Assessing Fiber Quality Variability Among Modern Cotton Cultivars and Integrating it into the GOSSYM-based Fiber Quality Simulation Model

Assessing Fiber Quality Variability Among Modern Cotton Cultivars and Integrating it into the GOSSYM-based Fiber Quality Simulation Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing Fiber Quality Variability Among Modern Cotton Cultivars and Integrating it into the GOSSYM-based Fiber Quality Simulation Model Sahila Beegum, Muhammad Adeel Hassan, Krishna N. Reddy, Vangimalla Reddy, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5198065/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 May, 2025 Read the published version in Journal of Cotton Research → Version 1 posted 4 You are reading this latest preprint version Abstract Background A fiber quality module developed in 2023 and integrated into the process-based mechanistic cotton crop growth and development model, GOSSYM is the first of its kind. In this fiber quality module, the functional relationships between fiber quality and the major factors influencing it (temperature, water, and nutrient status) are established based on experiments spanned four years conducted in the sunlit Soil Plant Atmospheric Research chambers. All these experiments were conducted only on the Texas Marker-1 cotton variety. Therefore, there is a possibility that the functional equations will be more aligned with this specific cultivar. Consequently, it's essential to assess how the model performs for other cotton cultivars and address any variability that arises. In this study, data from experiments conducted on 40 major cultivars currently grown in the USA, including the Texas Marker-1 variety, under the same environmental and management conditions is used to analyze the variability in fiber quality among the varieties. The measured fiber quality is then compared with the GOSSYM model-simulated fiber quality. Based on the relative variation between measured and simulated fiber quality, cultivar-dependent parameters were developed for the fiber quality model. Results Based on the relative variation between measured and simulated fiber quality, cultivar-dependent parameters were developed for the fiber quality model. The GOSSYM model, after incorporating the developed cultivar-dependent parameters, simulated the fiber quality (fiber length, strength, micronaire, and uniformity) with an average Pearson correlation coefficient value of 0.84 and index of agreement of 0.88. Conclusions This study aims to analyze the fiber quality variability among modern cotton cultivars and establish the cultivar-dependent parameters for cotton fiber quality simulation in the GOSSYM model. The parameter estimation methodology adopted and the estimated cultivar-specific parameters improved the simulation capabilities of the model. The model with cultivar-specific parameters for fiber quality will be helpful for model users, requiring less calibration effort and providing more accurate quality simulations. Cotton GOSSYM Crop modeling Fiber quality Cultivar parameters Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights - Relative variation in fiber quality among different modern cotton cultivars. - Cultivar-dependent parameters estimated for the fiber quality model - Estimated parameters improved the fiber quality simulation capabilities. 1. Introduction Cotton fiber quality is as crucial as cotton quantity, playing a pivotal role in market profitability for both cotton growers and the textile industry. Cotton fiber quality (length, strength, micronaire, length uniformity, color, and trash grade) affects yarn and fabric performance. Fiber length refers to the average length of the longer half of the fibers. Fiber uniformity is the ratio between two span lengths, expressed as a percentage of the longer length (50% span length/2.5% span length) of fibers in the test beard. Both fiber length and uniformity impact yarn (hairiness, evenness, strength, and spinning efficiency) as well as fabric (appearance, strength, and pilling). Fiber strength is defined as the grams of breaking load per tex (breaking tenacity), where tex is the linear fiber density in grams per kilometer. Fiber strength is vital for advanced spinning technologies and affects the hairiness and strength of both the yarn and fabric (Bradow and Davidonis, 2010 ). Fiber micronaire (an indirect measure of fiber fineness and maturity) influences fiber processing and dyeing consistency (Rodgers et al., 2017 ; Delhom et al., 2020b ). Color grade measures fiber’s reflectance, brightness, and yellowness, which influence dyeing properties (Delhom et al., 2020b ). Low-quality fiber can pose challenges during processing, leading to economic losses. Each bale of cotton produced in the USA undergoes quality measurements regulated by the United States Department of Agriculture-Agricultural Marketing Service (USDA-AMS) (Delhom et al., 2020a ; Pinnamaneni et al., 2021 ). Premium rates are offered for high-quality fibers, while lower-quality fibers face discounts. Despite numerous studies and simulation model development focusing on cotton crop growth and development models, a cotton fiber quality simulation model was only recently developed. Beegum et al. ( 2023a ) developed a cotton fiber quality simulation model and incorporated it into the process-based cotton crop model GOSSYM (Beegum et al., 2023b , 2023a , b ). The model can simulate four major fiber quality indices: fiber strength, length, micronaire, and uniformity. The functional relationships between fiber quality metrics (fiber strength, length, micronaire, and uniformity) and the major factors influencing fiber quality (temperature, water, and nutrient status) used in the developed model were established based on experiments conducted at the sunlit soil plant atmospheric research (SPAR) chambers at the Mississippi State University (Lokhande and Reddy, 2014a , 2014a , 2014b ; Lokhande and Reddy, 2015 ). The experiments took place at SPAR units located at the Rodney Foil Plant Science Research Center, Mississippi State University, MS. Information on these experiments and functional relationships between the quality and the influencing factors developed based on these experiments are detailed in separately published journal articles (Lokhande and Reddy, 2014a , 2014a , 2014b ; Lokhande and Raja Reddy, 2015) with the fiber quality model's development and incorporation into GOSSYM model explained by Beegum et al. ( 2023a , 2023b ) (Beegum et al., 2023a , 2023b ). One of the main highlights of all the experiments conducted to develop cotton quality functions was that they were all performed on the same cotton cultivar, TM1, a common cultivar used as a reference genome in cotton research (Kohel et al., 1970 ; Sreedasyam et al., 2024 ). The functions based on these experiments that used the TM1 cultivar are used to develop the fiber quality module in the GOSSYM model (Beegum et al., 2023a ). Therefore, the fiber quality module integrated into GOSSYM may be biased towards or more predictive of the TM1 variety. Thus, the model needs testing for its performance with other cultivars. If the model doesn't simulate well for other cultivars, cultivar-dependent parameters in the fiber quality module in GOSSYM will need to be developed. The study aims to assess fiber quality variability among different cotton cultivars and to evaluate the GOSSYM model's fiber quality simulations. The specific objectives are: (a) to analyze the variability in fiber strength, length, micronaire, and uniformity among major cotton cultivars grown in the USA, including the TM1 variety used for model development; (b) to evaluate the fiber quality module in GOSSYM for its accuracy in simulating fiber quality across cultivars; (c) to estimate cultivar-specific parameters based on observed variability in fiber quality. 2. Materials and methods 2.1. Cotton fiber quality module in GOSSYM Since the governing functions in the fiber quality model in GOSSYM are presented in multiple publications, only a general overview is provided here and in the Supplementary file section S1. In the fiber quality simulation module in GOSSYM, the potential fiber quality is estimated as a function of temperature ( supplementary file, Equation S1 ). The potential fiber quality is then reduced based on water and nutrient status to determine the actual fiber quality. The reduction factors for potential fiber quality, due to water status and nutrient status, are functions of leaf water potential and leaf nitrogen concentration, as given in the supplementary file Equations S2 and S3 , respectively. The fiber quality indices (fiber strength, length, micronaire, and uniformity) are estimated for each of the cotton bolls, and the plant level quality is estimated as a cotton boll mass-weighted average of the fiber quality of individual cotton bolls (supplementary file Equations S4 ). More details on the fiber quality model incorporated into GOSSYM, model evaluation, and applications are presented in Beegum et al., 2023a and 20204b (Beegum et al., 2023a , 2024b ). 