that the random parameters can be removed, thus arguing in favor of MXL compared to

MNL.

4.1.3 Latent class model

The LC model is a subset of the MXL model in which the mixing distribution consists of a finite set of

distinct values (Boxall and Adamowics, 2002; Hoyos, 2010). LC models assume that the population

consists of a certain number of latent segments with different utility functions, such that preference

parameters can differ between the segments. The LC model concurrently estimates both choice

probability and segment membership (Boxall and Adamowics, 2002; Swait, 1994).

We need to make assumptions on the explaining factors for the latent classes and the number of

segments in the model. For the former, we use the socio-economic variables that resulted in

significant interaction terms in the MNL and MXL models (omitting one variable due to

multicollinearity). It would be possible to include more variables in the membership function, but this

would highly increase the complexity of the model and the estimation effort (Boxall and Adamowicz,

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2002). In interpreting the results, we should keep in mind that the membership function for the

latent classes is not a behavioral or causal relation, but a statistical classification (Boxall and

Adamowics, 2002).

The decision on the number of classes is guided by statistical decision criteria and by interpretability

and simplicity of the model (Boxall and Adamowics, 2002; Swait, 1994). Although the Akaike

Information Criterion (AIC), the consistent Akaike Information Criterion (CAIC) and the Bayesian

Information Criterion (BIC) are lowest at two segments (table 4) we use the model with three

segments as this better allows to explain preference heterogeneity.

[Table 4]

4.1.4 Estimating and comparing the models

All calculations are done in Stata IC11.2. For the LC analysis we used the commands written by

Pacifico and Yoo (2012). The models are estimated with centered attribute levels (table 1). This does

not change the marginal effects in the analysis, but it facilitates interpretation of coefficients and

interaction terms (Brambor et al., 2006). To account for multiple choices by each respondent, we

cluster the data at respondent level (Hensher and Greene, 2003).

We compare both MNL models with each other and with the MXL and LC models using a likelihood

ratio test – as proposed by Greene (2008) and applied for example by Birol et al. (2006) and Gelo and

Koch (2012)7

7 The likelihood for clustered maximum likelihood estimation is not a true likelihood, and theoretically it is inappropriate to use in a likelihood ratio test (Greene, 2008). Yet, we follow the example of many other choice experiment studies and ignore the possible clustering effect to estimate the likelihood ratio.

. The likelihood ratio test is not suited to compare the MXL and LC models because it

only applies to nested models (Greene and Hensher, 2003) and therefore we use the Akaike

likelihood ratio test proposed by Ben-Akiva and Swait (1986). We find that the MNL model with

interaction terms is superior to the simple MNL, and that the MXL with and without interactions and

the LC are significant improvements over both MNL models. This indicates that it is important to

13

control for both observed and unobserved heterogeneity in this analysis. From the Akaike likelihood

ratio test we can conclude that the LC model is superior to the MXL model, indicating that splitting

the sample in segments according to the preferences and characteristics of the respondents is a

better option than allowing for continuous distribution of the parameters.

4.2 Opt-out

As explained previously, the choice cards include an opt-out option to allow respondents not to

adopt improved climbing bean varieties. In MNL such an opt-out alternative may produce strongly

biased estimates for preference parameters (Scarpa et al., 2005). To avoid such bias in our analyses,

we add an alternative-specific constant (ASC) for the opt-out alternative. Yet, it is possible that

respondents who chose the opt-out alternative have different preferences than those who chose a

specified alternative. This may conflict with the assumption of independence of irrelevant

alternatives (Faustin et al., 2010).

In the random utility framework the probability of observing an opt-out response is inversely related

to the quality of the specified alternatives (Kontoleon and Yabe, 2003). In our study only in 4 choice

occasions of the 1800 choice options the respondents chose the opt-out option. One respondent

opted out twice, and two other respondents opted out only once (in the same choice set) out of the

nine choice sets. The varieties in our sample were presented as equally or more favorable than the

current varieties for the soil fertility and yield attributes. They were equally or less favorable for the

price and maturation period. These properties reflected the varieties that were ready for

dissemination by our project partners. Yet, as will be confirmed in the analyses below, respondents

valued the first three attributes more highly than the latter two. Respondents may therefore have

found the opt-out to be largely inferior to the specified alternatives8

8 It could also be suggested that respondents wished to conform to the wishes of the questioner (the so-called

Hawthorne effect) by not opting out. However, it was carefully explained at the start of the experiment that

.

