Weather Intelligence for Climate-Resilient Agriculture

Climate-smart agriculture (CSA) is considered the best solution to cope with the adverse impacts of climate change by adopting resilient practices. By adopting CSA practices, farmers can adapt to climate changes through proper monitoring and forecasting of weather conditions. Therefore, this study aims to discuss different approaches and advancements in weather monitoring and forecasting systems relevant to CSA. Various weather monitoring systems based on remote sensing, IoT, and other techniques are discussed. Automatic weather stations are also explored as they help record, store, and retrieve real-time weather data. In addition, different regional climate models are presented that provide predictions of climate and regional-level climatic data for ungauged watersheds. This chapter also highlights different weather forecasting methods at various temporal and spatial scales, including Artificial Neural Networks (ANN) and Machine Learning (ML), as well as various soft computing tools. Overall, this study provides insights into the importance of weather monitoring and forecasting systems in agriculture and the potential for using modern technologies to address climate change impacts.