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[学术文献 ] Impacts of varying day and night environmental conditions on cotton flowering, yield, and fiber quality 进入全文
Frontiers in Plant Science
Introduction: Increases in the frequency of higher-than-optimum air temperatures can substantially reduce cotton production. Little is known about the influence of different combinations of day/nighttime temperature on cotton flowering and boll maturation under ambient and elevated CO2 conditions. Methods: This study examined the impacts of air temperature variations on the morphology of cotton flowers and seed yield under air CO2 concentrations at 425 ppm (ambient, aCO(2)) and elevated at 725 ppm (eCO(2)) in controlled Soil-Plant Atmospheric Research (SPAR) chambers. The four temperature conditions were: optimum (OT; 33/21 degrees C, day/night), high temperature (HT; 36/24 degrees C, day/night), high nighttime (OT+HNT; 33/24 degrees C, day/night), and high day/nighttime (HT+HNT; 36/28 degrees C, day/night). Results: Various reproductive and seed yield traits, as well as the phenology of the plants, differed significantly (p < 0.001) under the treatments. The boll maturation period significantly decreased in plants grown under HT+HNT, with only 39 days under aCO(2) and 38 days under eCO(2) compared to 47 days at OT. In the HT and OT+HNT conditions, the duration was 42 days at aCO(2) and 46 days at eCO(2), as opposed to 41 and 44 days, respectively, under aCO(2). Furthermore, there was a significant reduction in the number of pollen grains per anther, 13% for OT+HNT, 24% for HT, and 39% for HT+HNT, relative to OT treatments. The seed cotton weight also showed a drastic decline, decreasing from 105 g plant(-1) under OT to 90 g under OT+HNT, 47 g under HT, and 12 g plant(-1) under HT+HNT conditions. In the HT+HNT environment, lint percentage and seed weight per plant were reduced by 26% and 86%, respectively, when compared to OT. The eCO(2) did not alleviate the reductions in cotton yield caused by higher air temperature exposure. Discussion: This study highlights that high air temperature induces flower abscission and anther indehiscence, while diverting biomass allocation towards vegetative organs. The resulting source-sink imbalances between vegetative and reproductive structures resulted in significant reductions in seed and lint yield and growth patterns across CO2 and temperature environments. These findings provide insights into cotton management strategies under future environmental scenarios.
[前沿资讯 ] 浙江大学发布“AI育种家”将棉花杂交育种组合效率提升20倍 进入全文
科技日报
9月26日，浙江大学发布基于棉花全基因组大数据与AI加速算法的“一站式”育种智能体——“AI育种家”。该成果由浙大农业与生物技术学院张天真教授团队联合华为技术有限公司、北京大蚯蚓数字科技有限公司联合研发，可将棉花杂交育种组合效率比原来提升20倍，将棉花育种周期直接从传统的6到8年压缩到3到4年。 对二十年前的育种专家而言，研究棉花基因数据，就像翻阅着一本没有标点的书。张天真团队长期致力于破译这本“书”。为了解决传统育种的难题，其团队系统分析了全球5000余个棉花品种，一点点解析出79642个基因中隐藏的遗传密码，鉴定出1000多个和产量、品质相关的基因位点，此前成功构建了全球首个棉花多组学数据库平台COTTONOMICS。 三年来，该平台访问量已超9万次，成为棉花研究的“知识中心”。在解析底盘育种大数据之外，联合团队构建了几乎覆盖全部棉花优异基因的核心种质库，研发出全基因组选择育种液相芯片——“浙大棉芯”。搭载这款芯片的“AI育种家”，具备智能解析植株表型并输出直接结果的能力，同时采用对话式交互设计，支持基因信息检索、精准育种方案生成及后代性状预测。 使用“AI育种家”时，育种人员若想查询某个棉花品种的高产、优质或抗病基因，只需直接输入问题，平台即可迅速调出结果；若要设计棉花杂交亲本选配或基因聚合育种方案，只需输入目标性状，系统也能快速生成最优方案。值得一提的是，“AI育种家”在设计之初就具备多作物扩展能力，正逐步延伸应用于水稻、大豆、油菜、西瓜、西蓝花等作物的育种研究。
[前沿资讯 ] 华为、浙大联合开发基于棉花全基因组大数据与AI加速算法的“AI育种”芯片 进入全文
快科技
9月26日，浙江大学在“AI+生物育种”西湖学术论坛发布全球首款基于棉花全基因组大数据与AI加速算法的“一站式”育种智能体“AI育种家”。该平台由浙大张天真团队联合华为打造，集成昇腾910B芯片、EIHealth AI基因平台、盘古大模型与鸿蒙系统，依托团队构建的全球首个棉花多组学数据库COTTONOMICS，实现计算效率提升100倍、杂交育种组合效率提升20倍，将棉花育种周期从6—8年压缩至3—4年，并已具备向水稻、大豆、油菜、西瓜、西兰花等作物扩展的能力。 这是全球首款基于棉花全基因组大数据与AI加速算法的“一站式”育种智能体。标志着我国在智慧农业与生物育种技术领域跻身国际前列。 浙大张天真教授团队深耕二十余年，解析全球5000余个棉花品种的79642个基因，鉴定出1000多个产量、品质相关基因位点，构建全球首个棉花多组学数据库COTTONOMICS，为芯片研发奠定数据基础。 “AI育种家”搭载了华为AI芯片昇腾910B，采用华为EIHealth AI基因平台为基础，在盘古大模型和鸿蒙系统上构建。 团队成员介绍，这枚芯片能够加速计算效率能力提升100倍，棉花杂交育种组合效率提升20倍，棉花育种周期直接从传统的6-8年压缩到3-4年。 而且，“AI育种家”设计之初就具备多作物扩展能力，除了棉花，正在延伸应用于水稻、大豆、油菜、西瓜、西兰花等作物的育种研究。
[前沿资讯 ] 棉花纤维变长的关键“密码”找到了 进入全文
光明网
