African Dairy Genetic Gains (ADGG)
Permanent URI for this collectionhttps://hdl.handle.net/10568/69153
Africa Dairy Genetic Gains (ADGG) is an international Livestock Research Institute (ILRI) – led investment by the Bill and Melinda Gates Foundation (BMGF) that is developing and testing a multi-country genetic gains platform.
Visit the ADGG data portal/website
Funded by BMGF
Part of CGIAR Livestock program
Browse
Recent Submissions
Item Bull selection guideline for genetic improvement(Manual, 2024-11-30) Seyoum, K.; Mamo, S.; Birhanu, T.; Muluneh, D.; Kassa, T.Item Guidelines procedures and protocols for genetic evaluation and certification of breeding bulls and dams in Uganda(Report, 2024-12-30) Kugonza, D.R.Item Testing phenotypes for degree of resilience using fluctuations in milk yield of dairy cows in sub-Saharan Africa(Conference Paper, 2022-02-09) Oloo, Richard Dooso; Ekine-Dzivenu, Chinyere C.; Ojango, Julie M.K.; Mrode, Raphael A.; Okeyo Mwai, Ally; Chagunda, Mizeck G.G.Despite the relevance of dairy production in the fight against food insecurity and unemployment in sub-Saharan Africa (SSA), negative effects of climate change and general changes in the production environment pose huge challenges to its profitability. Thus, there is a need to improve resilience capacity of dairy animals to adapt to this changing environment. In the current study, we tested two indicators of resilience, logtransformed variance (LnVar) and Skewness (Skew) of deviation, based on fluctuations in animals’ milk yield. Further, we assessed the effects of genotype, agroecological zone, and genotype by agroecological zone (G×E) interaction for these phenotypes. Cows with less than 50% of exotic genetics had higher degree of resilience (P<0.05). Cows performing in semi-arid zones had higher resilience capacity compared to those in semi-humid environment (P<0.05). G×E did not significantly influence both indicators. The results provide valuable information that would inform dairy cattle improvement initiatives in SSA.Item On-Line LUKE-ADGG Training Course on MiX99 Genetic Genomic Analyses Software, 13─17 December 2022(Report, 2022-12-15) Mrode, Raphael A.Item Overview of the Africa Dairy Genetic Gains (ADGG) Project(Presentation, 2022-07) International Livestock Research InstituteItem E-Learning tools on best practices for dairy production(Presentation, 2022-07) Gitau, JenniferItem ODK for data capture: African Dairy Genetic Gains (ADGG)(Presentation, 2021-07) Gitau, JenniferItem Dairy profitability simulator mobile application: IPSR Innovation Profile(Brief, 2022-11-15) Rao, E.J.O.; Ojango, Julie M.K.; Menjo, Dominic; Kang'ethe, Edwin; Kipkosgei, Gideon; Mogaka, David; Jabes, YusufThe Dairy profitability simulator is a mobile application that integrates practical and scientific knowledge in an algorithm that projects the expected annual production and profitability of the small-holding enterprise based on known input variables affordable and accessible to the smallholder farmer. The application reveals to the smallholders, the inputs, services and practices that need to be adjusted to ensure profitability of their enterprise. It also enables structured engagement between extension service providers and farmers and enables targeted advisory services to smallholders.Item The African Dairy Genetic Gains program (ADGG) - Responding to dairy farmers’ needs and enabling multiple actors’ capacities through genetic innovations(Presentation, 2022-09-27) Okeyo Mwai, Ally; Mrode, Raphael A.; Ojango, Julie M.K.; Chinyere, Ekine; Gebreyohanes, GebregziabherItem Challenges and opportunities for genetic improvements in smallholder dairy systems in the tropics(Presentation, 2022-09-12) Ojango, Julie M.K.; Mrode, Raphael A.; Gebreyohanes, Gebregziabher; Chinyere, Ekine; Okeyo Mwai, AllyItem Identifying positive deviant farms using pareto-optimality ranking technique to assess productivity and livelihood benefits in smallholder dairy farming under contrasting stressful environments in Tanzania(Journal Article, 2022-09-05) Shija, D.S.; Okeyo