Modeling the Impact of Thermal Regimes on Cotton Fiber Elongation in High-Yield Xinjiang Production

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Using daily temperature data and regionally averaged fiber length data from 2016 to 2023, we conducted correlation analysis, linear regression, and piecewise regression, and linear regression analyses. Results revealed significant positive correlations between fiber length and average temperatures in June, July, and October (p < 0.01), while no significant correlations were observed in August and September.Critical temperature thresholds were identified at 25.43°C in July and 12.9°C in October, with July accounting for the highest proportion (55.4%) of fiber length variation among monthly temperatures. A positive linear relationship was found between accumulated growing degree days (GDD 12 , base temperature 12°C, June–October) and fiber length, expressed as y = 24.6503 + 0.0031x, indicating a 0.31 mm increase per 100°C·day rise in accumulated heat. However, the low explanatory power (R² = 0.6504) of the temperature-based models highlights that thermal factors alone play a limited role in Xinjiang’s integrated production system, where genetics, precision irrigation, and nutrient management are primary determinants of fiber quality. These findings highlight the importance of considering temperature thresholds in key growth stages while adopting holistic agronomic strategies to maintain high fiber quality under climate variability. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Plant sciences Cotton Fiber length Temperature threshold Accumulated growing degree days Xinjiang Climate adaptation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Xinjiang serves as China’s largest high-quality cotton production base, renowned for achieving super-high yields under conditions of severe water scarcity and thermal constraints. In 2024, its cotton output reached 5.686 million tons, accounting for over 90% of the national total (National Bureau of Statistics, 2025) and approximately 20% of the global output. This productivity is supported by advanced, integrated cultivation systems that efficiently utilize light, heat, water, and fertilizer resources. Endowed with a unique arid climate—characterized by abundant sunshine (2800–3400 hours annually) and a wide diurnal temperature range (12–16°C)—the region offers favorable conditions for cotton fiber development. Within this high-yield framework, fiber quality remains a critical determinant of economic value and market competitiveness. However, against the backdrop of global climate change, temperature instability poses a significant risk to quality stability. Over the past decade, the average temperature in the northern Xinjiang cotton-growing region has fluctuated by ± 2.3°C from June to October. Such fluctuations can disrupt key physiological processes; for instance, they may impair cellulose deposition during the secondary cell wall thickening stage (Li et al., 2022 ) and potentially affect the photosynthetic efficiency of the boll-leaf system, which is crucial for yield and quality formation (Wang et al., 2023 ). As a core determinant of yarn strength (USTER Technical Report, 2023), cotton fiber length is particularly sensitive to temperature variations. Previous studies have identified two critical, overlapping stages in fiber development: the primary elongation stage (0–20 days post-anthesis, DPA) and the secondary cell wall thickening stage (15–45 DPA) (Haigler et al., 2012 ). The growing degree day (GDD) model is widely adopted to quantify temperature impacts on crop development and quality. For example, Deutsch et al. ( 2016 ) established a linear relationship between effective GDD (> 15°C) and fiber strength in Texas, USA. In China, relevant research has primarily focused on the Yellow River region. Wang et al. ( 2019 ) reported that a 100°C increase in boll-stage accumulated temperature (≥ 10°C) was associated with a 0.3 mm increase in fiber length.The current academic research on the relationship between cotton fiber length and climate change, both domestically and internationally, mainly presents the following perspectives: First, cotton fiber length is primarily influenced by environmental and meteorological factors. Second, cotton fiber length is affected by both cultivar (variety) and meteorological factors. Third, cotton fiber length is mainly determined by the cultivated variety, with meteorological factors during climate change having a relatively minor impact. Most scholars believe that temperature is the primary meteorological factor affecting cotton fiber length, with either excessively high or low temperatures adversely affecting fiber elongation. Among these, some scholars argue that cotton fiber length is mainly influenced by the average night temperature. The temperature during the summer growing season is the main meteorological factor affecting cotton fiber length(Hesketh'set al., 1968).Relative humidity is the main factor affecting cotton fiber strength, while sunshine hours and temperature also exhibit a certain positive correlation with cotton fiber strength(Zhou Z G et al., 2022). While substantial research on Xinjiang cotton has focused on achieving yield breakthroughs via precision irrigation (e.g., Liu et al., 2025 ), optimized fertigation (e.g., Tian et al., 2024 ), and integrated cultivation systems, detailed studies dissecting the impact of temperature dynamics on specific quality traits like fiber length remain comparatively scarce. Most existing temperature-based models for Xinjiang rely on accumulated temperature over the entire growing period (e.g., Yang et al., 2020 ) without distinguishing the critical windows for fiber quality formation or identifying potential temperature thresholds. Therefore, within the context of Xinjiang's well-established high-yield agrosystem, quantifying the precise relationship between intra-seasonal temperature parameters (monthly averages and accumulated heat) and fiber length is essential. This knowledge will fill a critical gap and provide a scientific basis for fine-tuning climate-adaptive management practices to safeguard both yield and quality. This study addresses the following specific gaps: (1) To clarify the differential influence of monthly average temperatures (June to October) on fiber length and identify temperature-sensitive critical periods and potential thresholds for fiber development in Xinjiang. (2) To establish a quantitative relationship between accumulated growing degree days during key growth stages and fiber length, contributing to a more comprehensive understanding of environmental regulation within the region's high-performance cotton production system. 