2.2. Obtaining data for analyzing fiber quality variability among major cotton cultivars Data for analyzing fiber quality variability among major cotton cultivars, including the TM1 variety, was obtained as part of another experimental study that was focused on quantifying the growth and development of all the major currently grown cotton cultivars in the USA (a total of 40 cultivars- Table 2 ) under the same environmental and management conditions (Beegum et al., 2024a , c ). Growing all the cultivars under the same environmental and management conditions was done to isolate the variability in the growth and development and fiber quality to be only a function of the cultivar and not other factors. All the cultivars were grown under non-limiting water and nutrient conditions. The details of the experiments are published in two other separate studies (Beegum et al., 2024c , 2024a ). A general summary of the experiments is presented in Supplementary section S2. The data set on fiber quality was from a total of 40 different cultivars with three replications and three plants per replication. The information from the experiments that are used in this study is the four major fiber quality indices: fiber strength, length, micronaire, and uniformity. Quality indices were assessed using high-volume instrumentation (HVI) by the Fiber and Biopolymer Research Institute at Texas Tech University, Lubbock, TX, as described by Davidonis and Hinojosa (1994) (Davidonis and Hinojosa, 1994; Lokhande and Reddy, 2014a ). 2.3. Evaluating fiber quality variability and cultivar-specific parameters The cultivar-specific fiber quality parameters were estimated using the methodology developed by Beegum et al., 2024 (Beegum et al., 2024c ). Steps for the estimation include first running the GOSSYM model with the fiber quality module to obtain the model-simulated fiber quality. The observed and simulated fiber qualities are then compared. Since the model was developed using the TM1 cultivar, the model-simulated quality is specifically compared with the quality measured for the TM1 cultivar to analyze if the functions are more biased toward the TM1 cultivar. Based on the variability in the measured and simulated fiber quality, all the cotton cultivars are grouped into different categories according to the percentage variation of the measured fiber quality from the simulated fiber quality. Corresponding to the variation in the measured fiber strength, length, micronaire, and uniformity from the simulated values, the cultivar-specific parameters for the cultivars in each group are determined. Since the parameters in the fiber quality functional equations act as multipliers, the cultivar-specific parameters are determined by scaling the variation in the measured and simulated values from the base parameter value of 1.0 (specific details in Section 3.1). A similar scaling procedure was used to estimate cultivar-dependent parameters for several functions in GOSSYM during model development. For example, the cultivar-specific parameters for the time to square, time from square to open boll, time to flower, fruit loss, and plant height functions are developed as multipliers (Reddy and Baker, 1988 ). 2.4. Performance evaluation The comparison of the observed and simulated fiber quality, as well as the performance of the methodology used for cultivar-specific parameters estimation, are evaluated based on the absolute percentage error, root mean square error (RMSE), Willmott's Index of Agreement (IA), and Pearson correlation coefficient (r). Lower absolute percentage error values indicate higher accuracy in the simulation, as the simulated values are closer to the actual measured values. Lower RMSE values indicate the closeness of the measured values to the simulated ones. IA reflects the degree to which the simulated variable accurately estimates the measured variable. A value of 1.0 indicates perfect agreement, and 0.0 indicates no agreement (Willmott, 1981 ). The r is a statistical measure that describes the extent to which the simulated and measured variables are linearly related. The values range from − 1 to 1. An r value of 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 signifies no linear relationship. 3. Results 3.1. Fiber quality of the 40 cotton cultivars The mean and standard deviation of the fiber strength, length, micronaire, and uniformity are 31.6 ± 1.4 g/tex, 31.2 ± 1.3 mm, 3.7 ± 0.5, and 84.0 ± 0.68%, respectively. Fiber strength varied from 29 g/tex to 35.4 g/tex, fiber length ranged from 27.1 mm to 33.3 mm, fiber micronaire ranged from 2.7 to 4.6, and uniformity varied from 82.3–85.5%. The highest variability in quality was observed for micronaire, followed by strength and length, with the least variability among the cultivars observed for uniformity. Similar to this study, other studies have also observed that micronaire and strength showed the greatest genetic variability when comparing fiber quality among cotton cultivars (Meredith Jr. and Bridge, 1973 ; Snider et al., 2013 ). Significant variability in fiber quality among cultivars has been reported by Bakhsh et al. ( 2019 ) and Teodoro et al. ( 2019 ) ​(Bakhsh et al., 2019 ; Teodoro et al., 2019 ) A negative correlation was observed between micronaire and fiber length (r = -0.51) and between micronaire and fiber strength (r = -0.36). Cotton varieties with shorter fibers are usually coarser and have higher micronaire ratings than varieties with longer fibers. The negative correlation between micronaire and strength could be because when the micronaire decreases, it could result in more fibers, and hence, strength increases. Alternatively, higher micronaire fibers have thicker cell walls, resulting in less flexibility and capability of withstanding stress, thus reducing their ability to bear loads without breaking (Bradow and Davidonis, 2000 ; Meredith Jr, 2005 ). A positive correlation was observed between strength and length (r = 0.35) as well as between uniformity and micronaire (r = 0.43). Asif et al. (2008) and Sawhney et al. ( 2013 ) also observed a positive correlation between fiber length and strength (Muhammad Asif et al., 2008 ; Karademir et al., 2010 ; Sawhney et al., 2013 ). A mild positive correlation was observed between uniformity and length (r = 0.018). Based on the interpretation of the cotton fiber quality ratings from the major fiber quality indices (fiber strength, length, micronaire, and uniformity) by Cottonworks ( 2018 ), fiber strength varied from the strong to very strong category, fiber length ranged from the medium to long category, micronaire varied from the discount range to the premium range, and uniformity varied from the intermediate to very high category (Cottonworks, 2018 ). The standard interpretation of cotton quality is presented in Supplementary Table S2 . 