TY - JOUR
T1 - Forecast probability, lead time and farmer decision-making in rice farming systems in Northern Ghana
AU - Nyamekye, Andy Bonaventure
AU - Nyadzi, Emmanuel
AU - Dewulf, Art
AU - Werners, Saskia
AU - Van Slobbe, Erik
AU - Biesbroek, Robbert G.
AU - Termeer, Catrien J.A.M.
AU - Ludwig, Fulco
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Climate services
KW - Farmer decision-making
KW - Forecast lead times
KW - Forecast probability
U2 - 10.1016/j.crm.2020.100258
DO - 10.1016/j.crm.2020.100258
M3 - Article
AN - SCOPUS:85097093143
SN - 2212-0963
VL - 31
JO - Climate Risk Management
JF - Climate Risk Management
M1 - 100258
ER -