2. Materials and Methods 2.1 Data Sources Meteorological data Daily average temperature data from June 1 to October 30 for the years 2016–2023 were obtained from the Xinjiang Uygur Autonomous Region Meteorological Information Center(Fig. 1.Average cotton fiber length from 2016 to 2023). To ensure representativeness, data were sourced from a network of standard meteorological stations distributed across the major cotton production regions of Northern and Southern Xinjiang. Monthly average temperatures for June through October were calculated from this daily dataset. Agronomic data: Corresponding cotton fiber length (upper half mean length) data for the same period (2016–2023) were obtained from the official annual cotton quality inspection reports of the Xinjiang Uygur Autonomous Region Market Supervision and Administration Bureau(Figure 2,n = 3000,1000 samples each from Xayar County, Yuli County, and Usu City in Xinjiang). These data are derived from standardized, randomized sampling and testing protocols applied to lint from the region's main production areas, ensuring reliability and consistency. For the purpose of regional-scale climate-response analysis, the annual average fiber length for the entirety of Xinjiang was used as the representative value for each year. 2.2 Analysis methods The analysis utilized the daily average temperature data and the annual regional average fiber length data. A multi-step statistical framework was implemented using R software (version 4.5.1). First, Pearson correlation analysis was performed to assess the linear association between the monthly average temperature (for each month from June to October) and the annual average fiber length. Second, piecewise linear regression (segmented regression) was employed to identify potential breakpoints (thresholds) in the relationship between fiber length and the monthly average temperature for each month. The segmented package in R was used, and the statistical significance of each breakpoint was assessed using the Davies test. Third, to quantify the relative importance of monthly temperatures, the contribution rate of each month's average temperature to the variation in fiber length was calculated based on the standardized regression coefficients (beta weights) obtained from a multiple linear regression model that included the average temperatures of all five months. Finally, linear regression analysis was conducted to quantify the impact of accumulated thermal time on fiber length. Growing Degree Days (GDD) were calculated from the daily data with a base temperature of 12°C (GDD 12 ), summed from June 1 to October 30 for each year. The choice of a 12°C base, while unconventional, was made to capture the full spectrum of thermally active periods in this analysis, acknowledging that biological activity is minimal but not zero at low temperatures. A simple linear model was fitted between the annual GDD 12 total and the corresponding annual average fiber length. Model assumptions (linearity, homogeneity of variances, independence, and normality of residuals) were checked using diagnostic plots.Integrated Framewark of Intra-seasonal Temperature Dynamics and Cotton Fiber Quality. 3. Results 3.1 Correlation analysis between monthly average temperature and cotton fiber As shown in Fig. 3 , the correlation coefficients (based on n = 8 yearly observations) between cotton fiber length and the average temperatures in June (r = 0.20, p < 0.01), July (r = 0.31, p < 0.001), and October (r = 0.19, p 0.05). This indicates that inter-annual variations in average temperature during June, July, and October are significantly associated with variations in regional average fiber length in Xinjiang. 3.2 Threshold analysis of the relationship between cotton fiber length and monthly average temperature Table 1 Segmented regression thresholds and model statistics for monthly average temperature (June-October) versus cotton fiber length. Month Threshold (°C) SE R² P value June 28.16 1.066 0.0445 0.5715 July 25.43 0.774 0.1144 0.0353* August 21.60 2.019 0.0051 0.4051 September 14.82 3.240 0.0133 0.3867 October 12.89 0.909 0.0859 0.0030* Note: * denotes a statistically significant breakpoint (p < 0.05). As shown in Table 1 and Fig. 4 – 6 , statistically significant temperature thresholds were identified for July (25.43°C, p = 0.035) and October (12.9°C, p = 0.003). Figure 6 indicates that the average temperature in July made the largest relative contribution (approximately 55.4%) to the explained variance in fiber length among the monthly average temperatures, confirming its role as a key developmental period. July (Threshold ≈ 25.4°C) This period coincides with the peak of fiber elongation. Temperatures persistently below this threshold may be suboptimal for the cellular processes driving rapid elongation. October (Threshold ≈ 12.9°C) This period aligns with late fiber maturation and secondary wall thickening. Average temperatures dropping below this threshold may slow down metabolic activity and the final phase of cellulose deposition. It is important to emphasize that the low R² values associated with these threshold models (Table 1 ) indicate that monthly temperature thresholds alone explain only a small fraction of the total variance in fiber length. This underscores the significant influence of other factors, such as genetic cultivar differences, solar radiation, and management practices, which were not accounted for in this model. 3.3 Relationship between accumulated temperature and cotton fiber length Table 2 Linear regression analysis between accumulated Growing Degree Days (GDD 12 , June-October) and cotton fiber length Variable Estimate Std. Error t-value p-value Significance (Intercept) 24.6510 1.3207 18.6644 < 0.001 *** GDD₀ total 0.0031 0.0009 3.3410 0.0156 * Model Fit R² 0.6504 Adjusted R² 0.5921 Note: GDD 12 : Accumulated Growing Degree Days with a base temperature of 12°C. The accumulated GDD 12 during the June-October period showed a significant positive linear relationship with fiber length (Table 2 , Fig. 7 ). Both the intercept and the slope were highly significant (p < 0.001). The regression model is: Y = 24.6503 + 0.0031X where Y is fiber length (mm) and X is accumulated GDD 12 (°C·days). This model suggests that for every 100°C·day increase in GDD 12 accumulated from June to October, fiber length increases by approximately 0.31 mm. However, the R² value (0.651) clearly indicates that accumulated heat (under this 12°C base model) is a significant but minor predictor, explaining less than 10% of the observed inter-annual variation in regional average fiber length. 4. Discussion This study elucidates the significant, yet partial, role of intra-seasonal temperature parameters in explaining inter-annual variation in cotton fiber length within Xinjiang's high-yield production system. The findings confirm that specific thermal periods are key regulatory factors but operate within a complex agronomic context where advanced management and genetics are dominant forces. The identification of July and October as periods with significant temperature thresholds aligns with established cotton physiology. The July threshold (~ 25.4°C) coincides with the peak of fiber elongation (0–20 DPA). Temperatures below this optimum may limit the rate of cell expansion, a process highly sensitive to enzymatic activity and turgor pressure, which are thermally regulated (Haigler et al., 2012 ). The substantial contribution (~ 55.4%) of July temperature to the explained variance underscores this month as the most critical thermal window for determining potential fiber length. This highlights a potential synergy point: while agronomic practices like deficit irrigation and high density are optimized for yield and water use (Wu et al., 2024 ), July temperature independently exerts a strong influence on a key quality trait. The October threshold (~ 12.9°C) corresponds to the late maturation and secondary wall thickening phase. Temperatures dropping below this level likely slow down the final stages of cellulose biosynthesis and deposition, potentially leading to shorter fibers with