Farmer Willingness to Pay for Seed-Related Information - CiteSeerX

ENVIRONMENT AND PRODUCTION TECHNOLOGY DIVISION

SEPTEMBER 2005

EPT Discussion Paper 142

Farmer Willingness to Pay for Seed-Related Information: Rice Varieties in Nigeria and Benin J. Daniela Horna, Melinda Smale, and Matthias von Oppen

2033 K Street, NW, Washington, DC 20006-1002 USA • Tel.: +1-202-862-5600 • Fax: +1-202-467-4439 [email protected] www.ifpri.org IFPRI Division Discussion Papers contain preliminary material and research results. They have not been subject to formal external reviews managed by IFPRI's Publications Review Committee, but have been reviewed by at least one internal or external researcher. They are circulated in order to stimulate discussion and critical comment. Copyright 2005, International Food Policy Research Institute. All rights reserved. Sections of this material may be reproduced for personal and not-for profit use without the express written permission of but with acknowledgment to IFPRI. To reproduce the material contained herein for profit or commercial use requires express written permission. To obtain permission, contact the Communications Division at [email protected].

ACKNOWLEDGMENTS The project on which this research is based was initiated by the WARDA, with the University of Hohenheim and the German Ministry of Economic Cooperation (GTZ/BEAF). The authors thank Bonwoo Koo, Sevetlana Edmeades and Latha Naragajan for the valuable comments to this discussion paper. We would like as well thank Ekin Birol from Homerton College, Cambridge University, Volker Hoffmann from University of Hohenheim, Alicia Jimenez from CIATEJ, and Augustin Munyemana from WARDA, for the reviews and suggestions done to earlier versions of this work. Koffi Amegbeto from IITA, and James Hanson and William Rivera at the University of Maryland provided valuable suggestions and literature during the development of this research.

i

ABSTRACT A typical private good is defined by its excludability and rivalry characteristics. Information embodied in a technology might not generate rivalry among its users. By contrast, excludability is certainly a characteristic of this kind of information and its delivery can generate incentives for private participation. This study examines farmers’ preferences for seed of new rice varieties and their willingness to pay for seed-related information in villages of Nigeria and Benin. Conjoint analysis is used to estimate the structure of farmers’ preferences for rice seed given a set of alternatives. Farmers are considered to be consumers of seed as a production input, preferring one variety over another based on the utility they obtain from its attributes, which depends on their own social and economic characteristics, including whether or not they sell rice. Contingent methods are used to elicit preferences and willingness to pay (WTP) for rice seed. The marginal values of attributes, with and without information about the seed, are estimated with an ordered probit regression. WTP for information is derived from the analysis of WTP for rice seed. The results have implications for the best way to finance research and extension services in the areas of intervention, particularly for new rice varieties.

Keywords: WTP for seed-related information, conjoint analysis, rice attributes, and farmers’ preferences

ii

TABLE OF CONTENTS 1. Introduction

1

2. Theoretical Approach

6

3. Research design

9

4. Econometric Model

17

5. Results

22

6. Conclusion

31

References

35

iii

Farmer Willingness to Pay for Seed-Related Information: Rice Varieties in Nigeria and Benin J. Daniela Horna, 1 Melinda Smale, 2 and Matthias von Oppen1

1. INTRODUCTION The importance of extension in agricultural systems has been questioned due to its low effectiveness in improving agricultural performance and consequently farmer welfare (Rivera and Gustafson 1991; Carney 1998). Public provision of extension services represents a heavy fiscal burden for governments. Experiences in a number of locations around the world are demonstrating that inefficiencies in resource allocation are unavoidable if a service such as extension is provided free of charge to stakeholders who might be able or willing to contribute in order to obtain appropriate service (Schwartz 1994; Dinar 1996; Carney 1998; Chapman and Tripp 2003; McFeeters 2004). This study treats information and knowledge as agricultural inputs and the goods that extension services provide to farmers. Extension activities involve more than information provision; however, extension activities associated with dissemination of information and direct contact with farmers are those that have been commercialized, or transferred to the private sector (Le Gouis 1991). The underlying hypothesis is that as a good, information has a market with incentives for private participation. The objective of the study is to determine the marginal value to farmers of seedrelated information, and estimate their willingness to pay (WTP) for it. Though a

1

Institute of Agricultural Economics and Social Sciences for the Tropics and Subtropics, University of Hohenheim, [email protected], [email protected] 2 International Institute of Food Policy Research (IFPRI); International Plant Genetic Resources Institute (IPGRI), [email protected]

2 hypothetical value, WTP can help to establish boundaries for information supply price and guide the implementation of private participation in extension services. The project on which the research is based was initiated by the West African Rice Development Association (now Africa Rice Center, WARDA) in 1999 to develop participatory tools to enhance farmer participation in developing new rice varieties and explore alternative ways to increase the efficiency of agricultural extension. The project areas of intervention are villages in Ogun, Kogi and Ebonyi states in Nigeria and Dassa and Glazoue sub-prefectures in Benin (Figure 1). The project identified rice producers in study areas and conducted on-farm trials. The farmers participating in the project represent the population sampled for this research.

3 Figure 1--Location of the study sites

Data Source: WARDA project and Chris Legg, IITA GIS Lab. Map: Jan Dempewolf, University of Maryland

Rice is a main staple and cash crop in Nigeria and Benin, and these countries have a comparative advantage to produce it locally (Ahoyo 1996; Akpokoje et al. 2003). Nevertheless, agricultural growth and food production trends have not kept pace with the 3% annual growth in population, leading to increasing dependence on food imports. Although rice production levels and cultivated areas have tended to increase, there is a remarkable decrease in rice yield over the last 20 years, particularly in Nigeria (FAO

4 2004). A significant proportion of the population in both Nigeria and Benin remains food insecure (IFDC et al. 2000). Over 70% of the population in Nigeria, and 40% of the population in Benin, lives below the poverty line. Evidence suggests that the rate of adoption of rice technologies in West African countries has stagnated. Despite the importance of rice in the daily diet of the farm households and urban families, rice technologies appear not to have generated better yields or improved quality (FAO 2004). The public delivery of technologies has been inefficient, a problem accentuated by elevated costs (Akpokoje et al. 2003). Similar situations have been recognized in a number of different countries, where extension services have been handed out to private providers or the funding of this activity has been diversified (Carney 1998; Berdegué and Marchant 2002; Katz 2002; Rivera and Zijp 2002; Chapman and Tripp 2003; Davidson and Ahmad 2003; McFeeters 2004). The conclusion has been the same with either strategy: although there are economic and social reasons that justify public financing of agricultural extension services, not all services need be publicly provided (Sulaiman and Sadamate 2000). Different terminologies have been used in the literature to describe private participation in extension (financial participation in extension, financial diversification of extension, pluralism in extension, privatization of extension, etc) or different modes of private extension (paid extension, contracting for extension, fee-for-service extension, public funded private extension, etc) (Beynon 1996; Carney 1998; Kidd, Lamers et al. 1998; Sulaiman and Sadamate 2000; van den Ban 2000; Dinar and Keynan 2001; Hanson and Just 2001; Katz 2002; Rivera and Zijp 2002; Chapman and Tripp 2003). In this study, agricultural extension is viewed as a service that can be publicly funded with

5 additional contributions from farmers, but is delivered in a private way. Farmer contributions have two purposes other than supporting the system. First, they increase the incentives for extension delivery. Second, they enhance the accountability of extension agents to farmers. To assess the level of stakeholder contribution, a methodology is developed to estimate willingness to pay (WTP) for information. The rice seed evaluated includes inter-specific crosses (New Rice for Africa, or NERICA) and other improved rice varieties produced by WARDA. The innovative aspect of the research is the use of conjoint analysis to estimate the value of information and its effect on farmers’ perceptions and preferences. The model specified for estimating the farmers’ WTP for information could be calculated using a normal linear probability model (LPM). The LPM model is a simple ordinary least square (OLS) regression that relates the probability of a ranking occurrence to the attributes of the varieties. Due to the discrete nature of the dependent variable the LPM is constrained by its linear definition, heteroskedasticity and non-normality of the disturbances (Gujarati 1995). An ordered probit model is used instead, which is more appropriate when dealing with discrete dependent variables. Findings of this study demonstrate that farmers value information about seed and have a positive WTP for it. As a consequence, there are possibilities for private participation in providing agricultural extension, particularly for new varieties.

6 2. THEORETICAL APPROACH DEMAND FOR SEED VARIETIES In consumer theory, demand functions are derived by considering a model of preference maximizing behaviour coupled with underlying economic constraints. Under normal circumstances, the consumer chooses the good that satisfies better his needs or expectations, or that provides him with a higher utility. The consumer cannot choose a good that he cannot afford because his demand is constrained by his budget. Rice in West Africa is a staple good that is produced, consumed and sold by the household. Utility maximization has been used as the theoretical framework to model rice attribute preferences in several studies in West Africa, where markets are typically incomplete (Adesina and Zinnah 1993; Diagne and Arsene 2002; Dalton 2004). Farmer must select rice with traits that are desirable for on-farm consumption as well as production and sales. Given these conditions, the framework of utility maximization is more complete than that of profit maximization. These studies have also treated varieties as bundles of characteristics or traits, drawing on the Lancaster theory of consumer choice (1966). The Lancaster theory of consumer choice proposes that consumers choose attributes of goods rather than the goods themselves. In other words, utility is provided by the attributes a good possesses instead of the good per se. Individual preferences determine the relative weights given to the various attributes when choices are made. The relationship between goods and attributes and as well among attributes is objective and determined by the consumption technology.

