Cotton Genomics and Genetics 2025, Vol.16 http://cropscipublisher.com/index.php/cgg © 2025 CropSciPublisher, an online publishing platform of Sophia Publishing Group. All Rights Reserved. Sophia Publishing Group (SPG), founded in British Columbia of Canada, is a multilingual publisher.
Cotton Genomics and Genetics 2025, Vol.16 http://cropscipublisher.com/index.php/cgg © 2025 CropSciPublisher, an online publishing platform of Sophia Publishing Group. All Rights Reserved. Sophia Publishing Group (SPG), founded in British Columbia of Canada, is a multilingual publisher. Publisher CropSci Publisher Editedby Editorial Team of Cotton Genomics and Genetics Email: edit@cgg.cropscipublisher.com Website: http://cropscipublisher.com/index.php/cgg Address: 11388 Stevenston Hwy, PO Box 96016, Richmond, V7A 5J5, British Columbia Canada Cotton Genomics and Genetics (ISSN 1925-1947) is an open access, peer reviewed journal published online by CropSciPublisher. The journal is committed to providing a forum for the dissemination of high-quality papers within all aspects of cotton sciences, focusing on the basic theories, novel techniques, and the applications related to genetics, structural & functional genomics, and comparative genomics as well as proteomics. All the articles published in Cotton Genomics and Genetics are Open Access, and are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CropSci Publisher uses CrossCheck service to identify academic plagiarism through the world’s leading plagiarism prevention tool, iParadigms, and to protect the original authors’ copyrights. CropSci Publisher, operated by Sophia Publishing Group (SPG), is an international Open Access publishing platform that publishes scientific journals in the field of life science. Sophia Publishing Group (SPG), founded in British Columbia of Canada, is a multilingual publisher.
Cotton Genomics and Genetics (online), 2025, Vol. 16, No.2 ISSN 1925-1947 http://cropscipublisher.com/index.php/cgg © 2025 CropSci Publisher, registered at the publishing platform that is operated by Sophia Publishing Group, founded in British Columbia of Canada. All Rights Reserved. Latest Content Comprehensive Precision Agriculture Technology to Achieve Maximum Cotton Yield Shanjun Zhu, Mengting Luo Cotton Genomics and Genetics, 2025, Vol. 16, No. 2, 48-56 Study On the Influence of Irrigation Strategies On Cotton Growth and Yield Pingping Yang, Yuexin Zhu Cotton Genomics and Genetics, 2025, Vol. 16, No. 2, 57-71 Research on the Role of Micronutrient Management in Improving Cotton Fiber Quality Huijuan Xu, Xiaojing Yang, Yuxin Zhu Cotton Genomics and Genetics, 2025, Vol. 16, No. 2, 72-79 Best Practices for Sustainable Cotton Farming Systems Kaiwen Liang Cotton Genomics and Genetics, 2025, Vol. 16, No. 2, 80-94 Research on Post-Harvest Management Technology to Improve Cotton Quality Zhen Li Cotton Genomics and Genetics, 2025, Vol. 16, No. 2, 95-106
Cotton Genomics and Genetics 2025, Vol.16, No.2, 48-56 http://cropscipublisher.com/index.php/cgg 48 Research Insight Open Access Comprehensive Precision Agriculture Technology to Achieve Maximum Cotton Yield Shanjun Zhu, Mengting Luo Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China Corresponding email: mengting.luo@jicat.org Cotton Genomics and Genetics, 2025, Vol.16, No.2 doi: 10.5376/cgg.2025.16.0006 Received: 03 Jan., 2025 Accepted: 18 Feb., 2025 Published: 01 Mar., 2025 Copyright © 2025 Zhu and Luo, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Zhu S.J., and Luo M.T., 2025, Comprehensive precision agriculture technology to achieve maximum cotton yield, Cotton Genomics and Genetics, 16(2): 48-56 (doi: 10.5376/cgg.2025.16.0006) Abstract Cotton is an important cash crop and textile raw material globally, playing a vital role in the economies of many producing countries, but its yield growth has stagnated under conventional farming methods. This study comprehensively reviews the application of precision agriculture technologies in cotton production, focusing on key innovations such as remote sensing, GPS-guided machinery, variable rate technology (VRT), the internet of things (IoT), and data analytics platforms. It explores how these tools can help improve yields, resource efficiency, and environmental sustainability. The integration of big data, machine learning, and decision support systems (DSS) further enhances field decision-making, forecasting, and risk management. A case study in Xinjiang, China illustrates the real-world benefits and challenges of implementing precision agriculture in major cotton-producing regions. While these technologies have shown clear advantages in increasing productivity and reducing input costs, barriers such as high investment, technical skills gaps, and data management issues remain. Future advances in artificial intelligence, robotics, and