incomplete cell wall development. The significant but weak positive relationship between accumulated growing degree days (GDD 12 ) and fiber length quantifies the cumulative thermal effect. However, the very low coefficient of determination (R² = 0.6504) is a pivotal result. It unequivocally demonstrates that accumulated heat explains only a minor fraction of the total variation in fiber length. This strongly implies that other factors are primary drivers. Foremost among these is genetic variation. Secondly, advanced agronomic management directly influences quality. Precision irrigation strategies (Dai et al., 2024 ) affect plant water status and carbohydrate partitioning. Optimized fertigation, particularly nitrogen management (Wang et al., 2024 ), supports sustained canopy photosynthesis essential for fiber growth. Furthermore, salinity management practices (Xiao et al., 2024 ; Li et al., 2024 ) improve the root-zone environment, mitigating abiotic stresses. The results of this study indicate that although the average temperatures in June, July, and October showed highly significant positive correlations with cotton fiber length, and key temperature thresholds were identified, the explanatory power of temperature-based statistical models for fiber length variation was generally low. In particular, the growing degree day model (GDD 12 ) yielded a coefficient of determination of only 0.6504. On one hand, this confirms that temperature is a necessary but not sufficient condition for fiber development; on the other hand, it profoundly reveals the complexity of the cotton production system in Xinjiang. First, the low explanatory power of the models precisely highlights the high level of controllability and stability within Xinjiang's "integrated production system." Characterized by an arid climate with scarce rainfall and abundant sunshine, the Xinjiang cotton region has established a highly human-intervened production environment through the integration of modern agricultural techniques such as intensive drip irrigation under plastic film and precise water and fertilizer management. In such a system, while temperature is indispensable as a fundamental driving factor, its fluctuations can often be effectively buffered or compensated for by other management practices (e.g., irrigation, fertilization, chemical regulation). Consequently, inter-annual variations in fiber length more accurately reflect contributions from controllable factors such as cultivar renewal and optimized cultivation management, rather than being solely determined by natural temperature fluctuations. The low R² values suggest that the high yield and superior quality of Xinjiang cotton do not depend on a single climatic factor but are instead based on the comprehensive and efficient utilization of light, heat, water, and soil resources. Second, despite the overall low explanatory power of the models, the significant correlations and threshold information they reveal still provide irreplaceable "early warning windows" and "regulatory targets" for precise field management. One aspect is the identification of critical periods: the study found that July temperatures contributed the highest proportion (55.4%) to fiber length variation. Regardless of the models' overall explanatory power, July, as the critical period for cotton flowering, pollination, and fiber elongation, demands significant attention regarding temperature effects. Growers and managers can use this information to enhance precautions against temperature stress during July. Another aspect is the guiding significance of thresholds: the identified thresholds of 25.43°C in July and 12.9°C in October provide quantitative bases for formulating emergency management plans. When forecasted temperatures deviate from these thresholds, even without precise predictions of final fiber length, it is sufficient to trigger agronomic interventions. For instance, if persistent high temperatures (significantly above 25.43°C) are forecast for July, the frequency of drip irrigation could be increased in advance to regulate the field microclimate. If an early cold spell in October (temperatures dropping below 12.9°C) is predicted, applying harvest aids like ethephon could be considered to mitigate the adverse effects of low temperature on fiber maturity. The practical value of these threshold-based action guidelines far exceeds that of a statistical model with a high R² but lacking operational anchors. Therefore, the presented temperature thresholds and the GDD model represent one critical layer of understanding within the sophisticated, multi-factorial "cultivation technology system" that defines Xinjiang cotton production. For future climate adaptation, the practical implication lies in integrating thermal sensitivity knowledge with existing best practices. For example, during seasons with forecasted suboptimal July temperatures, management could be fine-tuned—such as ensuring optimal water and nitrogen status via drip fertigation to mitigate potential stress on fiber elongation. In conclusion, this analysis confirms that July and October average temperatures are key periods influencing cotton fiber length in Xinjiang and establishes a quantitative, albeit limited, link with seasonal accumulated heat. The modest explanatory power of the temperature-based models reinforces the concept that superior fiber quality in this high-yield system is the product of synergistic interactions between genetics, precision management, and environment. Sustainable intensification requires a holistic strategy that optimizes all factors in concert. 5. Conclusions This study systematically analyzed the relationship between temperature and cotton fiber length in Xinjiang. The key findings are as follows: Cotton fiber length showed significant positive correlations with the average temperatures in June, July, and October, but not with August and September; Piecewise regression identified critical temperature thresholds for fiber length regulation: 25.43°C in July and 12.9°C in October; Among monthly temperatures, July temperature contributed the most to fiber length variation (55.4%), confirming it as the key developmental period; A significant positive relationship exists between accumulated growing degree days (GDD₀, June-October) and fiber length, described by the model y = 24.6503 + 0.0031x, indicating a 0.31 mm increase per 100°C·day increase in GDD 12 . Ultimately, while temperature during specific windows is a significant regulator, its standalone predictive power is limited within Xinjiang's context. The findings underscore that achieving consistently high fiber quality necessitates an integrated approach that considers thermal effects alongside genetic selection, precision water and nutrient management, and other cultivation technologies that define the region's success. Declarations Conflicts of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This research was supported by the Xinjiang Tianshan Talent Training Program (2023TSYCTD004), the project “Research and Demonstration on Key Cultivation Technologies for Cotton Quality Improvement” (2024SNGGNT077), the Xinjiang Science and Technology Major Project (2023A02003-6), and the National Key Research and Development Program Project (2024YFD2300604). Author Contribution Tengfei Ma: Writing-review & editing, Formal analysis. Zhe Liang: Resources, Data curation. Chunyan Xiao:Resources, Data curation.Huiqing Zhang:Resources, Data curation.Weiping Liu:Resources, Data curation.Lingling Liu:Resources, Data curation.Chunwu Wang: Supervision, Project administration, Conceptualization.Paerhati Maimaiti: Supervision, Project administration. