Analysis of farmers valuation of common bean attributes and preference heterogeneity under environmental stresses of Kenya

Abstract

This study uses a choice experiment method to quantify farmers' valuation of key bean variety attributes under different climatic conditions of Kenya and assess their willingness to pay or accept for changes in those attributes. The study also tests for the gender related heterogeneity in attribute preferences at individual and household level while accounting for differences in production scenarios to understand when and where men and women preferences begin to diverge or converge. The key common bean attributes were: yield, tolerance to environmental stresses (intermittent drought and root rot), early maturing, taste and reduced cooking time. Choice data was collected from random selected 504 households from purposively selected districts of high drought prone areas and high rainfall parts of Kenya. A random parameter logit model with interactions that accounts for random heterogeneity and conditional heterogeneity was used to derive unbiased estimates. Results indicate that all attributes are important but farmer derive higher utility from changes in consumption and post-harvest attributes compared to those in production attributes. Farmer valuation of the changes in yield, tolerance to environmental stresses and cooking time are heterogeneous, partly explained by size of the household, gender, risk aversion and market access. Men generally are likely to derive higher values from improvements in these attributes than women. Results have important implications for breeding priority setting, seed dissemination and integration of gender into bean improvement research.

Farmers’ heterogeneous preferences for traits of improved varieties: Informing demand-oriented crop breeding in Tanzania

“…, 2022; Pambo et al. , 2017), preferences for animal welfare attributes (Otieno and Ogutu, 2020), designing of policies (Latacz-Lohmann and Breustedt, 2019; Regassa et.al, 2021), and modeling farmers’ preferences and WTP for crop attributes (Enid et al ., 2015; Miriti et al. , 2023).…”

“…Recent applications of CE reveal the versatility of the method in various development domains. The approach has been used, for instance, in assessing consumer preferences for food products and quality and safety attributes (Jada et al, 2022;Pambo et al, 2017), preferences for animal welfare attributes (Otieno and Ogutu, 2020), designing of policies (Latacz-Lohmann and Breustedt, 2019;Regassa et.al, 2021), and modeling farmers' preferences and WTP for crop attributes (Enid et al, 2015;Miriti et al, 2023).…”

“…However, there is a continued debate about whether current breeding priorities are adequately considering farmer preferences and needs (Lunduka et al, 2012;Valle et al 2022) and whether improved varieties are adapted to heterogeneous farmers' preferences (Miriti et al, 2023). Further, improving a typical trait would involve potential trade-offs with other traits that could compromise expected adoption and impact of a variety (Enid et al, 2015). However, participatory approaches do not allow quantifying the strength and heterogeneity of farmers' preferences and WTP for traits.…”

“…, 2023). Further, improving a typical trait would involve potential trade-offs with other traits that could compromise expected adoption and impact of a variety (Enid et al ., 2015). However, participatory approaches do not allow quantifying the strength and heterogeneity of farmers’ preferences and WTP for traits.…”

Information Barriers to Adoption of Agricultural Technologies: Willingness to Pay for Certified Seed of an Open Pollinated Maize Variety in Northern Uganda

“…In addition, we expand the discussion on adoption of improved (maize) varieties from valuing individual variety traits to WTP for seed as a quality product. WTP studies for seed have mainly focused on the WTP for individual variety traits such as drought tolerance or higher yields, rather than for the product itself (in our case a 2 kg pack of maize seed), using conjoint or discrete choice experiments (see, for example, Baidu-Forson et al, 1997;Bajari and Benkard, 2001;Dalton, 2004;Horna et al, 2007;Asrat et al, 2010;Enid et al, 2015;Kassie et al, 2017). In addition, Maredia et al (2019) point out that in most of these studies variation in quality of the product is not considered.…”