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As in Faustin et al. (2010) and Kontoleon and Yabe (2003), we argue that the opt-out option in our

experiment did not change farmers’ preferences for crop attributes but avoided forced answers. The

low number of opt-out choices inhibits us to conduct a full IIA test. However, a Hausman test

(Hausman and McFadden, 1984) shows us that the model with three alternatives and ASC is not

significantly different from the model with two alternatives.

5. RESULTS AND DISCUSSION

5.1 The MNL and MXL models

Results of the MNL and MXL models, with and without interaction terms, are given in table 5.

Interaction terms in the final model include the interaction between seed price and food insecurity,

between yield without mineral fertilizer and having sold climbing beans last season, between yield

with mineral fertilizer and the application of mineral fertilizer last season, and between yield with

mineral fertilizer and a dummy for households owning more fields than average.

[Table 5]

All models confirm that respondents are mainly interested in higher yields which is in line with

findings from other studies (Asrat et al., 2010; Birol et al., 2011, Baidu-Forson et al., 1997). The

results show that respondents have high preferences for increased climbing bean yields with and

without mineral fertilizer application, and for increased soil fertility or increased yields in subsequent

maize production. We find that maturation period is not an important attribute. It is insignificant in

all the models. This is against the expectations that a longer growing period would deter farmers

from using improved varieties and contradicts the finding by Birol et al. (2011) that a longer

maturation period decreases the likelihood of adopting bio-fortified millet among farmers in India.

respondents should act as if they were in a real situation and that choosing the opt-out option was equally valid

for the experiment.

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Seed price is not a significant attribute in the simple model, but becomes significant when an

interaction term between seed price and food security is included. The results of the MNL and MXL

models with interaction terms indicate that a high seed price has a positive effect on the willingness

to adopt new climbing bean varieties for food secure households, but a negative effect for food

insecure households. An insignificant or positive effect of seed price seems counterintuitive as a

higher seed price means a higher cost of production, which is a constraint for poor households. Other

studies (e.g. Birol et al., 2011) also found insignificant price effects and could not clearly explain this.

Our results can be explained by the fact that seed-saving is a common practice in the area and that

farmers regularly sell seeds to other farmers in case of surpluses. Our results indicate that food

secure households, who are more likely to sell seeds from their own harvest, like higher seed prices

while food insecure households dislike higher prices as this increases their production costs.

Further interaction terms in the model indicate that households who sell at least a part of their bean

harvest care less for the yield-without-mineral-fertilizer attribute while households who usually apply

mineral fertilizer on climbing beans have a higher preference for increased yields with mineral

fertilizer. The latter is straightforward and in the line with the expectations. The former requires

some more explanation. Households who commercialize part of their produce likely have better

access to agricultural inputs and, whether or not they already use mineral fertilizer on climbing

beans, they could more easily use fertilizer when varieties become available that have a good

response to mineral fertilizer. The results further indicate that households with more land care more

for yield increases with mineral fertilizer application. Finally, the negative and strongly significant ASC

shows that respondents in general dislike to opt-out.

The MXL provides us with more information on preference heterogeneity on unobserved factors. In

the simple MXL, we find randomness in all coefficients except for maturation period. This means that

respondents have varying preferences for these attributes, but confirms the insignificance of the

effect of maturation period on respondents’ choice. The random parameter for seed price is highly

16

significant. This indicates that, despite the non-significant coefficient in the simple MXL, respondents

do care about seed prices, but preferences may be both positive or negative. The probability of sign

reversal (from negative to positive) is as high as 35% for seed price. This confirms the double effect of

the seed price attribute, stemming from seed being a costly input on the one hand and a marketable

output from seed-saving on the other hand. For all other attributes in the simple MXL the signs of the

coefficients are unambiguous.

In the interacted MXL there remains some unexplained random variation and probability of sign

reversal for the interaction term between seed price and food insecurity. When adding up the

coefficients of the main price effect and the interaction term, we find that among food insecure

households the majority (82.5%) prefers a low seed price while only 17.5% prefers a high seed price.

5.2 THE LC MODEL

The LC model results in the identification of three population segments. We find two relatively large

segments (segment 2 and 3), and one segment with a small number of respondents (segment 1)9

[Table 6]

. In

Table 6 we report the estimated attribute coefficients for the three segments and the results of the

membership function. The a priori probability of a household to belong to the first segment is only

7.4%, while it is 44.1% and 48.5% for respectively the second and third segment. The third segment is

chosen as reference category. In table 7 we explore the socio-economic characteristics of the three

segments by appointing households to the class with the highest posterior membership probability

and compare socio-economic characteristics of these classes.

