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High-yield dairy cattle breeds improve farmer incomes, curtail greenhouse gas emissions and reduce dairy import dependency in Tanzania


Tanzania’s dairy sector is poorly developed, creating reliance on imports for processed, value-added dairy products and threatening food security, particularly when supply chains are disrupted due to market volatility or armed conflicts. The Tanzanian Dairy Development Roadmap is a domestic development initiative that aims to achieve dairy self-sufficiency by 2030. Here, we model different outcomes of the roadmap, finding that adoption of high-yield cattle breeds is essential for reducing dairy import dependency. Avoided land use change resulting from fewer, higher yielding dairy cattle would lead to lower greenhouse gas emissions. Dairy producers’ average incomes could increase despite capital expenditure and land allocation required for the adoption of high-yield breeds. Our findings demonstrate the importance of bottom-up development policies for sustainable food system transformations, which also support food sovereignty, increase incomes for smallholder farmers and contribute towards Tanzania’s commitments to reduce greenhouse gas emissions.

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Fig. 1: GHG emissions from different scenarios: baseline, meagre, middle road, high ambition and high ambition++.
Fig. 2: Herd sizes associated with dairy roadmap scenarios.
Fig. 3: Changes to dairy household income resulting from roadmap scenarios.

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Data availability

The data generated for this study are presented in the text and Supplementary Information and through the public GitHub repository ‘Tanzania Dairy Mitigation Assessment’ available from: Unprocessed, anonymized survey data used as parameters in the model are available from: External databases used in the study as cited in the text include: Feedipedia, available at; Gridded Livestock of the World, available at; and European Space Agency Land Cover Data, available at Source data are provided with this paper.

Code availability

The code used for this study is available in the public GitHub repository:


  1. Meat, Milk and More: Policy Innovations to Shepherd Inclusive and Sustainable Livestock Systems in Africa (Malabo Montpellier Panel, 2020).

  2. Value of Agricultural Production (FAO, accessed August 25, 2022);

  3. Jayne, T. & Sanchez, P. A. Agricultural productivity must improve in sub-Saharan Africa. Science 372, 1045–1047 (2021).

    Article  ADS  CAS  PubMed  Google Scholar 

  4. Dangal, S. R. S. et al. Methane emission from global livestock sector during 1890–2014: magnitude, trends and spatiotemporal patterns. Glob. Change Biol. 23, 4147–4161 (2017).

    Article  ADS  Google Scholar 

  5. Mottet, A. et al. Climate change mitigation and productivity gains in livestock supply chains: insights from regional case studies. Reg. Env. Change 17, 129–141 (2016).

    Article  Google Scholar 

  6. Valin, H. et al. Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security? Environ. Res. Lett. 8, 035019 (2013).

    Article  ADS  CAS  Google Scholar 

  7. González-Quintero, R. et al. Yield gap analysis to identify attainable milk and meat productivities and the potential for greenhouse gas emissions mitigation in cattle systems of Colombia. Agric. Syst. 195, 103303 (2022).

    Article  Google Scholar 

  8. Crops and Livestock Products (FAO, accessed August 17,2022);

  9. Ledo, J. et al. Persistent challenges in safety and hygiene control practices in emerging dairy chains: the case of Tanzania. Food Control 105, 164–173 (2019).

    Article  Google Scholar 

  10. Häsler, B. et al. Integrated food safety and nutrition assessments in the dairy cattle value chain in Tanzania. Glob. Food Sec. 18, 102–113 (2018).

    Article  Google Scholar 

  11. Supply Utilization Accounts (FAO, accessed August 26, 2022);

  12. Michael, S. et al. Tanzania Livestock Master Plan (International Livestock Research Institute, 2018).

  13. Tanzania Livestock Sector Analysis (2016/2017–2030/2031) (United Republic of Tanzania Ministry of Livestock and Fisheries, 2017);

  14. Nicholson, C. et al. Assessment of Investment Priorities for Tanzania’s Dairy Sector: Report on Activities and Accomplishments (International Livestock Research Institute, 2021).

