Data for: Role of Indigenous and local knowledge in seasonal forecasts and climate adaptation: A case study of smallholder farmers in Chiredzi, Zimbabwe

Published: 3 April 2023| Version 4 | DOI: 10.17632/dnzcszdnc2.4
Luckson Zvobgo,
Nicholas Simpson,


The dataset consists of survey data collected from 100 smallholder farmers in Chiredzi district, Zimbabwe on the indigenous and local knowledge used for weather and seasonal forecasting. In addition, the dataset consists of the questionnaire survey guide used to collect the data from smallholder farmers. What is enclosed in the spreadsheet are the climate-decisions that farmers after forecasting and the overall climate adaptation responses implemented by farmers. The dataset is behind all the analysis conducted and presented in the article "Role of Indigenous and local knowledge in seasonal forecasts and climate adaptation: A case study of smallholder farmers in Chiredzi, Zimbabwe" published by Environmental Science and Policy journal.


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1. 100 farmers from five communal and resettled wards in Chiredzi were surveyed. 2. Mixed sampling procedures were used from selection of wards, villages and the actual respondents. Two main factors to select the wards to sample from both communal and resettled areas: the proximity of a ward to the main river valleys in the study area (Save and Mtirikwi Valley), and the dryland conditions of a ward, specifically whether it had water available for irrigation. The sampling design was used to estimate the effects of access to irrigation for farmers in the Save and Mtirikwi valleys on the use of IK and LK for climate adaptation in Chiredzi. For resettled areas, one ward along the Mtirikwi Valley was selected (ward 27), and one ward from dryland conditions was selected (ward 32). For communal areas, two wards along Save Valley (wards 1 and 25) were selected, and one ward from dryland conditions (ward 3). Four villages were selected from each ward to ensure uniform sampling across the ward. In each village, five farmers were randomly selected for interviews. 3. Interviews were conducted in the local language (Shona) to ensure maximum participation and engagement with the farmers. Farmers’ participation was anonymous. Data were collected in October 2021. October was strategically selected for the interviews because the growing season usually starts from mid-November to early December. Therefore, October is a critical month in which climate-relevant farming decisions are top-of-mind for respondents, as smallholder farmers are preparing for the upcoming planting and growing season. 4. The interview guide was structured to explore four main areas: i) how smallholder farmers use IK and LK to perform weather and seasonal climate forecasting, ii) farmers’ reliance on IK and LK forecasts, iii) the influence of IK and LK forecasts on farmers’ decision-making for preparedness for potential climate risks, and iv) the connection of IK and LK forecasts to climate adaptation actions implemented by smallholder farmers. 5. Sociodemographic data of the respondents were also collected for use in the binary logistic regression model to assess which sociodemographic factors are associated with an increased or decreased probability of farmers using IK and LK forecasts. 6. Data collection focused on the types and varieties of knowledge used to forecast the weather and climate conditions of the forthcoming season, specifically storms, heavy precipitation, rainy days, season quality and length, and onset and cessation periods of the rainy season. 7. Ethical clearance was obtained from the University of Cape Town, Faculty of Science Research Ethics Committee (FSREC 002 – 2021). Permission from the headman was obtained before conducting the surveys in each village. Free informed consent was obtained before every interview with the farmers.


University of Cape Town


Weather Forecasting, Climate Prediction, Climate Change


International Development Research Centre

University of Cape Town


Foreign, Commonwealth and Development Office