Genetic evaluation results explained for extension support Julie M. K. Ojango1, Raphael Mrode1,2, Eliamoni T. Lyatuu1, Gilbert Msuta3, Daniel M. Komwihangilo3, Neema Keyla1, Chinyere C. Ekine-Dzivenu1, Gebregziabher Gebreyohanes1 and Ally Mwai Okeyo1 1International Livestock Research Institute (ILRI) 2Scotland’s Rural College (SRUC) 3Tanzania Livestock Research Institute (TALIRI) 2024 CGIAR Initiative on Sustainable Animal Productivity Tanzania information Brief https://www.ncbi.nlm.nih.gov/pubmed/?term=Mrode%20R%5BAuthor%5D&cauthor=true&cauthor_uid=30687382 https://www.ncbi.nlm.nih.gov/pubmed/?term=Okeyo%20AM%5BAuthor%5D&cauthor=true&cauthor_uid=30687382 July 2024 | Genetic evaluation results explained for extension support 2 Acknowledgements This brief is based on the Africa-Asia Dairy Genetic Gains project implemented by the International Livestock Research Institute (ILRI) and national and international partners in Ethiopia, Tanzania, Uganda, Kenya, Rwanda, and Nepal and financially supported by the Bill & Melinda Gates Foundation. The work was conducted as part of the CGIAR Initiative on Sustainable Animal Productivity for Livelihoods, Nutrition, and Gender Inclusion (SAPLING). CGIAR research is supported by contributions to the CGIAR Trust Fund. CGIAR is a global research partnership for a food-secure future dedicated to transforming food, land, and water systems in a climate crisis. https://www.cgiar.org/funders/ July 2024 | Genetic evaluation results explained for extension support 3 Introduction Genetic improvement or gain is a long-term strategy that promises a brighter future for livestock farming. By consistently using top-ranking animals as parents of the next generation, farmers can achieve permanent gains in livestock performance. This improvement accumulates over time, leading to enhanced efficiency and increased profit for farmers. Regular genetic evaluation and selection of animals provide a sustainable route to improving livestock performance at the national level and is a key component of any breed improvement program. The environment influences the ability of the animal to express its genetic superiority (or inferiority). On average, 75% of the differences among cows in production are attributed to the environment, while 25% are based on their genetic merit. Genetic evaluations are designed to account for genetic differences in the ability of animals to produce after correcting for the type of environment under which the animals were raised. Genetic evaluation Procedures used in the genetic evaluation are constantly revised and improved based on the quantity and quality of information available on the animals. These calculations, which account for many environmental and genetic factors, are designed to enhance accuracy. For young animals, genomic information plays a crucial role in predicting their potential performance, allowing for the earlier identification of superior animals and reducing the generation interval. Ancestor information is also included to enhance accuracy further. Genetic evaluations for milk production and growth are computed using an Animal Model. This means the review is based on the animal and its relationship with other animals being evaluated—i.e., its parents, ancestors, and progeny, all known relationships among the animals. Each animal additionally influences the evaluation of its relatives. The Africa Asia Dairy Genetic Gains (AADGG) project aims to provide results from the genomic evaluation of animals in the program countries each year to guide dairy genetic gains. Genetic gains depend on four main factors i. Accuracy of selection: Our ability to select animals that are truly genetically superior for the different traits. This is determined by the model and techniques used to evaluate the animals ii. Selection intensity: This depends on the proportion of animals that are identified to be parents for the next generation. It includes