9 In the two segment model, we also found a small population similar to segment 1 in the three segment model

above. The bigger segment then embraced both the other two larger segments. Results of the two-segment

model are not shown.

17

In the first segment we find farmers who have a preference for a low seed price, high yields without

mineral fertilizer application, and an improvement of soil fertility (table 6). Respondents have no

significant preference for yield with mineral fertilizer application or maturation period. This segment

can be classified as the poorest subsistence farmers. From the membership function, it is clear that

farmers in segment 1 own less fields, are less likely to sell surplus harvest, and are less likely to apply

mineral fertilizer than the baseline segment 3 (table 6). Results from the socio-economic comparison

of farmers in the three segments confirm that segment 1 includes farm-households who are less

wealthy, with less land, livestock and other assets, who have a lower level of education, and who

have a higher probability of being female-headed and severely food insecure (table 7).

Respondents in the second segment prefer varieties with a higher yield without mineral fertilizer

application, and with increased soil fertility. However, these farmers also have a significant and

positive preference for yield increases with mineral fertilizer application. The effects of seed price

and maturation period are not significant for this group of farmers. This segment consists of farmers

who are less likely to apply mineral fertilizer, own less fields, and sell less beans than the reference

segment (table 6). However, the coefficients in the membership function are smaller and less

significant compared to the first segment. Relative to the survey population, we can classify them as

middle-class farmers. Results in table 7 confirm that farmers in segment two have land, livestock and

asset holdings and education levels that are in between those of segment 1 and 3.

In the third segment farmers show a preference for yield increases with and without mineral

fertilizer, and increases in soil fertility (table 6). Compared to the other classes, the relative weight of

yield without fertilizer compared to yield with fertilizer is much lower. This segment includes farmers

who are more likely to apply mineral fertilizer, own more fields, and sell more beans than farmers in

the other segments. These are relatively better-off farmers with more land and assets, and a

somewhat higher level of education (table 7).

[Table 7]

18

5.3 SUMMARY OF RESULTS

Since models are parameterized differently, we cannot compare estimated coefficients across

different models (Greene and Hensher, 2003; Louviere et al., 2003). Comparing the signs of the

coefficients, we find consistency across the three models. Yield improvements are preferred in all

models, but observed and unobserved respondent characteristics relate to a relatively higher or

lower valuation of yield with or without mineral fertilizer application. Increases in soil fertility are

preferred by all, but are relatively less important for the poorest farmers. We find important

heterogeneity on unobservables for this attribute.

The seed price is an interesting attribute which shows us the power and need of modeling preference

heterogeneity. In the simple MNL model it is not a significant parameter. Yet, we find it is differently

appreciated for different respondents, and tastes can go from positive to negative preferences. Less

wealthy, more food insecure farmers generally prefer a lower seed price. These farmers sell no or

only a small part of their climbing harvest and do not benefit from a price premium. Yet, farmers who

sell a high proportion of their beans prefer a higher seed price.

The maturation period has no significant effect in our models, also we have found significant

preference heterogeneity on observable or unobservable characteristics. This is rather counter-

intuitive, as a higher maturation period was seen as a negative attribute by crop specialists and focus

group discussants. However, from this study its importance relative to other crop characteristics

appears to be overrated.

6. CONCLUSIONS

We use a choice experiment to explore the potential of ex-ante analysis of preferences for ISFM

technologies, with a focus on climbing beans. We find that farmers have high preferences for current

and future yield increases. The seed price also significantly influences respondents’ preferences, but

the effect can be either positive or negative. The maturation period is not a significant predictor of

choice.

19

We use different econometric methods that allow to introduce preference heterogeneity in the

model (MNL with interaction terms, MXL and LC). Based on our results, we argue that these models

are highly complementary (rather than substitutes), each providing specific and additional insights in

farmers’ adoption decisions for improved climbing bean varieties. Our study highlights a strong

heterogeneity in preferences depending on wealth, food security status, land ownership and current

agricultural practices of the respondent. We also find evidence of unexplained but significant

heterogeneity.

Choice experiments provide ex ante insights that can be applied to improve agricultural technology

development to better take into account the preferences and needs of local farmers, and improve

research and extension programs before they are implemented or out scaled. Our study shows that

choice experiments can be implemented in very poor settings, and with poorly educated

respondents. Preference heterogeneity in such a poor region is likely more closely related to

resource constraints than to taste or ideological differences. Hence, understanding and addressing

heterogeneity is key to design agricultural technologies that can also appeal the extremely poor, and

hence contribute to real pro-poor and sustainable agricultural development.