  15. Chagunda, M. G. C., Romer, D. A. M. & Roberts, D. J. Effect of genotype and feeding regime on enteric methane, non-milk nitrogen and performance of dairy cows during the winter feeding period. Livest. Sci. 122, 323–332 (2009).

    Article  Google Scholar 

  16. Notenbaert, A. et al. Towards environmentally sound intensification pathways for dairy development in the Tanga region of Tanzania. Reg. Environ. Change 20, 138 (2020).

  17. Yesuf, G. A. et al. Embedding stakeholders’ priorities into the low-emission development of the East African dairy sector. Env. Res. Lett. 16, 064032 (2021).

    Article  CAS  Google Scholar 

  18. GLS (Greening Livestock Survey) (International Livestock Research Institute, 2019);

  19. Intended Nationally Determined Contributions (United Republic of Tanzania, 2021);

  20. Ndung’u, P. W. et al. Farm-level emission intensities of smallholder cattle (Bos indicus; B. indicusB. taurus crosses) production systems in highlands and semi-arid regions. Animal 16, 100445 (2022).

    Article  PubMed  Google Scholar 

  21. Goopy, J. P. et al. Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle. Br. J. Nutr. 123, 1239–1246 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Goopy, J. P. et al. A new approach for improving emission factors for enteric methane emissions of cattle in smallholder systems of East Africa—results for Nyando, Western Kenya. Agric. Syst. 161, 72–80 (2018).

    Article  Google Scholar 

  23. Supporting Low Emissions Development in the Tanzanian Dairy Cattle Sector—Reducing Enteric Methane for Food Security and Livelihoods (FAO, 2019).

  24. Gerssen-Gondelach, S. J. et al. Intensification pathways for beef and dairy cattle production systems: impacts on GHG emissions, land occupation and land use change. Agric. Ecosyst. Environ. 240, 135–147 (2017).

    Article  Google Scholar 

  25. Havlik, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Herrero, M. et al. Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Change 6, 452–461 (2016).

    Article  ADS  Google Scholar 

  27. Dizyee, K., Baker, D. & Omore, A. Upgrading the smallholder dairy value chain: a system dynamics ex-ante impact assessment in Tanzania’s Kilosa district. J. Dairy Res. 86, 440–449 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Simões, A. R. P., Nicholson, C. F., Novakovicc, A. M. & Protil, R. M. Dynamic impacts of farm-level technology adoption on the Brazilian dairy supply chain. Int. Food Agribus. Manag. Rev. 23, 71–84 (2020).

    Article  Google Scholar 

  29. Rahimi, J. et al. Heat stress will detrimentally impact future livestock production in East Africa. Nat. Food. 2, 88–96 (2021).

    Article  Google Scholar 

  30. Mbululo, Y. & Nyihirani, F. Climate characteristics over southern highlands Tanzania. Atmos. Clim. Sci. 2, 454–463 (2012).

    Google Scholar 

  31. Kihoro, E. M., Schoneveld, G. C. & Crane, T. A. Pathways toward inclusive low-emission dairy development in Tanzania: producer heterogeneity and implications for intervention design. Agric. Syst. 190, 103073 (2021).

  32. Mruttu, H. et al. Animal Genetics Strategy and Vision for Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).

  33. Agricultural Sample Survey 2018/19 Report on Livestock and Livestock Characteristics (Private Peasant Holdings) (Central Statistical Agency, 2019).

  34. 2019/20 National Sample Census of Agriculture Main Report (Tanzania National Bureau of Statistics, 2022).

  35. Robinson, T. P. et al. Global Livestock Production Systems (FAO, 2011).

  36. Herrero, M. et al. Biomass use, production, feed efficiencies and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  37. Baseline Study of the Tanzania Dairy Value Chain (United Republic of Tanzania Ministry of Agriculture, Livestock and Fisheries, 2016).

  38. Mbwambo, N., Nandonde, S., Ndomba, C. & Desta, S. Assessment of Animal Feed Resources in Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).

  39. Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., & Borriello, G. Open data kit: tools to build information services for developing regions. Proc. 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (Association for Computing Machinery, 2010).

  40. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).

