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Background Landscape composition is known to affect both beneficial insect and pest communities on crop fields. Landscape composition therefore can impact ecosystem (dis)services provided by insects to crops. Though landscape effects on ecosystem service providers have been studied in large-scale agriculture in temperate regions, there is a lack of representation of tropical smallholder agriculture within this field of study, especially in sub-Sahara Africa. Legume crops can provide important food security and soil improvement benefits to vulnerable agriculturalists. However, legumes are dependent on pollinating insects, particularly bees (Hymenoptera: Apiformes) for production and are vulnerable to pests. We selected 10 pigeon pea (Fabaceae: Cajunus cajan (L.)) fields in Malawi with varying proportions of semi-natural habitat and agricultural area within a 1 km radius to study: (1) how the proportion of semi-natural habitat and agricultural area affects the abundance and richness of bees and abundance of florivorous blister beetles (Coleoptera: Melloidae ), (2) if the proportion of flowers damaged and fruit set difference between open and bagged flowers are correlated with the proportion of semi-natural habitat or agricultural area and (3) if pigeon pea fruit set difference between open and bagged flowers in these landscapes was constrained by pest damage or improved by bee visitation. Methods We performed three, ten-minute, 15 m, transects per field to assess blister beetle abundance and bee abundance and richness. Bees were captured and identified to (morpho)species. We assessed the proportion of flowers damaged by beetles during the flowering period. We performed a pollinator and pest exclusion experiment on 15 plants per field to assess whether fruit set was pollinator limited or constrained by pests. Results In our study, bee abundance was higher in areas with proportionally more agricultural area surrounding the fields. This effect was mostly driven by an increase in honeybees. Bee richness and beetle abundances were not affected by landscape characteristics, nor was flower damage or fruit set difference between bagged and open flowers. We did not observe a positive effect of bee density or richness, nor a negative effect of florivory, on fruit set difference. Discussion In our study area, pigeon pea flowers relatively late—well into the dry season. This could explain why we observe higher densities of bees in areas dominated by agriculture rather than in areas with more semi-natural habitat where resources for bees during this time of the year are scarce. Therefore, late flowering legumes may be an important food resource for bees during a period of scarcity in the seasonal tropics. The differences in patterns between our study and those conducted in temperate regions highlight the need for landscape-scale studies in areas outside the temperate region. 

Mapping crop types and land cover in smallholder farming systems in sub-Saharan Africa remains a challenge due to data costs, high cloud cover, and poor temporal resolution of satellite data. With improvement in satellite technology and image processing techniques, there is a potential for integrating data from sensors with different spectral characteristics and temporal resolutions to effectively map crop types and land cover. In our Malawi study area, it is common that there are no cloud-free images available for the entire crop growth season. The goal of this experiment is to produce detailed crop type and land cover maps in agricultural landscapes using the Sentinel-1 (S-1) radar data, Sentinel-2 (S-2) optical data, S-2 and PlanetScope data fusion, and S-1 C2 matrix and S-1 H/α polarimetric decomposition. We evaluated the ability to combine these data to map crop types and land cover in two smallholder farming locations. The random forest algorithm, trained with crop and land cover type data collected in the field, complemented with samples digitized from Google Earth Pro and DigitalGlobe, was used for the classification experiments. The results show that the S-2 and PlanetScope fused image + S-1 covariance (C2) matrix + H/α polarimetric decomposition (an entropy-based decomposition method) fusion outperformed all other image combinations, producing higher overall accuracies (OAs) (>85%) and Kappa coefficients (>0.80). These OAs represent a 13.53% and 11.7% improvement on the Sentinel-2-only (OAs < 80%) experiment for Thimalala and Edundu, respectively. The experiment also provided accurate insights into the distribution of crop and land cover types in the area. The findings suggest that in cloud-dense and resource-poor locations, fusing high temporal resolution radar data with available optical data presents an opportunity for operational mapping of crop types and land cover to support food security and environmental management decision-making.