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

Nature Food volume 3, pages 957–967 (2022)Cite this article

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Abstract

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: https://github.com/James-Hawkins/Tanzania-Dairy-Mitigation-Assessment. Unprocessed, anonymized survey data used as parameters in the model are available from: https://doi.org/10.17635/lancaster/researchdata/563. External databases used in the study as cited in the text include: Feedipedia, available at https://www.feedipedia.org; Gridded Livestock of the World, available at https://www.fao.org/livestock-systems/global-distributions/cattle/en/; and European Space Agency Land Cover Data, available at https://www.esa-landcover-cci.org. Source data are provided with this paper.

Code availability

The code used for this study is available in the public GitHub repository: https://github.com/James-Hawkins/Tanzania-Dairy-Mitigation-Assessment

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Acknowledgements

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.

Author information

Author notes

  1. Gabriel U. Yesuf

    Present address: Department of Geography & Environmental Science, University of Reading, Reading, UK

Authors and Affiliations

  1. Lancaster Environment Centre, Lancaster University, Lancaster, UK

    James W. Hawkins, Gabriel U. Yesuf & Mariana C. Rufino

  2. Center for International Forestry Research (CIFOR), Nairobi, Kenya

    James W. Hawkins & George C. Schoneveld

  3. School of Agriculture and Food Sciences, University of Queensland, St Lucia, Queensland, Australia

    Adam M. Komarek

  4. International Livestock Research Institute (ILRI), Nairobi, Kenya

    Esther M. Kihoro, Amos O. Omore & Polly J. Ericksen

  5. Department of Agricultural & Applied Economics, University of Wisconsin-Madison, Madison, WI, USA

    Charles F. Nicholson

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Contributions

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
Full size table
Extended Data Table 2 Description of five scenarios
Full size table
Extended Data Table 3 Household characteristics by type and district
Full size table
Extended Data Table 4 Milk price and dairy cost parameters used in estimation of dairy income by district and household type
Full size table

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). https://doi.org/10.1038/s43016-022-00633-5

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