Browsing by Author "Bararyenya, Astere"

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    Continuous Storage Root Formation and Bulking in Sweet potato
    (Gates Open Research, 2019) Bararyenya, Astere; Tukamuhabwa, Phinehas; Gibson, Paul; Grüneberg, Wolfgang; Ssali, Reuben; Low, Jan; Odong, Thomas; Ochwo-Ssemakula, Mildred; Talwana, Herbert; Mwila, Natasha; Mwanga, Robert
    Sweetpotato (Ipomoea batatas (L.) Lam, family Convolvulaceae.) is one of the most important food crops worldwide, with approximately 106 million tons produced in almost 120 countries from an area of about 8 million ha and an average global yield of 11.1 tons/ha (FAO, 2016). Asia is the world’s largest sweetpotato producing continent, with 79 million tons, followed by Africa (FAOstat, 2016). About 75% of this global production is from China alone. A total of 21.3 million tons is produced in Africa, with 48% from the Great Lakes region. In East Africa, the crop is the second most important root crop after cassava and has played an important role as a famine-relief crop during its long history and has recently been reevaluated as a health-promoting food (Low et al., 2017). Uganda ranks as the fourth largest sweetpotato producer in the world after China, Nigeria and Tanzania, with a production of 2.1 million t. In Africa, Uganda is ranked third after Nigeria and Tanzania. Sweetpotato is one of the main staple crops in the food systems of Uganda, Rwanda, and Burundi with a per capita consumption of 50.9, 80.1 and 57.0 kg, respectively
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    Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils
    (Plants, 2021) Badji, Arfang; Machida, Lewis; Kwemoi, Daniel Bomet; Kumi, Frank; Okii, Dennis; Mwila, Natasha; Agbahoungba, Symphorien; Ibanda, Angele; Bararyenya, Astere; Ndapewa Nghituwamhata, Selma; Odong, Thomas; Wasswa, Peter; Otim, Michael; Ochwo-Ssemakula, Mildred; Talwana, Herbert; Asea, Godfrey; Kyamanywa, Samuel; Rubaihayo, Patrick
    Genomic selection (GS) can accelerate variety improvement when training set (TS) size and its relationship with the breeding set (BS) are optimized for prediction accuracies (PAs) of genomic prediction (GP) models. Sixteen GP algorithms were run on phenotypic best linear unbiased predictors (BLUPs) and estimators (BLUEs) of resistance to both fall armyworm (FAW) and maize weevil (MW) in a tropical maize panel. For MW resistance, 37% of the panel was the TS, and the BS was the remainder, whilst for FAW, random-based training sets (RBTS) and pedigree-based training sets (PBTSs) were designed. PAs achieved with BLUPs varied from 0.66 to 0.82 for MW-resistance traits, and for FAW resistance, 0.694 to 0.714 for RBTS of 37%, and 0.843 to 0.844 for RBTS of 85%, and these were at least two-fold those from BLUEs. For PBTS, FAWresistance PAs were generally higher than those for RBTS, except for one dataset. GP models generally showed similar PAs across individual traits whilst the TS designation was determinant, since a positive correlation (R = 0.92***) between TS size and PAs was observed for RBTS, and for the PBTS, it was negative (R = 0.44**). This study pioneered the use of GS for maize resistance to insect pests in sub-Saharan Africa.