Market segmentation (G + Customer and Product Profile Tools) for Gender Responsive Bean Breeding in Zimbabwe (doi:10.7910/DVN/UD0L1F)

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Part 2: Study Description
Part 5: Other Study-Related Materials
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Market segmentation (G + Customer and Product Profile Tools) for Gender Responsive Bean Breeding in Zimbabwe

doi:10.7910/DVN/UD0L1F

Harvard Dataverse

2024-12-19

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Nchanji, Eileen Bogweh; Lutomia, Cosmas Kweyu, 2024, "Market segmentation (G + Customer and Product Profile Tools) for Gender Responsive Bean Breeding in Zimbabwe", https://doi.org/10.7910/DVN/UD0L1F, Harvard Dataverse, V1

Market segmentation (G + Customer and Product Profile Tools) for Gender Responsive Bean Breeding in Zimbabwe

doi:10.7910/DVN/UD0L1F

Nchanji, Eileen Bogweh (International Center for Tropical Agriculture - CIAT)

Lutomia, Cosmas Kweyu (International Center for Tropical Agriculture - CIAT)

International Center for Tropical Agriculture

2021-01

Harvard Dataverse

International Center for Tropical Agriculture

Alliance Data Management

Alliance Data Management

2024-10-28

2021-01

https://doi.org/10.7910/DVN/UD0L1F

Agricultural Sciences, Earth and Environmental Sciences, gender, gender-responsive approaches, nutrition, food security, socioeconomic development, Africa, Crops for Nutrition and Health

agriculture

The study aims to enhance gender-responsive bean breeding in Zimbabwe using the G+ Customer and Product Profile Tools. It focuses on profiling customer preferences and product traits to address food security, nutrition, and gender gaps. The data covers major topics like market segmentation, customer mapping, socioeconomic characteristics, and bean traits. Key questions include gender-specific preferences, constraints in accessing improved varieties, and the role of beans in livelihoods and food security <br><br> Methodology: The study employed a mixed-methods approach involving literature review, surveys, and participatory varietal selection to collect data from various bean value chain actors (e.g., farmers, processors, traders). The G+ tools guided the collection process, integrating gendered perspectives on customer segments and bean traits. Data were collected across different provinces, and the analysis used gender-disaggregated findings to inform breeding initiatives​

2021-01-2021-02

2021-01-2021-01

Zimbabwe

Manicaland, Masvingo, and Midlands​

Individual and group

The study population consists of smallholder bean farmers, processors, traders, and consumers in Zimbabwe. It includes both men and women, with an emphasis on women due to their significant role in bean production. The data covers participants of various ages, income levels, and educational backgrounds, focusing on regions like Manicaland, Masvingo, and Midlands

Qualitative Data

Quantitative Data

Survey Data

Socio-economic Data

Structured questionnaire and FGD guides were was used. The data collected included demographic characteristics, bean traits preferences, production practices, market dynamics, and gender roles in the value chain​Structured questionnaire and FGD guides were was used. The data collected included demographic characteristics, bean traits preferences, production practices, market dynamics, and gender roles in the value chain​Structured questionnaire and FGD guides were was used. The data collected included demographic characteristics, bean traits preferences, production practices, market dynamics, and gender roles in the value chain​Structured questionnaire and FGD guides were was used. The data collected included demographic characteristics, bean traits preferences, production practices, market dynamics, and gender roles in the value chain​

<p>The Alliance of Bioversity International and CIAT (hereinafter "the Alliance"), its partners, and the data authors have exercised utmost care in collecting and compiling the data. However, the data is provided "as is" without any express or implied warranty. Neither the Alliance, its partners, the data authors, nor any relevant funding agencies shall be held liable for any actual, incidental, or consequential damages arising from the use of this data.</p> <p>By utilizing the Alliance Dataverse, users explicitly acknowledge that the data may contain nonconformities, defects, or errors. No warranty is provided that the data will meet users' needs or expectations, nor that all nonconformities, defects, or errors can or will be corrected.</p> <p>Users are responsible for verifying the accuracy and suitability of the data for their intended use. It is strongly recommended that users refer to related publications as a baseline for their analysis whenever possible. This practice serves as an additional safeguard against misinterpretation of the data. Related publications are listed in the metadata section of the respective Dataverse study.</p>

Nchanji, E. B., Nyarai, C., Tsekenedza, S., Gutsa, F., Sondayi, L., Lutomia, C. K., ... & Onyango, P. (2022). Market segmentation (G+ customer and product profile tools) for gender responsive bean breeding in Zimbabwe: Piloting report. https://cgspace.cgiar.org/server/api/core/bitstreams/9610dd5a-89a3-4d3c-85ed-053982cb79d3/content

Nchanji, E. B., Nyarai, C., Tsekenedza, S., Gutsa, F., Sondayi, L., Lutomia, C. K., ... & Onyango, P. (2022). Market segmentation (G+ customer and product profile tools) for gender responsive bean breeding in Zimbabwe: Piloting report.

Nchanji, E. B., Nyarai, C., Tsekenedza, S., Gutsa, F., Sondayi, L., Lutomia, C. K., ... & Onyango, P. (2022). Market segmentation (G+ customer and product profile tools) for gender responsive bean breeding in Zimbabwe: Piloting report.

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01a. Data Dictionary for NUA45 Profiling Variables.xlsx

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Dictionary

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01b. NUA45 PRODUCT PROFILING TOOL.docx

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Questionnaire

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02a. NUA45 data - Submission.xlsx

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Data (Excel)

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