Mapping spatial distribution and geographic shifts of East African highland banana (Musa spp.) in Uganda.

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 options and policy advocacy to avert agriculture driven environmental degradation.

Emergence of rice yellow mottle virus in eastern Uganda: Recent and singular interplay between strains in East Africa and in Madagascar.

Epidemics of rice yellow mottle virus (RYMV) have developed recently in eastern Uganda, close to Lake Victoria in East Africa. Unexpectedly, all isolates from the affected area belonged to a single strain (named S4ug), a strain that is different from the S4lv strain that has been prevalent in the Lake Victoria basin for the past five decades. Interestingly, the S4ug strain is most closely related at the genomic level (except ORF1) to the strain present in Madagascar (S4mg), 2000km away. The minor parent of the S4mg recombinant strain could not be detected. Molecular clock dating analysis indicated that the singular sequence of events - that associated the emergence of a new strain (S4ug), a modular recombination between closely related strains (S4mg and S4ug) and a long distance transmission (S4mg) - occurred recently, within the past few decades. This finding is at variance with the process of gradual strain dispersal and diversification over two centuries throughout Africa that was previously established.