Abstract
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.
Highlights
East African Highland Banana (Musa acuminata genome group AAA-EA; hereafter referred to as banana) provides food and income for over 30 million inhabitants of the African Great Lakes Region [1]
We examined the performance of random forests (RF), gradient boosting machine (GBM), and neural network (NN) trained on the 12 and 17 selected covariates under diverse resampling scenarios (Figs 4; 5)
We found that adding significant three-way interactions to M1-12 that includes significant two-way interactions (M2-12) increased the likelihood and Receiver Operating Characteristic Area Under Curve (ROC AUC), but the model would suffer a penalty on the Bayesian Information Criterion (BIC)
Summary
East African Highland Banana (Musa acuminata genome group AAA-EA; hereafter referred to as banana) provides food and income for over 30 million inhabitants of the African Great Lakes Region [1]. Several abiotic and biotic factors are underlying causes of the yield gap of banana on smallholder farms in Uganda. Nutrient deficiencies combined with drought stress are the primary abiotic constraints [6, 7], with potassium and nitrogen deficiencies accounting for up to 68% of the banana yield gap [8–10]. Cubense) and Black Sigatoka (Mycosphaerella fijiensis) [14, 15]. Banana is affected by pests, notably banana weevil (Cosmopolites sordidus) and nematodes (Radopholus similis; Pratylenchus goodeyi) [11–13], and by diseases such as Xanthomonas wilt The effects of these constraints are exacerbated by poor agronomic management [6, 7].
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