3.2. Simulated and measured fiber quality without cultivar-specific parameters Figure 2 presents the GOSSYM model-simulated fiber quality and the measured fiber quality. First, the model-simulated quality is compared with the measured fiber quality from the TM1 variety. The model accurately predicted the TM1 variety for all fiber quality parameters. The simulated values for fiber strength, length, micronaire, and uniformity were 30.3 g/tex, 30.42 mm, 4.27, 83.1%, while the measured values were 30.03 g/tex, 30.44 mm, 4.48, 83.7%. The absolute percentage error was less than 1.6% for strength, length, and uniformity and 4.6% for micronaire. When comparing the absolute percentage difference between the measured and simulated fiber quality for all the cultivars, there is a difference of up to a maximum of 55.6% (cultivar: PHY332W3FE, micronaire). The r values between the simulated and measures are negative, and IA is less than 0.45 for all the fiber quality indices. The average absolute percentage difference between the measured and simulated fiber strength, length, micronaire, and uniformity is 5.4%, 3.8%, 16.7%, and 1.15%, respectively. These results demonstrate that the GOSSYM model effectively simulates the fiber quality of the TM1 variety as anticipated. However, there is a considerable disparity between the simulated and observed fiber quality for other cultivars, emphasizing the necessity for specific parameters tailored to each cultivar in order to model fiber quality accurately. Here, similar to the method proposed by Beegum et al. (2024) for estimating cultivar-dependent parameters for newer cultivars, a band of ± 2.5% width is determined around the simulated values (Beegum et al., 2024c ). This facilitates setting a cultivar-specific parameter value of 1.0 for all cultivars that have their measured fiber quality values within this band (-2.5% to + 2.5%). We did not use ± 5%, ± 10%, ± 15%, etc., from the simulated values because this would result in a larger band (-5% to + 5%) close to the simulated values compared to subsequent bands. Starting from the ± 2.5% band, an additional 5% is added on either side (-7.5%, + 7.5%) of the simulated values of fiber quality. The calibrated values for the cultivars within + 2.5% to + 7.5% were set to 1.05, and within − 2.5% to -7.5% were set to 0.95, which is based on the relative variation from a value of 1.0 for parameters for cultivars within − 2.5% to + 2.5%. Similarly, the calibrated values for the cultivars within + 7.5% to + 12.5% and within − 7.5% to -12.5% were set to 1.10 and 0.90 respectively. Following this procedure, cultivar parameters are estimated for the 40 cultivars. The cultivar-specific parameter values estimated using this approach are given in Table 1 . 3.3. Simulated and measured fiber quality after incorporating the cultivar-specific parameters Once the cultivar-specific parameters are estimated based on the simulated and measured fiber quality ( section 3.2 ), the GOSSYM model is rerun by including the cultivar-specific parameters. Figure 3 shows the simulated and measured fiber quality after incorporating the cultivar-specific parameters in the fiber quality functions in the GOSSYM model. The model simulated the fiber quality for all the cultivars with better accuracy, as shown by higher values of r (-0.06 versus 0.84) and IA (0.42 versus 0.88) and reduced RMSE compared to simulations without cultivar-specific parameters (Figs. 3 and 2 ). This highlights that the parameter estimation methodology efficiently improved the fiber quality simulations. Table 1 Cultivar-specific parameter values for fiber strength, length, micronaire, and uniformity for the 40 cotton cultivars estimated using the parameter estimation methodology adopted in this study. Cultivar Strength Length Micronaire Uniformity AR9371 1.05 1.05 0.8 1 ARMOR9831 1.1 1.05 0.9 1 AU 1.1 1.05 0.75 1 C315 1 1 0.9 1 DG3519 1.05 1.1 0.75 1 DG3615B3XF 1.1 1.05 0.9 1 DP1522B2XF 1.05 1 0.9 1 DP1646 1 1.1 0.9 1 DP2012B3XF 1.1 1.05 0.95 1 DP2020B3XF 1.05 1.05 0.85 1 DP20R733B3XF 1.05 1 0.95 1 DP2115B3XF 1 1.05 0.8 1 DP2127B3XF 1.1 1 1 1.05 DP2143NRB3XF 1.1 1 0.95 1 DP2239B3XF 1.05 1.1 0.75 1 DP90 1.1 1.05 0.85 1 FM958 1.05 1.05 1 1 FM966 1.15 1 0.8 1 HS26 1.05 0.95 1 1 M240 1 0.9 1 1 NG3195B3XF 1.05 1 0.9 1 NG3299B3XF 1.2 1.05 1 1.05 NG4190B3XF 1.05 1.05 0.9 1 PHY332W3FE 1.05 1.05 0.65 1 PHY360W3FE 1.1 1.05 0.9 1 PHY390W3FE 1.05 1.05 0.7 1 PHY400W3FE 1.1 1.05 0.75 1 PHY411W3FE 1.1 1 0.7 1 PHY443W3FE 1.1 1 0.75 1 PSC355 1.05 0.95 1 1 SG747 1 1 1.1 1 ST4595B3XF 1 1.05 0.9 1 ST474 1 1 1.05 1 ST5091B3XF 1.05 1.05 0.95 1 ST825 1 1 1.1 1 STNV4990 1 1 0.8 1 TM1 1 1 1.05 1 TP 1 0.95 1.05 1 UA222 1.05 1.05 0.75 1 UA48 1.15 1.1 0.8 1 4. Discussion Process-based crop models are essential for simulating crop growth and development under varying management and climatic conditions, analyzing the effectiveness of different cropping systems, optimizing agricultural productivity, etc. (Oteng-Darko et al., 2013 ). These models help assess interactions between cultivars, environmental factors, and management practices, aiding resource management and evaluating environmental impacts. Cultivar-specific parameters are employed in these models to represent different cultivars and reflect their phenological and physiological differences, thereby accurately simulating crop growth and development (Jones et al., 2011 ). Identifying these parameters typically requires extensive experimental data across multiple environmental and management conditions, which is time-consuming and resource-intensive. With the rapid development of new cultivars, it becomes increasingly challenging to develop cultivar-specific parameters for each new cultivar (Mongiano et al., 2019 ). Despite these challenges, identifying these parameters is crucial for effectively utilizing crop models. In crop models, cultivar-specific parameters can function as multipliers, modifiers of functional relationships, limits of variables, or arguments in equations. For example, in the GOSSYM model, parameters for potential cotton boll growth and stem growth act as limits, while parameters for the delay in fruiting node formation and cotton boll abscission act as arguments. For the fiber quality model in GOSSYM, all the functions are developed using the TM1 variety. The cultivar-specific parameters in the fiber quality module can act as multipliers, with the TM1 variety serving as a baseline for functional equations. By carrying out experiments with major cotton cultivars currently grown in the USA alongside the TM1 variety under the same environmental and management conditions, the present study was able to isolate the impact of cultivars on fiber quality variability as well as understand the relative variation in fiber quality with TM1. The experiments revealed significant variability in fiber quality among different cultivars, with micronaire showing the highest variability, followed by fiber strength and length, and uniformity exhibiting the least variability (Fig. 1 ). These findings indicate that certain fiber quality traits, such as uniformity, are more stable across cultivars, while others vary greatly, necessitating cultivar-specific calibration for accurate fiber quality simulations. The study first analyzed the variability between simulated and measured cotton fiber quality without adding cultivar parameters (Fig. 2 ). The model's predictions closely aligned with the TM1 variety, reflecting the specificity of the functional relationships developed from experiments on this cultivar. However, significant deviations were observed when applying the model to other cultivars, highlighting the need for cultivar-specific parameter estimation (Fig. 2 ). Given that cultivar parameters for fiber quality act as multipliers and all governing functions in the fiber quality module of GOSSYM were developed based on the same cultivar (TM1), it was reasonable to group the cultivars based on their relative variability from the simulated values and identify the cultivar-specific parameters. Incorporating these parameters into the GOSSYM model significantly improved the accuracy of fiber quality predictions across all evaluated cultivars, as evidenced by increased Pearson r and IA values and reduced RMSE (Figs. 1 and 2 ). The methodology focuses on reasonably estimating parameters by accounting for the crop model structure and functions rather than finding the most precise value to match observed fiber quality closely. There are various existing calibration methods, such as genetic algorithms (Pabico et al., 1999 ), Sequential Uncertainty Fitting (Abbaspour et al., 2004 ), Generalized Likelihood Uncertainty Estimation (GLUE), Parameter Estimation and Sensitivity Testing (PEST), weighted least squares methods, optimization algorithms, evolutionary and bio-inspired algorithms (Zuniga et al., 2014 ), Bayesian approaches, and trial-and-error searches (Seidel et al., 2018 ). Studies have used these methods to estimate cultivar parameters. For example, Fukui et al. ( 2015 ) used data from the variety trial experiments involving 15 rice cultivars to optimize parameters using a genetic algorithm (Fukui et al., 2015 ). Bannayan and Hoogenboom ( 2009 ) employed a pattern recognition approach in the DSSAT crop model, using the k-nearest neighbor method to find the best-matching cultivar combination(Bannayan and Hoogenboom, 2009 ). Most of these methods often treat the model as a black box, transforming inputs into outputs without considering the model structure or the functional relevance of the parameters being calibrated (Zhao et al., 2014 ). These frequentist or Bayesian approaches can also be used to estimate the