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References Dai, J. et al. Enhancing stand establishment and yield formation of cotton with multiple drip irrigation during emergence in saline fields of Southern Xinjiang. Field Crops Res. 309 , 109482 (2024). Deutsch, C. A. et al. Impacts of climate warming on cotton growth in US Southern Plains. Agric. Syst. 148 , 12–23 (2016). Feng, L., Wan, S., Zhang, Y. & Dong, H. Xinjiang cotton: Achieving super-high yield through efficient utilization of light, heat, water, and fertilizer by three generations of cultivation technology systems. Field Crops Res. 309 , 109401 (2024). 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Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 05 May, 2026 Editor invited by journal 15 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 03 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9309019","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":639034148,"identity":"71bddd92-7c08-411c-862a-761cd7c9aae5","order_by":0,"name":"Tengfei Ma","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tengfei","middleName":"","lastName":"Ma","suffix":""},{"id":639034149,"identity":"0f6f868e-f6ed-41d2-9c37-f6be4c8e0b7c","order_by":1,"name":"Zhe Liang","email":"","orcid":"","institution":"Biotechnology Research Institute, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Liang","suffix":""},{"id":639034150,"identity":"2b8d70e9-cd35-4487-98c8-f12f007f3ffc","order_by":2,"name":"Chunyan Xiao","email":"","orcid":"","institution":"Xinjiang Academy of Agricultural Technology Extension","correspondingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Xiao","suffix":""},{"id":639034151,"identity":"ea61f91d-d50b-4f6d-ab09-6540b4b5b0cb","order_by":3,"name":"Weiping Liu","email":"","orcid":"","institution":"Xinjiang Meteorological Information Center","correspondingAuthor":false,"prefix":"","firstName":"Weiping","middleName":"","lastName":"Liu","suffix":""},{"id":639034152,"identity":"c5b86241-bf1e-482f-866a-78a203622a35","order_by":4,"name":"Lingling LIU","email":"","orcid":"","institution":"Xinjiang Meteorological Information Center","correspondingAuthor":false,"prefix":"","firstName":"Lingling","middleName":"","lastName":"LIU","suffix":""},{"id":639034153,"identity":"80d261f5-9d14-4d06-b868-d56b167daf3c","order_by":5,"name":"Chunwu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYHACxscMFRJy/MyMjQ8+GNjYEaOF2ZjhjIWxZHvzYcMZBWnJxGhhk2ZsqUjccOZYmjDPh0OMDYTUGxw/YyZd2CDB2HAjx4zZxuAAMwP74aMb8Go5k2NsPXOHBDPjjByzxzkGd/gYeNLSbuDTYnaDd+Nt3jMSbMwSOebGOQbPmBkkeMwIadkgzdsmwcMmkWMmbWFwmLGBCC2bQFokeHiOpUkzEKPF/kz+Z+MZZyQMJNiBgdxjkJbMRsgvku3HEh8XVNTV7z8MjMoff2zs+NkPH8OrBROwkaZ8FIyCUTAKRgE2AAAqNEtVxY5ClQAAAABJRU5ErkJggg==","orcid":"","institution":"Xinjiang Academy of Agricultural Technology Extension","correspondingAuthor":true,"prefix":"","firstName":"Chunwu","middleName":"","lastName":"Wang","suffix":""},{"id":639034154,"identity":"335082e7-8e41-4b1f-8d1e-18b1bcf911d5","order_by":6,"name":"Parhati Maimaiti","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Uyghur Autonomous Region Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Parhati","middleName":"","lastName":"Maimaiti","suffix":""}],"badges":[],"createdAt":"2026-04-03 05:24:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9309019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9309019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109283264,"identity":"2985d0f5-a449-44bb-9e2a-222295c08d20","added_by":"auto","created_at":"2026-05-14 19:24:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18670456,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.Averagecottonfiberlengthfrom2016to2023.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9309019/v1/2128584c9c6e5efc06ca0ce4.jpg"},{"id":109283256,"identity":"067bc394-8d38-4c0e-8ad3-7a4d66e67c40","added_by":"auto","created_at":"2026-05-14 19:24:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34851658,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2.Averagecottonfiberlengthfrom2016to2023.png","url":"https://assets-eu.researchsquare.com/files/rs-9309019/v1/caddeb5777f7e0efef803518.png"},{"id":109283258,"identity":"f022cc40-f7a2-4754-b44b-52c99b659910","added_by":"auto","created_at":"2026-05-14 19:24:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1170506,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation coefficient matrix between monthly average temperature and cotton fiber length\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:* indicates P \u0026lt; 0.05, \u003c/em\u003e**\u003cem\u003eindicates P \u0026lt; 0.01, and *** indicates P \u0026lt; 0.001.x6–x10 denote the average temperature (1–30 days) from June to October, respectively, and y represents cotton fibelength.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.Pearsoncorrelationcoefficientmatrixbetweenmonthlyaveragetemperatureandcottonfiberlength.png","url":"https://assets-eu.researchsquare.com/files/rs-9309019/v1/cf382f9fe4a81b4c02b518d4.png"},{"id":109283255,"identity":"e260c3a0-293d-4794-846e-d8e0b3fd2f7c","added_by":"auto","created_at":"2026-05-14 19:24:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25802,"visible":true,"origin":"","legend":"\u003cp\u003eSegmented regression relationships and identified thresholds for July. \u003cem\u003e(Note: Conceptual figure showing two scatter plots with breakpoints at 25.43°C .)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.SegmentedregressionrelationshipsandidentifiedthresholdsforJuly.png","url":"https://assets-eu.researchsquare.com/files/rs-9309019/v1/906d590b8159d33b028474d3.png"},{"id":109283287,"identity":"2b1093d2-cd2b-4cdf-85cd-763f10a4d829","added_by":"auto","created_at":"2026-05-14 19:24:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22837,"visible":true,"origin":"","legend":"\u003cp\u003eSegmented regression relationships and identified thresholds for October. \u003cem\u003e(Note: Conceptual figure showing two scatter plots with breakpoints at 12.89°C.)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure5.SegmentedregressionrelationshipsandidentifiedthresholdsforOctober.png","url":"https://assets-eu.researchsquare.com/files/rs-9309019/v1/df2872664550325014019567.png"},{"id":109405291,"identity":"64ff7413-74e9-4ded-857e-8b5c52d4769b","added_by":"auto","created_at":"2026-05-17 13:16:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":206098,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of monthly average temperature to fiber length variation, derived from standardized regression coefficients. \u003cem\u003e(Note: Bar chart showing July ~55%, October ~20%, June ~15%, September ~6%, August ~4%).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure6.Contributionofmonthlyaveragetemperaturetofiberlengthvariationderivedfromstandardizedregressioncoefficients..png","url":"https://assets-eu.researchsquare.com/files/rs-9309019/v1/3395f776f0d0a56522ad3ff9.png"},{"id":109283281,"identity":"f7d8f3d8-7d03-45da-922e-de13abc3be50","added_by":"auto","created_at":"2026-05-14 19:24:27","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":860668,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLinear relationship between accumulated GDD\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e (June-October) and cotton fiber length.