supportive policy frameworks will play a key role in scaling up smart farming practices, ensuring sustainable and profitable cotton cultivation in the face of global agricultural challenges. Keywords Precision agriculture; Cotton yield; Remote sensing; IoT; Decision support systems 1 Introduction Cotton is a vital crop with significant economic importance, particularly in regions where it serves as a primary agricultural product. The global cotton industry supports millions of jobs and contributes substantially to the economies of many countries. Cotton production is not only crucial for the textile industry but also plays a role in the agricultural sector's overall economic health (Lambert et al., 2015; Jumanov et al., 2022). Precision agriculture has emerged as a transformative approach in cotton farming, aiming to enhance productivity and sustainability. This method leverages advanced technologies such as remote sensing, yield monitors, and soil testing to optimize resource use and improve crop yields (Neely et al., 2016). The integration of internet of things (IoT) devices and machine learning further enhances the ability to monitor and manage crops effectively, reducing environmental impact and increasing economic returns (Sharma et al., 2021; Nyéki and Neményi, 2022; Durai et al., 2024). The need for precision agriculture in cotton farming is driven by the challenges of resource limitations, climate variability, and the necessity to increase efficiency and profitability (Watson et al., 2016; Baio et al., 2017). This study aims to comprehensively analyze precision agriculture technologies and their applications in cotton cultivation and explore the economic benefits, technological advancements, and practical applications of these technologies in achieving maximum cotton yields. The scope of the study includes reviewing the current practices, challenges, and future prospects of precision agriculture with a focus on improving cotton production efficiency and sustainability. 2 Core Components of Precision Agriculture in Cotton 2.1 Remote sensing and satellite imaging Remote sensing and satellite imaging are pivotal in precision agriculture, particularly for cotton production. These technologies enable the monitoring and assessment of agricultural lands, providing critical data on crop biomass,
Cotton Genomics and Genetics 2025, Vol.16, No.2, 48-56 http://cropscipublisher.com/index.php/cgg 49 phenology, and yield at various scales. For instance, the use of Landsat 8 and other satellite technologies allows for the prediction and mapping of cotton lint yield by analyzing crop indices such as NDVI and other vegetation indices (Haghverdi et al., 2018; Sishodia et al., 2020). Remote sensing facilitates the application of variable rate technologies (VRT) by providing high-resolution images that inform precise input applications, such as fertilizers and water, thereby optimizing resource use and enhancing yield (Filintas et al., 2022). 2.2 GPS-guided machinery and variable rate technology (VRT) GPS-guided machinery and VRT are integral to precision agriculture, offering precise control over agricultural inputs. These technologies enable site-specific crop management by applying inputs like seeds, fertilizers, and water according to field variability. The integration of GPS with automatic controllers and sensors allows for the precise application of inputs, which is crucial for optimizing cotton yield and reducing environmental impact (Ali et al., 2024). Studies have shown that precision agriculture techniques, including VRT, can lead to significant increases in crop yield and reductions in water and fertilizer usage, highlighting their effectiveness in sustainable farming practices. 2.3 Internet of things (IoT) and sensor networks The internet of things (IoT) and sensor networks are transforming precision agriculture by providing real-time data on environmental conditions, crop health, and soil quality. IoT devices equipped with optical sensors can monitor critical indicators such as temperature, humidity, and chlorophyll content, which are essential for maintaining optimal growing conditions for cotton (Saha et al., 2023; Durai et al., 2024). These sensors transmit data wirelessly to central servers for analysis, enabling predictive analytics and informed decision-making regarding irrigation, pest management, and fertilizer application (Figure 1) (Alahmad et al., 2023). The use of IoT and wireless sensor networks enhances the efficiency of precision agriculture by reducing labor costs and increasing productivity (Shafi et al., 2019; Sanjeevi et al., 2020). Figure 1 Sensing technologies and their applications in agriculture (Adopted from Alahmad et al., 2023) 3 Data Analytics and Decision Support Systems (DSS) 3.1 Big data integration from multiple sources In precision agriculture, the integration of big data from various sources is crucial for optimizing crop yield and resource management. The use of unmanned aerial systems (UAS) and IoT sensors allows for the collection of real-time data on crop growth, soil conditions, and environmental factors. This data is then integrated into decision