cultivar parameters for fiber quality. They could provide results similar to or better than the methodology adopted in this study. The choice of method depends on user preference. This study did not focus on comparing existing calibration methods, as that was not its primary aim. However, a genetic algorithm (GA)--based parameter optimization was performed to compare the methodology used. GA was chosen randomly for this purpose, as the study does not explicitly focus on comparing parameter estimation procedures. Details of the GA optimization are in the Supplementary file. Figure 4 shows the simulated and measured fiber quality after parameter calibration using the GA, and Fig. 5 shows the comparison of the observed and simulated values using the two methods. It can be observed that both approaches yield comparable results. The methodology adopted in this study achieved an RMSE of 0.62, while the GA-based approach resulted in an RMSE of 0.68. This does not mean that the methodology adopted in the presented study is superior to the GA. In GA, the convergence criteria (population size and iterations) or early stopping criteria can be adjusted for more accuracy. For example, in the GA-based optimization carried out for this study, the early stopping criterion is triggered if the absolute percentage error between the simulated and measured values falls below 2.5%, which can be varied by the user. The methodology adopted in this study facilitates simulating fiber quality within an error margin of ± 2.5%. Similar to GA, the methodology for determining cultivar-specific parameters based on grouping used in this study is also flexible, allowing users to adjust the error margin to suit their specific needs, ranging from broad agricultural assessments to more precise applications. The error margin is a choice of the model user, depending on the precision and accuracy required for the model's purpose (Boote et al., 1996 ). Some general differences exist between the method adopted in this study and existing parameter calibration methods. Existing frequentist or Bayesian approaches do not inherently account for the model structure or functional equations where the parameters are used. Studies have shown that it is unwise to make adjustments without clearly understanding the parameters' relevance and the model structure, as it is essential to know how each cultivar parameter is used in the mathematical functions within crop models because individual parameters can be connected to the model structure and there can be interactions between parameters (Wallach et al., 2001 ; Zhao et al., 2014 ). In contrast to existing calibration methods, the methodology adopted in this study groups the cultivars based on the relative variation from simulated values and estimates the cultivar-specific parameters for the groups. This grouping approach allows for the assignment of the same cultivar parameters to all cultivars within a group, enabling a structured crop database. For example, GOSSYM can identify a particular cultivar, determine its group, and assign the corresponding parameters for specific functions. Existing approaches do not perform this grouping during parameter estimation. In Table 2 , the calibrated parameter values using GA for each of the cultivars are presented. Each cultivar has different values as opposed to the group approach (parameter values presented in Table 1 ). Figure 7 shows the same in the graphical format. Even if the parameter variation would have only resulted in minimal variation between the observed and simulated values, each cultivar will have one parameter value, which the user can decide if they would like to group based on the similarity or have independent parameter values for each cultivar. Most calibration methods calibrate the cultivar parameters of a cultivar at a time; the methodology adopted in this study identified the cultivar parameters of all the cultivars currently growing simultaneously. Only some studies have looked into estimating the cultivar parameters collectively by accounting for the relative variability in the growth and development of the cultivars. Table 2 Cultivar-specific parameter values for fiber strength, length, micronaire, and uniformity for the 40 cotton cultivars estimated using Genetic Algorithm-based parameter optimization. Cultivar Strength Length Micronaire Uniformity AR9371 1.15 0.79 0.84 0.98 ARMOR9831 1.14 0.97 0.94 1.05 AU 0.95 1.20 1.05 0.97 C315 0.79 0.95 0.87 0.79 DG3519 1.11 1.06 0.69 1.07 DG3615B3XF 0.84 0.91 0.88 1.18 DP1522B2XF 1.10 0.96 0.91 0.96 DP1646 0.92 1.00 0.90 1.08 DP2012B3XF 0.93 0.72 0.98 0.98 DP2020B3XF 0.92 0.96 0.88 1.11 DP20R733B3XF 1.06 0.97 0.91 0.87 DP2115B3XF 0.90 0.99 0.89 0.97 DP2127B3XF 1.19 1.11 1.03 1.05 DP2143NRB3XF 1.19 0.76 0.91 0.98 DP2239B3XF 1.09 1.02 0.71 0.71 DP90 1.04 1.00 0.80 1.09 FM958 1.02 0.94 1.02 1.07 FM966 1.03 1.06 0.82 1.05 HS26 0.99 0.92 0.96 0.97 M240 1.14 0.86 0.94 1.06 NG3195B3XF 1.01 0.96 0.89 0.66 NG3299B3XF 1.08 1.14 0.97 1.18 NG4190B3XF 0.99 1.00 0.78 0.97 PHY332W3FE 1.10 1.10 0.67 0.93 PHY360W3FE 1.06 1.09 0.93 0.98 PHY390W3FE 1.13 1.08 1.13 0.93 PHY400W3FE 1.17 1.19 0.74 1.03 PHY411W3FE 1.13 1.08 0.76 1.06 PHY443W3FE 1.07 0.90 0.93 0.85 PSC355 1.11 0.92 0.94 1.11 SG747 1.14 1.01 1.06 0.99 ST4595B3XF 0.92 1.03 1.13 1.16 ST474 0.97 0.93 0.95 0.95 ST5091B3XF 1.07 1.08 0.90 0.95 ST825 0.97 0.97 1.02 0.87 STNV4990 0.96 0.93 0.80 0.87 TM1 1.18 1.03 0.97 0.96 TP 1.01 0.97 1.08 1.05 UA222 1.07 1.12 0.73 0.74 UA48 1.09 1.05 0.78 0.88 From a practical standpoint, accurately predicting fiber quality for different cultivars using the GOSSYM model has significant implications for the cotton industry. This study facilitates less effort in parameter estimation for recent cultivars and results in more accurate quality estimation. The model can help decide management conditions that could improve cotton fiber quality. When adding newer cultivars beyond the 40 included in the study, experiments can be conducted in the same way (carrying out the experiments under the same environmental and management conditions), and the parameters can be estimated in comparison with the GOSSYM simulated values using a parameter value of 1.0. Another approach could be to include a few of the cultivars from the original 40 cultivars along with the new cultivars and find the cultivar parameter values in relation to the parameters of the cultivars already in the 40-cultivar group. While this study demonstrates the improved accuracy of the fiber quality simulation in the GOSSYM model with the developed cultivar-specific parameters, future studies should include additional validations by comparing the fiber quality of the cultivars in varying environmental and management conditions to validate further the cultivar parameters estimated in this study. 5. Conclusions The study examined the variability of four major fiber quality indices among the 40 cotton cultivars currently grown in the US Cotton Belt. Considering the importance of accurate simulation of fiber quality, as well as the variability in fiber quality even when cultivars are grown under the same environmental and management conditions, the study focused on estimating the fiber quality-related cultivar-specific parameters in the cotton crop growth and development model, GOSSYM. The methodology adopted considered the GOSSYM model structure and the fiber quality functional equations used in the model. The parameter estimation methodology adopted and the estimated cultivar-specific parameters improved the simulation capabilities of the model. The model with cultivar-specific parameters for fiber quality for the existing cultivars will be helpful for model users, requiring less calibration effort and providing more accurate quality simulations. Declarations Ethics approval and consent to participate: All authors have approved the manuscript and agree to its submission to the Journal of Cotton Research. Consent for publication: We confirm that the manuscript has not been published and is not under consideration for publication elsewhere. All authors have given their consent for publication. Competing interests: We have no conflicts of interest to disclose. Authors' information: Sahila Beegum ab* , Muhammad Adeel Hassan ac , Krishna N. Reddy d , Vangimalla Reddy a , Kambham Raja Reddy e a Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD 20705, USA b Nebraska Water Center, Robert B. Daugherty Water for Food Global Institute, 2021 Transformation Drive, University of Nebraska, Lincoln, NE 