Browsing by Author "Ochola, Dennis"

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    Evaluation of a lateral flow device for in-field detection of Banana Xanthomonas Wilt and its application in tracking the systemicity of Xanthomonas campestris pv. musacearum
    (African Journal of Agricultural Research, 2016) Karamura, Georgina; Ochola, Dennis; Smith, Julian; Kubiriba, Jerome; Karamura, Eldad
    Early detection of Banana Xanthomonas Wilt (BXW) in the field and immediate destruction of infected plants or plant tissue are key control methods to prevent the introduction and spread of BXW. This requires rapid, cost-effective and an on- site diagnostic tool to detect the bacterium, Xanthomonas campestris pv musacearum (Xcm). Polymerase chain reaction (PCR) detection technique for BXW is efficient but requires expensive equipment and knowledgeable expertise; this limits PCR application to the laboratory. This study therefore was carried out to evaluate the enzyme-linked immunosorbent assay (ELISA) tool configured as a lateral flow device (LFD) for detection of Xcm. Studies on the systemicity of Xcm in banana were carried out using the BXW-LFD in a field trial of 300 banana plants of Pisang Awak inoculated with the Xcm at Kiifu Forest, Mukono District, Uganda. Pseudo-stem samples from symptomatic and asymptomatic suckers were collected and tested with the LFD and the results compared with conventional PCR using the GspDm BXW primers. The LFD was able to detect Xcm 3 days post inoculation (dpi), 2 cm above and below inoculation site, 15 to 35 days in the pseudo-stem, 35 to 42 days to reach the corm and 81 days in the lateral roots. The rate of Xcm movement in banana was found to be sigmoid in nature, leveling off as the bacteria moved down the pseudo-stem towards the corm. Conventional PCR was only 24% more sensitive than the LFD. The use of the BXW LFD can therefore boost BXW control measures through improved surveillance and quarantine services to arrest the introduction and spread of the disease within and between national borders.
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    Manure matters: prospects for regional banana-livestock integration for sustainable intensification in South-West Uganda
    (Taylor & Francis, 2022-09) Braber, Harmen den; van de Ven, Gerrie; Ronner, Esther; Marinus, Wytze; Languillaume, Antoine; Ochola, Dennis; Taulya, Godfrey; Giller, Ken E; Descheemaeker, Katrien
    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.
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    Mapping spatial distribution and geographic shifts of East African highland banana (Musa spp.) in Uganda
    (Plos one, 2022) Ochola, Dennis; Boekelo, Bastiaen; van de Ven, Gerrie W. J.; Taulya, Godfrey; Kubiriba, Jerome; Asten, Piet J. A. van; Giller, Ken E.
    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.