纤维长度是决定棉花商业价值和工业应用的重要农艺性状，是棉花驯化和遗传改良的核心目标。自然变异产生的核苷酸多态性被认为是驱动纤维长度多样性的主要遗传基础。 近日，中国农业科学院棉花研究所和西部农业研究中心等单位合作，系统解析了支架蛋白GhTTL启动子的自然变异调节棉纤维伸长分子机制，阐明了棉纤维长度自然变化的分子基础，为棉花分子遗传改良提供了候选靶点。相关研究成果发表在《植物通讯（Plant Communications）》上。 该研究明确了支架蛋白GhTTL是棉纤维伸长的关键正调控因子，发现启动子区域的自然多态性显著影响上游转录因子对其的结合亲和力，从而调节该蛋白在不同棉花品种中的表达水平。 该研究还揭示了一个全新的调节机制，其中该蛋白与蛋白激酶相互作用并将其锚定在细胞膜上，这种相互作用降低了蛋白激酶的细胞质水平，阻止其与转录因子和下游底物的结合和磷酸化，促进纤维细胞的伸长。研究结果为棉纤维伸长的调控提供了更深入的见解。 该研究得到国家自然科学基金、新疆维吾尔自治区重大科技专项等项目资助。
[前沿资讯 ] What happens when AI comes to the cotton fields 进入全文
PHYS ORG
Precision agriculture uses tools and technologies such as GPS and sensors to monitor, measure and respond to changes within a farm field in real time. This includes using artificial intelligence technologies for tasks such as helping farmers apply pesticides only where and when they are needed. However, precision agriculture has not been widely implemented in many rural areas of the United States. We study smart communities, environmental health sciences and health policy and community health, and we participated in a research project on AI and pesticide use in a rural Georgia agricultural community. Our team, led by Georgia Southern University and the City of Millen, with support from University of Georgia Cooperative Extension, local high schools and agriculture technology company FarmSense, is piloting AI-powered sensors to help cotton farmers optimize pesticide use. Georgia is one of the top cotton-producing states in the U.S., with cotton contributing nearly US$1 billion to the state's economy in 2024. But only 13% of Georgia farmers use precision agriculture practices. Public-private-academic partnership Innovation drives economic growth, but access to it often stops at major city limits. Smaller and rural communities are frequently left out, lacking the funding, partnerships and technical resources that fuel progress elsewhere. At the same time, 75% of generative AI's projected economic impact is concentrated in customer operations, marketing, software engineering and research and development, according to a 2023 McKinsey report. In contrast, applications of AI that improve infrastructure, food systems, safety and health remain underexplored. Yet smaller and rural communities are rich in potential—home to anchor institutions like small businesses, civic groups and schools that are deeply invested in their communities. And that potential could be tapped to develop AI applications that fall outside of traditional corporate domains. The Partnership for Innovation, a coalition of people and organizations from academia, government and industry, helps bridge that gap. Since its launch almost five years ago, the Partnership for Innovation has supported 220 projects across Georgia, South Carolina, Kentucky, Tennessee, Virginia, Texas and Alabama, partnering with more than 300 communities on challenges from energy poverty to river safety. One Partnership for Innovation program provides seed funding and technical support for community research teams. This support enables local problem-solving that strengthens both research scholarship and community outcomes. The program has recently focused on the role of civic artificial intelligence—AI that supports communities and local governments. Our project on cotton field pesticide use is part of this program. Cotton pests and pesticides Our project in Jenkins County, Georgia, is testing that potential. Jenkins County, with a population of around 8,700, is among the top 25 cotton-growing counties in the state. In 2024, approximately 1.1 million acres of land in Georgia were planted with cotton, and based on the 2022 agricultural county profiles census, Jenkins County ranked 173rd out of the 765 counties producing cotton in the United States. The state benefits from fertile soils, a subtropical-to-temperate climate, and abundant natural resources, all of which support a thriving agricultural industry. But these same conditions also foster pests and diseases. Farmers in Jenkins County, like many farmers, face numerous insect infestations, including stink bugs, cotton bollworms, corn earworms, tarnished plant bugs and aphids. Farmers make heavy use of pesticides. Without