68588, USA c Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37830, USA d USDA-ARS, Crop Production Systems Research Unit, 141 Experiment Station Road, P.O. Box 350, Stoneville, MS 38776, USA e Department of Plant and Soil Sciences, Mississippi State University, Mississippi, Mississippi State, MS 39762, USA Funding: United States Department of Agriculture, Agricultural Research Service (under Agreement No. 58-8042-9-072), USDA NIFA 2019–34263 30552 and MIS 043050, and USDA-ARS NACA 58-6066-2-030 Authors' contributions: Sahila Beegum: Conceptualization, Methodology, Software, formal analysis, Writing- Original draft; Muhammad Adeel Hassan: Software, formal analysis, Writing- Original draft; Krishna N. Reddy: Supervision, review, and editing; Vangimalla Reddy: Supervision, review, and editing; Kambham Raja Reddy: Conceptualization, Methodology, Software, Experiments, Data acquisition, Writing- Reviewing and Editing preparation. Acknowledgements: This study is based on work supported by Mississippi State University, Mississippi, the United States Department of Agriculture, Agricultural Research Service (under Agreement No. 58-8042-9-072), and the USDA NIFA 2019–34263 30552 and MIS 043050, and USDA-ARS NACA 58-6066-2-030. The authors also received support from the University of Nebraska, Lincoln. The authors also received support from the University of Nebraska, Lincoln, and the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA). We thank David Brand, S. Poudel, R.R. Vennam, and P. Ramamoorthy for their help during the experiment. Availability of data and materials: Data used in this study are available within the article and the supplementary materials. The latest version of the GOSSYM source code with the fiber quality module can be accessed from https://github.com/USDA-ARS-ACSL/GOSSYM-2DSOIL . There are no restrictions for accessing the source code. References Abbaspour KC, Johnson CA, Van Genuchten MT. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J. 2004;3:1340–52. Bakhsh A, Rehman M, Salman S, Ullah R. (2019). Evaluation of cotton genotypes for seed cotton yield and fiber quality traits under water stress and non-stress conditions. https://www.researchgate.net/profile/Dr-Saad-Draman-Khail/publication/339616678_SJA_2019/links/5e5c90a192851cefa1d4b319/SJA-2019.pdf (Accessed June 4, 2024). Bannayan M, Hoogenboom G. Using pattern recognition for estimating cultivar coefficients of a crop simulation model. Field Crops Res. 2009;111:290–302. 10.1016/j.fcr.2009.01.007 . Beegum S, Hassan MA, Ramamoorthy P, Bheemanahalli R, Reddy KN, Reddy V, et al. Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton. Agriculture. 2024a;14:1054. 10.3390/agriculture14071054 . Beegum S, Reddy KR, Ambinakudige S, Reddy V. Planting for perfection: How to maximize cotton fiber quality with the right planting dates in the face of climate change. Field Crops Res. 2024b;315:109483. 10.1016/j.fcr.2024.109483 . Beegum S, Reddy KR, Reddy V. Algorithm for estimating cultivar-specific parameters in crop models for newer crop cultivars. Eur J Agron. 2024c;160:127308. 10.1016/j.eja.2024.127308 . Beegum S, Reddy V, Reddy KR. Development of a cotton fiber quality simulation module and its incorporation into cotton crop growth and development model: GOSSYM. Comput Electron Agric. 2023a;212:108080. 10.1016/j.compag.2023.108080 . Beegum S, Timlin D, Reddy KR, Reddy V, Sun W, Wang Z, et al. Improving the cotton simulation model, GOSSYM, for soil, photosynthesis, and transpiration processes. Sci Rep. 2023b;13:7314. Boote KJ, Jones JW, Pickering NB. Potential Uses and Limitations of Crop Models. Agron J. 1996;88:704–16. 10.2134/agronj1996.00021962008800050005x . Bradow JM, Davidonis GH. Quantitation of fiber quality and the cotton production-processing interface: A Physiologist’s Perspective. J Cotton Res. 2000;4:34–64. Bradow JM, Davidonis GH. Effects of environment on fiber quality. In: Stewart JM, Oosterhuis DM, Heitholt JJ, Mauney JR, editors. Physiology of Cotton. Dordrecht: Springer; 2010. pp. 229–45. https://doi.org/10.1007/978-90-481-3195-2_21 . Cottonworks. (2018). The classification of cotton . https://www.cottonworks.com/en/topics/sourcing-manufacturing/fiber-science/cotton-fiber-qualities-and-evaluation/ (Accessed December 19, 2022). Delhom CD, Hequet EF, Kelly B, Abidi N, Martin VB. Calibration of HVI cotton elongation measurements. J Cotton Res. 2020a;3:31. 10.1186/s42397-020-00073-1 . Delhom CD, Knowlton J, Martin VB, Blake C. Engineering and ginning. J Cotton Sci. 2020b;24:189–96. Fukui S, Ishigooka Y, Kuwagata T, Hasegawa T. A methodology for estimating phenological parameters of rice cultivars utilizing data from common variety trials. J Agricultural Meteorol. 2015;71:77–89. 10.2480/agrmet.D-14-00042 . Jones JW, He J, Boote KJ, Wilkens P, Porter CH, Hu Z. (2011). Estimating DSSAT cropping system cultivar-specific parameters using Bayesian techniques. In: Methods of Introducing System Models into Agricultural Research. L.R. Ahuja and L. Ma, editor Advances In Agricultural Systems Modeling Series 2. pp. 365–393, Madison, WI, USA. Karademir E, Karademir C, Ekininci R, Gencer O. Relationship between yield, fiber length and other fiber-related traits in advanced cotton strains. Notulae Botanicae Horti Agrobotanici Cluj-Napoca. 2010;38:111–6. 10.15835/nbha3834889 . Kohel RJ, Richmond TR, Lewis CF. Texas Marker-1. Description of a genetic standard for Gossypium hirsutum L. Crop Sci. 1970;10:670–1. 10.2135/cropsci1970.0011183X001000060019x . Lokhande SB, Reddy R, K. Cotton reproductive and fiber quality responses to nitrogen nutrition. Int J Plant Prod. 2015;9:191–210. Lokhande S, Reddy KR. Quantifying temperature effects on cotton reproductive efficiency and fiber quality. Agron J. 2014a;106:1275–82. Lokhande S, Reddy KR. Reproductive and fiber quality responses of upland cotton to moisture deficiency. Agron J. 2014b;106:1060–9. Meredith WR Jr. (2005). Influence of cotton breeding on yield and fiber quality problems. Cotton Incorporated Proceedings. June 6–8, 2005, Memphis, TN, USA. Meredith WR Jr., Bridge RR. Yield, Yield Component and Fiber Property Variation of Cotton (Gossypium hirsutum L.) Within and Among Environments1. Crop Sci. 1973;13. cropsci1973.0011183X001300030006x. Mongiano G, Titone P, Tamborini L, Pilu R, Bregaglio S. Advancing crop modelling capabilities through cultivar-specific parameters sets for the Italian rice germplasm. Field Crops Res. 2019;240:44–54. 10.1016/j.fcr.2019.05.012 . Muhammad Asif MA, Mirza JI, Yusuf Zafar YZ. (2008). Genetic analysis for fiber quality traits of some cotton genotypes. Pakistan Journal of Botany 40, 1209–1215. https://www.cabidigitallibrary.org/doi/full/10.5555/20103021764 (Accessed June 4, 2024). Oteng-Darko P, Yeboah S, Addy SNT, Amponsah S, Danquah EO. (2013). Crop modeling: A tool for agricultural research – A review. E3 Journal of Agricultural Research and Development , 2: 001–006. https://www.academia.edu/download/80926916/1364058163_Oteng-Darko_20et_20al.pdf (Accessed June 5, 2024). Pabico JP, Hoogenboom G, McClendon RW. Determination of cultivar coefficients of crop models using a genetic algorithm: a conceptual framework. Trans ASAE. 1999;42:223–32. Pinnamaneni SR, Anapalli SS, Sui R, Bellaloui N, Reddy KN. Effects of irrigation and planting geometry on cotton (Gossypium hirsutum L.) fiber quality and seed composition. J Cotton Res. 2021;4:2. 10.1186/s42397-020-00078-w . Reddy VR, Baker DN. Estimation of parameters for the cotton simulation model GOSSYM: Cultivar differences. Agric Syst. 1988;26:111–22. 10.1016/0308-521X(88)90064-9 . Rodgers J, Zumba J, Fortier C. Measurement comparison of cotton fiber micronaire and its components by portable near infrared spectroscopy instruments. Text Res J. 2017;87:57–69. Sawhney A, Reynolds M, Allen C, Slopek R, Condon B. Effects of greige cotton lint properties on hydroentangled nonwoven fabrics. Text Res J. 2013;83:3–12. 10.1177/0040517512452949 . Seidel S, Palosuo T, Thorburn P, Wallach D. Towards improved calibration of crop models – Where are we now and where should we go? Eur J Agron. 2018;94:25–35. 10.1016/j.eja.2018.01.006 . Snider JL, Collins GD, Whitaker J, Davis JW. Quantifying genotypic and environmental contributions to yield and fiber quality in Georgia: Data from seven commercial cultivars and 33 yield environments. J Cotton Sci. 2013;17:285–92. Sreedasyam A, Lovell JT, Mamidi S, Khanal S, Jenkins JW, Plott C, et al. Genome resources for three modern cotton lines guide future breeding efforts. Nat Plants. 2024;1–13. 