\u003c/strong\u003e\u0026nbsp;(Note: Scatter plot with a fitted regression line: y = 24.6503 + 0.0031x)\u003c/p\u003e","description":"","filename":"Figure7.LinearrelationshipbetweenaccumulatedGDD12JuneOctoberandcottonfiberlength.png","url":"https://assets-eu.researchsquare.com/files/rs-9309019/v1/83d09809ab788a7520853f18.png"},{"id":109406590,"identity":"a21fb7aa-b801-4faa-9e3c-493e9e744c7a","added_by":"auto","created_at":"2026-05-17 13:28:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":49947756,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9309019/v1/e9482b55-677f-4dfb-bad8-4db00a85cc27.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling the Impact of Thermal Regimes on Cotton Fiber Elongation in High-Yield Xinjiang Production","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eXinjiang serves as China\u0026rsquo;s largest high-quality cotton production base, renowned for achieving super-high yields under conditions of severe water scarcity and thermal constraints. In 2024, its cotton output reached 5.686\u0026nbsp;million tons, accounting for over 90% of the national total (National Bureau of Statistics, 2025) and approximately 20% of the global output. This productivity is supported by advanced, integrated cultivation systems that efficiently utilize light, heat, water, and fertilizer resources. Endowed with a unique arid climate\u0026mdash;characterized by abundant sunshine (2800\u0026ndash;3400 hours annually) and a wide diurnal temperature range (12\u0026ndash;16\u0026deg;C)\u0026mdash;the region offers favorable conditions for cotton fiber development. Within this high-yield framework, fiber quality remains a critical determinant of economic value and market competitiveness.\u003c/p\u003e \u003cp\u003eHowever, against the backdrop of global climate change, temperature instability poses a significant risk to quality stability. Over the past decade, the average temperature in the northern Xinjiang cotton-growing region has fluctuated by \u0026plusmn;\u0026thinsp;2.3\u0026deg;C from June to October. Such fluctuations can disrupt key physiological processes; for instance, they may impair cellulose deposition during the secondary cell wall thickening stage (Li et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and potentially affect the photosynthetic efficiency of the boll-leaf system, which is crucial for yield and quality formation (Wang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a core determinant of yarn strength (USTER Technical Report, 2023), cotton fiber length is particularly sensitive to temperature variations. Previous studies have identified two critical, overlapping stages in fiber development: the primary elongation stage (0\u0026ndash;20 days post-anthesis, DPA) and the secondary cell wall thickening stage (15\u0026ndash;45 DPA) (Haigler et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The growing degree day (GDD) model is widely adopted to quantify temperature impacts on crop development and quality. For example, Deutsch et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) established a linear relationship between effective GDD (\u0026gt;\u0026thinsp;15\u0026deg;C) and fiber strength in Texas, USA. In China, relevant research has primarily focused on the Yellow River region. Wang et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported that a 100\u0026deg;C increase in boll-stage accumulated temperature (\u0026ge;\u0026thinsp;10\u0026deg;C) was associated with a 0.3 mm increase in fiber length.The current academic research on the relationship between cotton fiber length and climate change, both domestically and internationally, mainly presents the following perspectives: First, cotton fiber length is primarily influenced by environmental and meteorological factors. Second, cotton fiber length is affected by both cultivar (variety) and meteorological factors. Third, cotton fiber length is mainly determined by the cultivated variety, with meteorological factors during climate change having a relatively minor impact. Most scholars believe that temperature is the primary meteorological factor affecting cotton fiber length, with either excessively high or low temperatures adversely affecting fiber elongation. Among these, some scholars argue that cotton fiber length is mainly influenced by the average night temperature. The temperature during the summer growing season is the main meteorological factor affecting cotton fiber length(Hesketh'set al., 1968).Relative humidity is the main factor affecting cotton fiber strength, while sunshine hours and temperature also exhibit a certain positive correlation with cotton fiber strength(Zhou Z \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eG\u003c/span\u003e et al., 2022).\u003c/p\u003e \u003cp\u003eWhile substantial research on Xinjiang cotton has focused on achieving yield breakthroughs via precision irrigation (e.g., Liu et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), optimized fertigation (e.g., Tian et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and integrated cultivation systems, detailed studies dissecting the impact of temperature dynamics on specific quality traits like fiber length remain comparatively scarce. Most existing temperature-based models for Xinjiang rely on accumulated temperature over the entire growing period (e.g., Yang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) without distinguishing the critical windows for fiber quality formation or identifying potential temperature thresholds. Therefore, within the context of Xinjiang's well-established high-yield agrosystem, quantifying the precise relationship between intra-seasonal temperature parameters (monthly averages and accumulated heat) and fiber length is essential. This knowledge will fill a critical gap and provide a scientific basis for fine-tuning climate-adaptive management practices to safeguard both yield and quality.\u003c/p\u003e \u003cp\u003eThis study addresses the following specific gaps: (1) To clarify the differential influence of monthly average temperatures (June to October) on fiber length and identify temperature-sensitive critical periods and potential thresholds for fiber development in Xinjiang. (2) To establish a quantitative relationship between accumulated growing degree days during key growth stages and fiber length, contributing to a more comprehensive understanding of environmental regulation within the region's high-performance cotton production system.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Sources\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eMeteorological data\u003c/strong\u003e \u003cp\u003eDaily average temperature data from June 1 to October 30 for the years 2016\u0026ndash;2023 were obtained from the Xinjiang Uygur Autonomous Region Meteorological Information Center(Fig.\u0026nbsp;1.Average cotton fiber length from 2016 to 2023). To ensure representativeness, data were sourced from a network of standard meteorological stations distributed across the major cotton production regions of Northern and Southern Xinjiang. Monthly average temperatures for June through October were calculated from this daily dataset.