20

7. TABLES

Table 1: Attributes and attribute levels used in the choice experiment

Attributes Definition Attribute levels

Coding

Maturation period The number of days from bean planting to harvest maturity

95 days 110 days (SQ) 125 days

- 15 0 +15

Productivity without mineral fertilizer

Average yield increase for each 1 kg of climbing beans sown without the addition of mineral fertilizer

0 kg (SQ) 2.5kg 5kg

-2.5 0 +2.5

Productivity with mineral fertilizer

Average yield increase for each 1 kg of climbing beans sown if mineral fertilizer is applied

0 kg (SQ) 10kg 20 kg

-10 0 +10

Soil fertility improvement

Improvement in soil fertility, expressed in yield increase of 1 kg sown of the rotating maize crop

0 kg (SQ) 7.5 kg 15kg

-7.5 0 +7.5

Seed price The amount of money the farmer needs to pay for 1 kg of seeds

1000 FBu (SQ) 1250 FBu 1500 FBu

-250 0 +250

Note: FBu= Burundian Francs; FBu 1250 = 1 USD at the time of the experiment (September 2011) (SQ) level of the status-quo or opt-out option

21

Table 2: Household and respondent characteristics across two communes

Variable Description Total sample Mutaho Makebuko

Mean se Mean se Mean se Gender Dummy for female-headed

household 0.10 0.02 0.02 0.01 0.19*** 0.04

Age head Age of household head 42.65 0.90 42.07 1.35 43.26 1.18

Age respondent Age of respondent 38.84 0.88 37.35 1.25 40.40 1.22

Education head Years of education of household head

3.45 0.19 2.89 0.24 4.04*** 0.27

Education respondent Years of education of respondent

2.94 0.19 2.07 0.24 3.85*** 0.27

Adults Number of adults in the household

2.79 0.09 2.67 0.12 2.93* 0.13

Children Number of children in the household

2.63 0.12 2.87 0.17 2.37** 0.16

Distance to road Distance to tarmac road (minutes on foot)

30.41 2.27 36.65 3.64 23.84*** 2.48

Association member Association membership (dummy)

0.55 0.04 0.35 0.05 0.76*** 0.04

TLU Tropical livestock unitsa 0.88 0.06 0.71 0.08 1.07*** 0.09

Fields Number of fields owned 4.31 0.20 3.75 0.25 4.83*** 0.29

Asset index Asset indexb 2.39 0.11 1.80 0.10 2.98*** 0.18

SFoodIns Severely food insecure (dummy) c

0.39 0.04 0.55 0.05 0.23*** 0.04

FoodIns Food insecure (dummy) d 0.91 0.02 0.99 0.01 0.82*** 0.04

N 100 95

Significantly different on *** p< 0.01; **p <0.05; *p <0.10 a. One cow equals 1 livestock unit, pig is 0.40, goat/sheep 0.20, chicken/rabbit 0.05, guinea pig 0.005 b. The asset index is the first term of a principal component analysis on household durables (excluding

productive assets) owned by the household c. FANTA classification (Coates et al., 2007), corresponding to the HFIAS category 4 d. Food insecure includes all households which are in HFIAS category 2 to HFIAS category 4

22

Table 3: Agronomic and climbing bean cultivation characteristics across two communes

Variable Description Total sample Mutaho Makebuko Mean se Mean se Mean se Try Chem F Household ever tried mineral

fertilizer (dummy) 0.96 0.01 0.95 0.02 0.97 0.02

Try Var Household ever tried improved varieties (dummy)

0.27 0.03 0.15 0.03 0.41*** 0.05

Use Var Household used improved varieties last year (dummy)

0.20 0.03 0.09 0.03 0.31*** 0.05

CB Subs1 Climbing bean is n° 1 important subsistence crop

0.58 0.04 0.70 0.05 0.45*** 0.05

CB Subs2 Climbing bean is n° 2 important subsistence crop

0.19 0.03 0.17 0.04 0.21 0.04

Mix CB Household mixes climbing bean varieties in the field

0.23 0.03 0.22 0.04 0.24 0.04

Local CB Number of local climbing bean varieties cultivated

1.78 0.06 2.08 0.07 1.47*** 0.08

Use ImprCB Household used an improved climbing bean variety last year (dummy)

0.05 0.02 0.04 0.02 0.06 0.02

Impr CB Number of improved climbing bean varieties cultivated

0.09 0.03 0.06 0.03 0.11 0.05

SoldCB Household sold climbing beans last year (dummy)