10.1038/s41477-024-01713-z . Teodoro PE, Farias FJC, de Carvalho LP, Ribeiro LP, Nascimento M, Azevedo CF, et al. Adaptability and Stability of Cotton Genotypes Regarding Fiber Yield and Quality Traits. Crop Sci. 2019;59:518–24. 10.2135/cropsci2018.04.0250 . Wallach D, Goffinet B, Bergez J-E, Debaeke P, Leenhardt D, Aubertot J-N. Parameter Estimation for Crop Models. Agron J. 2001;93:757–66. 10.2134/agronj2001.934757x . Willmott C. On the validation of models. Phys Geogr. 1981;2:184–94. 10.1080/02723646.1981.10642213 . Zhao G, Bryan BA, Song X. Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters. Ecol Model. 2014;279:1–11. 10.1016/j.ecolmodel.2014.02.003 . Zuniga E, Lopez-Cruz I, Ruiz Garcia A. Parameter estimation for crop growth model using evolutionary and bio-inspired algorithms. Appl Soft Comput. 2014;23:474–82. 10.1016/j.asoc.2014.06.023 . Supplementary Files SupplementaryFile.docx Cite Share Download PDF Status: Published Journal Publication published 13 May, 2025 Read the published version in Journal of Cotton Research → Version 1 posted Reviewers agreed at journal 12 Nov, 2024 Reviewers invited by journal 12 Nov, 2024 Editor assigned by journal 09 Oct, 2024 First submitted to journal 06 Oct, 2024 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. 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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-5198065","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377068952,"identity":"96ef8680-7563-4229-ac74-edf48634a747","order_by":0,"name":"Sahila Beegum","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYFACHoYDDxgYEiCcCiA+QIyWBLiWM0RqYYBrYWwjQot8+9mDBxJqGPL42duvSd2cd9ie7/gB5hcf23BrMTiTl3Ag4RhDsWTPmTLp3G2HE2eeSWCznIlPC0OOwYEENobEDTdy0kBaEkBcY54zeBzW/wao5h9D4v77b4Ba5hy2Nzj/AL8WhhtAWxLbgLZIsB+Tzm04zLjhRgLzY54KPA67AbQlsU+iWOJMDrN1zrH0xJk3HrYxzsCjRb4/x/jDh282efztxx/ezqmxtuc7n3z4wwcDPA6DAAkg5oEpY2yTIKgBAtgfwFjMH4jUMgpGwSgYBSMDAAAFPlwekZCHCwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3623-4426","institution":"USDA ARS: USDA Agricultural Research Service","correspondingAuthor":true,"prefix":"","firstName":"Sahila","middleName":"","lastName":"Beegum","suffix":""},{"id":377068953,"identity":"0804e51d-3d93-4fc0-bf73-cf78e1c482ce","order_by":1,"name":"Muhammad Adeel Hassan","email":"","orcid":"","institution":"USDA ARS: USDA Agricultural Research Service","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Adeel","lastName":"Hassan","suffix":""},{"id":377068954,"identity":"d1d84271-c5bc-449f-a592-6a73b2b86839","order_by":2,"name":"Krishna N. Reddy","email":"","orcid":"","institution":"USDA-ARS: USDA Agricultural Research Service","correspondingAuthor":false,"prefix":"","firstName":"Krishna","middleName":"N.","lastName":"Reddy","suffix":""},{"id":377068955,"identity":"c1bdf418-db23-4e1c-8b80-1a2028c02f58","order_by":3,"name":"Vangimalla Reddy","email":"","orcid":"","institution":"USDA-ARS: USDA Agricultural Research Service","correspondingAuthor":false,"prefix":"","firstName":"Vangimalla","middleName":"","lastName":"Reddy","suffix":""},{"id":377068956,"identity":"743127f2-402e-419c-9372-ef9abdcc1910","order_by":4,"name":"Kambham Raja Reddy","email":"","orcid":"","institution":"Mississippi State University","correspondingAuthor":false,"prefix":"","firstName":"Kambham","middleName":"Raja","lastName":"Reddy","suffix":""}],"badges":[],"createdAt":"2024-10-03 11:47:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5198065/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5198065/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42397-025-00221-5","type":"published","date":"2025-05-13T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70224925,"identity":"4fa8df32-847d-465a-934c-8cdbb677d6db","added_by":"auto","created_at":"2024-11-29 18:19:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1288880,"visible":true,"origin":"","legend":"\u003cp\u003eVariation in fiber strength (a), length (b), micronaire (c), and uniformity (d) among all the cultivars and the fiber strength (f), length (g), micronaire (h), and uniformity (i) for each of the 40 cultivars. The horizontal line inside the box plot (a-d) gives the median, and the red point represents the mean value. The distribution of the fiber quality and correlation among the fiber qualities are presented in Figure 1e, where ***, **, *, ., \" \", represents the significance at p \u0026lt; 0.001, p \u0026lt; 0.01, p \u0026lt; 0.05, p \u0026lt; 0.1, p \u0026lt; 1 respectively.\u003c/p\u003e","description":"","filename":"floatimage180.png","url":"https://assets-eu.researchsquare.com/files/rs-5198065/v1/618cc2336eaad43ec6f9ad6c.png"},{"id":70225172,"identity":"94d69987-bcc9-42ed-b918-18835f8eee6d","added_by":"auto","created_at":"2024-11-29 18:27:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1871401,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated and measured fiber strength (a), fiber length (b), micronaire (c), and uniformity (d). Horizonal dashed lines represents the simulated values ± 2.5% (Sim.+2.5%, Sim.-2.5%), ±7.5% (Sim.+7.5%, Sim.-7.5%), ± 12.5% (Sim.+12.5%, Sim.-12.5%), ± 17.5% (Sim.+17.5%, Sim.-17.5%), ± 22.5% (Sim.+22.5%, Sim.-22.5%). RMSE, r, and IA represent root mean square error, Pearson correlation coefficient, and Willmott's index of agreement, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5198065/v1/482935421d843a1f537d7c2d.png"},{"id":70224927,"identity":"e9a9c906-7365-469c-8c98-a107d2906ad8","added_by":"auto","created_at":"2024-11-29 18:19:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2246763,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated and measured fiber strength (a), length (b), micronaire (c), and uniformity after incorporating cultivar-specific parameters in the fiber quality simulation module in the GOSSYM. RMSE, r, and IA represent root mean square error, Pearson correlation coefficient, and Willmott's index of agreement, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5198065/v1/965499add21308dc6fdff16c.png"},{"id":70224928,"identity":"ad4d9079-8bf2-4073-b261-b108293760f0","added_by":"auto","created_at":"2024-11-29 18:19:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2378253,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated and measured fiber strength (a), length (b), micronaire (c), and uniformity after incorporating cultivar-specific parameters identified using Genetic Algorithm-based optimization into GOSSYM. RMSE, r, and IA represent root mean square error, Pearson correlation coefficient, and Willmott's index of agreement, respectively.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5198065/v1/c2f2661358f7904b4cbe5cfa.png"},{"id":70225173,"identity":"03004af7-8155-4964-8c7b-7f9034057a09","added_by":"auto","created_at":"2024-11-29 18:27:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":433020,"visible":true,"origin":"","legend":"\u003cp\u003eSimulated and measured fiber quality (fiber length, strength, micronaire, and uniformity) using the parameter estimation methodology adopted in this study (a) and the genetic algorithm-based parameter estimation method. RMSE, r, and IA represent root mean square error, Pearson correlation coefficient, and Willmott's index of agreement, respectively.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5198065/v1/4fbfa136165b4f012398c5de.png"},{"id":70225252,"identity":"df8c115d-87df-4454-a3fc-59965aaf884b","added_by":"auto","created_at":"2024-11-29 18:35:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1817081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 7.\u003c/strong\u003e Cultivar parameters for each of the 40 cultivars for (a) fiber strength, (b) length, (c) micronaire, and (d) uniformity estimated using parameter estimation methodology adopted in this study and genetic algorithm (GA)-based parameter estimation method.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5198065/v1/8cd7d2502fb318424533019d.png"},{"id":83067853,"identity":"29755735-42cb-432c-bb1d-26a797e95f94","added_by":"auto","created_at":"2025-05-19 16:07:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9229874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5198065/v1/26cc9912-7120-4972-8d83-b9ee159c8a09.pdf"},{"id":70224931,"identity":"26591c5b-4fca-4ad4-98f2-cd1c086b173b","added_by":"auto","created_at":"2024-11-29 18:19:15","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2417992,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-5198065/v1/8ba48f7031951dcaebad3bb0.docx"}],"financialInterests":"","formattedTitle":"Assessing Fiber Quality Variability Among Modern Cotton Cultivars and Integrating it into the GOSSYM-based Fiber Quality Simulation Model","fulltext":[{"header":"Highlights","content":"\u003cp\u003e- Relative variation in fiber quality among different modern cotton cultivars.