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Banana weevil (Cosmopolites sordidus, Germar) is a major pest in East African Highland Banana. The influence of crop nutritional status on weevil damage is poorly understood. Nutrient availability affects the nutritional quality of plants for weevils and may affect weevil damage. Here, we evaluate the effect of insecticides alone and in combination with fertilisers (N, P, K and Si) on weevil damage using data from two experiments in central and southwest Uganda. In the first experiment, we varied chlorpyrifos and application rates of N, P and K. In the second experiment, we varied the application rates of K and Si. Treatment effects were analysed using generalised linear mixed models with a negative binomial distribution. In the first experiment, chlorpyrifos reduced and N increased weevil damage, while P and K had no significant effect. In the K or Si application rates reduced weevil damage compared with the control. We conclude that the combined application of chlorpyrifos with K and Si fertilisers can contribute to weevil damage control on sites with low nutrient availability and should form part of integrated weevil management in bananas. Future studies should assess how much reduction in insecticide use is possible in EAHB with judicious input rates.

East African highland banana (Musa acuminata genome group AAA-EA; hereafter referred to as banana) is critical for Uganda’s food supply, hence our aim to map current distribution and to understand changes in banana production areas over the past five decades. We collected banana presence/absence data through an online survey based on high-resolution satellite images and coupled this data with independent covariates as inputs for ensemble machine learning prediction of current banana distribution. We assessed geographic shifts of production areas using spatially explicit differences between the 1958 and 2016 banana distribution maps. The biophysical factors associated with banana spatial distribution and geographic shift were determined using a logistic regression model and classification and regression tree, respectively. Ensemble models were superior (AUC = 0.895; 0.907) compared to their constituent algorithms trained with 12 and 17 covariates, respectively: random forests (AUC = 0.883; 0.901), gradient boosting machines (AUC = 0.878; 0.903), and neural networks (AUC = 0.870; 0.890). The logistic regression model (AUC = 0.879) performance was similar to that for the ensemble model and its constituent algorithms. In 2016, banana cultivation was concentrated in the western (44%) and central (36%) regions, while only a small proportion was in the eastern (18%) and northern (2%) regions. About 60% of increased cultivation since 1958 was in the western region; 50% of decreased cultivation in the eastern region; and 44% of continued cultivation in the central region. Soil organic carbon, soil pH, annual precipitation, slope gradient, bulk density and blue reflectance were associated with increased banana cultivation while precipitation seasonality and mean annual temperature were associated with decreased banana cultivation over the past 50 years. The maps of spatial distribution and geographic shift of banana can support targeting of context-specific intensification op

In South-West Uganda, manure is highly valued for sustaining yields of East African Highland Banana, but it is in short supply. As a result, banana growers import manure from rangelands up to 50 km away. We aimed to explore the potential of this regional banana-livestock integration to meet crop nutrient requirements for sustainable intensification of banana cropping systems. We used a mixed-methods approach supported by detailed data collection. Multiple spatial levels were integrated: field-level modelling to determine long-term nutrient requirements, a household-level survey to characterize farmer practices, and a regional-level spatial analysis to map banana production and manure source areas. For median to 90th percentile banana yields (37-52 t FW/ha/year), minimum K requirements were 118–228 kg/ha/year. To supply this with manure, 10.5–20.5 t DM manure/ha/year would be needed, requiring 47–91 tropical livestock units and 27–52 ha of rangeland, far more than what is potentially available currently. However, using only manure to satisfy potassium requirements increases the risk of N losses due to nutrient imbalances likely to result from large manure applications. For sustainable intensification, manure supplemented with K-based fertilizers is a better option than manure alone, as it is more cost-effective and reduces potential N losses.