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Climate change causes frequent and severe disasters. A comprehensive assessment of disaster hazards is thus crucial to understanding variations in disaster patterns and planning mitigation and adaptation strategies. This study obtained information from a crop disaster dataset of Taiwan covering the period from 2003 to 2022. Additionally, principal component analysis and K-means clustering were used to create climate clusters to evaluate the effect of climate patterns on disaster hazards. The results revealed that tropical storm frequency substantially decreased, whereas rain disasters exhibited an increasing trend. The four regions of Taiwan exhibited variations in terms of hazards of various disasters. The cold wave hazard showed a significant upward trend in the central region. An upward trend of rain disaster hazards was only detected in the southern region. However, a downward trend in tropical storm hazards was detected across all regions. A distinct climate pattern was identified over the study period. After 2012, high temperature and dry climate were the primary climate patterns. These patterns exhibited a high hazard value for cold waves, droughts, and rain disasters. Hence, the present study’s findings indicate that managing cold waves and rain disasters is crucial to protecting crop production in Taiwan.

Rice (Oryza sativa L.) is a crucial staple crop globally but is damaged under extreme precipitation. Risk assessment for heavy rain (HR) damage events is essential for developing strategies for adapting to climate change. In this study, weather and rice damage data were used to assess the risk of HR damage events in Taiwan. These events were classified into nontyphoon-caused HR (NTCHR) and typhoon-caused HR (TCHR) events. The temporal, spatial, and weather characteristics of HR damage events were selected as risk factors for rice HR damage. Logistic regression was used to evaluate the effects of the selected risk factors on the occurrence and severity of HR damage events. The odds of an NTCHR damage event were 4.33 and 4.17 times higher in the reproductive and ripening stages, respectively, than during the vegetative stage. Moreover, each 1 mm increase in the maximum daily precipitation increased the odds of an NTCHR and TCHR damage event by 2% and 3%, respectively. In this study, the documentary data of damage events present a potential for assessment of weather damage event risk. Moreover, the risk of rice HR damage events in Taiwan is affected by not only weather but also temporal and spatial factors.

Rice is a staple food crop in Asia. The rice farming industry has been influenced by global urbanization, rapid industrialization, and climate change. A combination of precise agricultural and smart water management systems to investigate the nutrition state in rice is important. Results indicated that plant nitrogen and chlorophyll content at the maximum tillering stage were significantly influenced by the interaction between water and fertilizer. The normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE), obtained from the multispectral images captured by a UAV, exhibited the highest positive correlations (0.83 and 0.82) with plant nitrogen content at the maximum tillering stage. The leave-one-out cross-validation method was used for validation, and a final plant nitrogen content prediction model was obtained. A regression function constructed using a nitrogen nutrition index and the difference in field cumulative nitrogen had favorable variation explanatory power, and its adjusted coefficient of determination was 0.91. We provided a flow chart showing how the nutrition state of rice can be predicted with the vegetation indices obtained from UAV image analysis. Differences in field cumulative nitrogen can be further used to diagnose the demand of nitrogen topdressing during the panicle initiation stage. Thus, farmers can be provided with precise panicle fertilization strategies for rice fields.