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Potential application of lessons from dairy genetics into beef: Lessons from ADGG

  1. Better lives through livestock Potential application of lessons from dairy genetics into beef Lessons from ADGG Okeyo A.M., Mrode R., Ojango J.M.K., Ekine C., Gebreyohanes G. CTLGH – ACIAR Convening Nairobi, Kenya, 30 September 2022
  2. 2 0 5 10 15 20 25 30 35 40 0 1 2 3 4 5 6 7 8 9 10 Kg of milk per day Months in Milk Figure 1: Realized lactation curves of improved (crossbred or higher) dairy cows achieved by different farmer types in Kenya Commercial/Intensive dairy farmers – ~6,500 kg/lactation --- ~2% of farmers Best smallholder farmers - ~2,500 kg/lactation --- ~5% of farmers Average smallholder farmers --- ~1,400 kg/lactation --- >90% of farmers Challenges and opportunities facing the small holder dairy system • Little or no systematic and sustainable breeding programs • Limited access to the appropriate genetics that best suit the different production systems • Inadequate access to various inputs and services, • Limited access to farmer education and extension • No sustained productivity gains and profitability
  3. 3 ADGG is designed to address key challenges facing the smallholder dairy systems through innovative use of genomics coupled with digital information technology enabled phenotyping across a wide range of very dispersed smallholder farms ADGG’s work includes developing breeding values, to drive national breeding schemes, based on performance data collected in the production environment to improve productivity and adaptive capacity. ADDG’s work is enabled by collaboration with key actors including Advanced research Institutions (ARIs), global and national dairy genetic companies, farmer organizations, national livestock seed regulators, national agricultural research and extension systems(NARES) to realize sustained genetic gains and scaled outcomes, which has been almost impossible to attain prior. About ADGG
  4. 4 ADGG’s Objectives • Establish/Revamp National livestock Performance Recording Centers (DPRCs) or Platforms for herd and cow data management • Develop & scale ICT platforms to capture herd, cow level & other related data & link it to DPRCs & other databases • Develop a pipeline for genomic evaluations to identify genetically superior bulls & cows for propagation using Artificial Insemination and natural mating • Establish a digital farmer extension and feedback system using genomic and performance data for improved herd management • Establish private-public partnerships to sustainably resource recording, genetic evaluation and digital extension at county & regional levels
  5. 5 ADGG innovative approach o Digital platforms for on-farm performance tracking o Decision-support and Farmer-to-Farmer performance benchmarking o Smart use of records & genomics tools for selection and AI service delivery Accelerate on-farm genetic gains • Milk Yield • Milk composition • Weight • Reproductive Performance • Heat tolerance • Survival rates • Lactation persistency • Mastitis incidences • Disease tolerance • Methane emission • Adaptability Indices o Target appropriate genotypes to the agro-ecology o Use young bulls with a focus on production & adaptation o Local feed/fodder resource use efficiency Genotypes adapted to local agro-ecology Economically and Environmentally relevant Traits Adapted, and Genetically superior "seed” animals for local production systems
  6. 7 Country Access to ADGG Platform: Webpage The ADGG landing page:
  7. 8 Overview of the ADGG data platform Country Data platforms Data Extraction API Management System Administration Data Validation User management Data Analytics Other Database integration Data feedback systems API API Mobile applications Web applications Farm Data Capture National governments Livestock stakeholders Big Data systems API API API DATA PLATFORM API = Application Program interphase An agile, robust, flexible & scalable system
  8. 9 ADGG Dashboard:
  9. 10 Useful data summaries for different stake holders: Example from Tanzania
  10. Useful data summaries for different stake holders: Visualization tool for National Artificial Insemination Centers (NAICs)
  11. 12 Useful data summaries for different stake holders: Feedback to farmers
  12. Useful data summaries for different stake holders:
  13. Demonstration of evidence for need to support medium scale farms in supplying genetically superior bulls, evaluated and ranked in an objective manner to local genetic institutions and small holder farmers ………many bulls that were evaluated by you are in fact imported semen and their ancestry, almost all these bulls have INTERBULL data available or other international breeding values, many were top ranking bulls in their time. I have much of their breeding data available…………. It was very interesting to see that some of the internationally acclaimed best bulls did not fare so well when evaluated/performance tested under our challenging Kenyan coast environment….. I was truly overwhelmed about all the information you were able to extract from all the records sent to you, I have still not managed to go through them all but will give my comments as I proceed over time. So many thanks for the very comprehensive data evaluation report! I have not yet had the pleasure to read such a brilliant summary of our 30 years of dairying and breeding efforts in such a compacted and highly accurate format! What a great effort done by you!!! Farmer’s responses Useful data summaries for different stake holders:
  14. 15 • ILRI director general, Jimmy Smith (left) and Minister for Livestock and Fisheries Luhaga Mpina (2nd to left) present the award for best bull at a special bull and cow show at the Nane Nane exhibition center in Dodoma, Tanzania, June 2019. Photo ILR Highlights of some of ADGG’s Achievements … • Ethiopia First dairy animal parade held on Tuesday March 30, 2020, Fikiru Regessa, State Minister of Agriculture (extreme left), Selam Meseret ADGG Ethiopia National Coordinator (middle), and Asrat Tera, Director General of National Animal Genetics Institute (NAGII) Ethiopia.
  15. 16
  16. Objective: Evaluate changes in genetic parameters for milk yield and estimate breeding values for milk production under heat stress conditions. Data –phenotype, genotype, weather Variance Components THI Thresholds 69 71 78 σ2 a 9.83 9.16 7.52 σ2 aht 0.10 1.96 2.15 σa (a, aht) -1.00 -2.10 -0.24 rg(a, aht) -0.99 -0.50 -0.06 Genomic analysis of milk yield and heat tolerance in small holder dairy system of sub-Saharan Africa C.C. Ekine-Dzivenu, R. Mrode1, R.D. Oloo, D. Komwihangilo, E. Lyatuu, G. Msuta, J.M. Ojango, A.M. Okeyo Phenotype Monthly milk collected, between Nov 2016 and May 2020 Genotype Cows and bulls genotyped on the 50K chip and imputed to the Illumina HD chip Statistical Model Variance components estimated using a fixed regression model with random regressions on a function of THI for additive genetic and permanent environmental effects Climate Data Obtained using GPS coordinates of the farms, data includes daily maximum and minimum relative humidity. -1 0 1 2 3 4 69 70 71 72 73 74 76 77 78 79 80 81 82 Milk EBV THI 65769 55133 59078 63039 51938 59188 59155 Results Antagonistic relationships between milk yield and thermal tolerance. Milk production reduced by 4.16% to 14.42% across THI groups (heat stress levels). Reaction norm shows genetic variations exist between sires, indicating the possibility to select animals that perform optimally in different environments. Chinyere Ekine-Dzivenu
  18. 19 Acknowledgment of Partners Dairy Farmers & Farmer organizations National/regional Institutions/govts.

