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Global warming

Climate Change Impacts on Agricultural Systems: New Research Reveals Essential Role of Climate Data and Services

Strategic Insights

— 1 min read

Researchers at KLE Technological University have emphasized the critical importance of climate data and services in helping farmers adapt to the growing impacts of climate change on agricultural systems. According to a new study published in the Journal of Smart Internet of Things, climate change is considered one of the most significant economic factors affecting agricultural production, making it essential for farmers to prepare ahead of time using accurate weather forecasts and climate data.

Key Takeaways:

  • Climate change has a detrimental effect on agricultural production, making climate data and services essential for farmers to survive.
  • Weather forecasts are crucial for agricultural resource management, enabling farmers to prepare ahead of time and safeguard their crops from natural calamities.
  • Climate data has been affected by global warming, resulting in unexpected hurricanes that have harmed agriculture's production roots.
  • The daily forecasting of weather variables, such as rainfall, maximum temperature, and humidity, is primarily done using artificial intelligence, machine learning, and deep learning approaches.
  • Current climate condition models require innovation in terms of high-performance computing and complexity.
  • The proposed Harris Hawk Optimised deep learning network and ensemble residual Long Short-term memory (R-LSTM) model has shown significant improvement in crop-yield output compared to other state-of-the-art models.
  • The suggested model achieved a 97.3% accuracy rate, a 96.9% precision rate, a 96.6% recall rate, and a 97.4% F1-score.
  • The study concluded that the proposed model is a very good choice for predicting climate change, enabling farmers to increase crop output productivity and contribute to raising their standard of living.

Statistics:

  • 97.3% accuracy rate achieved by the proposed model
  • 96.9% precision rate achieved by the proposed model
  • 96.6% recall rate achieved by the proposed model
  • 97.4% F1-score achieved by the proposed model

Sources:

  • NewsRx. KLE Technological University Researcher Reports Recent Findings in Climate Change (A Cognitive IoT Learning Models for Agro Climatic Estimation Aiding Farmers in Decision making). Global Warming Focus. July 7, 2025; p 272.
  • A Cognitive IoT Learning Models for Agro Climatic Estimation Aiding Farmers in Decision making. Journal of Smart Internet of Things, 2024,2024(1). The publisher for Journal of Smart Internet of Things is Walter de Gruyter GmbH.
  • https://doi-org.sdpl.idm.oclc.org/10.2478/jsiot-2024-0004

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