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAgronomic data: Corresponding cotton fiber length (upper half mean length) data for the same period (2016\u0026ndash;2023) were obtained from the official annual cotton quality inspection reports of the Xinjiang Uygur Autonomous Region Market Supervision and Administration Bureau(Figure 2,n\u0026thinsp;=\u0026thinsp;3000,1000 samples each from Xayar County, Yuli County, and Usu City in Xinjiang). These data are derived from standardized, randomized sampling and testing protocols applied to lint from the region's main production areas, ensuring reliability and consistency. For the purpose of regional-scale climate-response analysis, the annual average fiber length for the entirety of Xinjiang was used as the representative value for each year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis methods\u003c/h2\u003e \u003cp\u003eThe analysis utilized the daily average temperature data and the annual regional average fiber length data. A multi-step statistical framework was implemented using R software (version 4.5.1).\u003c/p\u003e \u003cp\u003eFirst, Pearson correlation analysis was performed to assess the linear association between the monthly average temperature (for each month from June to October) and the annual average fiber length.\u003c/p\u003e \u003cp\u003eSecond, piecewise linear regression (segmented regression) was employed to identify potential breakpoints (thresholds) in the relationship between fiber length and the monthly average temperature for each month. The segmented package in R was used, and the statistical significance of each breakpoint was assessed using the Davies test.\u003c/p\u003e \u003cp\u003eThird, to quantify the relative importance of monthly temperatures, the contribution rate of each month's average temperature to the variation in fiber length was calculated based on the standardized regression coefficients (beta weights) obtained from a multiple linear regression model that included the average temperatures of all five months.\u003c/p\u003e \u003cp\u003eFinally, linear regression analysis was conducted to quantify the impact of accumulated thermal time on fiber length. Growing Degree Days (GDD) were calculated from the daily data with a base temperature of 12\u0026deg;C (GDD\u003csub\u003e12\u003c/sub\u003e), summed from June 1 to October 30 for each year. The choice of a 12\u0026deg;C base, while unconventional, was made to capture the full spectrum of thermally active periods in this analysis, acknowledging that biological activity is minimal but not zero at low temperatures. A simple linear model was fitted between the annual GDD\u003csub\u003e12\u003c/sub\u003e total and the corresponding annual average fiber length. Model assumptions (linearity, homogeneity of variances, independence, and normality of residuals) were checked using diagnostic plots.Integrated Framewark of Intra-seasonal Temperature Dynamics and Cotton Fiber Quality.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Correlation analysis between monthly average temperature and cotton fiber\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the correlation coefficients (based on n\u0026thinsp;=\u0026thinsp;8 yearly observations) between cotton fiber length and the average temperatures in June (r\u0026thinsp;=\u0026thinsp;0.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), July (r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and October (r\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were statistically significant. In contrast, correlations for August (r\u0026thinsp;=\u0026thinsp;0.02) and September (r\u0026thinsp;=\u0026thinsp;0.10) were non-significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This indicates that inter-annual variations in average temperature during June, July, and October are significantly associated with variations in regional average fiber length in Xinjiang.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Threshold analysis of the relationship between cotton fiber length and monthly average temperature\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSegmented regression thresholds and model statistics for monthly average temperature (June-October) versus cotton fiber length.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThreshold (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0353*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0030*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: * denotes a statistically significant breakpoint (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e, statistically significant temperature thresholds were identified for July (25.43\u0026deg;C, p\u0026thinsp;=\u0026thinsp;0.035) and October (12.9\u0026deg;C, p\u0026thinsp;=\u0026thinsp;0.003). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicates that the average temperature in July made the largest relative contribution (approximately 55.4%) to the explained variance in fiber length among the monthly average temperatures, confirming its role as a key developmental period.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eJuly (Threshold\u0026thinsp;\u0026asymp;\u0026thinsp;25.4\u0026deg;C)\u003c/strong\u003e \u003cp\u003eThis period coincides with the peak of fiber elongation. Temperatures persistently below this threshold may be suboptimal for the cellular processes driving rapid elongation.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOctober (Threshold\u0026thinsp;\u0026asymp;\u0026thinsp;12.9\u0026deg;C)\u003c/strong\u003e \u003cp\u003eThis period aligns with late fiber maturation and secondary wall thickening. Average temperatures dropping below this threshold may slow down metabolic activity and the final phase of cellulose deposition.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIt is important to emphasize that the low R\u0026sup2; values associated with these threshold models (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) indicate that monthly temperature thresholds alone explain only a small fraction of the total variance in fiber length. This underscores the significant influence of other factors, such as genetic cultivar differences, solar radiation, and management practices, which were not accounted for in this model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Relationship between accumulated temperature and cotton fiber length\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression analysis between \u003cb\u003eaccumulated\u003c/b\u003e Growing Degree Days \u003cb\u003e(GDD\u003c/b\u003e\u003csub\u003e\u003cb\u003e12\u003c/b\u003e\u003c/sub\u003e, \u003cb\u003eJune-October) and cotton fiber length\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.6510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.3207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.6644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDD₀ total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel Fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eNote: GDD\u003c/b\u003e\u003csub\u003e\u003cb\u003e12\u003c/b\u003e\u003c/sub\u003e: \u003cb\u003eAccumulated Growing Degree Days with a base temperature of 12\u0026deg;C.