precise data on the bugs, farmers end up using more pesticides than they likely need, risking residents' health and adding costs. While there are some existing tools for integrated pest management, such as the Georgia Cotton Insect Advisor app, they are not widely adopted and are limited to certain bugs. Other methods, such as traditional manual scouting and using sticky traps, are labor-intensive and time-consuming, particularly in the hot summer climate. Our research team set out to combine AI-based early pest detection methods with existing integrated pest management practices and the insect advisor app. The goal was to significantly improve pest detection, decrease pesticide exposure levels and reduce insecticide use on cotton farms in Jenkins County. The work compares different insect monitoring methods and assesses pesticide levels in both the fields and nearby semi-urban areas. We selected eight large cotton fields operated by local farmers in Millen, four active and four control sites, to collect environmental samples before farmers began planting cotton and applying pesticides. The team was aided by a new AI-based insect monitoring system called the FlightSensor by FarmSense. The system uses a machine learning algorithm that was trained to recognize the unique wingbeats of each pest insect species. The specialized trap is equipped with infrared optical sensors that project an invisible infrared light beam—called a light curtain—across the entrance of a triangular tunnel. A sensor monitors the light curtain and uses the machine learning algorithm to identify each pest species as insects fly into the trap. FlightSensor provides information on the prevalence of targeted insects, giving farmers an alternative to traditional manual insect scouting. The information enables the farmers to adjust their pesticide-spraying frequency to match the need. What we've learned Here are three things we have learned so far: Predictive pest control potential—AI tools can help farmers pinpoint exactly where pest outbreaks are likely—before they happen. That means they can treat only the areas that need it, saving time, labor and pesticide costs. It's a shift from blanket spraying to precision farming—and it's a skill farmers can use season after season. Stronger decision-making for farmers—The preliminary results indicate that the proposed sensors can effectively monitor insect populations specific to cotton farms. Even after the sensors are gone, farmers who used them get better at spotting pests. That's because the AI dashboards and mobile apps help them see how pest populations grow over time and respond to different field conditions. Researchers also have the ability to access this data remotely through satellite-based monitoring platforms on their computers, further enhancing the collaboration and learning. Building local agtech talent—Training students and farmers on AI pest detection is doing more than protecting cotton crops. It's building digital literacy, opening doors to agtech careers and preparing communities for future innovation. The same tools could help local governments manage mosquitoes and ticks and open up more agtech innovations. Blueprint for rural innovation By using AI to detect pests early and reduce pesticide use, the project aims to lower harmful residues in local soil and air while supporting more sustainable farming. This pilot project could be a blueprint for how rural communities use AI generally to boost agriculture, reduce public health risks and build local expertise. Just as important, this work encourages more civic AI applications—grounded in real community needs—that others can adopt and adapt elsewhere. AI and innovation do not need to be urban or corporate to have a significant effect, nor do you need advanced technology degrees to be innovative. With the right partnerships, small towns, too, can harness innovations for economic and community growth.
[前沿资讯 ] Ultra-Low Gossypol Cotton: Transforming Cottonseed into a Global Protein Source 进入全文
ISAAA Inc.
