Assessing the Economic Value of El Niño-Based Seasonal Climate Forecasts for Smallholder Farmers in Zimbabwe

  • Ephias M. MakaudzeEmail author


This study demonstrates the potential value of seasonal forecasts to smallholder farmers in Zimbabwe—the majority of whom often suffer severely from drought impacts. Using simulation models to compare crop yield performances of farmers with and without forecasts, results indicate that: during a “drought year”, farmers with forecasts (WF) record higher yield gains (28 %) compared to those without forecasts (WOF); during a “neutral year” WF farmers obtain higher yield gains (20 %) than those WOF; however, during a “good year”, results show no yield gains as WOF farmers perform better. This suggests that during a good year, forecasts may not have a significant impact. Using gross margin analysis, results show WF farmers realizing higher returns (US$0.14/ha) during a drought than WOF farmers who net a negative return (−US$0.15/ha).To conclude, El Niño-based seasonal forecasts could play an important role as loss mitigation measures particularly during a drought.


Seasonal climate forecasts Smallholder farmers El Niño Economic value Drought With forecasts and without forecasts 



This paper received support from the Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria, in partnership with the Swedish International Development Authority. The author benefited from various comments provided by CEEPA resource personnel. In addition, the author acknowledges support received from Economic Research Southern Africa (ERSA). Finally, special thanks to Dr. Sebinasi Dzikiti of the Centre for Scientific and Industrial Research (CSIR Stellenbosch, Cape Town), who offered technical expertise on the DSSAT (Decision Support System for Agricultural Technology) program.


  1. Anyamba A, Eastman JR (1996) Interannual variability of NDVI over Africa and its relation to EL Niño/Southern oscillation. Int J Remote Sensing 17:2533–2548Google Scholar
  2. Blench R (1999) Seasonal climatic forecasting: who can use it and how should it be disseminated? Overseas Development Institute, London, UK. Natural Resource Perspective 47. Available at Accessed 20 Dec 2014)
  3. Easterling WE, Mjelde JW (1987) The importance of seasonal climate prediction lead time in agricultural decision making. Agric For Meteorol 40:37–50CrossRefGoogle Scholar
  4. Hansen JW, Challinor A, Ines A, Wheeler T, Moron V (2006) Translating climate forecasts into agricultural terms: advances and challenges. Clim Res 33:27–41CrossRefGoogle Scholar
  5. Hansen JW, Simons JM, Liqiang S, Tall A (2010) Review of seasonal climate forecasting for agriculture in Sub-Saharan Africa. Exp Agric 47(2):205–240CrossRefGoogle Scholar
  6. Kogan FN (1998) A typical pattern of vegetation conditions in southern Africa during El-Nino years detected from AVHRR data using three-channel numerical index. Int J Remote Sens 19(18):3689–3695CrossRefGoogle Scholar
  7. Lobell DB, Burke MB, Tebaldi C, Mastrandrea MD (2008) Prioritizing climate change adaptation needs for food security in 2030. Science 319(5863):607–610CrossRefPubMedGoogle Scholar
  8. Makarau A (1992) National drought and desertification polices. The Zimbabwe situation. SADC regional workshop on climate change, 1992, Windhoek, NamibiaGoogle Scholar
  9. Makaudze E (2009) Do seasonal forecasts and crop insurance really matter for smallholder farmers in Zimbabwe? Using contingency valuation and remote sensing techniques. VCD Verlag Dr. Muller, Weinheim, GermanyGoogle Scholar
  10. Mason SJ, Jury MR (1997) Climatic variability and change over southern Africa: a reflection on the underlying process. Prog Phys Geogr 21:23–50CrossRefGoogle Scholar
  11. Mason SJ (2001) El Nino, climate change and southern African climate. Environ-metrics 12:327–345Google Scholar
  12. Meza FJ, Wilks DS, Riha SJ, Stedinger JR (2003) Value of perfect forecasts of the sea surface temperature anomalies for selected rain-fed agricultural locations of Chile. Agric For Meteorol 116(3–4):117–135CrossRefGoogle Scholar
  13. Mjelde JW, Dixon BL (1993) Valuing the lead time of periodic forecasts in dynamic production systems. Agric Syst 42(1–2):41–55CrossRefGoogle Scholar
  14. Mjelde JW, Peel DS, Sonka ST, Lamb PJ (1993) Characteristics of climate forecast quality: implications for economic value to Midwestern corn producers. J Clim 11:2175–2187Google Scholar
  15. Mjelde JW, Penson JB, Nixon CJ (2000) Dynamic aspects of the impact of the use of perfect climate forecasts in the corn belt region. J Appl Meteorol 39:67–79CrossRefGoogle Scholar
  16. Msangi S, Rosegrant MW, You L (2006) Ex-post assessment methods of climate forecast impacts. Clim Res 33:67–79CrossRefGoogle Scholar
  17. Patt A (2001) Understanding uncertainty: Forecasting seasonal climate for farmers in Zimbabwe. Risk Decis Policy 6:105–119CrossRefGoogle Scholar
  18. Patt A, Gwata C (2002) Effective seasonal climate forecast applications: examining constraints for subsistence farmers in Zimbabwe. Glob Environ Change 12:185–195CrossRefGoogle Scholar
  19. Patt A, Suarez P, Gwata C (2005) Effects of seasonal climate forecasts and participatory workshops among subsistence farmers in Zimbabwe. PNAS 102(35):12623–12628CrossRefPubMedPubMedCentralGoogle Scholar
  20. Reyes CM, Gonzales KG, Predo CD, de Guzman RG (2009) Assessing the value of seasonal climate forecasts on farm-level corn production through simulation modeling. Discussion paper series no. 2009–2005, Makati City, PhilippinesGoogle Scholar
  21. Rubas DJ, Hill HSJ, Mjelde JW (2006) Economics and climate applications: exploring the frontier. Clim Res 33:43–54CrossRefGoogle Scholar
  22. SADC (Southern African Development Community) (1993) Report of the regional drought management workshop, volume 1, workshop proceedings. SADC Drought Management Workshops in Southern Africa, Harare, ZimbabweGoogle Scholar
  23. Solow AR, Adams RF, Bryant KJ et al (1998) The value of improved ENSO predictions of U.S. agriculture. Clim Change 39:47–60CrossRefGoogle Scholar
  24. Vogel C (2000) Usable science: an assessment of long-term seasonal forecasts amongst farmers in rural areas of South Africa. S Afr Geogr J 82:107–116CrossRefGoogle Scholar
  25. WMO (World Meteorological Organization) (1995) The global climate system review: climate system monitoring, June 1991–Nov 1993. WMO, No. 819Google Scholar

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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of the Western CapeBellville, Cape TownSouth Africa

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