7 In this study, the consumption technology is the seed variety, or genotype that is consumed by the farmer as a production input. Building on the Lancaster theoretic framework, conjoint analysis (CA) methodology allows a utility function to be modeled as the sum of utilities that product attributes generate to a consumer. The methodology is a survey-based, stated preference technique for valuation that relies on individual backgrounds to estimate the marginal contribution of a specific product characteristic to overall preference ratings (Hamath et al. 1997). The underlying premise is that consumers evaluate a product by adding utilities from each attribute (Baidu-Forson1997a). A number of studies have employed CA to evaluate farmers’ preferences in crops and other technical innovations in West Africa, particularly where revealed preference approaches are not feasible because of incomplete markets or regulated pricing that constrains price differentials among varieties (Adesina and Zinnah 1993; Baidu-Forson, Ntare et al. 1997a; Baidu-Forson, Waliyar et al. 1997b; Ndjeunga and Nelson 2005). Similarly, prices for rice varieties included in this study were not observable. CA was used to decompose the structure of farmers’ preferences given his/her overall evaluations of a set of rice seed alternatives that were pre-specified in terms of level of different attributes (Green and Srinivasan 1978). Farmers prefer one variety based on its attributes and how they perceive them. Choices among alternatives depend on the attributes of the variety of rice seed, the characteristics of farmers, including whether or not they sell it, and the level of information they have about the variety. DEMAND FOR SEED-RELATED INFORMATION Literature on willingness to pay (WTP) for information provided by extension services is scant. Dinar (Dinar) estimated demand for and supply of extension visits in

8 Israel, deriving WTP for these services from the per hectare value added by subtracting the production cost (including extension) from the revenue. This approach provides a factual estimate of the extension price, but if the value added is not due exclusively to an increase in the number of extension visits, the extension activity might be overvalued. Where extension services are not strong and structured, as they are in Israel, the validity of the approach is questionable. Application of the method requires detailed information not only about farm production but also about extension performance. Holloway and Ehui (2001) focused on the cash income constraint in a model of consumer demand, deriving the amount of income that a dairy producer in Ethiopia is willing to forgo to obtain an additional unit of service rendered. The decision the dairy producer has to take is whether or not to participate in the market. Market participation depends on the increased price he can obtain for a better product. The WTP is estimated only for individual extension visits. The suitability of this approach depends on the reliability of market prices and the extent to which production is commercially oriented. Sulaiman and Sadamate (2000) estimated the WTP for extension services for Indian conditions. Farmers were asked directly about their WTP for extension services and valid agricultural information. The authors used a linear discriminant function to predict farmers’ behaviour and evaluate the determinants of their willingness or unwillingness to pay. One caveat of this approach, as in any contingent valuation exercise, is that the WTP is a hypothetical value. Hypothetical values cannot always be correlated with capability or readiness to pay. When farmers are familiar with fee based extension services and can give a plausible value, as was the case in the Indian study, the methodology is appropriate. By

9 contrast, financial participation for extension is rare in West Africa. In this study, the WTP value for information had to be estimated indirectly. Conjoint analysis, most commonly applied in market research, provides a powerful theoretical basis for doing so. By treating information about the seed variety as a product attribute, it is possible not only to evaluate how preferences change but also to estimate the marginal value of the information itself. The product delivered by extension services is information about new technologies, which are improved rice varieties. The information variables account for extension activities conducted during the introduction of the new varieties.

3. RESEARCH DESIGN SITE SELECTION The sites studied were originally selected by the WARDA project based on ecological criteria used to classify the production conditions of upland rice and test varieties. While there is a preference for lowland cultivation in Nigeria as well as Benin, upland production has great potential in both countries. Villages in three states of Nigeria (Ogun, Kogi and Ebonyi) and two sub-prefectures in Benin (Dassa and Glazoue) are included. Each site has unique ecological features, social and economic conditions, summarized in Table 1 (WARDA 1999).

10 Table 1--Ecological characterization of sites NIGERIA Variables

Ogun State

BENIN

Kogi State

Ebonyi State

Dassa /Glazoue

Geology

Igneous / metamorphic rocks

Basement complex rocks / alluvial materials

Sedimentary rocks

Gneiss / granites

Relief

Lowland

Undulated plateau and flood plains

Lowland

Mixture of plateau and valley

Ecology

Rainforest

Southern guinea savannah

Derived savannah resulting from human activities

Guinea savannah

Annual rainfall (mm)

1400 - 1700

1000 – 1500

1500 - 1800

900 – 1200

Length of rain season

March – Mid November

May- October

April – Earlier November

May – October

Length of growing period

> 270 days

180 – 210

230 – 270

180 – 210

Annual average temperature

23

25

28

23

Soils

Acrisols / ferrisols (upland)

Ferralitic and alluvial soils

Lateritic clays (upland)

Ferrigineus tropical (upland)

Ultisols (valleys)

Hydromorphic (lowland)

Gleysols (valleys Population density (persons/Km2)

139

66

250

50

Ethnic group

Yoruba

Igbira, Igala, Yoruba

Igbo

Idatcha, Mahi, Peuhl

Road density

High

Medium

High

Medium

Market density

Very high both urban and rural

Medium, mainly rural

High, both urban and rural

High

Rural financial systems

Few formal institutions

Few formal institutions

Few formal and informal institutions

Many formal and informal organizations

Source: WARDA (1999)

Dassa and Glazoue in Benin, and Kogi in Nigeria are classified as Guinea savannah. Rainfall patterns in these sites varies from 900 to 1200 mm per year in Benin

11 and from 1000 to 1500 mm per year in Kogi, allowing in both cases a crop-growing period of 180 to 210 days. There is a marked dry period in the sub-prefectures of Benin, which as has been extending in length over the last several years. Drought resistant varieties represent an important alternative for rice producers in this area. Kogi state, located in the central plateau of Nigeria, also has a dry period but the villages under evaluation are close to the Lokoja River, which carries water throughout the year. Lowland rice production is more important than upland production in this state, although upland rice is an alternative for small farmers with limited access to good quality land. Kogi and Benin farmers participating in the project had limited experience in upland rice production. Ogun state in the Southeast is classified as a rainforest area but it also has some savannah areas. The average annual rainfall in this state varies from 1400 to 1700 mm per year, allowing a crop-growing period of around 270 days. Upland and lowland rice production are important and farmers are organized in an association called ORGA (Ogun State Rice Growers Association). Ogun state is relatively rich with high road density, and with a stronger extension office (OGADEP, Ogun State Agricultural Development Project). Farmers have access to village and regional markets and are quite familiar with rice varieties. Ebonyi state, located in the Southwestern part of Nigeria, is a secondary savannah as a result of human activities and high population density. The volume of rainfall is greater than in Ebonyi than in the other sites (1500 to 1800 mm per year) and is distributed from April to early November, with a growing period of 230 to 270 days. Traditionally, Ebonyi farmers are rice producers and they are very familiar with rice

12 varieties. The rice produced in Ebonyi is marketed to states around the region, and the rice production and commercialization chain is therefore more developed. Farmers participating in the project take other roles in the rice production chain in addition to farming, including parboiling, milling and trading. This diversity of functions affects the way they perceive and assess rice quality attributes. DATA The sample of farmers was defined by the WARDA project, since these represent the population with potential willingness to pay for seed and seed-related information. Nonetheless, willingness to pay for seed related information is not necessarily a function of the factors influencing the decision to participate in a project. Participating farmers were identified in a two-step process. First, rice-growing villages were located with the help of the agricultural extension officials. Next, farmers were called to community planning meetings and invited to participate in the project. A total of 272 farmers decided to enroll in the project activities, including 176 from Nigeria (57 in Kogi, 49 in Ogun and 70 in Ebonyi), and 96 from Benin (46 in Dassa and 50 in Glazoue). Three tools generated the primary data. The first tool was a household survey conducted during 2002 – 2003, addressed to each farmer participating in the project. The survey covered general household characteristics, rice production, market information, and farmer perceptions of rice attributes. Survey data provided the explanatory variables for the econometric analysis. The second tool was an on-farm trial. Each of the 272 participating farmers agreed to implement an on-farm trial during two cropping seasons of 2001 and 2002 to test an improved rice variety and compare it to the local variety in use. Farmers were

13 responsible for: a) managing the plot; b) evaluating rice varieties already available in the area and the new varieties available in stock; c) selecting the most preferred rice varieties to be tested in WARDA experimental fields in each site; d) selecting the improved variety to be tested in their own on-farm trial; and e), providing periodic information to the WARDA technicians about the status of the on-farm trials. WARDA provided the seed and the necessary inputs for the on-farm trials free of charge, most importantly fertilizer and extension assistance. Participation in the on-farm trial is one of the two extension variables included in the analysis. The third tool was a field day activity where the contingent ranking method (CR) was used to elicit farmers’ preferences for rice varieties. The exercise was carried out in the WARDA experimental field in each site in 2002 after the first cropping season of the on-farm trial. Only 201 farmers of the 272 participating in the project could attend the field day. The task for farmers was to rank a sample of varieties in order of preference, with and without information about the seed. While the ranking in itself is the dependent variable in the analysis, the information obtained in the field day experience represents the second extension variable. The ranking system is considered to have estimation efficiency that is superior to the binary preference system (Mackenzie 1993). Unlike rating systems, ranking levels are comparable across respondents. In the study sites, the ability of farmers to order or rank varieties and multiple attributes is well developed, although the probability of ranking inconsistency increases when the sample size increases (Baidu-Forson et al. 1997a). For this reason, the number of varieties evaluated was limited to 5 in Kogi, 6 in Ogun, 7 in Ebonyi and 8 in Dassa and Glazoue.

14 Two scenarios were implemented. In the scenario without information, the rice seed samples were presented without identification to the farmers. Their judged the seed samples solely on the characteristics they could see or deduce from the physical appearance of the seed. Observable seed characteristics can provide some genetic information—depending on the farmer’s prior knowledge. For example, experienced farmers might recognize seed of a well-known local variety and the variety that they were evaluating in the on-farm trials. In the scenario with information, seed samples were labeled with their local and WARDA names, and information about variety characteristics was provided orally and on paper, as shown in Table 2. Farmers were guided around the experimental field where the varieties were at the final stage of development and could visually compare the performance, similarities and differences among the varieties.

