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Due to increasing climate variability and change, the need for more accessible, timely, and reliable climate information has risen, particularly in African rain-fed smallholder farming communities. Yet, studies on the role of information sources in climate resilience are limited. Given the plurality of climate information sources, it is uncertain which medium offers better chances to build resilience against the changing climate. To fill this gap, we employed quantitative survey data from smallholder agricultural households in the Mzimba District in Malawi (n =1090) and the Upper West Region of Ghana (n =1100). Our findings reveal that in Malawi, households whose primary source of climate information was the mass media (OR =2.37; p ≤ 0.001) and external organizations (government, private sector, and nonprofit sector) (OR =2.11; p ≤0.001) were over two times more likely to rate their resilience as good compared to those who relied primarily on self-experience. While in Ghana, interpersonal sources (other farmers, friends/ relatives, special activities by the community) significantly increased a household’s odds (OR = 3.46; p ≤0.001) of reporting good resilience, while external sources reduced farmers’ likelihood of reporting climate resilience (OR =0.06; p ≤0.001) compared to those who relied primarily on self-experience. Farmers in Malawi who practiced intercropping were also more likely to rate their resilience as good than those engaged in monocropping. The findings suggest that the relevance of information sources on climate change resilience is place-specific and that some sources may impede resilience-building if contextual factors are sidelined. This finding reaffirms the need for context-specific policies due to the heterogeneity of agrarian communities across Africa. 

Crop yield is related to household food security and community resilience, especially in smallholder agricultural systems. As such, it is crucial to accurately estimate within-season yield in order to provide critical information for farm management and decision making. Therefore, the primary objective of this paper is to assess the most appropriate method, indices, and growth stage for predicting the groundnut yield in smallholder agricultural systems in northern Malawi. We have estimated the yield of groundnut in two smallholder farms using the observed yield and vegetation indices (VIs), which were derived from multitemporal PlanetScope satellite data. Simple linear, multiple linear (MLR), and random forest (RF) regressions were applied for the prediction. The leave-one-out cross-validation method was used to validate the models. The results showed that (i) of the modelling approaches, the RF model using the five most important variables (RF5) was the best approach for predicting the groundnut yield, with a coefficient of determination (R2) of 0.96 and a root mean square error (RMSE) of 0.29 kg/ha, followed by the MLR model (R2 = 0.84, RMSE = 0.84 kg/ha); in addition, (ii) the best within-season stage to accurately predict groundnut yield is during the R5/beginning seed stage. The RF5 model was used to estimate the yield for four different farms. The estimated yields were compared with the total reported yields from the farms. The results revealed that the RF5 model generally accurately estimated the groundnut yields, with the margins of error ranging between 0.85% and 11%. The errors are within the post-harvest loss margins in Malawi. The results indicate that the observed yield and VIs, which were derived from open-source remote sensing data, can be applied to estimate yield in order to facilitate farming and food security planning.