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe accumulated GDD\u003csub\u003e12\u003c/sub\u003e during the June-October period showed a significant positive linear relationship with fiber length (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Both the intercept and the slope were highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The regression model is:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;24.6503\u0026thinsp;+\u0026thinsp;0.0031X\u003c/p\u003e \u003cp\u003ewhere Y is fiber length (mm) and X is accumulated GDD\u003csub\u003e12\u003c/sub\u003e (\u0026deg;C\u0026middot;days). This model suggests that for every 100\u0026deg;C\u0026middot;day increase in GDD\u003csub\u003e12\u003c/sub\u003e accumulated from June to October, fiber length increases by approximately 0.31 mm. However, the R\u0026sup2; value (0.651) clearly indicates that accumulated heat (under this 12\u0026deg;C base model) is a significant but minor predictor, explaining less than 10% of the observed inter-annual variation in regional average fiber length.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study elucidates the significant, yet partial, role of intra-seasonal temperature parameters in explaining inter-annual variation in cotton fiber length within Xinjiang's high-yield production system. The findings confirm that specific thermal periods are key regulatory factors but operate within a complex agronomic context where advanced management and genetics are dominant forces.\u003c/p\u003e \u003cp\u003eThe identification of July and October as periods with significant temperature thresholds aligns with established cotton physiology. The July threshold (~\u0026thinsp;25.4\u0026deg;C) coincides with the peak of fiber elongation (0\u0026ndash;20 DPA). Temperatures below this optimum may limit the rate of cell expansion, a process highly sensitive to enzymatic activity and turgor pressure, which are thermally regulated (Haigler et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The substantial contribution (~\u0026thinsp;55.4%) of July temperature to the explained variance underscores this month as the most critical thermal window for determining potential fiber length. This highlights a potential synergy point: while agronomic practices like deficit irrigation and high density are optimized for yield and water use (Wu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), July temperature independently exerts a strong influence on a key quality trait. The October threshold (~\u0026thinsp;12.9\u0026deg;C) corresponds to the late maturation and secondary wall thickening phase. Temperatures dropping below this level likely slow down the final stages of cellulose biosynthesis and deposition, potentially leading to shorter fibers with incomplete cell wall development.\u003c/p\u003e \u003cp\u003eThe significant but weak positive relationship between accumulated growing degree days (GDD\u003csub\u003e12\u003c/sub\u003e) and fiber length quantifies the cumulative thermal effect. However, the very low coefficient of determination (R\u0026sup2; = 0.6504) is a pivotal result. It unequivocally demonstrates that accumulated heat explains only a minor fraction of the total variation in fiber length. This strongly implies that other factors are primary drivers. Foremost among these is genetic variation. Secondly, advanced agronomic management directly influences quality. Precision irrigation strategies (Dai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) affect plant water status and carbohydrate partitioning. Optimized fertigation, particularly nitrogen management (Wang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), supports sustained canopy photosynthesis essential for fiber growth. Furthermore, salinity management practices (Xiao et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) improve the root-zone environment, mitigating abiotic stresses.\u003c/p\u003e \u003cp\u003eThe results of this study indicate that although the average temperatures in June, July, and October showed highly significant positive correlations with cotton fiber length, and key temperature thresholds were identified, the explanatory power of temperature-based statistical models for fiber length variation was generally low. In particular, the growing degree day model (GDD\u003csub\u003e12\u003c/sub\u003e) yielded a coefficient of determination of only 0.6504. On one hand, this confirms that temperature is a necessary but not sufficient condition for fiber development; on the other hand, it profoundly reveals the complexity of the cotton production system in Xinjiang.\u003c/p\u003e \u003cp\u003eFirst, the low explanatory power of the models precisely highlights the high level of controllability and stability within Xinjiang's \"integrated production system.\" Characterized by an arid climate with scarce rainfall and abundant sunshine, the Xinjiang cotton region has established a highly human-intervened production environment through the integration of modern agricultural techniques such as intensive drip irrigation under plastic film and precise water and fertilizer management. In such a system, while temperature is indispensable as a fundamental driving factor, its fluctuations can often be effectively buffered or compensated for by other management practices (e.g., irrigation, fertilization, chemical regulation). Consequently, inter-annual variations in fiber length more accurately reflect contributions from controllable factors such as cultivar renewal and optimized cultivation management, rather than being solely determined by natural temperature fluctuations. The low R\u0026sup2; values suggest that the high yield and superior quality of Xinjiang cotton do not depend on a single climatic factor but are instead based on the comprehensive and efficient utilization of light, heat, water, and soil resources.\u003c/p\u003e \u003cp\u003eSecond, despite the overall low explanatory power of the models, the significant correlations and threshold information they reveal still provide irreplaceable \"early warning windows\" and \"regulatory targets\" for precise field management. One aspect is the identification of critical periods: the study found that July temperatures contributed the highest proportion (55.4%) to fiber length variation. Regardless of the models' overall explanatory power, July, as the critical period for cotton flowering, pollination, and fiber elongation, demands significant attention regarding temperature effects. Growers and managers can use this information to enhance precautions against temperature stress during July. Another aspect is the guiding significance of thresholds: the identified thresholds of 25.43\u0026deg;C in July and 12.9\u0026deg;C in October provide quantitative bases for formulating emergency management plans. When forecasted temperatures deviate from these thresholds, even without precise predictions of final fiber length, it is sufficient to trigger agronomic interventions. For instance, if persistent high temperatures (significantly above 25.43\u0026deg;C) are forecast for July, the frequency of drip irrigation could be increased in advance to regulate the field microclimate. If an early cold spell in October (temperatures dropping below 12.9\u0026deg;C) is predicted, applying harvest aids like ethephon could be considered to mitigate the adverse effects of low temperature on fiber maturity. The practical value of these threshold-based action guidelines far exceeds that of a statistical model with a high R\u0026sup2; but lacking operational anchors.