Climate Risk Management in Agriculture: Monthly and Seasonal Forecast Application

Sustainable agricultural production is vital for food security and agricultural productivity. It is greatly influenced by weather and climate conditions. This book focuses on understanding weather and climate systems and crop yield productions, including integrated weather-crop prediction systems for climate risk management in agriculture. It examines the impact of climate change and its variability on different crops, and possible ways to minimize the loss for farmers. This book also describes different weather and climate hazards, including the fundamentals of weather/climate prediction systems and numerical weather prediction (NWP) models. It presents the need for seamless weather/climate predictions and their impact on agriculture. The status and availability of different monthly and seasonal scale forecasts worldwide is explored and how the forecasting models or products can be evaluated using statistical methods. The book concisely elucidates systematic model bias removal techniques and a reliable approach based on multi-statistics in producing a single forecast from the multi-model grand ensemble. Since crop models need daily weather sequence, several standard disaggregation methods for generating daily weather sequences from monthly/seasonal products are presented. This book describes several aspects that are needed for agricultural practices and crop modelling. It encapsulates different components of crop models and their application, preparation methods of Crop Weather Calendar, application of disaggregated weather sequence in crop models, and generation of Climate Risk Matrices (CRM). A detailed methodology is presented for hands-on practice, including downloading and processing data, model evaluation and bias corrections, generating a single forecast, disaggregation, and preparing CRM based on crop model products. This book contains a total 11 chapters and appeals to students, researchers, scientists, and operational agencies.