Cotton Genomics and Genetics 2025, Vol.16, No.2, 48-56 http://cropscipublisher.com/index.php/cgg 51 major deterrents for many farmers, particularly those managing smaller operations (John et al., 2023). The economic cost barrier is further exacerbated by the size and income differences among farmers, which influence their ability to invest in new technologies (Barnes et al., 2019). Additionally, the lack of adequate infrastructure, such as reliable internet connectivity and access to advanced machinery, poses a significant challenge, especially in rural and underdeveloped regions (Lowenberg‐DeBoer and Erickson, 2019). 5.2 Technical knowledge and training gaps A critical challenge in the adoption of precision agriculture is the gap in technical knowledge and training among farmers. Many farmers lack the necessary skills and understanding to effectively implement and manage these technologies (Pathak et al., 2019). This knowledge gap is particularly pronounced among small-scale farmers, where digital literacy and technological interoperability are significant hurdles. The complexity of precision agriculture technologies requires comprehensive training programs to equip farmers with the skills needed to utilize these tools effectively (Lambert et al., 2015). Without adequate training and support, the potential benefits of precision agriculture remain largely untapped. 5.3 Data management and privacy concerns Data management and privacy concerns are increasingly becoming significant barriers to the adoption of precision agriculture technologies. The vast amounts of data generated by these technologies require robust data management systems, which many farmers find challenging to implement (Ofori and El-Gayar, 2020). Moreover, concerns about data privacy and security are prevalent, as farmers are wary of how their data might be used or shared without their consent. These concerns are compounded by the lack of clear regulatory frameworks to protect farmers' data, leading to hesitancy in adopting technologies that rely heavily on data collection and analysis (Lambert et al., 2015). 6 Case Study: Precision Agriculture Implementation in a Cotton-Producing Region 6.1 Background and cultivation system of Xinjiang, China Xinjiang, located in northwestern China, is a major cotton-producing region, known for its arid climate and challenging agricultural conditions, including limited water and heat resources, as well as prevalent soil salinity issues. Over the past three decades, Xinjiang has seen significant advancements in cotton cultivation techniques, leading to a consistent increase in cotton yields. The region has developed three generations of cultivation technology systems, focusing on efficient utilization of light, heat, water, and fertilizers (Feng et al., 2024). These advancements have transformed Xinjiang into one of the world's largest cotton producers, despite its environmental challenges. 6.2 Applied precision technologies and interventions In Xinjiang, precision agriculture technologies have been implemented to optimize resource use and improve cotton yields. Drip irrigation has been a key intervention, significantly increasing boll weight, yield, and water productivity compared to traditional furrow irrigation methods (Kuang et al., 2024). Additionally, a decision-making system based on reinforcement learning has been developed to provide precise irrigation strategies, maximizing cotton yield while reducing water consumption. Remote sensing and crop models have also been utilized to estimate cotton yield accurately, integrating satellite and environmental data to enhance yield predictions (Figure 2) (Lang et al., 2023). Furthermore, management zones have been delineated using machine learning and remote sensing to address soil salinization and optimize resource allocation. 6.3 Outcomes, benefits, and lessons learned The implementation of precision agriculture technologies in Xinjiang has led to several positive outcomes. Drip irrigation and optimized fertigation strategies have improved cotton yield and fiber quality, while also enhancing water and nitrogen use efficiency (Hou et al., 2024). The use of reinforcement learning for irrigation decision-making has further increased yields and reduced water usage, aligning with sustainable water management goals (Chen et al., 2023). The delineation of management zones has allowed for more targeted resource application, addressing soil salinity issues and improving overall farm management (Wang et al., 2023). These interventions have collectively contributed to the region's ability to achieve high cotton yields despite
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