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

Simple item page
dc.contributor.authorOchola, Dennis
dc.contributor.authorBoekelo, Bastiaen
dc.contributor.authorvan de Ven, Gerrie W. J.
dc.contributor.authorTaulya, Godfrey
dc.contributor.authorKubiriba, Jerome
dc.contributor.authorAsten, Piet J. A. van
dc.contributor.authorGiller, Ken E.
dc.date.accessioned2022-06-09T17:16:23Z
dc.date.available2022-06-09T17:16:23Z
dc.date.issued2022
dc.description.abstractEast 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.en_US
dc.identifier.citationOchola D, Boekelo B, van de Ven GWJ, Taulya G, Kubiriba J, van Asten PJA, et al. (2022) Mapping spatial distribution and geographic shifts of East African highland banana (Musa spp.) in Uganda. PLoS ONE 17(2): e0263439. https://doi. org/10.1371/journal.pone.0263439en_US
dc.identifier.urihttps://doi. org/10.1371/journal.pone.0263439
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/3852
dc.language.isoenen_US
dc.publisherPlos oneen_US
dc.subjectSpatial distributionen_US
dc.subjectGeographic shiftsen_US
dc.subjectEast African highland banana (Musa spp.)en_US
dc.subjectUgandaen_US
dc.titleMapping spatial distribution and geographic shifts of East African highland banana (Musa spp.) in Ugandaen_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Mapping spatial distribution and geographic.pdf
Size:
5.5 MB
Format:
Adobe Portable Document Format
Description:
Article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Agricultural and Veterinary Sciences