Seasonal climate forecasts (SCFs) have the potential to improve productivity and profitability in agricultural industries, but are often underutilised due to insufficient evidence of the economic value of forecasts and uncertainty about their reliability. In this study we developed a bio-economic model of forecast use, explicitly incorporating forecast uncertainty. Using agricultural systems (ag-systems) production simulation software calibrated with case study information, we simulated pasture growth, herd dynamics and annual economic returns under different climatic conditions. We then employed a regret and value function approach to quantify the potential economic value of using SCFs (at both current and improved accuracy levels) in decision making for a grazing enterprise in north-eastern Queensland, Australia – a region subject to significant seasonal and intra-decadal climate variability. Applying an expected utility economic modelling approach, we show that skilled SCF systems can contribute considerable value to farm level decision making. At the current SCF skill of 62% (derived by correlating the El Niño Southern Oscillation (ENSO) signal and historical climate data) at Charters Towers, an average annual forecast value of AU$4420 (4.25%) was realised for the case study average annual net profit of AU$104000, while a perfect (no regret) forecast system could result in an increased return of AU$13475 per annum (13% of the case study average annual net profit). Continued improvements in the skill and reliability of SCFs is likely to both increase the value of SCFs to agriculture and drive wider uptake of climate forecasts in on-farm decision making. We also anticipate that an integrated framework, such as that developed in this study, may provide a pathway for better communication with end users to support improved understanding and use of forecasts in agricultural decision making and enhanced sustainability of agricultural enterprises.
Engaging farmers on climate risk through targeted integration of bio-economic modelling and seasonal climate forecasts