the superiority of these animals compared to the average of the overall population. The selection intensity in bulls is high because only a small number of bulls are needed to produce semen for a large population of cows. A good dairy farmer retains 75% of his females based on genetic merit to produce replacements. iii. Genetic Variation: This indicates the relative differences among animals that are controlled by genetic factors. It is measured as the heritability of the trait iv. Generation interval: The average age of the parents when the offspring are born. The rate of genetic change increases when the generation interval decreases (this is why the Age at first calving and the calving interval are important traits in monitoring dairy performance) To achieve genetic gains, the accuracy of selection must be high and the parents must be genetically superior compared to the average population. July 2024 | Genetic evaluation results explained for extension support 4 Note: The cost of feeding animals is a critical variable cost in producing milk. The feed consumed is used for production (milk quantity and quality (composition)), body maintenance, and reproduction. About one-third of the feed consumed is used for body maintenance, which is directly related to body size. Heavier cows need more feed and water for body maintenance. In smallholder farming systems where feed is a major limiting factor to productivity gains, an important goal is to get as much milk as possible with less feed. The index Using data on animals registered on the AADGG platform in Tanzania, we evaluated milk production and body weights to generate GEBVs for each trait. These genetic parameters, obtained through the evaluation, have been used to develop an index that improves the rate of milk production while keeping body weight constant. This index, which allows us to rank and select bulls, empowers farmers to promote more efficient dairy bulls and future cows that produce more milk without increasing feed consumption. The index weights derived are 1.00062 for milk and -0.0189 for body weight. This means that animals with heavy body weight will be penalized since the aim is to improve milk production efficiency. Results of 2024 genomic evaluation of animals in Tanzania The results from the genomic animals assessment registered on the ADGG data platform are shared in the Excel spreadsheets in separate lists for bulls and cows. The results are further presented in separate lists for cows registered from the different regions. Comparison with previous evaluation The correlation between the INDEX for sires in 2021 and 2024 was 0.75, while that for cows in 2021 and 2024 was 0.70, implying a significant re-ranking of both bulls and cows in 2024 (Figure 1). Terms used in the evaluation results Breeding value: This is the total genetic ability of an animal for a given trait. An individual receives one-half of their breeding value from each parent: Breeding value = Sire’s Transmitting ability + Dams Transmitting ability Estimated breeding values (EBV) present the genetic superiority (or inferiority) that a bull or cow will transmit to their offspring. Genomic breeding values (GEBV): The information derived from the genes inherent in an animal is used to improve the accuracy of selection in populations. This information enhances details on relationships among animals. Genomic breeding values are thus calculated to improve the accuracy and the rate of genetic gains in populations. Reliability is a measure that estimates the accuracy of the breeding value based on the amount of information used in the evaluation. The more the information available for an individual, the higher the reliability. When an individual has a large number of offspring (e.g. daughters of sires), the reliability increases. July 2024 | Genetic evaluation results explained for extension support 5 Figure 1: Correlation between bull index 2021 and 2024 Using