Cotton plants produce significantly more seed than lint, with about 1.6 pounds of seed for every pound of lint. While the oil from these seeds can be used for human food, the protein is typically not because it contains a natural toxin called gossypol. Gossypol is a naturally occurring compound in the cotton plant and is present in the plant's stems, leaves, flower buds, and especially the seeds. Gossypol is the main toxic component in cottonseed meal. Gossypol acts as a natural defense mechanism for the cotton plant, protecting it from insects, pests, and pathogens. It is toxic to monogastric animals such as pigs and poultry, and pre-ruminant/immature ruminants like young calves and lambs. In humans, high levels of gossypol can be detrimental, limiting the use of cottonseed protein in food products. The United States Food and Drug Administration (USFDA) approves cottonseed with no more than 450 ppm free gossypol for human consumption. Ultra-low gossypol cotton Scientists tried to breed cotton varieties with less gossypol, but these plants became vulnerable to insect damage. However, researchers at Texas A&M AgriLife Research used genetic engineering to create a dual-purpose cotton variety with an ultra-low gossypol trait that can be used for fiber and human and animal consumption. In 2018, the U.S. Department of Agriculture's (USDA) Animal and Plant Health Inspection Service (APHIS) announced the deregulation of genetically engineered cotton with ultra-low levels of gossypol in its seed, developed by experts led by plant biotechnologist Keerti Rathore at Texas A&M AgriLife Research. In 2019, the USFDA approved the ultra-low gossypol cottonseed (ULGCS) to be used as human food and animal feed. ULGCS is derived from a transgenic cotton variety TAM66274. It is a unique cotton plant with ultra-low gossypol levels in the seed, which makes the protein from the seeds safe for food use, but also maintains normal plant-protecting gossypol levels in the rest of the plant, making it ideal for the traditional cotton farmer. According to Rathore, "the amount of protein locked up in the annual output of cottonseed worldwide is about 10.8 trillion grams. That is more than what is present in all the chicken eggs produced globally, and enough to meet the basic protein requirements of over 500 million people." The human food ingredients from TAM66274 cottonseed can be roasted cottonseed kernels, raw cottonseed kernels, cottonseed kernels, partially defatted cottonseed flour, defatted cottonseed flour, and cottonseed oil. For animal feed, the low-gossypol cottonseed can be used in the aquaculture and poultry industries. Ultimately, Rathore’s goal is for global adoption of TAM66274 to help address protein malnutrition in impoverished parts of the world that cultivate cotton. Gossypol-free cottonseed In 2022, Rathore's team successfully developed gossypol-free cottonseed. Using RNA interference, they were able to silence the gene d-cadinene synthase to reduce gossypol concentration in the seed by 97%, without lowering the gossypol in other parts of the cotton plant where it is needed as defense against insects and diseases. According to an open-access article in Critical Reviews in Plant Sciences, field trials conducted in multiple states from 2009 to 2016 validated the stability and heritability of the trait, with no effect on agronomic performance. ULGCS takes next step toward humanitarian use In 2025, after decades of research, Uzbekistan will become the first country to formally partner with the Texas A&M University System to integrate the ULGCS trait into its cotton crops. The agreement, facilitated by Uzbekistan’s Center of Genomics and Bioinformatics of the Academy of Sciences, will support the incorporation of the trait into cotton varieties adapted for Uzbekistan. This collaboration will also support Uzbekistan's national food security goals. Ibrokhim Abdurakhmonov, Ph.D., a former student at Texas A&M Department of Soil and Crop Sciences and current Uzbekistan Minister of Agriculture, facilitated this humanitarian relationship. “The transfer of cutting-edge cotton innovation offers a significant opportunity for Uzbekistan’s cotton industry,” Abdurakhmonov said. “It is of interest to the research community, government, and farmers, aligning fully with Uzbekistan’s food security agenda.” The partnership is a significant step toward Rathore's goal of making cotton a dual-purpose crop—valued for both its fiber and its seed as a protein source. This development is expected to improve the sustainability of cotton farming worldwide and holds potential for U.S. cotton growers to benefit from shared genetic material in the future. Conclusion Gossypol has long been a significant barrier to using cottonseed as a protein source for human food and animal feed. While this natural toxin protects the cotton plant from pests, its presence in the seeds makes them unsafe for human and animal consumption. The groundbreaking research by Texas A&M AgriLife has changed the future of the cotton industry. With the recent partnership between Texas A&M and Uzbekistan, the global adoption of ULGCS is now a reality. This development represents a major step toward addressing global protein malnutrition, improving food security, and transforming cotton into a dual-purpose crop—valuable for both its fiber and its high-protein seed.
