Forecast probability, lead time and farmer decision-making in rice farming systems in Northern Ghana

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Abstract

Rice farmers in Northern Ghana are susceptible to climate variability and change with its effects in the form of drought, water scarcity, erratic rainfall and high temperatures. In response, farmers resort to weather and seasonal forecast to manage uncertainties in decision-making. However, there is limited empirical research on how forecast lead time and probabilities influence farmer decision-making. In this study, we posed the overall question: how do rice farmers respond to forecast information with different probabilities and lead times? We purposively engaged 36 rice farmers (12 rainfed, 12 irrigated and 12 practising both) in Visually Facilitated Scenario Mapping Workshops (VFSMW) to explore how probabilities and lead times inform their decision-making. Results of the VFSMW showed rainfed rice farmers are most sensitive to forecast probabilities because of their over-reliance on rainfall. Also, an increase in forecast probability does not necessarily mean farmers will act. The decision to act based on forecast probability is dependent on the stage of the farming cycle. Also, seasonal forecast information provided at a 1 month lead time significantly informed farmer decision-making compared to a lead time of 2 or 3 months. Also, weather forecast provided at a lead time of 1 week is more useful for decision-making than at a 3 day or 1 day lead time. We conclude that communicating forecast information with their probabilities and at an appropriate lead time has the potential to help farmers manage risks and improve decision-making. We propose that climate services in Northern Ghana should aim at communicating weather and seasonal climate forecast information at 1 week and 1 month lead times respectively. Farmers should also adapt their decisions to the timing and probabilities of the forecast provided.

Original languageEnglish
Article number100258
JournalClimate Risk Management
DOIs
Publication statusE-pub ahead of print - 25 Nov 2020

Keywords

  • Climate services
  • Farmer decision-making
  • Forecast lead times
  • Forecast probability

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