15 Table 2--Varieties evaluated by farmers and information provided at field day Characteristics Local 1: Atila Local 2: China Local 3: Ofada Local 4: China Local 5: Gambiaka Improved 1: ITA 150 Improved 2: WAB 450 P31 Improved 3: WAB 450 P38 Improved 4: ITA 212 Improved 5: ITA 128 Improved 6: ITA 306 Improved 7: ITA 257 Improved 8: WAB 189 Improved 9: WAB 224

Potential yield (mt/ha)

Days to maturity

Number of tillers

Plant height (cm)

1.0 – 2.0

120 – 130

7 – 10

120 – 140

1.0 – 2.0

90 – 100

7–9

80 – 100

1.5 – 2.5

120 – 130

9 – 12

130 – 150

1.0 – 2.0

90 – 100

7–9

80 – 100

2.0 – 3.0

140 – 150

10 –20

110 – 130

2.5 – 3.0

100 – 105

6

120 – 140

2.5 – 3.0

95 – 105

10

100 – 115

2.5 – 3.0

105 – 110

8

110 – 120

3.5 – 7.5

120 – 125

15 – 18

100 – 115

2.0 – 3.0

120

12

130

4.0 – 6.0

125 – 130

15 – 30

100 – 115

1.5 – 3.0

100

10

100

2.0 – 2.5

105 – 110

6–9

110 – 130

2.0 – 2.5

120 – 125

6 –9

110 – 120

Grain size

Evaluated in

medium, bold short, slender

Kogi

medium, bold short, bold medium

Ogun Ebonyi

long, bold medium, slender medium, slender medium

Kogi. Ogun, Ebonyi, Benin Kogi. Ogun, Ebonyi, Benin Kogi. Ogun, Ebonyi, Benin Benin

medium, bold long, slender medium, bold medium, bold long, slender

Ebonyi, Benin

Kogi

Benin

Benin Kogi. Ebonyi, Benin Ogun, Ebonyi

Source: WARDA, Nigeria

The contingent valuation method (CV) was used to elicit farmers’ willingness to pay for seed of improved varieties in both scenarios, since seed prices were not available in each case. Not all of the improved varieties have been officially released, although several have been used by farmers for some years. Other improved varieties, such as the NERICA varieties, are completely new to farmers. Questions were open-ended because farmers surveyed are accustomed to buying rice seed in the market, even though they were exposed to new varieties in the field day activity.

Ogun

16 Rice Varieties The varieties ranked by farmers correspond to both lowland and upland rice production. They included the most preferred local variety and improved varieties with ITA and WAB prefixes. ITA varieties are relatively old varieties produced by the International Institute of Tropical Agriculture (NCRI 2000), however not all of them were officially released. WAB varieties are entirely new varieties bred at WARDA and adapted to upland conditions. Among them, WAB 450 P31 and WAB 450 P38 are the only NERICA (New Rice for Africa) varieties (see Table 2). NERICA varieties are the result of a novel, inter-specific cross between rice originating in Asia (O. sativa) and rice originating in Africa (O. glaberrima) with the purpose of cross-introgressing traits between these species. These crosses are characterized by their high, stable yield (from O.sativa) and the ability to resist or tolerate adverse environmental and biological conditions (from O. glaberrima). NERICA varieties have short stalks, mature early, and compete well with weeds. In addition, they have higher protein content (WARDA 2005). NERICA thus represents a technology with the potential to increase farmers’ welfare. WAB 450 P38 has been recently officially released in Nigeria as NERICA 1, though this release does not correspond to the period of our analysis.

17 4. ECONOMETRIC MODEL SPECIFICATION The model states that when a farmer selects rice seed among alternative options, he or she actually compares single attributes (ri) of the variety (R). The selection process is influenced by the characteristics (fi) of the farmer (F) and information about the seed (Y). As in a random utility approach, the variety selected is assumed to provide the farmer the highest level of utility. The utility function is therefore defined as: U = U [ R (r1...rn), F (f1...fm), Y ] + U [Z] .

(1)

The farmer selects the best of the options according to the level of related information, Y. Rice choices are made independent of the consumption of the other goods, Z. Since utility is not directly observable, an indirect utility function (V) is specified. In theory, seed price is an argument in the indirect utility function. Price signals are poor and markets incomplete for rice seed alternatives in the study sites. Instead, farmers were asked to express their WTP for the rice variety of their choice (R). The indirect utility function is then expressed as V = U [ R, F, Y, WTP]

(2)

Plausibly, the farmers’ best choice could be a variety of seed that is not available. Then the farmer would select the next accessible variety with the attributes that fit his requirements, as his second best choice that attains a lower level of utility. Utility is not amenable to direct estimation. The utility level generated by each variety can be indirectly expressed by a preference ranking, implying that: R1 [ r1,…rn, WTP1] ≥ R2 [ r1,…rn,, WTP2] ≥ … ≥ Rn [ r1,…rn, WTPn,] The indirect utility function can be linearly specified as:

(3)

18 V = a + br1r1 + br2r2 +... + bf1f1 + bf2f2 +... + by1y1 + by2y2+ bwtpWTP

(4)

The marginal utility (also called marginal effect in the probit models) of an independent variable represents the relative change in the value of the independent variable needed to change utility by one unit (keeping the other variables constant). These marginal utilities can be derived from the coefficients of the independent variables. The ratio of marginal utilities of two attributes is the marginal rate of substitution (MRS). The MRS expresses the amount of an attribute the consumer is willing to give up in order to obtain one more unit of another attribute at the same level of utility: MRSr1r2 = MUr1 / MUr2. When one of the attributes is price, the equation is the inversed compensated demand for the attribute. The ratio of the marginal utility of a given attribute or extension variable to the marginal utility of the WTP is the inverse compensated demand for the attribute: WTPi = MUi / MUwtp

(5)

Logit or probit models could be used to accomplish the model estimation, and are appropriate in dealing with discrete dependent variables. Both models adjust better to a probability curve by using a logistic and a normal distribution function to estimate the probability of a certain ranking occurrence. They also have the advantage that the utility function itself remains linear in the parameters. The difference between the output of an ordered probit and logit is minimal (Gujarati 1995; Greene 2003), although probit is preferred when the order value of the dependent variable is important. The model is built around a latent regression in the same manner as a binomial probit model, where the respondents have their own intensity of opinion that depends on certain measurable factors x and certain unobservable factors ε: K* = x’β + ε

(6)

19 K* is unobserved, but what it is observed are threshold values of K: K=0

if K* < 0

K=1

if 0 < K* < μ1

K=2

if μ1 < K* < μ2

… K=

J

if μJ-1 ≤ K*

Then, for polytomous responses with ranking outcomes Ki = 1, 2, 3, ..., i, the probability p of observing Ki is: Prob (K=0) = p1 = Φ (–x’β)

(7)

Prob (K=1) = p2 = Φ (μ1 – x’β) – Φ (– x’β) Prob (K=2) = p3 = Φ (μ2 – x’β) – Φ (μ1 – x’β) … Prob (K=i) = pi*1 = 1 – Φ (μi-1 –x’β) Where Φ represents a normal cumulative distribution function, x a vector of independent variables, and the μ’s are unknown parameters to be estimated with β. Equation (4) was estimated with a multinomial ordered probit model specified as the probability of ranking K [Pr (K)], where: K=

α + δ R + ϕ F + γ Y * + φ WTP + ε.

(8)

The vector of parameter estimates δ, ϕ, γ and φ are embedded in the coefficient vector β’ (equation 6), R accounts for variety attributes, F for farmer characteristics, Y for seedrelated information from extension activities, WTP for willingness to pay for the rice seed, and α represents the conventional intercept plus the additional intercept dummies or

20 threshold variables (Hamath et al. 1997). In addition, in order to have positive probabilities: 0 < μ1 < μ2 < … < μj-2 (Greene 2003). EXPLANATORY VARIABLES As compared to Asian or American rice, there are no pre-established market standards for rice grain or agronomic quality properties in Nigeria or Benin. Farmers surveyed identified 5 attributes they consider when selecting varieties of rice seed: potential yield, days to maturity, tillering capacity, plant height, and size of the grain. Each of these is a fundamental agronomic characteristic of importance to farmers who consume seed as a production input, although grain size is also an attribute sought by those who consume the product. Grain size coded as an effect dummy (best option = 1; indifferent = 0; worst option= −1). The best option corresponds to rice with long, bold grains, while the worst option is represented by small, slender grains. Attributes omitted from the analysis were grain color, height uniformity and milling capacity. Grain color, although important, was not included because of the sizable difference between the color of the local varieties that were parboiled and milled locally and the color of the improved varieties that were parboiled and milled under experimental conditions. Farmers listed height uniformity only because the latest materials were released unfinished and still demonstrated low field stability and height disparities. Milling attributes reflect a more complex problem concerning processing methods and grain quality. Technical information about the levels of related agronomic characters per variety was insufficient. In similar studies, an orthogonal array was developed to generate product (seed variety) profiles that consumers then evaluated (Mackenzie 1993; Baidu-Forson et

21 al.1997a; Baidu-Forson et al.1997b). As compared to hypothetical varieties in the research development pipeline, we are dealing with finished varieties. Finished varieties have attribute levels that vary under farmer conditions depending on interactions of the genotype with the environment. Here, expected (in a statistical sense) levels of variety attributes are drawn from multi-year, multi-locational trial data that account statistically for variation in management and growing conditions (Table 2). Farmer characteristics (F) include: age, gender, years in school, marital status, experience in rice production, the size of the household, income from rice production, and whether or not the farmer sells rice (summarized in Table 3). Age, gender and marital status characterize rice producers, and affect variety preferences. Years of school and experience in rice production express farmer knowledge. Farmer knowledge could affect preferences for rice seed and ability to assimilate seed-related information effectively. Income earned from rice sales reflects the extent of commercial orientation and the economic situation of the farmers. In West Africa, household size is also an indicator of better economic status.

