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  • JAMES W. HANSEN (a1) (a2), SIMON J. MASON (a2), LIQIANG SUN (a2) and ARAME TALL (a3)

We review the use and value of seasonal climate forecasting for agriculture in sub-Saharan Africa (SSA), with a view to understanding and exploiting opportunities to realize more of its potential benefits. Interaction between the atmosphere and underlying oceans provides the basis for probabilistic forecasts of climate conditions at a seasonal lead-time, including during cropping seasons in parts of SSA. Regional climate outlook forums (RCOF) and national meteorological services (NMS) have been at the forefront of efforts to provide forecast information for agriculture. A survey showed that African NMS often go well beyond the RCOF process to improve seasonal forecast information and disseminate it to the agricultural sector. Evidence from a combination of understanding of how climatic uncertainty impacts agriculture, model-based ex-ante analyses, subjective expressions of demand or value, and the few well-documented evaluations of actual use and resulting benefit suggests that seasonal forecasts may have considerable potential to improve agricultural management and rural livelihoods. However, constraints related to legitimacy, salience, access, understanding, capacity to respond and data scarcity have so far limited the widespread use and benefit from seasonal prediction among smallholder farmers. Those constraints that reflect inadequate information products, policies or institutional process can potentially be overcome. Additional opportunities to benefit rural communities come from expanding the use of seasonal forecast information for coordinating input and credit supply, food crisis management, trade and agricultural insurance. The surge of activity surrounding seasonal forecasting in SSA following the 1997/98 El Niño has waned in recent years, but emerging initiatives, such as the Global Framework for Climate Services and ClimDev-Africa, are poised to reinvigorate support for seasonal forecast information services for agriculture. We conclude with a discussion of institutional and policy changes that we believe will greatly enhance the benefits of seasonal forecasting to agriculture in SSA.

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M. Fafchamps (2003). Rural Poverty, Risk and Development. Cheltenham, UK: Edward Elgar Publishing.

S. J. Mason (2008). ‘Flowering walnuts in the wood’ and other bases for seasonal climate forecasting. In Seasonal Forecasts, Climatic Change and Human Health, 1329 (Eds M. C. Thomson , R. Garcia-Herrera and M. Beniston ). Dordrecht: Springer.

M. Morris , V. A. Kelly , R. Kopicki and D. Byerlee (2007). Fertilizer Use in African Agriculture: Lessons Learned and Good Practice Guidelines. Washington, DC: World Bank.

R. Rengalakshmi (2007). Localized climate forecasting system: seasonal climate and weather prediction for farm level decision-making. In Climate Prediction and Agriculture: Advances and Challenges, 129134 (Eds M. V. K. Sivakumar and J. W. Hansen ). Berlin: Springer.

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Experimental Agriculture
  • ISSN: 0014-4797
  • EISSN: 1469-4441
  • URL: /core/journals/experimental-agriculture
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