Cotton Genomics and Genetics 2025, Vol.16, No.2, 48-56 http://cropscipublisher.com/index.php/cgg 50 support systems to enhance agricultural practices. For instance, a study highlights the use of UAS to capture RGB data for developing a Digital Twin framework, which forecasts cotton crop features such as canopy cover and height, thereby aiding in yield prediction and biomass estimation (Pal et al., 2019). Additionally, the integration of diverse data sources, including historical weather data and soil nutrient analysis, enables personalized recommendations for farmers, enhancing decision-making and risk management (Singh et al., 2024). 3.2 Machine learning and predictive modeling Machine learning (ML) plays a pivotal role in precision agriculture by analyzing complex datasets to predict crop yields and optimize farming practices. Various ML models, such as random forests, XGboost, and artificial neural networks, have been employed to predict cotton yield and determine the impact of management and environmental variables (Dhaliwal et al., 2022). These models facilitate informed decision-making by predicting suitable crops, detecting diseases, and optimizing irrigation (Mohyuddin et al., 2024). Moreover, ML techniques, including support vector regression and ensemble methods, have been used to enhance prediction accuracy and decision-making capabilities, contributing to sustainable farming practices (Bachu et al., 2024). 3.3 DSS tools for cotton farmers Decision support systems (DSS) are essential tools for cotton farmers, providing insights into irrigation scheduling, crop management, and yield optimization. For example, an irrigation DSS based on forecasted rainfall and water stress indices has been shown to significantly increase cotton yield and water productivity in arid climates (Chen et al., 2020). Furthermore, IoT-based DSS frameworks integrate multiple soil and environmental parameters to predict soil moisture content and optimize irrigation control schemes, ensuring efficient water use and maintaining uniform moisture levels across fields (Keswani et al., 2020). These tools empower farmers to make data-driven decisions, ultimately enhancing crop yield and resource efficiency. 4 Impact of Precision Agriculture on Cotton Yield and Sustainability 4.1 Yield enhancement through site-specific management Precision agriculture significantly enhances cotton yield through site-specific management techniques. By utilizing technologies such as GPS, IoT sensors, and variable rate technology (VRT), farmers can apply inputs precisely where needed, optimizing crop yield. For instance, precision nitrogen management in Bt cotton has shown to improve seed cotton yield by aligning nitrogen application with crop demand, thereby enhancing nitrogen use efficiency (Gupta et al., 2022). Additionally, precision agriculture practices have demonstrated a 20% increase in crop yield by addressing inter- and intravariability in cropping systems. 4.2 Efficient use of resources and input cost reduction Precision agriculture contributes to the efficient use of resources and reduction of input costs by minimizing waste and optimizing input application. For example, precision nitrogen management not only improves yield but also reduces nitrous oxide emissions, showcasing a dual benefit of resource efficiency and environmental protection. Moreover, precision agriculture has been shown to reduce water and fertilizer usage by 40%, leading to significant cost savings. The use of site-specific management strategies also results in economic benefits through cost savings and increased profits, as evidenced by studies on various crops (Bahmutsky et al., 2024). 4.3 Environmental and ecological benefits The environmental and ecological benefits of precision agriculture are substantial. By reducing the overuse of fertilizers and pesticides, precision agriculture minimizes environmental impacts such as greenhouse gas emissions and pesticide runoff. Precision farming techniques, such as site-specific sensing and management, allow for targeted input use, reducing agrichemical residuals and promoting environmental sustainability (Finger et al., 2019). Additionally, precision agriculture practices have been shown to decrease greenhouse gas emissions by 14% in sugarcane production, highlighting their potential for broader ecological benefits (Sanches et al., 2023). 5 Challenges and Limitations in Adoption 5.1 Economic and infrastructure barriers The adoption of precision agriculture technologies (PATs) is often hindered by significant economic and infrastructure barriers. High initial costs and the need for substantial investments in technology infrastructure are