22 Table 3--Descriptive statistics for farmers surveyed Farmer characteristic Age (years)

Nigeria Kogi Mean SD

Ogun Mean SD

Benin Dassa Glazoue Mean SD Mean SD

Ebonyi Mean SD

51.1

14.9

38.9

9.6

40.9

8.2

47.5

14.4

41.7

10.2

Female (1=yes, else 0)

0.2

-

0.1

-

0.1

-

0.4

-

0.3

-

Married (1=yes, else 0)

0.9

-

0.8

-

0.9

-

0.9

-

0.9

-

Average education (years)

5.6

4.3

8.5

5.4

7.7

4.3

3.3

4.7

3.0

3.6

18.0

15.4

10.3

6.0

17.6

8.4

11.3

7.3

9.6

7.3

9.7

3.5

11.2

7.3

13.2

8.4

9.7

5.2

9.8

5.6

Rice income (US$)

244.7

287.9

368.0

788.6

519.9

882.9

155.3

289.4

90.57

150.5

Total income (US$)

910.1 1001.6 1191.4 1729.5 2169.4 3576.6

374.3

376.3

596.2

733.9

Rice experience (years) Household size

Percent selling rice

0.6

-

0.9

-

0.9

-

0,7

-

0.5

-

WTP for seed (US$/Kg)

0.31

0.18

0.35

0.29

0.17

0.14

0.19

0.20

0.30

0.21

Farm price of rice (US$/Kg)

0.27

0.12

0.20

0.05

0.27

0.12

0.17

0.09

0.17

0.05

Information provided through extension activities (Y) was measured by two dummy variables. “On-farm trial experience” took the value of 1 when the farmer evaluated the variety he or she grew in the on-farm trial, and zero otherwise. After observing the variety on the farm, farmers became familiar with its attributes and their judgment was affected by the information acquired with this activity. The value of “field day information” was 1 when the information in Table 2 was provided at the field day activity, and zero if the farmer was presented only with unidentified seed samples.

5. RESULTS DESCRIPTIVE STATISTICS Farmer characteristics, WTP for rice seed and the farm price of rice are summarized in Table 3, by site. Farmers are considerably older in Dassa and Ogun than

23 in other sites. Women participate more in rice production in Benin, where it is more likely to be a task for which they are responsible. In Nigeria, men’s agricultural work is often related to crops like yam or cassava while women undertake production of vegetables and cash crops to meet their own consumption needs, including tomatoes, groundnuts, and pepper. In Nigeria either the husband or wife manages rice plots. Women are often in charge of smaller rice plots for family consumption needs, while men manage the larger rice plots for commercial sales. On the other hand, in Benin, rice production is most likely a task for the wife that explains the difference in gender composition of the sample across countries. Households in the sites surveyed are often organized around a male household head with several wives. Polygamy is socially accepted and it also provides additional household labor (White 2002). This fact is especially important for rice production since labor is the most expensive variable cost. Female household heads are mainly widows. The average household size of a rice producing family in either country is above 10 members. The household size is particularly large in Ebonyi, which also shows the highest labor cost among the sites. Rice production is more extensive in Nigeria than in Benin and the production chain is more developed, especially in Ebonyi state. A higher percent of farmers selling rice was reported in the Nigerian sites, which is also linked to a relatively higher quality of the final product sold. Except for Kogi, in the other two states in Nigeria, farmers sell processed rice (parboiled and milled). Survey data also indicate that farmers in the Nigerian states of Ogun and Ebonyi generate a larger percentage of their income from rice production, which is probably due to a greater experience cropping rice in these two

24 states relative to the other sites. Total income is considerably higher in Nigeria than in Benin. Table 3 also shows that, except for Ebonyi, WTP for the improved seed samples is higher than the actual farm gate price of the local rice. One explanation for this finding could be that Ebonyi is the site with the greatest degree of specialization in rice production and marketing. Farmers in Ebonyi are involved in more than one step of the production chain and almost all of them sell parboiled-milled rice. The milling facilities are organized in a milling company and have developed essential quality standards based on the characteristics of the most important rice types produced in the state. A large percentage of the population in the state is involved in rice production, generating an excess supply that is traded in neighboring states. An improved rice variety must demonstrate similar or better quality characteristics to compete with the local varieties. ORDERED PROBIT RESULTS Complete and valid information was gathered for 201 farmers (30 from Kogi, 38 from Ogun, 49 from Ebonyi, 40 from Dassa, and 44 from Glazoue), generating for each case scenario a total of 150 usable rankings in Kogi (30 observations * 5 varieties evaluated), 228 in Ogun (38 * 6), 343 in Ebonyi (49 * 7), 320 in Dassa (40 * 8), and 352 in Glazoue (44 * 8). The model was estimated for each site and for all respondents. Each site had a different number of varieties evaluated. For the model considering all respondents, rankings were adjusted to include only the four varieties evaluated in every site: the local one, ITA 150, WAB 450 P31 and WAB 450 P38. The transitivity property of the

25 rankings enables recoding. Given the reduction in the number of variety attributes, samples pooled at country levels revealed no additional information and are not reported. A number of formal approaches can be used for testing for model specification, the most common of which are the Lagrange multiplier (LM) test, the likelihood ratio (LR) test, and the Wald test. In each of these approaches, two models are formulated, a restricted model and an unrestricted model. The likelihood ratio test is preferred when a direct comparison is made between nested hypotheses that can be treated with parameter restrictions (Ramanathan 1998). The test-statistic is defined by the ratio of the maximum value of the likelihood function under the null hypothesis (the restricted model) divided by its maximum value when no restrictions are imposed. The test statistics is asymptotically distributed as chi-squared, with degrees of freedom equal to the difference in estimated parameters (Mazzanti 2001). Table 4 summarizes the results of six separate probit models representing each site and the pooled sample. The pooled model was compared to an unrestricted model that allows for site-specific effects. The null hypothesis that separate site effects are equal to zero was rejected with a Swait-Louviere log-likelihood ratio test at the 1% significance level. In other words, data do not support the hypothesis that the underlying population parameters are similar across sites.

26 Table 4--Ordered probit results All Respondents Variables Constant Variable Attributes Potential yield Days to maturity Tillering Height plant Grain size Producer Characteristics Farmer age Farmer gender Married (1=yes) Schooling years Rice experience Household size Rice income Sell rice (1=yes) Extension Practices On-farm trial Field day

Kogi

Ogun

Ebonyi

Dassa

Glazoue

Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio Coeff. t-ratio -2.13 -4.13 *** 8.29 5.77 *** 43.28 7.44 *** 0.28 0.29 -3.44 -4.33 *** -5.74 -6.40 *** 0.88 0.02 -0.19 -0.01 0.52

7.46 *** -0.51 -1.29 3.95 *** -0.09 -4.24 *** -7.21 *** -0.27 -3.14 *** -0.86 0.05 2.45 *** *** 7.60 0.01 0.05

-9.73 -0.22 -1.12 0.13 -1.81

-6.25 *** 0.64 2.39 ** 0.21 3.79 *** 0.33 7.67 *** -7.29 *** -0.06 -4.00 *** 0.00 -0.14 0.00 -0.19 *** *** -6.97 -0.03 -0.82 -0.05 -2.73 -0.04 -3.04 *** 5.02 *** 0.05 4.00 *** 0.02 3.11 *** 0.05 7.37 *** -3.59 *** 0.32 4.01 *** 0.13 1.84 * -0.06 -0.95

0.00 0.08 -0.11 0.02 0.01 0.01 0.00 0.06

-1.15 0.00 0.40 0.95 0.15 0.42 -1.04 -0.21 -0.85 2.35 ** 0.02 1.23 1.59 0.01 0.95 * 1.76 -0.00 -0.15 2.74 *** 0.00 0.60 0.88 -0.16 -0.39

0.00 -0.10 -0.08 -0.02 -0.00 -0.01 -0.00 0.18

-0.11 -0.64 -0.23 -1.09 -0.44 -0.37 -0.24 1.52

0.31 4.10 *** 0.03 0.15 -0.08 -1.42 -0.03 -0.20 1.03 3.22 ***

1.43 4.34 ***

-0.17 0.01 1.54 -0.09 0.07 0.55 -0.60 -0.14 -0.71 2.64 *** 0.00 0.01 0.23 -0.00 -0.44 1.58 0.01 1.00 1.45 0.00 2.04 ** -0.98 -0.34 -2.99 ***

0.15 1.21 -0.11 -1.25 4.12 12.19 ***

0.10 0.81 -0.05 -0.47

-0.01 0.31 -0.44 -0.03 -0.01 0.03 0.00 -0.23

-0.98 2.44 *** -1.55 -2.07 ** -2.38 ** 3.01 *** 0.68 -2.15 **

0.48 4.02 *** -0.07 -0.85

5.76 18.16 ***

3.81 14.70 ***

0.68 19.26 *** 0.68 8.65 *** 0.77 9.42 *** 0.38 8.09 *** 0.18 4.31 *** γ1 1.54 31.37 *** 1.33 13.11 *** 1.51 14.62 *** 0.77 12.52 *** 0.38 6.44 *** γ2 2.17 16.93 *** 2.24 19.90 *** 1.20 16.70 *** 0.63 8.71 *** γ3 3.03 23.64 *** 1.70 20.54 *** 0.98 11.41 *** γ4 2.44 24.01 *** 1.47 14.97 *** γ5 2.15 19.01 *** γ6 Sample size 1608 300 456 686 640 Log Likelihood 1774.57 value Swait-Louviere test19.75*** statistic * Note: denotes significances at the 10% level, ** at the 5% level, and *** at the 1% level For the Swait-Louviere test, the chi-squared critical value at 1% with 4 degrees of freedom is 13.2767.