0.48 0.04 0.45 0.05 0.51 0.05

ChemFert Household applied mineral fertilizer to the climbing beans last year (dummy)

0.81 0.03 0.90 0.03 0.72*** 0.05

N 103 96

Significantly different on *** p< 0.01; **p <0.05; *p <0.10

23

Table 4: Information criteria for LC models with different segments

Number of segments

Log likelihood

Number of parameters

AIC CAIC BIC

1 (MNL model) -729.4455 6 1470.891 1510.456 2 -702.0905 16 1436.190 1504.954 1488.954 3 -693.8977 26 1440.469 1551.552 1525.552 4 -689.8516 36 1451.975 1606.443 1570.443 5 -684.5204 46 1459.455 1658.763 1612.763

24

Table 5: Parameter estimates for simple and interacted MNL and MXL model

MNL simple MNL interacted MXL simple MXL interacted Mean ASC -2.5112*** -2.4874*** -5.7390*** -4.6402*** Seedprice -0.0001 0.0014** -0.0003 0.0016** Yield no chem fert 0.2455*** 0.2974*** 0.2758*** 0.3273*** Yield with chem fert 0.1359*** 0.1039*** 0.1575*** 0.1098*** Soil fertility increase 0.1043*** 0.1068*** 0.1229*** 0.1228*** Maturation period -0.0012 -0.0013 -0.0014 -0.0013 Seedprice*FoodIns -0.0017** -0.0020*** YnF *SoldCB -0.0995** -0.1121** YwF*ChemFert 0.0300** 0.0424*** YwF*FieldsDummy 0.0312** 0.0383*** Standard deviation ASC 2.6832*** 2.0637*** Seedprice 0.0013*** Yield no chem fert 0.0912 Yield with chem fert 0.0534*** Soil fertility increase 0.0347** 0.0366** Maturation period 0.0024 Seedprice*FoodIns 0.0012** YwF*ChemFert 0.0538***

chi2 423.9134 438.4818 16.5759 18.6747 ll -729.4455 -716.7938 -721.1575 -707.4564

aic 1470.8909 1453.5875 1464.315 1442.9128 bic 1510.4558 1519.529 1536.8507 1535.2309

N 5400 5400 5400 5400

Coëfficients are significant at *** p< 0.01; **p <0.05; *p <0.10

25

Table 6: Parameter estimates for LC models

Class1 Class 2 Class 3 Attributes ASC -39.0715 0.0488 -38.6148 Seedprice -0.0033*** -0.0006 0.0003 Yield no chem fert 0.3731*** 0.5021*** 0.1276* Yield with chem fert -0.0081 0.1972*** 0.1483*** Soil fertility increase 0.0865** 0.1701*** 0.1005*** Maturation period -0.0122 0.0095 -0.006 Membership function ChemFert -2.0801** -1.0109 Fields -0.4019* -0.0531 SoldCB -1.9371** -1.3238* Constant 1.621 1.4014

Class probability (%) 7.4 44.1 48.5

Coefficients are significant at *** p< 0.01; **p <0.05; *p <0.10

26

Table 7: Comparison of socio-economic characteristics of different segments

Class 1 Class 2 Class 3 Mean (se) Mean (se) Mean (se) Asset index 2.00 (0.39) 2.42 (0.17) 2.42 (0.15) FoodInsec 0.93 (0.07) 0.87 (0.04) 0.93 (0.03) * SFoodInsec 0.46 (0.14) 0.38 (0.04) 0.41 (0.05) TLU 0.74 (0.19) 0.91 (0.09) 0.90 (0.09) Fields 2.86 (0.49) 3.87 (0.27) 4.967 (0.32) *** ChemFert 0.57 (0.14) 0.79 (0.04) 0.88 (0.03) *** SoldBeans 0.21 (0.11) 0.30 (0.05) 0.71 (0.05) *** Children 2.57 (0.34) 2.60 (0.18) 2.67 (0.17) Gender 0.14 (0.10) 0.10 (0.03) 0.09 (0.03) Commune (Makebuko=1) 0.36 (0.13) 0.49 (0.05) 0.51 (0.05) * Distance to road 35.14 (9.10) 25.91 (2.60) 34.42 (3.91) ** Education of respondent 1.50 (0.57) 2.89 (0.30) 3.34 (0.27) ***

Number of observations 14 87 90

Variables significantly different at *** p< 0.01; **p <0.05; *p <0.10 for at least two classes

27

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