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
Cotton Genomics and Genetics 2025, Vol.16, No.2, 48-56 http://cropscipublisher.com/index.php/cgg 52 environmental constraints. The lessons learned from Xinjiang's experience highlight the importance of integrating advanced technologies and tailored management practices to overcome regional agricultural challenges and enhance productivity. Figure 2 Testing performance [R2 (A, B), RMSE (C, D) and rRMSE (E, F)] of cotton yield prediction only with remote sensing variables and combined with climate variables using the LSTM model for the whole growing season during 2012-2018 and 2019, respectively (Adopted from Lang et al., 2023) 7 Future Directions and Recommendations 7.1 Integration of AI, robotics, and automation The integration of AI, robotics, and automation in precision agriculture is pivotal for maximizing cotton yield. AI technologies, such as machine learning and computer vision, are transforming traditional farming by enabling real-time data analysis and decision-making, which enhances efficiency and sustainability (Hoque and Padhiary, 2024; Padhiary et al., 2024). Robotics, including autonomous tractors and drones, facilitate precise operations like planting, monitoring, and harvesting, reducing labor costs and increasing operational efficiency (Agrawal and Arafat, 2024). Future advancements should focus on developing energy-efficient AI models and improving sensor technologies to overcome current limitations such as high operational costs and technical complexity. 7.2 Policies and support for technology adoption To fully realize the potential of precision agriculture technologies, supportive policies and infrastructure are essential. Governments and private sectors must collaborate to provide training, infrastructure, and region-specific solutions for farmers (Yousafzai et al., 2024). Policy support can address challenges such as high implementation
Cotton Genomics and Genetics 2025, Vol.16, No.2, 48-56 http://cropscipublisher.com/index.php/cgg 53 costs and data privacy concerns, fostering an environment conducive to technological adoption (Akintuyi, 2024). Additionally, initiatives to bridge the digital divide in rural areas and ensure affordable access to technology for small-scale farmers are crucial (Daraojimba et al., 2024). 7.3 Research needs and innovation opportunities Ongoing research is needed to explore new frontiers in precision agriculture, such as the integration of blockchain, big data analytics, and cloud computing to enhance transparency and decision-making. Innovation opportunities lie in developing robust AI solutions that are accessible and scalable, particularly for smallholder farmers (Naresh et al., 2024). Further research should also focus on ethical considerations and the environmental impact of AI technologies, ensuring sustainable practices that align with global food security goals (Debnath and Basu, 2023). By addressing these research needs, precision agriculture can continue to evolve, offering innovative solutions for sustainable crop production and maximum yield. 8 Concluding Remarks Precision agriculture technologies have significantly contributed to enhancing cotton yield by optimizing resource use and improving management practices. The integration of GPS, IoT sensors, and variable rate technology (VRT) has led to a 20% increase in crop yield and a 40% reduction in water and fertilizer usage, demonstrating the effectiveness of these technologies in promoting sustainable farming practices. UAV-based systems have enabled real-time monitoring of crop responses to environmental and management factors, allowing for more informed agronomic decisions. AI-driven systems have further enhanced yield prediction accuracy by 15% and reduced water and fertilizer use by up to 30% and 20%, respectively, without compromising yields. These advancements underscore the potential of precision agriculture to maximize cotton yield while minimizing environmental impact. The successful implementation of precision agriculture technologies in cotton farming requires context-specific strategies that consider local environmental conditions, soil variability, and socio-economic factors. For instance, site-specific variable-rate (SSVR) technologies allow for targeted nematicide applications, reducing costs and sustaining yield levels. Adoption patterns among cotton farmers indicate that larger operations with access to diverse information sources are more likely to adopt technology bundles, highlighting the need for tailored strategies that address the unique challenges faced by smaller