  41. Rufino, M. C. et al. Lifetime productivity of dairy cows in smallholder farming systems of the central highlands of Kenya. Animal 3, 1044–1056 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Hawkins, J. et al. Feeding efficiency gains can increase the greenhouse gas mitigation potential of the Tanzanian dairy sector. Sci. Rep. 11, 4190 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. Python Software Foundation (Python Software Foundation, 2019);

  44. Kashoma, I. P. B. et al. Predicting body weight of Tanzania shorthorn zebu cattle using heart girth measurements. Livest. Res. Rural. Dev. 23, Table 1 (2011).

  45. Galukande, E. B., Mahadevan, P. & Black, J. G. Milk production in East African zebu cattle. Anim. Sci. 4, 329–336 (1962).

    Article  Google Scholar 

  46. Gillah, K. A., Kifaro, G. C. & Madsen, J. Effects of pre partum supplementation on milk yield, reproduction and milk quality of crossbred dairy cows raised in a peri urban farm of Morogoro town Tanzania. Livest. Res. Rural. Dev. 26 (2014).

  47. Njau, F. B. C., Lwelamira, J. & Hyandye, C. Ruminant livestock production and quality of pastures in the communal grazing land of semi-arid central Tanzania. Livest. Res. Rural. Dev. 8, Table 4 (2013).

  48. Mwambene, P. L. et al. Selecting indigenous cattle populations for improving dairy production in the Southern Highlands and Eastern Tanzania. Livest. Res. Rural. Dev. 26 (2014).

  49. Rege, J. E. O. et al. Cattle of Kenya: Uses, Performance, Farmer Preferences, Measures of Genetic Diversity and Options for Improved Use (International Livestock Research Institute, 2001).

  50. Beffa, L. M. Genotype × Environment Interaction in Afrikaner Cattle. PhD thesis, Univ. of the Free State (2005).

  51. Meaker, H. J., Coetsee, T. P. N. & Lishman, A. W. The effects of age at 1st calving on the productive and reproductive-performance of beef-cows. S. Afr. J. Anim. Sci. 10, 105–113 (1980).

    Google Scholar 

  52. Chenyambuga, S. W. & Mseleko, K. F. Reproductive and lactation performances of Ayrshire and Boran crossbred cattle kept in smallholder farms in Mufindi district, Tanzania. Livest. Res. Rural. Dev. 21, 100 (2009).

    Google Scholar 

  53. Ojango, J. M. K. et al. Dairy production systems and the adoption of genetic and breeding technologies in Tanzania, Kenya, India and Nicaragua. Anim. Genet. Resour. 59, 81–95 (2016).

    Article  Google Scholar 

  54. Feedipedia—Animal Feed Resources Information System (FAO, accessed 2021);

  55. Lukuyu, B. et al. (eds) Feeding Dairy Cattle in East Africa (East Africa Dairy Development Project, 2012).

  56. Rubanza, C. D. K. et al. Biomass production and nutritive potential of conserved forages in silvopastoral traditional fodder banks (Ngitiri) of Meatu District of Tanzania. Asian-Aust. J. Anim. Sci. 19, 978–983 (2006).

    Article  Google Scholar 

  57. Food Balances (2010-) (FAO, accessed September 29, 2021);

  58. Crop Data for the United Republic of Tanzania (FAO, accessed September 22, 2021); at/en/#data/QC

  59. Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data. 5, 180227 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. 2014/15 Annual Agricultural Sample Survey Report (The United Republic of Tanzania, 2016).

  61. Basic Data for Livestock and Fisheries (The United Republic of Tanzania Ministry of Livestock and Fisheries, 2013).

  62. IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 Agriculture, Forestry and Other Land Use (IPCC, 2006).

  63. 2019 Refinement to the IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2019).

  64. Fertilizers by Nutrient (FAO, accessed July 6, 2022);

  65. Hutton, M. O. et al. Toward a nitrogen footprint calculator for Tanzania. Env. Res. Lett. 12, 034016 (2017).

    Article  Google Scholar 

  66. Tanzania Fertilizer Assessment (International Fertilizer Development Center, 2012);

  67. A Common Carbon Footprint Approach for the Dairy Sector: The IDF Guide to Standard Life Cycle Methodology (International Dairy Federation, 2015);

  68. Bruzzone, L., Bovolo, F. & Arino, O. European Space Agency land cover climate change initiative. ESA LC CCI data: high resolution land cover data via Centre for Environmental Data Analysis; (2021)

  69. Characteristics of Markets for Animal Feeds Raw Materials in the East African Community: Focus on Maize Bran and Sunflower Seed Cake (Kilimo Trust, 2017).