0.34 6.82 *** 0.67 10.30 *** 1.02 13.35 *** 1.42 17.07 *** 1.89 21.24 *** 2.46 25.14 *** 704

WTP rice variety

2.07 16.57 ***

0.39 2.08 ** -0.00 -0.01

0.00 -0.02 -0.17 0.03 0.00 0.01 0.00 -0.21

Threshold variables

The signs of the regression coefficients indicate the direction of effect of each variety attribute, seed-related information, and farmer characteristics. The ranks ranged in value from 0 to 3 for the pooled model, 4 for Kogi, 5 for Ogun, 6 for Ebonyi, or 7 for

27 Dassa and Glazoue. Since a higher rank indicates a stronger preference for the rice variety, utility increases with a positive sign and decreases with a negative sign. The magnitude of the coefficients is more complex to interpret because the probit function uses a normal distribution to adjust the probability curve for the different utility thresholds (rankings). Hamath et al. (1997) explain that since the utility of preference is an ordinal measure, the relative magnitude of the coefficients is more important than their absolute magnitude. The sign and significance of the variables allow inferences to be drawn about: a) the importance of variety attributes to farmers, b) the effects of farmer characteristics on the way they rank varieties; and c) the influence of information in the ranking process. Farmers in each site stated preferences for a distinct set of variety attributes, as expressed in the divergent combinations of signs and significance of coefficients. The model estimated for all respondents suggests that, in general, farmers prefer varieties with higher potential yield. Still, while a strong preference for yield potential is observable in Ogun, Ebonyi, Dassa and Glazoue, the same cannot be said for Kogi. Yield is typically the primary advantage of improved varieties over local ones, although farmers are not always looking for the single variety with the greatest yield potential but for greater stability, as well as other characteristics. Furthermore, farmers do not generally attain yield potential on their farms, due to a combination of environmental and management factors. Though surprising, these findings coincide with those obtained by Dalton (2004) in his hedonic analysis of the economic value of rice traits. Days-to-maturity is a significant attribute for farmers in the Nigerian site regressions as well as the pooled regression. In Nigeria, farmers prefer early-maturing

28 varieties. In Ebonyi, China is a short cycle variety that allows farmers to enter the market early in the season, but its yield is still modest and consumers pay a lower market price for its short grain. The indifference of farmers in Dassa and Glazoue to early maturity could be explained by the varieties used as local controls in these sites and their grain size characteristics. In Dassa and Glazoue, Gambiaka is a variety with an extra-long cycle and low drought tolerance, but a good-sized grain that consumers appreciate. The coefficient on grain size has the expected positive sign in Kogi, Ebonyi, and Dassa, as well as the pooled model—demonstrating farmer preference for long-bold grains. Grain size is of statistical significance in all sites except Kogi and specially Glazoue, where the local, medium-sized grain is highly demanded by farmers. The tillering capacity of the plant is an important attribute to rice farmers in all of the sites and in the pooled model except Ebonyi,. Nonetheless, the highest tillering plants are not the most preferred. For some farmers high tillering capacity is an indicator of higher yield, although a plant that produces more tillers has less vigor to produce bold grains (Myers McClung 2002). Plant height is a statistically significant factor in variety rank for farmers in the all sites except for the pooled sample. Farmers state a clear preference for taller varieties. In the sites, where only the panicle is cut at harvest, short varieties demand more effort for an activity that already demands a lot of labor (Jones and Wopereis-Pura). The set of farmer characteristics that influence how they rank varieties also depends on the site. The extent of formal education is significant only in Ebonyi and Glazoue sites and in the pooled model. Similar results were obtained in India, where analysis of demand for agricultural services revealed a higher WTP among better

29 educated farmers (Sulaiman and Sadamate 2000; Katz 2002). The sample included relatively few cases of single-headed households, and marital status had no effect on variety rank. The gender of the farmer is a statistically significant determinant only in Glazoue, where women participate intensively in rice production. Participation in the market as a rice seller mattered in the Benin sites. The effect of selling rice is negative, indicating that to keep rice for home consumption gives farmers a higher utility. Income generated from rice production is a determining farmer characteristic only in Dassa and Glazoue, which are the sites with the lowest total income. The information provided to farmers during the field day has no significant effect on their variety ranking in any site. Moreover, the coefficient for this variable is negative in all cases, suggesting that the information provided during the field day activity did not increase the utility level of farmers. On the other hand, the second information variable, reflecting farmers’ experience in on-farm trials, is statistically significant in Ogun, Glazoue and the pooled sample. Across sites, the willingness to pay (WTP) for seed is a statistically significant and positive factor in a variety’s rank. Consistent with consumer theory, these results confirm that the higher the preference for the variety and its attributes, the higher the willingness to pay for seed. The threshold variables can be interpreted as the numerical linkages between the utility function of respondents and the preference rankings of rice varieties. As shown in Table 4 the threshold coefficients obtained are consecutively higher, positive and significant, which implies that the model specification is correct (Hamath, Faminov et al. 1997; Greene 2003).

30 WTP estimation In the context of this study, information is understood as a message including data, ideas, or facts that are new and valuable to the person that obtains them. Knowledge is the ability to use information, and the development of this ability involves a learning process. The ratio of a given attribute or extension variable coefficient to the WTP coefficient is the inverse compensated demand for the attribute (Mackenzie 1993) or the WTP value for each attribute. These values are presented in Table 5. Table 5--WTP for information variables WTP (US$) On-farm experience Field day experience

All Respondents

Nigeria

Benin

Kogi

Ogun

Ebonyi

Dassa

Glazoue

0.149*

0.032

0.273*

0.036

0.018

0.127*

-0.040

-0.026

-0.001

-0.026

-0.008

-0.019

(*) Significant in the probit analysis

Positive as well as negative WTP values were discovered. The negative values could be interpreted as a lack of farmer interest in field days or on-farm trials unless there is compensation. The on-farm trial experiment included the seed, fertilizer and extension service and the opportunity to observe the variety closely throughout a cropping period. In comparison, the field day activity is a single day of exposure to rice varieties where farmers are told about some characteristics of improved varieties and can visually compare their field performance to that of the local check only at that point in time. In the pooled regression model, WTP is significant and positive for the on-farm experiment (US$ 0.149), but the field day activity does not influence variety rank or increase the utility of farmers. Farmers in Ogun state are willing to pay more for on-farm trials than farmers in other sites. Ogun farmers are willing to pay US$ 0.273 for the whole on-farm trial package, as compared to US$ 0.127 in Glazoue. Farmers from Dassa, Kogi and

31 Ebonyi show no interest in paying for the on-farm trial activity. In Dassa, Ebonyi and Kogi the improved varieties tested appear not to have represented a real advantage compared to the local variety included in the trial, or to alternative activities. Rice production is a labor demanding activity and many farmers opt either for other crops like cassava or yam, or for other non-agricultural activities like commerce. The coefficients for the field day activity suggest a negative WTP for this activity, although they are not statistically significant. Clearly, the information provided to farmers was not enough in quantity or quality to affect their preferences. Nor do improved varieties show a strong technological advantage.

6. CONCLUSION An ordered probit regression was specified to estimate the indirect utility function of rice farmers in selected project sites of Benin and Nigeria, where the ratio of an extension activity coefficient to the WTP coefficient is the inverse compensated demand for the activity. Farmers were asked to rank a sample of rice varieties with and without seed-related information. The rankings represent the relative level of farmer utility. The model was built around a latent regression where the respondents have their own intensity of opinion. Explanatory factors included variety attributes, farmer characteristics, and extension variables in addition to a price, measured as the willingness to pay for the rice seed. The willingness to pay for rice seed was elicited through an open contingent valuation approach. The sample of farmers evaluated in this study included rice farmers who decided to participate in the project, enabling us to generalize only over a comparable population rather than the entire rice-farming population.

32 The variety attributes evaluated in this study (potential yield, tillering capacity, size of the grain, height of the plant and days to maturity) are those that have been addressed specifically by plant breeders. The probit coefficients indicate that these attributes are important determinants of the seed preferences stated by farmers, although in many cases, the sign of coefficient seems to contradict that sought by rice researchers. This is most evident for potential yield and plant height. Finding similar patterns with the application of a hedonic analysis, Dalton (2004) concluded that although yield has served as a defining factor for promoting a new variety for official release, high yield was not a significant determinant of farmer WTP for new rice varieties in West Africa. Most farmers do not obtain yield potential under their growing conditions, and yield stability, as well as other attributes, may also be of concern to them. Plant height is an important indicator for farmers and conventionally, rice breeders tend to select stronger and shorter rice plants to avoid lodging from high winds. Plant height was a Green Revolution breeding priority because it indirectly affected yield performance. Depending on the method they use to harvest and other uses of rice straw, farmers might have a preference for rice plants with medium stature. In the study sites, farmers cut rice panicles and leave the plant on the ground, so that short plants are inconvenient. Tall plants are easier for harvesting, since farmers do not have to bend and the process can be accomplished more effectively. Furthermore, rice production is already a fundamentally labor-intensive activity that competes for inputs with other crops such as yam and cassava, which demand less labor and have a higher transformation and storage capacity. It is understandable that farmers decide to crop a rice variety that demand less effort.