\u003c/p\u003e\u003cp\u003e- Cultivar-dependent parameters estimated for the fiber quality model\u003c/p\u003e\u003cp\u003e- Estimated parameters improved the fiber quality simulation capabilities.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eCotton fiber quality is as crucial as cotton quantity, playing a pivotal role in market profitability for both cotton growers and the textile industry. Cotton fiber quality (length, strength, micronaire, length uniformity, color, and trash grade) affects yarn and fabric performance. Fiber length refers to the average length of the longer half of the fibers. Fiber uniformity is the ratio between two span lengths, expressed as a percentage of the longer length (50% span length/2.5% span length) of fibers in the test beard. Both fiber length and uniformity impact yarn (hairiness, evenness, strength, and spinning efficiency) as well as fabric (appearance, strength, and pilling). Fiber strength is defined as the grams of breaking load per tex (breaking tenacity), where tex is the linear fiber density in grams per kilometer. Fiber strength is vital for advanced spinning technologies and affects the hairiness and strength of both the yarn and fabric (Bradow and Davidonis, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Fiber micronaire (an indirect measure of fiber fineness and maturity) influences fiber processing and dyeing consistency (Rodgers et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Delhom et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Color grade measures fiber\u0026rsquo;s reflectance, brightness, and yellowness, which influence dyeing properties (Delhom et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLow-quality fiber can pose challenges during processing, leading to economic losses. Each bale of cotton produced in the USA undergoes quality measurements regulated by the United States Department of Agriculture-Agricultural Marketing Service (USDA-AMS) (Delhom et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Pinnamaneni et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Premium rates are offered for high-quality fibers, while lower-quality fibers face discounts.\u003c/p\u003e \u003cp\u003eDespite numerous studies and simulation model development focusing on cotton crop growth and development models, a cotton fiber quality simulation model was only recently developed. Beegum et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) developed a cotton fiber quality simulation model and incorporated it into the process-based cotton crop model GOSSYM (Beegum et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003eb\u003c/span\u003e). The model can simulate four major fiber quality indices: fiber strength, length, micronaire, and uniformity. The functional relationships between fiber quality metrics (fiber strength, length, micronaire, and uniformity) and the major factors influencing fiber quality (temperature, water, and nutrient status) used in the developed model were established based on experiments conducted at the sunlit soil plant atmospheric research (SPAR) chambers at the Mississippi State University (Lokhande and Reddy, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e; Lokhande and Reddy, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The experiments took place at SPAR units located at the Rodney Foil Plant Science Research Center, Mississippi State University, MS. Information on these experiments and functional relationships between the quality and the influencing factors developed based on these experiments are detailed in separately published journal articles (Lokhande and Reddy, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e; Lokhande and Raja Reddy, 2015) with the fiber quality model's development and incorporation into GOSSYM model explained by Beegum et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) (Beegum et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the main highlights of all the experiments conducted to develop cotton quality functions was that they were all performed on the same cotton cultivar, TM1, a common cultivar used as a reference genome in cotton research (Kohel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Sreedasyam et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The functions based on these experiments that used the TM1 cultivar are used to develop the fiber quality module in the GOSSYM model (Beegum et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Therefore, the fiber quality module integrated into GOSSYM may be biased towards or more predictive of the TM1 variety. Thus, the model needs testing for its performance with other cultivars. If the model doesn't simulate well for other cultivars, cultivar-dependent parameters in the fiber quality module in GOSSYM will need to be developed.\u003c/p\u003e \u003cp\u003eThe study aims to assess fiber quality variability among different cotton cultivars and to evaluate the GOSSYM model's fiber quality simulations. The specific objectives are: (a) to analyze the variability in fiber strength, length, micronaire, and uniformity among major cotton cultivars grown in the USA, including the TM1 variety used for model development; (b) to evaluate the fiber quality module in GOSSYM for its accuracy in simulating fiber quality across cultivars; (c) to estimate cultivar-specific parameters based on observed variability in fiber quality.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Cotton fiber quality module in GOSSYM\u003c/h2\u003e \u003cp\u003eSince the governing functions in the fiber quality model in GOSSYM are presented in multiple publications, only a general overview is provided here and in the Supplementary file section S1. In the fiber quality simulation module in GOSSYM, the potential fiber quality is estimated as a function of temperature (\u003cb\u003esupplementary file, Equation S1\u003c/b\u003e). The potential fiber quality is then reduced based on water and nutrient status to determine the actual fiber quality. The reduction factors for potential fiber quality, due to water status and nutrient status, are functions of leaf water potential and leaf nitrogen concentration, as given in the supplementary file \u003cb\u003eEquations S2\u003c/b\u003e and \u003cb\u003eS3\u003c/b\u003e, respectively. The fiber quality indices (fiber strength, length, micronaire, and uniformity) are estimated for each of the cotton bolls, and the plant level quality is estimated as a cotton boll mass-weighted average of the fiber quality of individual cotton bolls (supplementary file \u003cb\u003eEquations S4\u003c/b\u003e). More details on the fiber quality model incorporated into GOSSYM, model evaluation, and applications are presented in Beegum et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e and 20204b (Beegum et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Obtaining data for analyzing fiber quality variability among major cotton cultivars\u003c/h2\u003e \u003cp\u003eData for analyzing fiber quality variability among major cotton cultivars, including the TM1 variety, was obtained as part of another experimental study that was focused on quantifying the growth and development of all the major currently grown cotton cultivars in the USA (a total of 40 cultivars- Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) under the same environmental and management conditions (Beegum et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003ec\u003c/span\u003e). Growing all the cultivars under the same environmental and management conditions was done to isolate the variability in the growth and development and fiber quality to be only a function of the cultivar and not other factors. All the cultivars were grown under non-limiting water and nutrient conditions. The details of the experiments are published in two other separate studies (Beegum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024c\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). A general summary of the experiments is presented in \u003cb\u003eSupplementary section S2.