Editor's Notes

  1. At the inception, we established a national dairy recording center in ET and Tz and developed digital tools for data capture and farmer feedback and extension service initially targeting 2000 herds per country and eventually registering more than 30000 herds and 90000 animals The goal is to develop a regional platform and so in the 2nd phase, we expanded to Kenya and Uganda and we started exploring having farmers send us data directly instead of enumerators and engaged more partners at country and global levels to to extract benefit from the platform and invest in its resourcing and we started collecting milk quality traits in addition to milk growth and other data already being collected In phase 3, we are developing more robust data capture tools and including climate resilience traits in our selection index to improve the value of our animal certification system
  2. As you scroll down the front page you see which countries have their data recorded on the platform, and get a glimpse on the quantity of data available from each country. It is a very secure and participating countries are allowed to see only their country data
  3. We have the data platform with different users interacting with it. Data capture happens via differrnt means, feedback generated for farmers and different stakeholders, capability for other data bases to get connected via APIs
  4. Dash boards with visualizations of important summaries
  5. And you can query the database to produce reports for specific needs
  6. This is one example of how a parter derives benefit from the platform ….here the Nartional Artificial insemination center can monitor inseniations across counties can tell farmer demographics across the conties, what breed of semen is in the population and the use and even monitor how the inseminators are performing
  7. For medium to large scale farms we have a platform where farmers can have summaries for their herds as well as for individual animals----lactation curves, growth cuves
  8. Within herd genetic evaluations for milk afc and ci……some of these farms deliver genetics to small holder farms …..this is some of what this farmer had to say……….
  9. Too early to see genetic progress but exciting to see the needle is moving in the right direction especiallyresults showing improved breeding value among female cows over the years and the related improvement in reliabilities of such predictions
  10. The effect of our changing climate on food systems will be disproportionately felt, affect small holder food systems in sub-Saharan Africa much more than others. A lack of infrastructure for systematic data capture in these food systems make it more difficult to address this challenge and would require innovative solutions. Now, this is one innovation. using the science of genomics coupled with milk data captured using ICT technology and mobile devices coupled with weather data in the form of temperature humidity index, we evaluated changes in genetic and phenotypic parameters and estimated breeding value for milk yield under heat stress conditions in the small holder dairy cattle population of Tanzania. our results as presented in the table and figure show antagonism between milk yield and heat tolerance with milk yield decreasing by 4 -14% across heat stress spectrum indicating that selection for increased milk yield without considering heat tolerance will reduce heat tolerance and the figure shows variation between bulls for milk breeding values by across the heat stress spectrum and suggests that it is possible to select sires that perform optimally across the THI spectrum. This result can be used in designing selection and breeding program for sustainably increasing milk yield under rising global temperatures in this population Ekine-Dzivenu, C., Mrode, R., Oloo., D, Komwihangilo, D., Lyatuu., E, Msuta., G, Ojango., J, M, Okeyo., A.M. 2022. Genomic analysis of milk yield and heat tolerance in small holder dairy system of sub-Saharan Africa. In Proceedings, 12th World Congress of Genetics Applied to Livestock Production. Rotterdam, The Netherlands, 3-12 July 2022. Ekine-Dzivenu C., Mrode R., Oyieng E., Komwihangilo D., Lyatuu E., Msuta G., Ojango JMK., Okeyo AM., 2020 Evaluating the impact of heat stress as measured by temperature-humidity index (THI) on test-day milk yield of small holder dairy cattle in a sub-Sahara African climate. Livestock Science 242(2):104314
  11. Developing this platform and providing all this information is a collective effort of many partners, many institutions and the dairy Farmers. Thank you to everybody who has contributed and enabled us to have data from all types of dairy farmers in Africa all in one portal.