Mwai, Ally; Migwi, P, K.; Komwihangilo, Daniel M.; Bebe, B.O.In smallholder dairy-cattle farming, identifying positive deviants that attain outstanding performance can inform targeted improvements in typical, comparable farms under similar environmental stresses. Mostly, positive deviants are identified subjectively, introducing bias and limiting generalisation. The aim of the study was to objectively identify positive deviant farms using the Pareto-optimality ranking technique in a sample of smallholder dairy farms under contrasting stressful environments in Tanzania to test the hypothesis that positive deviant farms that simultaneously outperform typical farms in multiple performance indicators also outperform in yield gap, productivity and livelihood benefits. The selection criteria set five performance indicators: energy balance ≥ 0.35 Mcal NEL/d, disease-incidence density ≤ 12.75 per 100 animal-years at risk, daily milk yield ≥ 6.32 L/cow/day, age at first calving ≤ 1153.28 days and calving interval ≤ 633.68 days. Findings proved the hypothesis. A few farms (27: 3.4%) emerged as positive deviants, outperforming typical farms in yield gap, productivity and livelihood benefits. The estimated yield gap in typical farms was 76.88% under low-stress environments and 48.04% under high-stress environments. On average, total cash income, gross margins and total benefits in dairy farming were higher in positive deviants than in typical farms in both low- and high-stress environments. These results show that the Pareto-optimality ranking technique applied in a large population objectively identified a few positive deviant farms that attained higher productivity and livelihood benefits in both low- and high-stress environments. However, positive deviants invested more in inputs. With positive deviant farms objectively identified, it is possible to characterise management practices that they deploy differently from typical farms and learn lessons to inform the uptake of best practices and extension messages to be directed to improving dairy management.Item Breeding programs to accelerate dairy productivity in Nepal: Opportunity for adapting the Africa Dairy Genetic Gains (ADGG) program(Brief, 2022-03-15) Ojango, Julie M.K.; Okeyo Mwai, Ally; Varijakshapanicker, PadmakumarItem Online Natural Resources Institute Finland (Luke)-African Dairy Genetic Gains (ADGG) training course on Mix99 genetic genomic analyses software(Report, 2021-12-30) Mrode, Raphael A.Item African Dairy Genetic Gains Project Overview(Presentation, 2021-12-02) Gebreyohanes, Gebregziabher; Okeyo Mwai, AllyItem ADGG Tanzania - Top bulls and cows 2021: Genomic evaluation(Report, 2021) International Livestock Research InstituteItem Application of ICT tools and genomics technology for the transformation of dairy cattle genetic improvement in Ethiopia: ADGG approaches, experiences, and prospects(Conference Paper, 2021-10-30) Gebreyohanes, Gebregziabher; Meseret, Selam; Mrode, Raphael A.; Ojango, Julie M.K.; Ekine, C.; Tessema, E.; Jufare, B.; Negussie, E.; Lidauer, M.; Tera, Asrat; Kahumbu, S.; Okeyo Mwai, AllyItem ADGG: Boresha mbari za ng'ombe wa maziwa Tanzania–sehemu ya tatu(Video, 2021-09-30) International Livestock Research InstitutePart 3 of the African Dairy Genetic Gains (ADGG) project work in improving dairy productivity in Tanzania.Item መሠረታዊ የጤና አጠባበቅ ትምህርት ለወተት ከብት (ADGG dairy tool: Ensuring dairy herd hygiene). Amharic version(Extension Material, 2021-11-15) Ojango, Julie M.K.; Rao, E.J.O.; Bett, Bernard K.; Omore, Amos O.; Kang'ethe, Edwin; Okeyo Mwai, AllyItem ላምን ጤነኛ ጥጃ እንድትገላገል ስለመርዳት (ADGG dairy tool: Helping your cow to calve). Amharic version(Extension Material, 2021-11-15) Ojango, Julie M.K.; Chinyere, Ekine; Rao, E.J.O.; Kang'ethe, Edwin; Okeyo Mwai, AllyItem Sustaining genetic improvement for more milk in Tanzania(Brief, 2021-10-15) Lyatuu, Eliamoni T.R.; Komwihangilo, Daniel M.; Msuta, G.; Ojango, Julie M.K.; Gebreyohanes, Gebregziabher; Mrode, Raphael A.; Ekine, C.; Omore, Amos O.; Okeyo Mwai, Ally