Surveys on agricultural practices, perceptions of climate change, and adaptation strategies in the Sudanian-Sahelian zone of Cameroon

Description

This dataset contains the results of a household survey conducted in May 2023 among farmers in the Sudanian–Sahelian zone of Cameroon. The survey covers a total of 260 agricultural producers distributed across six villages located in two administrative regions: the Far North (Douroum and Gambour) and the North region (Bamé, Bang, Pintchoumba, and Tolloré).

The main objective of the survey was to document farmers’ perceptions of climate change, describe prevailing agricultural practices, and identify local adaptation strategies developed in response to climate change and variability. The dataset also enables an assessment of the perceived effects of climate change on maize and cotton yields, which are the two main rainfed crops in the study area.

Data were collected using a structured questionnaire implemented through Kobo Collect. The questionnaire is organized into four main components: (i) agricultural activities and practices (cropping systems, cultivated varieties, planted areas, sowing dates), (ii) farmers’ perceptions of climate change (onset and cessation of the rainy season, rainfall amounts, temperature, wind intensity, droughts, and floods), (iii) perceived effects of climate change on agricultural production and yields, and (iv) adaptation strategies implemented by farmers to cope with climatic constraints.

The sampling strategy combined cluster sampling and purposive selection. The Sudanian–Sahelian agroecological zone of Cameroon was first subdivided into agroclimatic zones based on climatic and hydrological criteria. Villages were then selected within the relevant zones where both maize and cotton are cultivated. In each village, 45 farmers were selected based on predefined criteria: being at least 30 years old and having cultivated maize and/or cotton during at least two of the four agricultural seasons preceding the survey (2020–2023). After data quality control, 10 inconsistent questionnaires were excluded, resulting in a final sample of 260 respondents.

Following data collection, the dataset was cleaned and standardized using OpenRefine. Cleaning procedures included unit harmonization (conversion of maize production reported in 100-kg bags into kilograms), removal of obvious data entry errors (e.g., unrealistically large cultivated areas), standardization of approximate dates (e.g., “mid-June” converted to the 15th of the month), and semantic grouping of open-ended responses to reduce redundancy while preserving meaning.

The dataset is provided as an Excel file containing two worksheets. The first sheet (“Agricultural_survey_climate_adaptation_Northern_Cameroon_v1.xlsx”) includes the cleaned and coded survey data ready for analysis. The second sheet (“Dictionary”) presents the original survey questions and the corresponding variable codes used in the dataset.

This dataset is intended for use in climate change impact studies, agricultural adaptation research, and policy-oriented analyses focusing on smallholder farming systems in semi-arid regions of Sub-Saharan Africa. It forms part of the doctoral research of Victor Hugo Nenwala on the spatialized modeling and prospective analysis of maize and cotton yields in the Sudanian–Sahelian zone of Cameroon.