Gender and social targeting in plant breeding

Gender and social targeting can improve the relevance and effectiveness of plant-breeding programs serving resource-poor farmers, traders, processors, and consumers. Generally, these breeding programs have limited information about their clients, which makes it difficult to prioritize breeding objectives. As a result, products from these breeding programs may not meet the needs of their intended users.

We argue that plant breeding for resource-poor farmers, sellers, and processors requires a marketing approach. We show how the Segmenting-Targeting-Positioning (STP) framework from consumer marketing can be adapted for gender and social targeting in these programs. First, Segment the market, or identify groups of consumers with homogeneous preferences (“market segments”). Second, Target those market segments that meet the programs’ equity objectives, are big enough to justify the investment, and whose preferences match physical traits. Third, Position new products in the market by showing how these new products meet the preferences of their intended users.

The STP framework is broken down into eight logical steps which provide a checklist for gender and social targeting. The result is a “customer profile” (just like a breeders’ “product profile”), which combines demographic, behavioral, and geographic variables with a set of trait preferences to describe a market segment. A customer profile gives the program a clear picture of whom the program is breeding for, the expected number of customers, and why they prefer specific traits.

To prioritize breeding objectives, breeders must have an accurate picture of the relative size and social character of different client groups. Currently, information about these clients and their trait preferences is based on small-scale studies, which makes it difficult to set breeding priorities at the national or regional level. But the growing number and availability of large datasets make it possible to define growers and crop utilization on a much bigger scale. We inventory large datasets, identify a minimum dataset of biophysical and socioeconomic variables, and show how these variables can be layered for gender and social targeting at the national level. Datasets include the Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS–ISA), the Women’s Empowerment in Agriculture Index (WEAI), and the Demographic and Health Surveys (DHS) Program. We use the example of cassava in Nigeria to illustrate how these datasets can help breeding programs incorporate gender into their customer profiles.