bulls GEBVs When using AI, service providers, and livestock keepers should try to use semen from bulls with a positive GEBV. This will ensure they incorporate better genes suited for milk production and efficiency in their herds. In cases where bulls are used for natural mating, farmers should be advised not to use any bulls with negative GEBVs but be encouraged to use bulls with positive and higher GEBVs. Using cows GEBVs Farmers should be advised as follows: 1. When looking to replace a cow in their herd, the cows with the poorest GEBVs should be the ones to cull or sell off. 2. When looking for cows to buy, select cows with GEBVs higher than the average GEBV for their herd. 3. When looking for a bull to use for mating, ensure the GEBV for the bull you have chosen is higher than the average GEBV for their herd, or ensure the GEBV for the bull is higher than that for the cow to which the bull will be mated. Note i. Farmers/producers who do not mind keeping or feeding heavier cows can choose bulls based on milk yield only (Milk GEBVs). ii. Farmers/producers interested in higher efficiency will select bulls based on the index. -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 -1.5 -1 -0.5 0 0.5 1 1.5 BU LL IN D EX 2 02 1 BULL INDEX 2024 Correlation between Bull INDEX 2021 and 2024 June 2024 | Genetic evaluation results explained for extension support 6 Evaluation results: Top 20 bulls—2024 Region District farm_id BullID BrthYr Mlk-Reliability Mlk-GEBV Wt-Reliability Wt-GEBV Rank-Mlk-GEBV-2024 Index-2024 Rank-Index-2024 Dar es Salaam Temeke Municipal Council 32532 TZN000403755694 0 0.42 1.542 0.42 17.921 1 1.2042 1 Tanga Muheza District Council 3015 TZN000192800309 2015 0.84 1.278 0.78 6.716 4 1.1519 2 Tanga Muheza District Council 30296 TZN000192800139 2015 0.75 1.294 0.74 15.257 3 1.0064 3 Mbeya Mbozi District Council 27587 TZN000404014789 2018 0.5 1.086 0.5 4.394 9 1.0036 4 Tanga Muheza District Council 8446 TZN000192796157 2014 0.63 1.109 0.63 5.935 7 0.9975 5 Tanga Tanga City Council 1751 TZN000365699389 2015 0.52 1.296 0.52 15.892 2 0.9964 6 Tanga Lushoto District Council 4667 TZN000404026067 2014 0.66 1.156 0.66 12.011 5 0.9297 7 Iringa Iringa Municipal Council 32534 TZN000365696328 2005 0.78 1.054 0.78 7.108 13 0.9203 8 Tanga Tanga City Council 411440 TZN000404024975 0 0.51 0.927 0.51 1.372 20 0.9016 9 Arusha Meru District Council 19112 TZN000192813492 2011 0.95 1.007 0.93 5.988 16 0.8945 10 June 2024 | Genetic evaluation results explained for extension support 7 Mbeya Mbeya District Council 10570 TZN000404008337 2017 0.61 1.093 0.61 11.395 8 0.8783 11 Arusha Arusha District Council 19602 TZN000192807884 2014 0.57 1.064 0.57 10.802 11 0.8605 12 Mbeya Mbozi District Council 18486 TZN000404015038 2016 0.51 1.145 0.51 15.735 6 0.8483 13 Mbeya Mbeya District Council 16153 TZN000404009379 2017 0.65 0.907 0.65 3.892 24 0.834 14 Arusha Arusha District Council 14649 TZN000362735725 2016 0.7 0.992 0.7 8.477 18 0.8324 15 Njombe Makete District Council 32549 TZN000404007358 0 0.44 0.903 0.44 4.11 25 0.8259 16 Tanga Tanga City Council 32551 TZN000404025331 0 0.61 1.009 0.61 10.117 15 0.8184 17 Njombe Makete District Council 32549 TZN000404007354 0 0.4 1.033 0.4 11.584 14 0.8147 18 217 0 0.43 0.869 0.28 3.061 29 0.8117 19 Njombe Njombe District Council 25256 TZN000365707024 2014 0.4 0.914 0.4 5.934 23 0.8024 20 June 2024 | Genetic evaluation results explained for extension support 8 Breed composition of top 20 sires Region District Farm_id BullID Rank-Index-2024 IND AFT AY BF GU HO JE %Bos Taurus Dar es Salaam Temeke Municipal Council 32532 TZN000403755694 1 0.0000 0.0000 0.1315 0.0413 0.0000 0.8208 0.0064 1.0000 Tanga Muheza District Council 3015 TZN000192800309 2 0.0876 0.0136 0.0520 0.2909 0.1160 0.4341 0.0059 0.8989 Tanga Muheza District Council 30296 TZN000192800139 3 0.0237 0.0107 0.0000 0.0869 0.0102 0.8393 0.0294 0.9657 Mbeya Mbozi District Council 27587 TZN000404014789 4 