Climate change will create significant challenges to agriculture. The effects on livestock productivity and crop production are highly dependent on weather conditions with consequences for food security. If agriculture is to remain a viable industry and to maintain future food security, the adaptations and the ideal timeframes for their implementation to mitigate against climate change impacts will be essential knowledge. This study aims to show how farms will be affected and will need to adapt to climate change, based on a holistic examination of the entire farming process. A modified Bayesian belief network (BBN) was used to investigate climate change impacts on livestock, crops, soil, water use, disease, and pesticide use through the use of 48 indicators (comprising climate, agricultural, and environmental). The seasonal impact of climate change on all aspects of farming was investigated for three different climate forcing scenarios (RCPs 2.6, 4.5, and 8.5) for four timeframes (2030, 2050, 2080, and 2099). The results suggest that heat stress and disease in both livestock and crops will require adaptations (e.g., shelter infrastructure being built, new crops, or cultivators grown). Pest intensity is expected to rise, leading to increased pesticide use and greater damage to crops and livestock. Higher temperatures will likely cause increased drought and irrigation needs, while increasing rain intensity might lead to winter flooding. Soil quality maintenance will rely increasingly on fertilisers, with significant decreases in quality if unsustainable. Crop yield will be dependent on new crops or cultivators that can cope with a changing climate being successful and market access; failure to do so could lead to substantial decrease, in food security. Impacts are more significant from 2080 onwards, with the severity of impacts dependent on season.

Tea is a vital agricultural product in Taiwan. Due to global warming, the increasing extreme weather events have disrupted tea garden conditions and caused economic losses in agriculture. To address these challenges, a comprehensive tea garden risk assessment model, a Bayesian network (BN), was developed by considering various factors, including meteorological data, disaster events, tea garden environment (location, altitude, tea tree age, and soil characteristics), farming practices, and farmer interviews, and constructed risk assessment indicators for tea gardens based on the climate change risk analysis concept from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The results demonstrated an accuracy of over 92% in both validating and testing the model for tea tree damage and yield reduction. Sensitivity analysis revealed that tea tree damage and yield reduction were mutually influential, with weather, fertilization, and irrigation also impacting tea garden risk. Risk analysis under climate change scenarios from various global climate models (GCMs) indicated that droughts may pose the highest risk with up to 41% and 40% of serious tea tree growth damage and tea yield reduction, respectively, followed by cold events that most tea gardens may have less than 20% chances of serious impacts on tea tree growth and tea yield reduction. The impacts of heavy rains get the least concern because all five tea gardens may not be affected in terms of tea tree growth and tea yield with large chances of 67 to 85%. Comparing farming methods, natural farming showed lower disaster risk than conventional and organic approaches. The tea plantation risk assessment model can serve as a valuable resource for analyzing and offering recommendations for tea garden disaster management and is used to assess the impact of meteorological disasters on tea plantations in the future.