farms. Additionally, the integration of remote sensing and soil analyses has proven effective in optimizing irrigation and fertilization practices, further emphasizing the importance of adapting technologies to specific agricultural contexts. The future of smart cotton farming lies in the continued development and adoption of precision agriculture technologies that are scalable and adaptable to diverse farming landscapes. Overcoming barriers such as high initial costs, technical expertise requirements, and data privacy concerns will be crucial for broader adoption. Collaborative efforts from policymakers, agricultural organizations, and technology providers are essential to develop accessible and cost-effective solutions that empower farmers with actionable insights for improved farm management. As these technologies evolve, they hold the promise of transforming cotton farming into a more sustainable, efficient, and profitable endeavor, ultimately contributing to global food security and environmental sustainability. Acknowledgments We are grateful to Mr. Xu for critically reading the manuscript and providing valuable feedback that improved the clarity of the text. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Agrawal J., and Arafat M., 2024, Transforming farming: a review of AI-powered UAV technologies in precision agriculture, Drones, 8(11): 664. https://doi.org/10.3390/drones8110664
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Cotton Genomics and Genetics 2025, Vol.16, No.2, 57-71 http://cropscipublisher.com/index.php/cgg 57 Feature Review Open Access Study On the Influence of Irrigation Strategies On Cotton Growth and Yield Pingping Yang, Yuexin Zhu Hainan Provincial Key Laboratory of Crop Molecular Breeding, Sanya, 572025, Hainan, China Corresponding email: yuexin.zhu@hitar.org Cotton Genomics and Genetics, 2025, Vol.16, No.2 doi: 10.5376/cgg.2025.16.0007 Received: 11 Jan., 2025 Accepted: 25 Feb., 2025 Published: 10 Mar., 2025 Copyright © 2025 Yang and Zhu, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Yang P.P., and Zhu Y.X., 2025, Study on the influence of irrigation strategies on cotton growth and yield, Cotton Genomics and Genetics, 16(2): 57-71 (doi: 10.5376/cgg.2025.16.0007) Abstract Cotton is an important fiber and cash crop in the world, but major producing areas generally face the challenge of water shortage. This study reviews the water demand characteristics of cotton at various growth stages, as well as the effects of water shortage and excessive irrigation on cotton physiology, growth and yield, and evaluates the effects of traditional irrigation and advanced irrigation technologies (such as drip irrigation, sprinkler irrigation, and underground infiltration irrigation) and precision irrigation strategies (such as deficit irrigation). The analysis shows that different irrigation methods significantly affect the root development, plant height, leaf area index, and flowering and boll setting process of cotton, thereby affecting seed cotton yield and fiber quality. A reasonable irrigation system can improve water use efficiency while ensuring yield and fiber quality. As a field case, the large-scale drip irrigation practice under plastic film in Xinjiang cotton fields has demonstrated significant yield-increasing and water-saving effects, but it also faces problems such as secondary salinization of the soil and residual film pollution. Looking forward to the future, intelligent irrigation integrating remote sensing and the Internet of Things, breeding of drought-resistant cotton varieties, and policy and training support will be the key directions for optimizing cotton irrigation and achieving sustainable production. This study summarizes the research progress on the impact of irrigation strategies on cotton growth and yield in recent years, aiming to provide a scientific reference for efficient water use and stable yield of cotton in arid areas. Keywords Cotton; Water requirement; Irrigation strategy; Growth and development; Yield and quality 1 Introduction Cotton (Gossypium hirsutum L.) is one of the most important natural fiber crops in the world and a major source of income for farmers in many countries. The global cotton planting area is about 31.92 million hectares, with an annual turnover of about US $5.68 billion, and it occupies a key position in the textile industry (Koudahe et al., 2021). As an economic crop, cotton production is related to the livelihoods of farmers in many developing countries and the development of related industries. China, India, and the United States are major cotton producers, among which Xinjiang, China, accounts for more than 73% of the country's cotton production and plays a strategic role in the country's textile raw material supply (He et al., 2023). However, cotton cultivation is usually