0.0385 0.0027 0.1848 0.0580 0.1245 0.5743 0.0173 0.9588 Tanga Muheza District Council 8446 TZN000192796157 5 0.0397 0.0124 0.0000 0.0000 0.0000 0.9076 0.0403 0.9479 Tanga Tanga City Council 1751 TZN000365699389 6 0.0596 0.0089 0.1133 0.1113 0.0782 0.6236 0.0052 0.9315 Tanga Lushoto District Council 4667 TZN000404026067 7 0.0865 0.0459 0.0328 0.0447 0.1050 0.6276 0.0575 0.8676 Iringa Iringa Municipal Council 32534 TZN000365696328 8 0.0147 0.0149 0.0000 0.9348 0.0000 0.0327 0.0029 0.9704 Tanga Tanga City Council 411440 TZN000404024975 9 0.1581 0.0750 0.0000 0.0339 0.0383 0.6947 0.0000 0.7669 Arusha Meru District Council 19112 TZN000192813492 10 0.0000 0.0000 0.0000 0.0000 0.0000 0.9999 0.0000 1.0000 Mbeya Mbeya District Council 10570 TZN000404008337 11 0.0524 0.0311 0.0067 0.3830 0.0000 0.5075 0.0193 0.9165 Arusha Arusha District Council 19602 TZN000192807884 12 0.0202 0.0000 0.4772 0.0341 0.0894 0.3680 0.0111 0.9798 June 2024 | Genetic evaluation results explained for extension support 9 Mbeya Mbozi District Council 18486 TZN000404015038 13 0.0106 0.0009 0.1048 0.3786 0.0029 0.4890 0.0131 0.9885 Mbeya Mbeya District Council 16153 TZN000404009379 14 0.0000 0.0081 0.2932 0.6761 0.0226 0.9919 Arusha Arusha District Council 14649 TZN000362735725 15 0.0000 0.0000 0.2202 0.1640 0.0283 0.5842 0.0033 1.0000 Njombe Makete District Council 32549 TZN000404007358 16 0.0072 0.0160 0.0091 0.8614 0.0000 0.0811 0.0253 0.9768 Tanga Tanga City Council 32551 TZN000404025331 17 0.1123 0.0150 0.0228 0.2901 0.0000 0.5256 0.0343 0.8728 Njombe Makete District Council 32549 TZN000404007354 18 0.0000 0.0000 0.0261 0.5226 0.0000 0.4368 0.0146 1.0000 217 19 Njombe Njombe District Council 25256 TZN000365707024 20 0.0188 0.0290 0.3340 0.6145 0.0038 0.9523 June 2024 | Genetic evaluation results explained for extension support 10 Evaluation results: Top 20 cows—2024 Region District Farm Animal_i d tag_id BrthY r No- TDys Avg- My Mlk- Rel Mlk- GEBV Wt- Avg Wt- Rel Wt- GEBV Mlk-Rank- 2024 Index- 2024 Rank-Ind- 2024 Arusha Arusha District Council 1872 8 9576 TZN0003627359 07 2012 12 18.667 0.64 2.881 313.29 3 0.61 5.381 1 2.7811 1 Arusha Meru District Council 3252 7 355006 TZN0004040384 62 2015 24 14.5 0.67 1.757 0 0.36 -51.901 33 2.739 2 Kilima njaro Rombo District Council 2012 3 14285 TZN0003656954 07 2017 1 26 0.18 2.588 0 0 -0.73 4 2.6034 3 Kilima njaro Hai District Council 1779 7 23259 TZN0004040040 35 2016 11 20.273 0.71 2.764 327.84 9 0.68 13.953 2 2.502 4 Arusha Arusha City Council 1465 7 6036 TZN0001928098 04 2015 10 20 0.52 2.109 313.43 2 0.51 -8.13 17 2.264 5 Arusha Arusha City Council 1546 4 5962 TZN0001928096 97 2015 14 16.286 0.58 2.609 400.75 0.55 19.163 3 2.2484 6 Tanga Muheza District Council 4005 2731 TZN0001928000 92 2012 22 16.455 0.59 2.505 309.07 6 0.53 14.638 7 2.2299 7 Njombe Njombe Town Council 2199 7 12570 TZN0003656932 93 2012 5 20.4 0.65 2.355 357.91 0.69 9.54 10 2.1762 8 Kilima njaro Moshi Rural District Council 1748 4 20726 TZN0004040001 98 0 0 0 0.71 2.55 0 0.71 21.059 5 2.1536 9 Arusha Meru District Council 3979 10804 TZN0003627387 08 2011 6 17.667 0.61 2.549 370.31 5 0.61 23.069 6 2.1146 10 Arusha Arusha City Council 1290 7 5972 TZN0001928097 08 2012 19 15.579 0.77 2.24 313.25 1 0.76 7.732 11 2.0953 11 Arusha Arusha City Council 1465 7 6035 TZN0001928098 03 2015 11 20 0.54 2.068 328.51 9 0.51 -1.001 18 2.0882 12 June 2024 | Genetic evaluation results explained for extension support 11 Tanga Lushoto District Council 2793 9 20073 TZN0004032581 25 2014 16 9.75 0.72 2.386 283.97 4 0.7 19.089 8 2.0267 13 Kilimanjar o Moshi Rural District Council 1748 4 20724 TZN0004040001 96 2014 33 15.061 0.77 2.165 248.89 5 0.74 9.451 12 1.9877 14 Kilimanjar o Moshi Rural District Council 1748 4 20724 TZN0004040001 96 2014 33 15.061 0.77 2.165 248.89 5 0.74 9.451 13 1.9877 15 Kilimanjar o Hai District Council 2431 5 7141 TZN0001928117 34 2014 8 17 0.68 2.371 338.77 4 0.69 20.883 9 1.9778 16 Mbeya Mbeya City