33 In general, differences across the regions for preferred attributes express the high degree of physical, social, and economic heterogeneity among the sites. This heterogeneity can help to explain the mixed results obtained with respect to the role of farmer characteristics in predicting rice variety preferences. Education was a significant factor in several sites. In theory, educated farmers can assimilate information and convert it into knowledge more effectively than farmers with limited education, although this is not always the case. The extent of farming experience, expressed as the number of years growing rice, generates relevant knowledge. Surprisingly, this variable was significant only in Ebonyi, Glazoue and the pooled model. The importance of the introduced technology, the content of the activities, and the experience of the farmers regarding specific topics of discussion can influence their WTP for extension activities. Extension activities are usually time-consuming, but if the content is new and valuable to farmers the incentives to participate may be sufficient without a direct financial compensation. In this case, the content is information about improved rice varieties, some of which are already known to farmers. This research suggests that rice farmers participating in the project have a positive WTP value for information but mainly for hands-on experience (on-farm trials). Since all farmers evaluated are project participants, there is probably an upward bias in WTP, if any, relative to the general rice-growing population. Willingness to participate in the project does not necessarily translate into willingness to pay for extension, however. Overall, findings concerning the willingness to pay for seed-related information among participants support the hypothesis that extension activity has potentially positive marginal benefits. The level of marginal benefits may still be too low to encourage the

34 participation of the private sector in extension. Even when farmers are willing to pay for a better service, their contribution is limited by their incomes. Group contributions could be a much feasible alternative to increase incentives for private participation. If incentives are to be increased, the technology offered with the extension service has to create a technological advantage that is sufficient to make farmers “willing to pay”—or even better—“able to pay”. Moreover, the fact that WTP is positive does not necessarily mean that private delivery of technologies is optimal. The size of WTP may be too small to attain the socially optimal level of delivery. Paid extension involves not only the interests of farmers, but also those of extension agents and the government. Findings indicate only that extending seed-related information in a private way is feasible. Determining the optimal level of delivery is beyond the scope of this paper, and more research is needed to explore the appropriate institutional arrangement that would suit the specific conditions in the target areas. Outcomes from experiences in other countries can help to guide the process.

35 REFERENCES

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36 Diagne, A. and K. K. Arsene (2002). The demand for rice varietal characteristics technologies. Green revolution in Asia and its transferability to Sub-Saharan Africa, December 8 - 10, Tokyo, Japan. Dinar, A. (1996). "Extension commercialization: How much to charge for extension services." American Journal of Agricultural Economics 78(February 1996): 1 12. Dinar, A. and G. Keynan (2001). "Economic of paid extension: lessons from experience in Nicaragua." American Journal of Agricultural Economics 83(3): 769-776. FAO (2004). Agricultural Data, FAOSAT. 2004. Green, P. E. and V. Srinivasan (1978). "Conjoint analysis in consumer research: Issues and outlook." Journal of Consumer Research 5(September 1978): 103 - 123. Greene, W. H. (2003). Econometric Analysis. Upper Sadler River, New Jersey 07458, Prentice Hall. Gujarati, D. (1995). Basic Econometrics, McGraw-Hill Book Co. Hamath, A. S., M. D. Faminov, et al. (1997). "Estimating the values of cattle characteristics using an ordered probit model." American Journal of Agricultural Economics 79: 463-476. Hanson, J. C. and R. E. Just (2001). "The potential for transition to paid extension: some guiding economic principles." American Journal of Agricultural Economics 83(3): 777-784. Holloway, G. J. and S. K. Ehui (2001). "Demand, supply and willingness-to-pay for extension services in an emerging-market setting." American Journal of Agricultural Economics 83(3): 764-768. IFDC, IITA, et al. (2000). The agricultural input market in Nigeria: An assessment and a strategy for development, International Fertilizer Development Center (IFDC), International Institute of Tropical Agriculture (IITA), West African Rice Development Association (WARDA): 1-35. Jones, M. and M. Wopereis-Pura History of NERICA and PVS. Bouaké: 18. Katz, E. (2002). Financial participation in practice: experiences with participation of clients in the financing of extension services. BeraterInnen News: 16 - 23. Kidd, A., J. Lamers, et al. (1998). "Towards pluralism in agricultural extension - A growing challenge to the public and private sectors." Agriculture and Rural Development 5(1): 7 - 10.

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EPTD DISCUSSION PAPERS

LIST OF EPTD DISCUSSION PAPERS 01

Sustainable Agricultural Development Strategies in Fragile Lands, by Sara J. Scherr and Peter B.R. Hazell, June 1994.

02

Confronting the Environmental Consequences of the Green Revolution in Asia, by Prabhu L. Pingali and Mark W. Rosegrant, August 1994.

03

Infrastructure and Technology Constraints to Agricultural Development in the Humid and Subhumid Tropics of Africa, by Dunstan S.C. Spencer, August 1994.

04

Water Markets in Pakistan: Participation and Productivity, by Ruth Meinzen-Dick and Martha Sullins, September 1994.

05

The Impact of Technical Change in Agriculture on Human Fertility: District-level Evidence from India, by Stephen A. Vosti, Julie Witcover, and Michael Lipton, October 1994.

06

Reforming Water Allocation Policy through Markets in Tradable Water Rights: Lessons from Chile, Mexico, and California, by Mark W. Rosegrant and Renato Gazri S, October 1994.

07

Total Factor Productivity and Sources of Long-Term Growth in Indian Agriculture, by Mark W. Rosegrant and Robert E. Evenson, April 1995.

08

Farm-Nonfarm Growth Linkages in Zambia, by Peter B.R. Hazell and Behjat Hoijati, April 1995.

09

Livestock and Deforestation in Central America in the 1980s and 1990s: A Policy Perspective, by David Kaimowitz (Interamerican Institute for Cooperation on Agriculture. June 1995.

10

Effects of the Structural Adjustment Program on Agricultural Production and Resource Use in Egypt, by Peter B.R. Hazell, Nicostrato Perez, Gamal Siam, and Ibrahim Soliman, August 1995.

11

Local Organizations for Natural Resource Management: Lessons from Theoretical and Empirical Literature, by Lise Nordvig Rasmussen and Ruth Meinzen-Dick, August 1995.

EPTD DISCUSSION PAPERS

12

Quality-Equivalent and Cost-Adjusted Measurement of International Competitiveness in Japanese Rice Markets, by Shoichi Ito, Mark W. Rosegrant, and Mercedita C. Agcaoili-Sombilla, August 1995.

13

Role of Inputs, Institutions, and Technical Innovations in Stimulating Growth in Chinese Agriculture, by Shenggen Fan and Philip G. Pardey, September 1995.

14

Investments in African Agricultural Research, by Philip G. Pardey, Johannes Roseboom, and Nienke Beintema, October 1995.

15

Role of Terms of Trade in Indian Agricultural Growth: A National and State Level Analysis, by Peter B.R. Hazell, V.N. Misra, and Behjat Hoijati, December 1995.

16

Policies and Markets for Non-Timber Tree Products, by Peter A. Dewees and Sara J. Scherr, March 1996.

17

Determinants of Farmers’ Indigenous Soil and Water Conservation Investments in India’s Semi-Arid Tropics, by John Pender and John Kerr, August 1996.

18

Summary of a Productive Partnership: The Benefits from U.S. Participation in the CGIAR, by Philip G. Pardey, Julian M. Alston, Jason E. Christian, and Shenggen Fan, October 1996.

19

Crop Genetic Resource Policy: Towards a Research Agenda, by Brian D. Wright, October 1996.

20

Sustainable Development of Rainfed Agriculture in India, by John M. Kerr, November 1996.

21

Impact of Market and Population Pressure on Production, Incomes and Natural Resources in the Dryland Savannas of West Africa: Bioeconomic Modeling at the Village Level, by Bruno Barbier, November 1996.

22

Why Do Projections on China’s Future Food Supply and Demand Differ? by Shenggen Fan and Mercedita Agcaoili-Sombilla, March 1997.

23

Agroecological Aspects of Evaluating Agricultural R&D, by Stanley Wood and Philip G. Pardey, March 1997.

EPTD DISCUSSION PAPERS

24

Population Pressure, Land Tenure, and Tree Resource Management in Uganda, by Frank Place and Keijiro Otsuka, March 1997.

25

Should India Invest More in Less-favored Areas? by Shenggen Fan and Peter Hazell, April 1997.

26

Population Pressure and the Microeconomy of Land Management in Hills and Mountains of Developing Countries, by Scott R. Templeton and Sara J. Scherr, April 1997.

27

Population Land Tenure and Natural Resource Management: The Case of Customary Land Area in Malawi, by Frank Place and Keijiro Otsuka, April 1997.

28

Water Resources Development in Africa: A Review and Synthesis of Issues, Potentials, and Strategies for the Future, by Mark W. Rosegrant and Nicostrato D. Perez, September 1997.

29

Financing Agricultural R&D in Rich Countries: What’s Happening and Why? by Julian M. Alston, Philip G. Pardey, and Vincent H. Smith, September 1997.

30

How Fast Have China’s Agricultural Production and Productivity Really Been Growing? by Shenggen Fan, September 1997.

31

Does Land Tenure Insecurity Discourage Tree Planting? Evolution of Customary Land Tenure and Agroforestry Management in Sumatra, by Keijiro Otsuka, S. Suyanto, and Thomas P. Tomich, December 1997.

32

Natural Resource Management in the Hillsides of Honduras: Bioeconomic Modeling at the Micro-Watershed Level, by Bruno Barbier and Gilles Bergeron, January 1998.

33

Government Spending, Growth, and Poverty: An Analysis of Interlinkages in Rural India, by Shenggen Fan, Peter Hazell, and Sukhadeo Thorat, March 1998. Revised December 1998.

34

Coalitions and the Organization of Multiple-Stakeholder Action: A Case Study of Agricultural Research and Extension in Rajasthan, India, by Ruth Alsop, April 1998.

EPTD DISCUSSION PAPERS

35

Dynamics in the Creation and Depreciation of Knowledge and the Returns to Research, by Julian Alston, Barbara Craig, and Philip Pardey, July, 1998.

36

Educating Agricultural Researchers: A Review of the Role of African Universities, by Nienke M. Beintema, Philip G. Pardey, and Johannes Roseboom, August 1998.

