Developing an Index-Based Methodology to Forecast the Integrated Risk of Extreme Weather to Agricultural Production Systems
Citation
Newlands, N.K. Developing an Index-Based Methodology to Forecast the Integrated Risk of Extreme Weather to Agricultural Production Systems. Section on Risk Analysis, Invited Session on Statistical Modeling for Climate Risk Assessment at the Interface of Climate Change and Insurance -Invited Papers, Joint Statistical Meetings (JSM) , July 30-August 4th, 2016, Chicago, Illinois, USA.
Plain language summary
Farmers, agri-business, the insurance/reinsurance industry, corporate risk/liability analysts, commodity market traders and multi-jurisdictional government policy analysts and decision-makers all require timely, reliable and useful information on the real-world impacts of extreme weather. The agricultural sector is highly exposed to a wide range of weather-related threats and risks that have cumulative, integrated impacts on crop and livestock production. I will discuss a new integrated risk-based methodology using machine-learning techniques to forecast integrated agricultural risk due to extreme weather. New indices derived from remote-sensing data, ground-based weather networks and insurance data aim to be included. A prototype design for a new decision-support tool that enables agricultural stakeholders/end-users an ability to benchmark their risk and explore adaptation options for maximizing risk benefits and minimizing exposure and disaster costs (disease, pests, floods, droughts) will also be discussed.
Abstract
Farmers, agri-business, the insurance/reinsurance industry, corporate risk/liability analysts, commodity market traders and multi-jurisdictional government policy analysts and decision-makers all require timely, reliable and useful information on the real-world impacts of extreme weather. The agricultural sector is highly exposed to a wide range of weather-related threats and risks that have cumulative, integrated impacts on crop and livestock production. I will discuss a new integrated risk-based methodology using machine-learning techniques to forecast integrated agricultural risk due to extreme weather. New indices derived from remote-sensing data, ground-based weather networks and insurance data aim to be included. A prototype design for a new decision-support tool that enables agricultural stakeholders/end-users an ability to benchmark their risk and explore adaptation options for maximizing risk benefits and minimizing exposure and disaster costs (disease, pests, floods, droughts) will also be discussed.