Utilizing conjoint analysis to design modern crop varieties: Empirical example for groundnut in Niger

Abstract

Preferences for monetary and non‐monetary plant traits influence modern crop variety adoption decisions of farmers. To enhance adoption probability of modem crop varieties, it is necessary to identify and locus research on traits that significantly contribute to utility while de‐emphasizing insignificant plant attributes. This paper illustrates the potential for applying conjoint analysis to aid the design and targeting of client‐responsive modern crop varieties, farmers ranked eight orthogonally‐derived plant trail combinations used in an illustrative example. Utilities were estimated using the choice‐probability‐based method of ordered probit. Results showed that conjoint analysis can differentiate significant and non‐significant trails of modern crop varieties. The usefulness of applying conjoint analysis over identifiable disaggregated groups of a sample was also evident. Future application of conjoint analysis to the design and targeting of modern crop varieties should carefully consider sample composition and size to permit the estimation of relevant sub‐models for desired farmer segments.

Farmer willingness to pay for seed-related information: rice varieties in Nigeria and Benin

“…Building on the Lancaster theoretic framework, conjoint analysis (CA), or choice modeling methodology (Bateman et al, 2003) allows a utility function to be modeled as the sum of utilities that product attributes generate to a consumer. The underlying premise is that consumers evaluate a product by adding utilities from each attribute (Baidu-Forson et al, 1997a). The underlying premise is that consumers evaluate a product by adding utilities from each attribute (Baidu-Forson et al, 1997a).…”

“…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 et al, 1997a;Baidu-Forson et al, 1997b;Ndjeunga and Nelson, 2005). Similarly, prices for rice varieties included in this study were not observable.…”

“…In choice experiments, respondents choose their most preferred option among different alternatives. Contingent ranking was selected because: (1) it provides more statistical information than choice experiments, which leads to tighter confidence intervals (Hanley et al, 2001); (2) unlike rating systems (contingent rating and paired comparisons), ranking levels are comparable across respondents (Mackenzie, 1993); (3) rice farmers have a well-developed skill for ordering or ranking a set of varieties based on their different attributes (Baidu-Forson et al, 1997a). For contingent rating, individuals are asked to rate a series of alternatives individually on a semantic or numeric scale.…”

An Application of Rating Conjoint Analysis to Study the Importance of Quality-, Access- and Price-attributes to Health Care Consumers

“…Different fractional factorial designs are possible depending on the effects that should be estimated (main effects only vs. main effects and interactions), the presentation of the profiles to respondents (all profiles to all respondents vs. a part of the profiles to a group of respondents) and the estimation of the utility function (single vs. multiple stage procedure) (Green, 1974;Kuhfeld et al, 1994). In conjoint studies, orthogonal main effect designs are still most often applied (e.g., Baidu-Forson et al, 1997;Ryan, 1999;Bryan et al, 2000;Earnhart, 2002;Telser and Zweifel, 2002). These designs are attractive for researchers because they are usually very small in size even when the number of the attributes and their levels is considerably high (Green, 1974).…”

Using Conjoint Analysis to Estimate Farmers' Preferences for Cattle Traits in West Africa

“…Conjoint analysis is an experimental-based tool that can also be used to guide the development of breed improvement programs (Baidu-Forson et al, 1997). Data are generated through a survey in which respondents are asked to rate products with alternative levels of important characteristics.…”