37

The Changing Organizational Basis of African Agricultural Research, by Johannes Roseboom, Philip G. Pardey, and Nienke M. Beintema, November 1998.

38

Research Returns Redux: A Meta-Analysis of the Returns to Agricultural R&D, by Julian M. Alston, Michele C. Marra, Philip G. Pardey, and T.J. Wyatt, November 1998.

39

Technological Change, Technical and Allocative Efficiency in Chinese Agriculture: The Case of Rice Production in Jiangsu, by Shenggen Fan, January 1999.

40

The Substance of Interaction: Design and Policy Implications of NGOGovernment Projects in India, by Ruth Alsop with Ved Arya, January 1999.

41

Strategies for Sustainable Agricultural Development in the East African Highlands, by John Pender, Frank Place, and Simeon Ehui, April 1999.

42

Cost Aspects of African Agricultural Research, by Philip G. Pardey, Johannes Roseboom, Nienke M. Beintema, and Connie Chan-Kang, April 1999.

43

Are Returns to Public Investment Lower in Less-favored Rural Areas? An Empirical Analysis of India, by Shenggen Fan and Peter Hazell, May 1999.

44

Spatial Aspects of the Design and Targeting of Agricultural Development Strategies, by Stanley Wood, Kate Sebastian, Freddy Nachtergaele, Daniel Nielsen, and Aiguo Dai, May 1999.

45

Pathways of Development in the Hillsides of Honduras: Causes and Implications for Agricultural Production, Poverty, and Sustainable Resource Use, by John Pender, Sara J. Scherr, and Guadalupe Durón, May 1999.

46

Determinants of Land Use Change: Evidence from a Community Study in Honduras, by Gilles Bergeron and John Pender, July 1999.

EPTD DISCUSSION PAPERS

47

Impact on Food Security and Rural Development of Reallocating Water from Agriculture, by Mark W. Rosegrant and Claudia Ringler, August 1999.

48

Rural Population Growth, Agricultural Change and Natural Resource Management in Developing Countries: A Review of Hypotheses and Some Evidence from Honduras, by John Pender, August 1999.

49

Organizational Development and Natural Resource Management: Evidence from Central Honduras, by John Pender and Sara J. Scherr, November 1999.

50

Estimating Crop-Specific Production Technologies in Chinese Agriculture: A Generalized Maximum Entropy Approach, by Xiaobo Zhang and Shenggen Fan, September 1999.

51

Dynamic Implications of Patenting for Crop Genetic Resources, by Bonwoo Koo and Brian D. Wright, October 1999.

52

Costing the Ex Situ Conservation of Genetic Resources: Maize and Wheat at CIMMYT, by Philip G. Pardey, Bonwoo Koo, Brian D. Wright, M. Eric van Dusen, Bent Skovmand, and Suketoshi Taba, October 1999.

53

Past and Future Sources of Growth for China, by Shenggen Fan, Xiaobo Zhang, and Sherman Robinson, October 1999.

54

The Timing of Evaluation of Genebank Accessions and the Effects of Biotechnology, by Bonwoo Koo and Brian D. Wright, October 1999.

55

New Approaches to Crop Yield Insurance in Developing Countries, by Jerry Skees, Peter Hazell, and Mario Miranda, November 1999.

56

Impact of Agricultural Research on Poverty Alleviation: Conceptual Framework with Illustrations from the Literature, by John Kerr and Shashi Kolavalli, December 1999.

57

Could Futures Markets Help Growers Better Manage Coffee Price Risks in Costa Rica? by Peter Hazell, January 2000.

58

Industrialization, Urbanization, and Land Use in China, by Xiaobo Zhang, Tim Mount, and Richard Boisvert, January 2000.

EPTD DISCUSSION PAPERS

59

Water Rights and Multiple Water Uses: Framework and Application to Kirindi Oya Irrigation System, Sri Lanka, by Ruth Meinzen-Dick and Margaretha Bakker, March 2000.

60

Community natural Resource Management: The Case of Woodlots in Northern Ethiopia, by Berhanu Gebremedhin, John Pender and Girmay Tesfaye, April 2000.

61

What Affects Organization and Collective Action for Managing Resources? Evidence from Canal Irrigation Systems in India, by Ruth Meinzen-Dick, K.V. Raju, and Ashok Gulati, June 2000.

62

The Effects of the U.S. Plant Variety Protection Act on Wheat Genetic Improvement, by Julian M. Alston and Raymond J. Venner, May 2000.

63

Integrated Economic-Hydrologic Water Modeling at the Basin Scale: The Maipo River Basin, by M. W. Rosegrant, C. Ringler, DC McKinney, X. Cai, A. Keller, and G. Donoso, May 2000.

64

Irrigation and Water Resources in Latin America and he Caribbean: Challenges and Strategies, by Claudia Ringler, Mark W. Rosegrant, and Michael S. Paisner, June 2000.

65

The Role of Trees for Sustainable Management of Less-favored Lands: The Case of Eucalyptus in Ethiopia, by Pamela Jagger & John Pender, June 2000.

66

Growth and Poverty in Rural China: The Role of Public Investments, by Shenggen Fan, Linxiu Zhang, and Xiaobo Zhang, June 2000.

67

Small-Scale Farms in the Western Brazilian Amazon: Can They Benefit from Carbon Trade? by Chantal Carpentier, Steve Vosti, and Julie Witcover, September 2000.

68

An Evaluation of Dryland Watershed Development Projects in India, by John Kerr, Ganesh Pangare, Vasudha Lokur Pangare, and P.J. George, October 2000.

69

Consumption Effects of Genetic Modification: What If Consumers Are Right? by Konstantinos Giannakas and Murray Fulton, November 2000.

EPTD DISCUSSION PAPERS

70

South-North Trade, Intellectual Property Jurisdictions, and Freedom to Operate in Agricultural Research on Staple Crops, by Eran Binenbaum, Carol Nottenburg, Philip G. Pardey, Brian D. Wright, and Patricia Zambrano, December 2000.

71

Public Investment and Regional Inequality in Rural China, by Xiaobo Zhang and Shenggen Fan, December 2000.

72

Does Efficient Water Management Matter? Physical and Economic Efficiency of Water Use in the River Basin, by Ximing Cai, Claudia Ringler, and Mark W. Rosegrant, March 2001.

73

Monitoring Systems for Managing Natural Resources: Economics, Indicators and Environmental Externalities in a Costa Rican Watershed, by Peter Hazell, Ujjayant Chakravorty, John Dixon, and Rafael Celis, March 2001.

74

Does Quanxi Matter to NonFarm Employment? by Xiaobo Zhang and Guo Li, June 2001.

75

The Effect of Environmental Variability on Livestock and Land-Use Management: The Borana Plateau, Southern Ethiopia, by Nancy McCarthy, Abdul Kamara, and Michael Kirk, June 2001.

76

Market Imperfections and Land Productivity in the Ethiopian Highlands, by Stein Holden, Bekele Shiferaw, and John Pender, August 2001.

77

Strategies for Sustainable Agricultural Development in the Ethiopian Highlands, by John Pender, Berhanu Gebremedhin, Samuel Benin, and Simeon Ehui, August 2001.

78

Managing Droughts in the Low-Rainfall Areas of the Middle East and North Africa: Policy Issues, by Peter Hazell, Peter Oram, Nabil Chaherli, September 2001.

79

Accessing Other People’s Technology: Do Non-Profit Agencies Need It? How To Obtain It, by Carol Nottenburg, Philip G. Pardey, and Brian D. Wright, September 2001.

80

The Economics of Intellectual Property Rights Under Imperfect Enforcement: Developing Countries, Biotechnology, and the TRIPS Agreement, by Konstantinos Giannakas, September 2001.

EPTD DISCUSSION PAPERS

81

Land Lease Markets and Agricultural Efficiency: Theory and Evidence from Ethiopia, by John Pender and Marcel Fafchamps, October 2001.

82

The Demand for Crop Genetic Resources: International Use of the U.S. National Plant Germplasm System, by M. Smale, K. Day-Rubenstein, A. Zohrabian, and T. Hodgkin, October 2001.

83

How Agricultural Research Affects Urban Poverty in Developing Countries: The Case of China, by Shenggen Fan, Cheng Fang, and Xiaobo Zhang, October 2001.

84

How Productive is Infrastructure? New Approach and Evidence From Rural India, by Xiaobo Zhang and Shenggen Fan, October 2001.

85

Development Pathways and Land Management in Uganda: Causes and Implications, by John Pender, Pamela Jagger, Ephraim Nkonya, and Dick Sserunkuuma, December 2001.

86

Sustainability Analysis for Irrigation Water Management: Concepts, Methodology, and Application to the Aral Sea Region, by Ximing Cai, Daene C. McKinney, and Mark W. Rosegrant, December 2001.

87

The Payoffs to Agricultural Biotechnology: An Assessment of the Evidence, by Michele C. Marra, Philip G. Pardey, and Julian M. Alston, January 2002.

88

Economics of Patenting a Research Tool, by Bonwoo Koo and Brian D. Wright, January 2002.

89

Assessing the Impact of Agricultural Research On Poverty Using the Sustainable Livelihoods Framework, by Michelle Adato and Ruth Meinzen-Dick, March 2002.

90

The Role of Rainfed Agriculture in the Future of Global Food Production, by Mark Rosegrant, Ximing Cai, Sarah Cline, and Naoko Nakagawa, March 2002.

91

Why TVEs Have Contributed to Interregional Imbalances in China, by Junichi Ito, March 2002.

92

Strategies for Stimulating Poverty Alleviating Growth in the Rural Nonfarm Economy in Developing Countries, by Steven Haggblade, Peter Hazell, and Thomas Reardon, July 2002.

