Nutrient profiling of ruminant feed resources in Ghana

Scientific African(2023)

引用 0|浏览19
暂无评分
摘要
A study was carried out to evaluate the available ruminant feed resources for their nutrient compositions. Two hundred and seventy-eight (278) feed resources were sampled from 27 farms in six regions in Ghana: Ashanti, Brong Ahafo, Greater Accra, Northern, Upper East and Volta. Parameters measured included proximate composition, detergent fibre, and in vitro gas production of the samples. Other parameters such as apparent digestibilities, relative feed values, short-chain volatile fatty acid (SCFAs) content and dry matter intake were estimated using the established models. A Kruskal-Wallis H test was carried out with the nutritional parameters serving as random factors whilst the different feed materials served as fixed factors. The results obtained indicated that root crops and their residues had higher (p < 0.001) CP contents than the other categories of plant-based feed materials whilst cereals and their residues recorded the least CP (p < 0.001). The crude fat content was generally low for all samples, however, the fibre content was high in the cereals, legumes and their residues, grasses, and forbs. The mixed feeds and mashes produced the highest (p < 0.001) quantities of gas after 96 h of incubation (19.38 ml/200 mg DM) and also had a faster rate (p < 0.001) of gas production than the other materials (0.025 ml/h). However, the root crops and their residues were the materials with the highest (p < 0.001) potential gas produced. Digestibilities, SCFA, metabolisable energy and other estimated nutritional attributes were relatively low (p < 0.05) for the varying categories of feed materials except for the mixed feeds and mashes. The study revealed that the nutrient composition and the nutritional value of the feed resources were generally lower than the requirement of ruminant livestock and this may subsequently affect their productivity.
更多
查看译文
关键词
Sub-Saharan Africa,Ruminants,Feed materials,Dry season,Nutrients
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要