Tree Genetics and Molecular Breeding 2024, Vol.14, No.5, 256-268 http://genbreedpublisher.com/index.php/tgmb 264 7.3 Tea production in Kenya: thriving under unique climatic conditions Kenya, as the third-largest tea producer globally, benefits from unique climatic conditions that support tea production. The country's tea yield is closely linked to soil moisture and temperature dynamics, with satellite-derived observations showing a strong correlation between these factors and yield variability. Despite the challenges posed by rising temperatures, projections suggest that increases in soil moisture could offset potential yield losses, resulting in a relatively modest decrease in yields by 2040-2070 (Rigden et al., 2020). This highlights the importance of soil moisture conservation strategies in maintaining tea productivity under changing climatic conditions. Adaptation strategies in Kenyan tea plantations focus on enhancing resilience to climate change. By improving soil moisture retention and implementing advanced planning measures, tea producers can mitigate the adverse effects of temperature increases and ensure sustainable yields (Rigden et al., 2020). These efforts are crucial for maintaining Kenya's position as a leading tea producer and for supporting the livelihoods of those dependent on the tea industry. 7.4 Comparative analysis of ecological factors in major tea-producing regions A comparative analysis of ecological factors across major tea-producing regions reveals diverse challenges and adaptation strategies. In China, the focus is on managing monsoon dynamics and promoting ecological tea gardens to balance production with environmental sustainability (Boehm et al., 2016; Wang et al., 2023a). In India, traditional practices are being adapted to address climate variability and improve yield through effective management of environmental and agronomic factors (Dutta et al., 2010; Duncan et al., 2016). Meanwhile, Kenya's approach emphasizes soil moisture conservation to counteract the effects of rising temperatures, ensuring continued productivity (Rigden et al., 2020). 8 Emerging Research and Technological Innovations 8.1 Advances in precision agriculture for tea cultivation Precision agriculture is revolutionizing tea cultivation by integrating advanced technologies to enhance productivity and sustainability. The use of information and communication technology (ICT) in tea cultivation is becoming increasingly prevalent, allowing for more precise management of resources and improved decision-making processes. ICT tools are utilized in various stages of tea cultivation, including land preparation, pest control, and nutrient management, which are crucial for optimizing yield and quality. Additionally, machine learning techniques are being employed to evaluate the suitability of land for tea cultivation, ensuring that resources are allocated efficiently and sustainably. For instance, a study in Xinming Township, China, utilized machine learning models to assess land suitability, which aids in making informed decisions about land use and cultivation practices (Xing et al., 2022). Furthermore, the integration of computer vision and machine learning in the tea industry has led to the development of vision-based harvesting equipment and disease detection methods. These technologies enable more efficient and accurate identification of tea buds and detection of common diseases, which are essential for maintaining high-quality tea production. Despite the current limitations, such as the need for more comprehensive applications, the ongoing advancements in sensor technologies and artificial intelligence are paving the way for more intelligent and sustainable tea garden management (Wang et al., 2023b). 8.2 Use of remote sensing and AI in monitoring ecological factors Remote sensing and artificial intelligence (AI) are playing pivotal roles in monitoring ecological factors that influence tea yield. These technologies provide valuable insights into environmental conditions, enabling tea growers to adapt to changing climates and optimize their cultivation practices. The application of remote sensing allows for the continuous monitoring of large tea plantations, providing data on soil moisture, temperature, and other critical factors that affect tea growth (Wang et al., 2023b). This data-driven approach helps in making timely decisions to mitigate adverse effects caused by environmental stresses. AI, particularly machine learning, is being used to analyze complex datasets obtained from remote sensing technologies. This analysis helps in predicting the impact of various ecological factors on tea yield and quality. For example, machine learning models have been developed to evaluate the suitability of different areas for tea
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