The Effect of El Niño Southern Oscillation (ENSO) on Climate Change and Cereal Crops Production in Thailand Using Artificial Intelligence Based Models

Certainly! Here's a translation of the provided text into English:

The abnormalities of the El Niño Southern Oscillation (ENSO) phenomenon impact weather variability across many regions of the world, especially in Southeast Asia, including Thailand. ENSO affects temperature and rainfall variations, leading to large-scale climate anomalies such as severe droughts, hurricanes, and floods. These anomalies directly affect agricultural yields. According to related documents and research, the variability in cereal crop yields and harvesting areas correlates with annual weather conditions, with significant yield reductions occurring during El Niño events.

The objective of this research is to apply fundamental artificial intelligence techniques to investigate and assess the impact of ENSO on cereal production in Thailand and to predict future impacts on cereal production. This study will be significant for policymakers as it aims to utilize ENSO signals to manage the impacts on production and ensure production resilience, which is crucial for Thailand. The research aims to develop AI-based models that will help in assessing the extent of El Niño and La Niña impacts on cereal production across various regions in Thailand. 

This study will highlight how ENSO anomalies contribute to weather variability affecting agricultural yields. It will identify specific climate variables with significant influence on cereal production in Thailand. Understanding these links will aid in developing targeted adaptation strategies for improved crop management. The study will create and identify the impacts of El Niño and La Niña on temperature and rainfall patterns in Thailand. This knowledge is essential for forecasting and preparing for extreme weather events that adversely affect crop production.

The research will use developed models to predict future cereal production scenarios influenced by ENSO. These forecasts will enable policymakers and stakeholders to make informed decisions to mitigate risks and ensure production readiness amidst changing weather conditions. The findings of this research will have policy implications by providing evidence-based insights on how to use ENSO signals to mitigate production disruptions and ensure food security in Thailand. Crop producers will find adaptation strategies derived from the analysis useful for making informed decisions during El Niño and La Niña events. 

The research will improve crop management practices by offering recommendations on crop selection, planting schedules, and irrigation techniques based on anticipated El Niño and La Niña events. This could lead to enhanced agricultural resilience and productivity. The study will use AI models to gain a deeper understanding of the complex interactions between climate factors, El Niño, La Niña, and cereal production. This knowledge will enhance climate risk management and sustainable agricultural practices.

The results of this research proposal will contribute to the existing knowledge on the impacts of ENSO on cereal production in Thailand. Integrating AI methodologies with weather and crop data can increase prediction accuracy, optimize management strategies, and bolster resilience in agriculture amidst changing climate conditions. The outcomes will be valuable for policymakers, farmers, and stakeholders involved in promoting food security and development in Thailand.