@article{Bakhit_Abdelkreim_Fadlalla_2024, title={Rangeland Plants Preferred by Goats Grazing at Western Jebel Marra Locality, Central Darfur State, Sudan}, volume={10}, url={https://oiccpress.com/journal-of-rangeland-science/article/rangeland-plants-preferred-by-goats-grazing-at-western-jebel-marra-locality-central-darfur-state-sudan/}, abstractNote={The study was conducted to determine the plants preference by goats grazing at Central Darfur State, Sudan in 2016. The objective was to identify desirable plants that would assist in range rehabilitation. Five mature female goats were used to determine diet selection using the bite count technique. The total number of bites from each plant species was recorded and the selected diet and preference indices were calculated. It was found that forbs constituted 52.6% of the diet of goats followed by trees and shrubs (43.6%) and then grasses (3.6%). Among the forbs Ipomoea sinensis (Desr.), Kohautia aspera and Haemanthus multiflorus were the most selected forbs with average values of 7.17%, 5.53% and 4.06% respectively. Faidherbia albida, Ziziphus spina-christi and Albizia amara were the most selected trees with average values of 18.29%, 7.77% and 7.66% respectively. The grass species that appeared most in the diet of goats was Pennisetum pedicellatum (3.53%). In this study forbs had higher Relative Preference Indices (RPI) than grasses. The higher values of RPI in forbs as Abelmoschus esculentus, Kohautia aspera, Commelina kotschyi, Portulaca quadrifida, Talinum portulacifolium and Setaria acromelaena were 25.2 7.9, 3.7, 3.68, 3.64 and 3.42, respectively. Plants with higher RPI were suggested for reseeding rehabilitation projects. These findings may be considered as a basis for an informed management system in the study area which will be invaluable in developing sustainable management strategies.}, number={2}, journal={Journal of Rangeland Science}, publisher={OICC Press}, author={Bakhit, Gafar and Abdelkreim, Mohammed and Fadlalla, Babo}, year={2024}, month={Jan.}, keywords={Botanical composition, Climatic change, Bite count, Relative preference index} }