Browsing Department of Crop Science by Subject "Parametric and nonparametric models"

Now showing items 1-2 of 2

Factors Influencing Genomic Prediction Accuracies of Tropical Maize Resistance to Fall Armyworm and Weevils

Badji, A.; Machida, L.; Kwemoi, D. B.; Kumi, F.; Okii, D.; Mwila, N.; Agbahoungba, S.; Ibanda, A.; Bararyenya, A.; Nghituwamhata, S. N.; Odong, T.; Wasswa, P.; Otim, M.; Ochwo-Ssemakula, M.; Talwana, H.; Asea, G.; Kyamanywa, S.; Rubaihayo, P. (University of Cape Coast, 2020-09)

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 (PA) of genomic prediction (GP) models. Sixteen ...
Genomic prediction of tropical maize resistance to fall armyworm and weevils: genomic selection should focus on effective training set determination

T. Odong, A. Badji; Machida, L.; Kwemoi, D. B.; Kumi, F.; Okii, D; Mwila, N.; Agbahoungba, S.; Ibanda, A.; Bararyenya, A.; Nghituwamhata, S. N.; Odong, T; Wasswa, P; Otim, M; Ochwo-Ssemakula, M.; H. Talwana, H.; G. Asea, G. Asea; Kyamanywa, S.; Rubaihayo, P. (University of Cape Coast, 2020-07)

Genomic selection (GS) can accelerate variety release by shortening variety development phase when factors that influence prediction accuracies (PA) of genomic prediction (GP) models such as training set (TS) size and ...

Now showing items 1-2 of 2

Search UCC IR