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East African food security as influenced by future climate change and land use change at local to regional scales
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  • Published: 10 June 2011

East African food security as influenced by future climate change and land use change at local to regional scales

  • Nathan Moore1,2,8,
  • Gopal Alagarswamy2,
  • Bryan Pijanowski3,
  • Philip Thornton4,
  • Brent Lofgren5,
  • Jennifer Olson6,
  • Jeffrey Andresen2,
  • Pius Yanda7 &
  • …
  • Jiaguo Qi2 

Climatic Change volume 110, pages 823–844 (2012)Cite this article

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Abstract

Climate change impacts food production systems, particularly in locations with large, vulnerable populations. Elevated greenhouse gases (GHG), as well as land cover/land use change (LCLUC), can influence regional climate dynamics. Biophysical factors such as topography, soil type, and seasonal rainfall can strongly affect crop yields. We used a regional climate model derived from the Regional Atmospheric Modeling System (RAMS) to compare the effects of projected future GHG and future LCLUC on spatial variability of crop yields in East Africa. Crop yields were estimated with a process-based simulation model. The results suggest that: (1) GHG-influenced and LCLUC-influenced yield changes are highly heterogeneous across this region; (2) LCLUC effects are significant drivers of yield change; and (3) high spatial variability in yield is indicated for several key agricultural sub-regions of East Africa. Food production risk when considered at the household scale is largely dependent on the occurrence of extremes, so mean yield in some cases may be an incomplete predictor of risk. The broad range of projected crop yields reflects enormous variability in key parameters that underlie regional food security; hence, donor institutions’ strategies and investments might benefit from considering the spatial distribution around mean impacts for a given region. Ultimately, global assessments of food security risk would benefit from including regional and local assessments of climate impacts on food production. This may be less of a consideration in other regions. This study supports the concept that LCLUC is a first-order factor in assessing food production risk.

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Authors and Affiliations

  1. College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China

    Nathan Moore

  2. CGCEO, Michigan State University, 202 Manly Miles Bldg, East Lansing, MI, 48823, USA

    Nathan Moore, Gopal Alagarswamy, Jeffrey Andresen & Jiaguo Qi

  3. Department of Forestry and Natural Resources, Purdue University, 195 Marsteller St, FORS203, West Lafayette, IN, 47906, USA

    Bryan Pijanowski

  4. International Livestock Research Institute, PO Box 30709, Nairobi, 00100, Kenya

    Philip Thornton

  5. Great Lakes Env. Research Lab, 4840 S. State Road, Ann Arbor, MI, 48108-9719, USA

    Brent Lofgren

  6. Communication Arts and Sciences, Michigan State University, 202 Manly Miles Bldg, East Lansing, MI, 48823, USA

    Jennifer Olson

  7. Institute of Resources Assessment, University of Dar Es Salaam, PO Box 35097, Dar Es Salaam, Tanzania

    Pius Yanda

  8. Department of Geography, Michigan State University, 202 Manly Miles Bldg, East Lansing, MI, 48823, USA

    Nathan Moore

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  1. Nathan Moore
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Correspondence to Nathan Moore.

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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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Moore, N., Alagarswamy, G., Pijanowski, B. et al. East African food security as influenced by future climate change and land use change at local to regional scales. Climatic Change 110, 823–844 (2012). https://doi.org/10.1007/s10584-011-0116-7

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  • Received: 11 August 2009

  • Accepted: 16 May 2011

  • Published: 10 June 2011

  • Issue Date: February 2012

  • DOI: https://doi.org/10.1007/s10584-011-0116-7

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Keywords

  • Regional Climate Model
  • Maize Yield
  • Crop Model
  • Yield Change
  • Community Climate System Model
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