  70. Ngunga, D. & Mwendia, S. Forage Seed System in Tanzania: A Review Report (Alliance of Biodiversity and CIAT, 2020).

  71. Nkombe, B.M. Investigation of the Potential for Forage Species to Enhance the Sustainability of Degraded Rangeland and Cropland Soils. MSc thesis, Ohio State Univ. (2016).

  72. Producer Prices (FAO, accessed 2021);

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The paper was in part supported by the International Fund for Agricultural Development within the ‘Greening Livestock: Incentive-Based Interventions for Reducing the Climate Impact of Livestock in East Africa’ project, implemented by the ILRI, the CIFOR and The CGIAR Research Program for Climate Change, Agriculture and Food Security.

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Authors and Affiliations



J.H. and E.K. designed and implemented the household survey. J.H., M.R., A.K., A.O., and C.N. contributed to the implementation of the scenario analysis. J.H. developed and parameterized the model and scenario analysis code with input from M.R., A.K., G.Y. and C.N. J.H., M.R., A.K. and C.N. designed the economic impact indicators. P.E., G.S. and M.R. supervised the Greening Livestock project. J.H. led the writing of the paper with contributions from all coauthors.

Corresponding author

Correspondence to James W. Hawkins.

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The authors declare no competing interests.

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Nature Food thanks Ricardo González Quintero, Klaus Butterbach-Bahl, Elizabeth Meier and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Direct greenhouse gas emissions by dairy sub-sector.

Local cattle shown on left (panels; a, c, e) and improved cattle on right (panels; b,d,f). Error bars show one standard error from the estimated value based on Monte Carlo uncertainty analysis. FPCM = fat- and protein-corrected milk. TLU = tropical livestock unit.

Source data

Extended Data Fig. 2 Feed intake (a) and dairy land use (b,c) for Baseline and dairy roadmap scenarios.

All values represent the average across production systems and districts included in the simulations. Error bars denote one standard error from the mean.

Source data

Extended Data Fig. 3 Spatial data and depiction of main cattle breeds considered in study.

(a) location of study region within Tanzania showing regions and districts used in simulations, (b) improved (Bos taurus x Bos indicus) and (c) local cattle (Bos indicus) breeds considered in model, (d), production systems simulated (MRT = Mixed rainfed tropical, MRH = mixed rainfed humid), (e) dairy breed composition for base year (2018) as % improved cattle for each simulated district. Base year herd genetic compositions are based on the Greening Livestock Survey (GLS 2019). Maps and photographs attributable to authors of this paper.

Extended Data Fig. 4 Model overview.

Calibration involves specifying parameters from the household survey, for local and improved cattle in the livestock simulation model LivSim, herd population and activity data for life cycle assessment (LCA), and number of dairy households per district. Simulations represent respectively a Baseline (‘Business as usual’) and four scenarios involving variations of roadmap objectives. Impact indicators include dairy GHG emissions quantified using the LCA and land footprint indicator, and household income based on the milk yield, herd sizes, and input use associated with each scenario.

Extended Data Table 1 Milk yields and cattle populations to 2030 per scenario
Extended Data Table 2 Description of five scenarios
Extended Data Table 3 Household characteristics by type and district
Extended Data Table 4 Milk price and dairy cost parameters used in estimation of dairy income by district and household type

Supplementary information

Supplementary Information

Supplementary Sections 1–4, Tables 1–10 and equations (1)–(3).

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Source Data Fig. 2

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Source Data Fig. 3

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Source Data Extended Data Fig. 1

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Source Data Extended Data Fig. 2

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Hawkins, J.W., Komarek, A.M., Kihoro, E.M. et al. High-yield dairy cattle breeds improve farmer incomes, curtail greenhouse gas emissions and reduce dairy import dependency in Tanzania. Nat Food 3, 957–967 (2022).

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