Rice Imports, Domestic Production, and Climate-resilience Proxies in Cameroon: An Exploratory Time-Series Assessment
Godom Moise, Tchouassi Gérard, Soulemanou Soulemanou
International Journal of Environment and Climate Change · pp. 692–703 · Published 5 Aug 2026
10.9734/ijecc/2026/v16i85604Abstract
Rice is an important staple in Cameroon, yet domestic production remains below the volume reported as imported. This study provides an exploratory assessment of the reported import–production difference, the relationships between climate variability and domestic rice production, and the associations between rice yield and three weather-derived proxy indicators intended to represent conditions favouring short-cycle, long-cycle, and heat- or drought-resistant varieties. Annual secondary data for 2010–2021 were drawn from FAOSTAT, Cameroonian administrative sources identified in the manuscript, and the World Bank Group Climate Change Knowledge Portal. Descriptive analysis showed a persistent difference between reported imports and domestic production, with a mean annual value of 384,338.59 tonnes. Pearson correlations between domestic production and rainfall variability (r = −.249, p = .434) and temperature variability (r = −.420, p = .174) were negative but not statistically significant. In an exploratory ordinary least-squares model based on 12 observations, the short-cycle and heat- or drought-resistance proxies had positive coefficients at the 10% level, whereas the long-cycle proxy was not significant. Nitrogen fertiliser input also had a positive coefficient at the 10% level. The model explained 53.0% of observed yield variation, but its adjusted R² was 26.2% and the overall F-test was not significant, F(4, 7) = 1.975, p = .203. These findings do not estimate the effects of observed farmer adoption because the varietal indicators were inferred from climatic conditions. The results should therefore be interpreted as hypothesis-generating evidence requiring validation with farm-level adoption data, longer time series, spatially consistent variables, and fully reproducible coding.
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