Browsing by Author "Ilukor, John"

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    Application of the TOA-MD model to assess adoption potential of improved sweet potato technologies by rural poor farm households under climate change: the case of Kabale district in Uganda
    (Food Security, 2014) Ilukor, John; Bagamba, Fredrick; Bashaasha, Bernard
    Sweet potato technologies that increase productivity, such as drought resistant varieties and virus free planting material are being promoted in order to reduce the vulnerability of poor farm households to climate change. In this paper, the Trade-off Analysis, Minimum Data Model Approach (TOAMD) was used to assess the adoption potential of these technologies by resource poor farmers under climate change in Uganda. The model was calibrated and validated using household survey data collected in 2009 from Kabale district. To simulate adoption potential, the base system data was generated from household data and adjusted to reflect impact of climate change on crop yields and prices by 2050. The percentage increase in yields resulting from the use of climate resilient sweet potato technologies were used to estimate yields for alternative systems based on the results from sweet potato trials by the National Agricultural Research Organization (NARO), Uganda. Adoption potential of sweet potato technologies varied across altitudes. Compared with the high and lower altitudes, adoption potential is lowest at moderate altitude despite higher yields and lower costs of production. Paying farmers to adopt new sweet potato technologies is economically rational at the higher and moderate altitudes but not at the lower altitudes. The provision of free planting material (subsidy) for the evaluated technologies resulted in a modest increase of 2 % in adoption potential. Therefore, providing this as a way of increasing adoption of sweet potato technologies to reduce vulnerability of poor farm households to climate change will have a very small impact. Instead, climate change adaptation policy should focus on creating enabling environments for farmers tomarket their produce so as to raise returns and reduce the opportunity costs of climate change adaptation strategies.
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    The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda
    (ISPRS Journal of Photogrammetry and Remote Sensing, 2020) Sinha, Priyakant; Robson, Andrew; Schneider, Derek; Kilic, Talip; Mugera, Harriet K.; Ilukor, John; Tindamanyire, Jimmy M.
    Bananas and plantains provide food and income for more than 50 million smallholder farmers in East and Central African (ECA) countries. However, banana productivity generally achieves less than optimal yield potential (< 30%) in most regions, including Uganda. Numerous studies have been undertaken to identify the key challenges that smallholder banana growers face at different stages of the banana value chain, with one of the main constraints being a lack of policy-relevant agricultural data. The World Bank (WB) initiated a methodological survey design aimed at identifying the distribution of banana varieties across a number of key Ugandan growing regions, at the individual household scale. To achieve this outcome a number of approaches including ground-based surveys, DNA tissue collection of selected banana plants and remote sensing were evaluated. For the remote sensing component, the set objectives were to develop statistical models from the hyperspectral reflectance properties of individual leaves that could differentiate typical ECA banana varieties, as well as their parentage (usage). The study also explored the potential of extrapolating the ground-based hyperspectral measures to high-resolution WorldView-3 (WV3) satellite imagery, therefore creating the potential of mapping the distribution of banana varieties at a regional scale. The DNA testing of 43 banana varieties propagated at the National Banana Research Program site at National Agricultural Research Organization (NARO) research station in Kampala, Uganda, identified 12 genetically different varieties. A canonical powered partial least square (CPPLS) model developed from hyperspectral reflectance properties of the sampled banana leaves successfully differentiated BLU, BOG, GON, GRO and KAY genotypes. The Random Forest (RF) algorithm was also evaluated to determine if spectral bands coinciding with those provided by WV3 data could segregate banana varieties. The results suggested that this was achievable and as such presents an opportunity to extrapolate the hyperspectral classifications to broader areas of land. The ability to spectrally differentiate these five genotypes has merit as they are not typical east African varieties. As such, identifying the distribution and density of these varieties across Uganda provides vital information to the banana breeders of NARO of where their new varieties are being disseminated too, data that has been previously difficult to obtain. Although the results from this pilot study indicated that not all banana varieties could be spectrally differentiated, the methodology developed and the positive results that were achieved do present remote sensing as a complimentary technology to the ongoing surveying of banana and other crop types grown within Ugandan household farming systems.