A global meta-analysis of climate services and decision-making in agriculture

• Main Authors: Lorna Born, Steven Prager, Julian Ramirez-Villegas, Pablo Imbach
• Format: Article
• Language: English
• Published: Elsevier 2021-04-01
• Series: Climate Services
• Subjects: Climate services; Farmer decision-making; Co-production; Climate forecast; Agricultural management decisions
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405880721000194

Harmonizing the supply of climate information with the type of information needed by next-users is crucial for effective weather and climate services (CS). Understanding of information demand could help reshape supply-side based CS that have dominated the field over the last few decades. Most CS have been developed using a ‘loading dock’ model, whereby products are designed by information suppliers with little input from or consultation with users of climate services. Notably, a focus on climate modelling and prediction has largely resulted in a lack of consideration of the demand-side when producing climate services. Here, we contribute to understanding of CS demand by presenting a global meta-analysis – a ‘decision matrix’ - of farmers’ climate-influenced decisions. We identify 41 studies that encompass 186 decisions, three forecast timescales (weather, dekadal, seasonal), and five forecast variables (precipitation, temperature, wind, soil moisture and soil temperature). Several insights were offered by this literature review into the value of climate services and the way forward in considering users’ needs. We find that the seasonal precipitation is the most frequently used forecast variable for decision-making, particularly of crop sowing date. Forecasts such as temperature, soil moisture and soil temperature appeared to be less used by farmers, according to the decision matrix. It is apparent that more investigation is necessary into how farmers use climate information in their decision-making to better establish the value of CS. We suggest that different sectors should make their respective decision matrices to explore decision spaces and engage with users of climate information in various sectors.