concentrated in arid and semi-arid areas with limited precipitation, and irrigation is required to meet its water needs for growth, making water resources one of the decisive factors in cotton production. Drought and water scarcity are severe challenges faced by cotton-producing areas around the world. In major cotton-producing areas such as northwest China, the South Asian subcontinent, and southwestern America, precipitation is low and varies greatly from year to year, and competition for irrigation water is fierce. Especially in Xinjiang, China, extremely dry climate conditions and unreasonable use of water and soil resources have led to secondary salinization of cotton fields, exacerbating the agricultural water crisis (Yang et al., 2024). Water stress will limit the growth and development of cotton, reduce photosynthesis and nutrient absorption, and ultimately lead to reduced yields. On the other hand, unreasonable excessive irrigation not only wastes water resources, but may also cause problems such as poor soil aeration, nutrient leaching and increased diseases. Therefore, achieving a balance between water supply and demand and improving irrigation water utilization efficiency in cotton-producing areas are crucial to ensuring cotton yield and quality (Hussain et al., 2020). The current challenge is how to formulate scientific irrigation strategies to cope with water shortages based on the water demand characteristics of cotton in each growth period and regional water resource conditions.
Cotton Genomics and Genetics 2025, Vol.16, No.2, 57-71 http://cropscipublisher.com/index.php/cgg 58 This study aims to systematically review the research progress on the effects of cotton irrigation strategies on its growth, yield and fiber quality in the past five years. The focus was on the water demand of cotton at different growth stages, the effects of water stress and over-irrigation on cotton physiology and ecology; the classification and comparison of traditional and advanced irrigation technologies, especially the application of sub-film drip irrigation and other technologies in arid areas; the mechanism of the influence of irrigation system on cotton growth parameters (root system, plant height, canopy development, flowering and boll formation); the influence of different irrigation methods on cotton final yield and fiber quality; water use efficiency and sustainability issues of water-saving irrigation; and the results and experience of large-scale application of sub-film drip irrigation in Xinjiang, China. This study summarizes the key experience of efficient cotton irrigation and proposes future research and practice innovation directions, in order to provide a reference for sustainable cotton production in arid areas. 2 Overview of Cotton Water Requirements 2.1 Cotton growth stages and their specific water requirements The entire growth cycle of cotton includes the seedling stage, vegetative growth stage (before budding), reproductive growth stage (flowering and boll formation stage) and boll opening and maturity stage, and the water requirements in different stages vary significantly. In the seedling and bud stages, cotton water requirements are relatively low, and excessive water may lead to shallow root development and leggy growth; in the mid-summer when flowering and boll formation occurs, cotton plants reach the maximum leaf area and heavy boll load, and evapotranspiration rises rapidly at this time, reaching 542 mm, accounting for 88% of the total transpiration water loss, which is the peak water requirement throughout the year (Figure 1) (Zhao et al., 2023). Hussain et al. (2020) showed that under sufficient irrigation conditions, the total water requirement of cotton during the entire growth period is approximately between 500-800 mm depending on the regional climate. For example, in the cooler climate of the eastern cotton region of the North China Plain, the seasonal water requirement is about 620-670 mm, while in the hot Mediterranean climate the seasonal water requirement is higher (Yang et al., 2021). Usually, cotton is most sensitive to water and has the greatest demand for water from budding to flowering and in full bloom and boll formation, requiring timely and sufficient water supply; while the requirements for water are relatively low during the sowing and seedling stage and the end of boll opening. Based on this water demand law, phased water supply is often adopted in irrigation practice, with irrigation strengthened during the critical growth period and water control appropriately in the late growth period to ensure both yield and improve water use efficiency (Shen et al., 2012). The water consumption characteristics of cotton at each stage lay the scientific foundation for formulating a phased irrigation system. 