\u003c/p\u003e \u003cp\u003eTherefore, the presented temperature thresholds and the GDD model represent one critical layer of understanding within the sophisticated, multi-factorial \"cultivation technology system\" that defines Xinjiang cotton production. For future climate adaptation, the practical implication lies in integrating thermal sensitivity knowledge with existing best practices. For example, during seasons with forecasted suboptimal July temperatures, management could be fine-tuned\u0026mdash;such as ensuring optimal water and nitrogen status via drip fertigation to mitigate potential stress on fiber elongation.\u003c/p\u003e \u003cp\u003eIn conclusion, this analysis confirms that July and October average temperatures are key periods influencing cotton fiber length in Xinjiang and establishes a quantitative, albeit limited, link with seasonal accumulated heat. The modest explanatory power of the temperature-based models reinforces the concept that superior fiber quality in this high-yield system is the product of synergistic interactions between genetics, precision management, and environment. Sustainable intensification requires a holistic strategy that optimizes all factors in concert.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study systematically analyzed the relationship between temperature and cotton fiber length in Xinjiang. The key findings are as follows: Cotton fiber length showed significant positive correlations with the average temperatures in June, July, and October, but not with August and September; Piecewise regression identified critical temperature thresholds for fiber length regulation: 25.43\u0026deg;C in July and 12.9\u0026deg;C in October; Among monthly temperatures, July temperature contributed the most to fiber length variation (55.4%), confirming it as the key developmental period; A significant positive relationship exists between accumulated growing degree days (GDD₀, June-October) and fiber length, described by the model y\u0026thinsp;=\u0026thinsp;24.6503\u0026thinsp;+\u0026thinsp;0.0031x, indicating a 0.31 mm increase per 100\u0026deg;C\u0026middot;day increase in GDD\u003csub\u003e12\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eUltimately, while temperature during specific windows is a significant regulator, its standalone predictive power is limited within Xinjiang's context. The findings underscore that achieving consistently high fiber quality necessitates an integrated approach that considers thermal effects alongside genetic selection, precision water and nutrient management, and other cultivation technologies that define the region's success.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was supported by the Xinjiang Tianshan Talent Training Program (2023TSYCTD004), the project \u0026ldquo;Research and Demonstration on Key Cultivation Technologies for Cotton Quality Improvement\u0026rdquo; (2024SNGGNT077), the Xinjiang Science and Technology Major Project (2023A02003-6), and the National Key Research and Development Program Project (2024YFD2300604).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eTengfei Ma: Writing-review \u0026amp; editing, Formal analysis. Zhe Liang: Resources, Data curation. Chunyan Xiao:Resources, Data curation.Huiqing Zhang:Resources, Data curation.Weiping Liu:Resources, Data curation.Lingling Liu:Resources, Data curation.Chunwu Wang: Supervision, Project administration, Conceptualization.Paerhati Maimaiti: Supervision, Project administration.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDai, J. et al. Enhancing stand establishment and yield formation of cotton with multiple drip irrigation during emergence in saline fields of Southern Xinjiang. \u003cem\u003eField Crops Res.\u003c/em\u003e \u003cb\u003e309\u003c/b\u003e, 109482 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeutsch, C. A. et al. 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(2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cotton, Fiber length, Temperature threshold, Accumulated growing degree days, Xinjiang, Climate adaptation","lastPublishedDoi":"10.21203/rs.3.rs-9309019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9309019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the influence of intra-seasonal temperature dynamics on cotton fiber length in Xinjiang, China\u0026mdash;a region renowned for its super-high yield under arid conditions. Using daily temperature data and regionally averaged fiber length data from 2016 to 2023, we conducted correlation analysis, linear regression, and piecewise regression, and linear regression analyses. Results revealed significant positive correlations between fiber length and average temperatures in June, July, and October (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while no significant correlations were observed in August and September.Critical temperature thresholds were identified at 25.43\u0026deg;C in July and 12.9\u0026deg;C in October, with July accounting for the highest proportion (55.4%) of fiber length variation among monthly temperatures. A positive linear relationship was found between accumulated growing degree days (GDD\u003csub\u003e12\u003c/sub\u003e, base temperature 12\u0026deg;C, June\u0026ndash;October) and fiber length, expressed as y\u0026thinsp;=\u0026thinsp;24.6503\u0026thinsp;+\u0026thinsp;0.0031x, indicating a 0.31 mm increase per 100\u0026deg;C\u0026middot;day rise in accumulated heat. However, the low explanatory power (R\u0026sup2; = 0.6504) of the temperature-based models highlights that thermal factors alone play a limited role in Xinjiang\u0026rsquo;s integrated production system, where genetics, precision irrigation, and nutrient management are primary determinants of fiber quality. These findings highlight the importance of considering temperature thresholds in key growth stages while adopting holistic agronomic strategies to maintain high fiber quality under climate variability.\u003c/p\u003e","manuscriptTitle":"Modeling the Impact of Thermal Regimes on Cotton Fiber Elongation in High-Yield Xinjiang Production","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 19:24:13","doi":"10.21203/rs.3.rs-9309019/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-12T12:40:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112075317497774928556966712203629181001","date":"2026-05-06T06:32:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-06T03:58:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-15T17:19:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T12:46:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T12:45:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-03T05:18:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ebfb007-fb4a-4b06-b77b-29ff5794c0d2","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-12T12:40:12+00:00","index":75,"fulltext":""},{"type":"reviewerAgreed","content":"112075317497774928556966712203629181001","date":"2026-05-06T06:32:20+00:00","index":74,"fulltext":""},{"type":"reviewersInvited","content":"12","date":"2026-05-06T03:58:50+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":68015060,"name":"Earth and environmental sciences/Climate sciences"},{"id":68015061,"name":"Biological sciences/Ecology"},{"id":68015062,"name":"Earth and environmental sciences/Ecology"},{"id":68015063,"name":"Earth and environmental sciences/Environmental sciences"},{"id":68015064,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-05-14T19:24:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 19:24:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9309019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9309019","identity":"rs-9309019","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}