EPTD DISCUSSION PAPERS

93

Local Governance and Public Goods Provisions in Rural China, by Xiaobo Zhang, Shenggen Fan, Linxiu Zhang, and Jikun Huang, July 2002.

94

Agricultural Research and Urban Poverty in India, by Shenggen Fan, September 2002.

95

Assessing and Attributing the Benefits from Varietal Improvement Research: Evidence from Embrapa, Brazil, by Philip G. Pardey, Julian M. Alston, Connie Chan-Kang, Eduardo C. Magalhães, and Stephen A. Vosti, August 2002.

96

India’s Plant Variety and Farmers’ Rights Legislation: Potential Impact on Stakeholders Access to Genetic Resources, by Anitha Ramanna, January 2003.

97

Maize in Eastern and Southern Africa: Seeds of Success in Retrospect, by Melinda Smale and Thom Jayne, January 2003.

98

Alternative Growth Scenarios for Ugandan Coffee to 2020, by Liangzhi You and Simon Bolwig, February 2003.

99

Public Spending in Developing Countries: Trends, Determination, and Impact, by Shenggen Fan and Neetha Rao, March 2003.

100

The Economics of Generating and Maintaining Plant Variety Rights in China, by Bonwoo Koo, Philip G. Pardey, Keming Qian, and Yi Zhang, February 2003.

101

Impacts of Programs and Organizations on the Adoption of Sustainable Land Management Technologies in Uganda, Pamela Jagger and John Pender, March 2003.

102

Productivity and Land Enhancing Technologies in Northern Ethiopia: Health, Public Investments, and Sequential Adoption, Lire Ersado, Gregory Amacher, and Jeffrey Alwang, April 2003.

103

Animal Health and the Role of Communities: An Example of Trypanasomosis Control Options in Uganda, by Nancy McCarthy, John McDermott, and Paul Coleman, May 2003.

104

Determinantes de Estrategias Comunitarias de Subsistencia y el uso de Prácticas Conservacionistas de Producción Agrícola en las Zonas de Ladera en Honduras, Hans G.P. Jansen, Angel Rodríguez, Amy Damon, y John Pender, Juno 2003.

EPTD DISCUSSION PAPERS

105

Determinants of Cereal Diversity in Communities and on Household Farms of the Northern Ethiopian Highlands, by Samuel Benin, Berhanu Gebremedhin, Melinda Smale, John Pender, and Simeon Ehui, June 2003.

106

Demand for Rainfall-Based Index Insurance: A Case Study from Morocco, by Nancy McCarthy, July 2003.

107

Woodlot Devolution in Northern Ethiopia: Opportunities for Empowerment, Smallholder Income Diversification, and Sustainable Land Management, by Pamela Jagger, John Pender, and Berhanu Gebremedhin, September 2003.

108

Conservation Farming in Zambia, by Steven Haggblade, October 2003.

109

National and International Agricultural Research and Rural Poverty: The Case of Rice Research in India and China, by Shenggen Fan, Connie Chan-Kang, Keming Qian, and K. Krishnaiah, September 2003.

110

Rice Research, Technological Progress, and Impacts on the Poor: The Bangladesh Case (Summary Report), by Mahabub Hossain, David Lewis, Manik L. Bose, and Alamgir Chowdhury, October 2003.

111

Impacts of Agricultural Research on Poverty: Findings of an Integrated Economic and Social Analysis, by Ruth Meinzen-Dick, Michelle Adato, Lawrence Haddad, and Peter Hazell, October 2003.

112

An Integrated Economic and Social Analysis to Assess the Impact of Vegetable and Fishpond Technologies on Poverty in Rural Bangladesh, by Kelly Hallman, David Lewis, and Suraiya Begum, October 2003.

113

Public-Private Partnerships in Agricultural Research: An Analysis of Challenges Facing Industry and the Consultative Group on International Agricultural Research, by David J. Spielman and Klaus von Grebmer, January 2004.

114

The Emergence and Spreading of an Improved Traditional Soil and Water Conservation Practice in Burkina Faso, by Daniel Kaboré and Chris Reij, February 2004.

115

Improved Fallows in Kenya: History, Farmer Practice, and Impacts, by Frank Place, Steve Franzel, Qureish Noordin, Bashir Jama, February 2004.

EPTD DISCUSSION PAPERS

116

To Reach The Poor – Results From The ISNAR-IFPRI Next Harvest Study On Genetically Modified Crops, Public Research, and Policy Implications, by Atanas Atanassov, Ahmed Bahieldin, Johan Brink, Moises Burachik, Joel I. Cohen, Vibha Dhawan, Reynaldo V. Ebora, José Falck-Zepeda, Luis HerreraEstrella, John Komen, Fee Chon Low, Emeka Omaliko, Benjamin Odhiambo, Hector Quemada, Yufa Peng, Maria Jose Sampaio, Idah Sithole-Niang, Ana Sittenfeld, Melinda Smale, Sutrisno, Ruud Valyasevi, Yusuf Zafar, and Patricia Zambrano, March 2004

117

Agri-Environmental Policies In A Transitional Economy: The Value of Agricultural Biodiversity in Hungarian Home Gardens, by Ekin Birol, Melinda Smale, And Ágnes Gyovai, April 2004.

118

New Challenges in the Cassava Transformation in Nigeria and Ghana, by Felix Nweke, June 2004.

119

International Exchange of Genetic Resources, the Role of Information and Implications for Ownership: The Case of the U.S. National Plant Germplasm System, by Kelly Day Rubenstein and Melinda Smale, June 2004.

120

Are Horticultural Exports a Replicable Success Story? Evidence from Kenya and Côte d’Ivoire, by Nicholas Minot and Margaret Ngigi, August 2004.

121

Spatial Analysis of Sustainable Livelihood Enterprises of Uganda Cotton Production, by Liangzhi You and Jordan Chamberlin, September 2004

122

Linkages between Poverty and Land Management in Rural Uganda: Evidence from the Uganda National Household Survey, 1999/00, by John Pender, Sarah Ssewanyana, Kato Edward, and Ephraim Nkonya, September 2004.

123

Dairy Development in Ethiopia, by Mohamed A.M. Ahmed, Simeon Ehui, and Yemesrach Assefa, October 2004.

124

Spatial Patterns of Crop Yields in Latin America and the Caribbean, by Stanley Wood, Liangzhi You, and Xiaobo Zhang, October 2004.

125

Variety Demand within the Framework of an Agricultural Household Model with Attributes: The Case of Bananas in Uganda, by Svetlana Edmeades, Melinda Smale, Mitch Renkow and Dan Phaneuf, November 2004.

EPTD DISCUSSION PAPERS

126

Assessing the Spatial Distribution of Crop Production Using a Cross-Entropy Method, Liangzhi You and Stanley Wood, November 2004.

127

Water Allocation Policies for the Dong Nai River Basin in Vietnam: An Integrated Perspective, by Claudia Ringler and Nguyen Vu Huy, December 2004.

128

Participation of Local People in Water Management: Evidence from the Mae Sa Watershed, Northern Thailand, by Helene Heyd and Andreas Neef, December 2004.

129

Improved Water Supply in the Ghanaian Volta Basin: Who Uses it and Who Participates in Community Decision-Making? by Stefanie Engel, Maria Iskandarani, and Maria del Pilar Useche, January 2005.

130

Improved Fallows in Eastern Zambia: History, Farmer Practice and Impacts, by Freddie Kwesiga, Steven Franzel, Paramu Mafongoya, Olu Ajayi, Donald Phiri, Roza Katanga, Elias Kuntashula, Frank Place, and Teddy Chirwa, February 2005.

131

The Case of Smallholder Dairying in Eastern Africa, by Margaret Ngigi, February 2005.

132

Incorporating Project Uncertainty in Novel Environmental Biotechnologies: Illustrated Using Phytoremediation, by Nicholas A. Linacre, Steven N. Whiting, and J. Scott Angle, May 2005.

133

Ecological Risks of Novel Environmental Crop Technologies Using Phytoremediation as an Example, by J. Scott Angle and Nicholas A. Linacre, May 2005.

134

Policy Options for Increasing Crop Productivity and Reducing Soil Nutrient Depletion and Poverty in Uganda, Ephraim Nkonya, John Pender, Crammer Kaizzi, Kato Edward, and Samuel Mugarura, March 2005.

135

Local Seed Systems and Village-Level Determinants of Millet Crop Diversity in Marginal Environments of India, by Latha Nagarajan and Melinda Smale, June 2005.

EPTD DISCUSSION PAPERS

136

The Emergence of Insect Resistance in Bt-Corn: Implication of Resistance Management Information under Uncertainty, by Nicholas A. Linacre and Colin J. Thompson, June 2005.

137

Incorporating Collateral Information Using an Adaptive Management Framework for the Regulation of Transgenic Crops, by Nicholas Linacre, Mark A. Burgman, Peter K. Ades, And Allen Stewart-Oaten, August 2005.

138

Security Analysis for Agroterrorism: Applying the Threat, Vulnerability, Consequence Framework to Developing Countries, by Nicholas A. Linacre, Joanne Gaskell, Mark W. Rosegrant, Jose Falck-Zepeda, Hector Quemada, Mark Halsey, and Regina Birner, August 2005.

139

Comparing Farm and Village-Level Determinants of Millet Diversity in Marginal Environments of India: The Context of Seed Systems, Latha Nagarajan, Melinda Smale, and Paul Glewwe, August 2005.

140

Analysis for Biotechnology Innovations Using Strategic Environmental Assessment (SEA), by Nicholas A. Linacre, Joanne Gaskell, Mark W. Rosegrant, Jose Falck-Zepeda, Hector Quemada, Mark Halsey, and Regina Birner, July 2005.

141

Water Pricing and Valuation in Indonesia: Case Study of the Brantas River Basin, by Charles Rodgers and Petra J.G.J. Hellegers, August 2005.