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Measuring baseline agriculture-related sustainable development goals index for southern Africa

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Listed:
  • Nhemachena, Charles
  • Matchaya, Greenwell
  • Nhemachena, C. R.
  • Karuaihe, S.
  • Muchara, B.
  • Nhlengethwa, Sibusiso

Abstract

Sustainable development has become the main focus of the global development agenda as presented in the 2015 Sustainable Development Goals (SDGs). However, for countries to assess progress, they need to have reliable baseline indicators. Therefore, the objective of this paper is to develop a composite baseline index of the agriculture-related SDGs in Southern Africa to guide progress reporting. The paper identified eight of the SDG indicators related to the agriculture sector. The paper relies on data for indicators from five SDGs (SDGs 1, 2, 6, 7 and 15). Applying the arithmetic mean method of aggregation, an agriculture-related SDG composite index for Southern Africa between zero (0 = poor performance) and 100 (best possible performance) was computed for thirteen countries that had data on all identified indicators. The results show that the best performing countries (Botswana, Angola, Namibia, Zambia and South Africa) in the assessment recorded high scores in SDGs 1, 2 and 7. The three countries (Democratic Republic of Congo, Zimbabwe and Madagascar) that performed poorly on both SDG 1 and 2 also had the least scores on the overall agriculture-related SDG composite index. The water stress indicator for SDG 6 recorded the worst performance among most countries in the region. Possible approaches to improve the contribution of agriculture to SDGs may include investing more resources in priority areas for each agriculture-related SDG depending on baseline country conditions. The implementation, monitoring and evaluation of regional and continental commitments in the agriculture sector and the SDGs are critical for achievement of the targets at the national and local levels. While the methods employed are well-grounded in literature, data unavailability for some of the SDGs in some countries presented a limitation to the study, and future efforts should focus on collecting data for the other SDGs in order to permit a wider application.

Suggested Citation

  • Nhemachena, Charles & Matchaya, Greenwell & Nhemachena, C. R. & Karuaihe, S. & Muchara, B. & Nhlengethwa, Sibusiso, 2018. "Measuring baseline agriculture-related sustainable development goals index for southern Africa," Papers published in Journals (Open Access), International Water Management Institute, pages 10(3):1-16..
    • Handle: RePEc:iwt:jounls:h048613
    DOI: 10.3390/su10030849
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    References listed on IDEAS

    as
    1. Nhemachena, Charles & Matchaya, Greenwell & Nhlengethwa, Sibusiso & Nhemachena, C. R., . "Exploring ways to increase public investments in agricultural water management and irrigation for improved agricultural productivity in Southern Africa," Papers published in Journals (Open Access), International Water Management Institute, pages 44(3):474-4.
    2. George Kararach & Godwell Nhamo & Maurice Mubila & Senia Nhamo & Charles Nhemachena & Suresh Babu, 2018. "Reflections on the Green Growth Index for developing countries: A focus of selected African countries," Development Policy Review, Overseas Development Institute, vol. 36(S1), pages 432-454, March.
    3. L Cherchye & W Moesen & N Rogge & T Van Puyenbroeck & M Saisana & A Saltelli & R Liska & S Tarantola, 2008. "Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 239-251, February.
    4. Andrea Saltelli, 2007. "Composite Indicators between Analysis and Advocacy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 81(1), pages 65-77, March.
    5. M. Saisana & A. Saltelli & S. Tarantola, 2005. "Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 307-323, March.
    6. Zhou, P. & Ang, B.W. & Poh, K.L., 2007. "A mathematical programming approach to constructing composite indicators," Ecological Economics, Elsevier, vol. 62(2), pages 291-297, April.
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    Sustainable Development Goals;

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