2.2 Effects of water shortage and overwatering on cotton physiology Water stress directly affects the physiological process and growth vitality of cotton. When the available soil water is lower than the cotton water requirement, the plant's stomatal conductance decreases, the photosynthesis rate decreases, the relative water content and chlorophyll content of the leaves decrease, and the plant growth is hindered (Luo et al., 2016). Mild to moderate drought stress first inhibits the growth of cotton stems and leaves and the development of flower buds. Severe drought will lead to increased shedding of young buds and flowers. Shareef et al. (2018) showed that drought stress reduced plant height and leaf area, and caused a decrease in photosynthesis rate under increased water deficit. Under moderate drought, photosynthesis decreased by about 30%, growth traits decreased by more than 20%, and significantly increased the content of osmotic regulating substances such as proline and abscisic acid to help plants resist water deficit. On the other hand, excessive water (such as long-term soil overwetting or excessive irrigation) can also have an adverse effect on cotton. Excessive irrigation can cause soil hypoxia, inhibit root respiration and deep rooting, and cause "waterlogging" symptoms. Cotton is manifested as yellowing leaves, root rot, and increased bud and boll shedding rates (Yan et al., 2009). An overly humid environment can also easily induce the occurrence of soil-borne diseases such as cotton wilt, reducing the number of bolls per plant and the yield of cotton. In general, whether it is water shortage or overwatering, it will disrupt the normal physiological metabolic balance of cotton and affect dry matter accumulation and reproductive growth. Therefore, extreme water stress and long-term overhumidity should be
Cotton Genomics and Genetics 2025, Vol.16, No.2, 57-71 http://cropscipublisher.com/index.php/cgg 59 avoided in cotton planting, and soil moisture should be controlled in an appropriate range through reasonable irrigation to maintain the healthy growth of cotton (Pchelkin et al., 2023). Figure 1 Changes in the evapotranspiration components and the proportion of transpiration to evapotranspiration at different growth stages of cotton (Adopted from Zhao et al., 2023) 2.3 Effect of climate and soil conditions on water use efficiency The water use efficiency (WUE, dry matter or yield obtained per unit of water consumption) of cotton is significantly affected by the climate and soil conditions of the planting area. In an environment of high temperature, low humidity and strong radiation, the transpiration rate and evaporation loss of cotton are increased, and WUE is often lower under the same irrigation conditions; on the contrary, in a milder climate, cotton can convert more dry matter per unit of water. For example, improper irrigation in arid and hot areas such as Xinjiang and Texas will cause a large amount of water evaporation or deep seepage, resulting in low water use efficiency (Li et al., 2016; Evett et al., 2019). Soil texture and water storage capacity also affect cotton's use of water. Sandy soil has poor water storage capacity and requires more frequent irrigation, but the amount of water each time should be small, otherwise it is easy to leak and waste; clay soil has poor aeration, and excessive irrigation is prone to waterlogging (Wang et al., 2021). The degree of soil salinization is also a key factor: in saline soil, cotton needs to maintain a high irrigation leaching rate to suppress the increase in salt, but excessive irrigation will reduce WUE. Studies have shown that by optimizing irrigation scheduling, controlling soil salinity while ensuring cotton growth, efficient water use can be achieved that takes into account both water and salt. Zhang et al. (2021) found that under saline-alkali water irrigation conditions, when soil salinity is controlled within a certain threshold, cotton yield is equivalent to freshwater irrigation, thus achieving a relatively high water productivity. Therefore, when formulating a cotton irrigation system, it is necessary to consider the local climate evaporation requirements and soil characteristics, and determine the irrigation intensity and frequency according to local conditions to maximize the efficiency of cotton's use of every drop of water. 3 Classification of Cotton Irrigation Strategies 3.1 Conventional irrigation methods Before the popularization of modern water-saving technologies, conventional ground irrigation methods dominated cotton cultivation. The most traditional of these is flood irrigation (sometimes called flood irrigation), which is to flood the field at one time on flat land. This method is simple and easy to implement, but it uses a large
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