Council 1215 0 25024 TZN0004040075 31 2017 10 22.1 0.52 1.989 0 0 0.795 19 1.9752 17 Mbeya Mbeya City Council 1490 7 25167 TZN0004040078 33 2016 11 14.182 0.55 2.124 0 0 10.14 15 1.9337 18 Arusha Arusha City Council 1281 5 6011 TZN0001928097 66 2016 10 19.6 0.66 1.888 359.14 6 0.64 -2.19 23 1.9306 19 Arusha Arusha City Council 2540 6 5108 TZN0001928072 86 2012 25 13.92 0.63 1.942 320.21 2 0.57 3.357 20 1.8798 20 June 2024 | Genetic evaluation results explained for extension support 12 Breed composition of top 20 cows Region District Farm_id Animal_id tag_id Rank-Ind-2024 IND AFT AY BF GU HO JE % Exotic Arusha Arusha District Council 18728 9576 TZN000362735907 1 0.10718 0.03603 0.06383 0.17935 0.35105 0.17487 0.08769 0.85679 Arusha Meru District Council 32527 355006 TZN000404038462 2 0.00492 0.01525 0.33491 0.62767 0.01724 0.97982 Kilimanjaro Rombo District Council 20123 14285 TZN000365695407 3 Kilimanjaro Hai District Council 17797 23259 TZN000404004035 4 0.04236 0.03143 0.17702 0.67707 0.07213 0.92622 Arusha Arusha City Council 14657 6036 TZN000192809804 5 Arusha Arusha City Council 15464 5962 TZN000192809697 6 Tanga Muheza District Council 4005 2731 TZN000192800092 7 Njombe Njombe Town Council 21997 12570 TZN000365693293 8 0.00001 0.00695 0.00001 0.85401 0.00001 0.12497 0.01404 0.99304 Kilimanjaro Moshi Rural District Council 17484 20726 TZN000404000198 9 0.0111 0.0019 0.15431 0.79175 0.04095 0.98701 Arusha Meru District Council 3979 10804 TZN000362738708 10 0.06001 0.01996 0.37049 0.11243 0.26391 0.08574 0.08745 0.92002 Arusha Arusha City Council 12907 5972 TZN000192809708 11 0.0037 0.00001 0.05461 0.24131 0.00123 0.63836 0.06077 0.99628 Arusha Arusha City Council 14657 6035 TZN000192809803 12 Tanga Lushoto District Council 27939 20073 TZN000403258125 13 0.20121 0.05224 0.00001 0.0418 0.00774 0.69107 0.00594 0.74656 Kilimanjaro Moshi Rural District Council 17484 20724 TZN000404000196 14 0.00971 0.00001 0.01209 0.71655 0.03369 0.2071 0.02085 0.99028 Kilimanjaro Moshi Rural District Council 17484 20724 TZN000404000196 15 0.00971 0.00001 0.01209 0.71655 0.03369 0.2071 0.02085 0.99028 Kilimanjaro Hai District Council 24315 7141 TZN000192811734 16 0.02416 0.00781 0.13099 0.00001 0.10981 0.60263 0.12458 0.96802 Mbeya Mbeya City Council 12150 25024 TZN000404007531 17 Mbeya Mbeya City Council 14907 25167 TZN000404007833 18 Arusha Arusha City Council 12815 6011 TZN000192809766 19 0.20018 0.11157 0.4005 0.17736 0.11039 0.68825 Arusha Arusha City Council 25406 5108 TZN000192807286 20 June 2024 | Genetic evaluation results explained for extension support 0 Contact : Julie Ojango, ILRI – j.ojango@cgiar.org SAPLING Initiative Lead, Isabelle Baltenweck - I.Baltenweck@cgiar.org SAPLING Initiative Deputy Lead, Rekik Mourad - M.Rekik@cgiar.org CGIAR is a global research partnership for a food-secure future. CGIAR science is dedicated to transforming food, land, and water systems in a climate crisis. Its research is carried out by 13 CGIAR Centers/Alliances in close collaboration with hundreds of partners, including national and regional research institutes, civil society organizations, academia, development organizations, and the private sector. www.cgiar.org We would like to thank all funders who support this research through their contributions to the CGIAR Trust Fund: www.cgiar.org/funders. To learn more about this Initiative, please visit this webpage. To learn more about this and other Initiatives in the CGIAR Research Portfolio, please visit www.cgiar.org/cgiar-portfolio © 2024. International Livestock Research Institute. Some rights reserved. This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 International Licence (CC by 4.0). | | | mailto:j.ojango@cgiar.org mailto:I.Baltenweck@cgiar.org mailto:M.Rekik@cgiar.org http://www.cgiar.org/funders https://www.cgiar.org/initiative/sustainable-animal-productivity/ http://www.cgiar.org/cgiar-portfolio https://creativecommons.org/licenses/by/4.0/