Publication Type

Journal Article

Publication Date (Issue Year)

2026

Journal Name

Meteorological Applications

Abstract

In Zimbabwe, the production, dissemination and use of seasonal weather forecasts in maize production is a system that involves the flow of information from a production point to a final point for farmers, through dissemination channels such as agricultural extension officers and more experienced farmers and elders, in the case of indigenous seasonal weather forecasts. This paper examines the perspectives of maize farmers (the general public or the masses) alongside the views of agricultural extension officers, policy shapers and influencers (key informants or elites) regarding seasonal weather forecasts and their role in improving farmers' access to this information. The findings reveal a broad consensus that indigenous seasonal weather forecasts can complement modern forecasts, aiding farmers' adaptation to climate change mainly through selecting suitable crop varieties, scheduling planting dates and planning other agricultural activities. Both farmers and key informants agreed on the need to downscale and disseminate locality-specific seasonal weather forecasts and co-production involving the integration of indigenous seasonal forecasts with modern seasonal weather forecasts. However, many farmers feel marginalised, with limited access to localised and customised forecasts. Elites often underestimate this marginalisation, creating asymmetric information gaps. This asymmetry in information between farmers and elites highlights the need for more frequent interaction between the two groups, especially through co-production processes, to enhance access to seasonal weather forecasts and strengthen climate adaptation.

Keywords

maize farming, qualitative analysis, seasonal weather forecasts, Zimbabwe

Rsif Scholar Name

Joseph Manzvera

Rsif Scholar Nationality

Zimbabwe

Cohort

Cohort 3

Thematic Area

Food security and Agribusiness

Africa Host University (AHU)

University of Ghana (UoG), Ghana

Funding Statement

This work was supported by the Partnership for Skills in Applied Sciences, Engineering and Technology-Regional Scholarship and Innovation Fund (PASET RSIF) PhD Scholarship and Carnegie Corporation of New York.

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