Integrating seasonal forecast information with crop models to inform decision making in small-scale farming under climate variability

Abstract

Integrating seasonal forecast information and crop models has the potential to inform farm management decisions under climate variability. The study assessed the feasibility of integrating seasonal forecast information into crop models for decision making in small-scale farming conditions in South Africa. Seasonal forecast outputs from the GCM, CFSv2, were coupled into the DSSAT v4.7 crop model to evaluate the impact of farm management decisions in Limpopo, South Africa. Historical weather and seasonal forecast data for the 2011–2017 and 2017/2018 seasons were utilised to set up and validate decision scenarios. The analysis of maize yield data under different combinations of management practices and seasonal forecasts yielded a range of decision scenarios. Overall, there were no notable differences in farm management decision scenarios among different farmer types. Integrating seasonal forecast information into crop models offers valuable insights in cases where decision capacity is low and climate sensitivity is high, as well as where decision capacity is high and climate sensitivity is weak. The decision support system proved more effective for cereal and vegetable crops than for legumes. In conclusion, integrating seasonal forecast information into crop models is a feasible approach for enhancing farm management decision making in South African small-scale farming systems.