\u003c/b\u003e The data set on fiber quality was from a total of 40 different cultivars with three replications and three plants per replication. The information from the experiments that are used in this study is the four major fiber quality indices: fiber strength, length, micronaire, and uniformity. Quality indices were assessed using high-volume instrumentation (HVI) by the Fiber and Biopolymer Research Institute at Texas Tech University, Lubbock, TX, as described by Davidonis and Hinojosa (1994) (Davidonis and Hinojosa, 1994; Lokhande and Reddy, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Evaluating fiber quality variability and cultivar-specific parameters\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe cultivar-specific fiber quality parameters were estimated using the methodology developed by Beegum et al., 2024 (Beegum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024c\u003c/span\u003e). Steps for the estimation include first running the GOSSYM model with the fiber quality module to obtain the model-simulated fiber quality. The observed and simulated fiber qualities are then compared. Since the model was developed using the TM1 cultivar, the model-simulated quality is specifically compared with the quality measured for the TM1 cultivar to analyze if the functions are more biased toward the TM1 cultivar. Based on the variability in the measured and simulated fiber quality, all the cotton cultivars are grouped into different categories according to the percentage variation of the measured fiber quality from the simulated fiber quality. Corresponding to the variation in the measured fiber strength, length, micronaire, and uniformity from the simulated values, the cultivar-specific parameters for the cultivars in each group are determined. Since the parameters in the fiber quality functional equations act as multipliers, the cultivar-specific parameters are determined by scaling the variation in the measured and simulated values from the base parameter value of 1.0 (specific details in Section 3.1). A similar scaling procedure was used to estimate cultivar-dependent parameters for several functions in GOSSYM during model development. For example, the cultivar-specific parameters for the time to square, time from square to open boll, time to flower, fruit loss, and plant height functions are developed as multipliers (Reddy and Baker, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Performance evaluation\u003c/h2\u003e \u003cp\u003eThe comparison of the observed and simulated fiber quality, as well as the performance of the methodology used for cultivar-specific parameters estimation, are evaluated based on the absolute percentage error, root mean square error (RMSE), Willmott's Index of Agreement (IA), and Pearson correlation coefficient (r). Lower absolute percentage error values indicate higher accuracy in the simulation, as the simulated values are closer to the actual measured values. Lower RMSE values indicate the closeness of the measured values to the simulated ones. IA reflects the degree to which the simulated variable accurately estimates the measured variable. A value of 1.0 indicates perfect agreement, and 0.0 indicates no agreement (Willmott, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). The r is a statistical measure that describes the extent to which the simulated and measured variables are linearly related. The values range from \u0026minus;\u0026thinsp;1 to 1. An r value of 1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 signifies no linear relationship.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Fiber quality of the 40 cotton cultivars\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mean and standard deviation of the fiber strength, length, micronaire, and uniformity are 31.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 g/tex, 31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 mm, 3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5, and 84.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68%, respectively. Fiber strength varied from 29 g/tex to 35.4 g/tex, fiber length ranged from 27.1 mm to 33.3 mm, fiber micronaire ranged from 2.7 to 4.6, and uniformity varied from 82.3\u0026ndash;85.5%. The highest variability in quality was observed for micronaire, followed by strength and length, with the least variability among the cultivars observed for uniformity. Similar to this study, other studies have also observed that micronaire and strength showed the greatest genetic variability when comparing fiber quality among cotton cultivars (Meredith Jr. and Bridge, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Snider et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Significant variability in fiber quality among cultivars has been reported by Bakhsh et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Teodoro et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) ​(Bakhsh et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Teodoro et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eA negative correlation was observed between micronaire and fiber length (r = -0.51) and between micronaire and fiber strength (r = -0.36). Cotton varieties with shorter fibers are usually coarser and have higher micronaire ratings than varieties with longer fibers. The negative correlation between micronaire and strength could be because when the micronaire decreases, it could result in more fibers, and hence, strength increases. Alternatively, higher micronaire fibers have thicker cell walls, resulting in less flexibility and capability of withstanding stress, thus reducing their ability to bear loads without breaking (Bradow and Davidonis, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Meredith Jr, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). A positive correlation was observed between strength and length (r\u0026thinsp;=\u0026thinsp;0.35) as well as between uniformity and micronaire (r\u0026thinsp;=\u0026thinsp;0.43). Asif et al. (2008) and Sawhney et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) also observed a positive correlation between fiber length and strength (Muhammad Asif et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Karademir et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sawhney et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A mild positive correlation was observed between uniformity and length (r\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e \u003cp\u003eBased on the interpretation of the cotton fiber quality ratings from the major fiber quality indices (fiber strength, length, micronaire, and uniformity) by Cottonworks (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), fiber strength varied from the strong to very strong category, fiber length ranged from the medium to long category, micronaire varied from the discount range to the premium range, and uniformity varied from the intermediate to very high category (Cottonworks, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The standard interpretation of cotton quality is presented in \u003cb\u003eSupplementary Table S2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Simulated and measured fiber quality without cultivar-specific parameters\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the GOSSYM model-simulated fiber quality and the measured fiber quality. First, the model-simulated quality is compared with the measured fiber quality from the TM1 variety. The model accurately predicted the TM1 variety for all fiber quality parameters. The simulated values for fiber strength, length, micronaire, and uniformity were 30.3 g/tex, 30.42 mm, 4.27, 83.1%, while the measured values were 30.03 g/tex, 30.44 mm, 4.48, 83.7%. The absolute percentage error was less than 1.6% for strength, length, and uniformity and 4.6% for micronaire.\u003c/p\u003e \u003cp\u003eWhen comparing the absolute percentage difference between the measured and simulated fiber quality for all the cultivars, there is a difference of up to a maximum of 55.6% (cultivar: PHY332W3FE, micronaire). The r values between the simulated and measures are negative, and IA is less than 0.45 for all the fiber quality indices. The average absolute percentage difference between the measured and simulated fiber strength, length, micronaire, and uniformity is 5.4%, 3.8%, 16.7%, and 1.15%, respectively. These results demonstrate that the GOSSYM model effectively simulates the fiber quality of the TM1 variety as anticipated. However, there is a considerable disparity between the simulated and observed fiber quality for other cultivars, emphasizing the necessity for specific parameters tailored to each cultivar in order to model fiber quality accurately.\u003c/p\u003e \u003cp\u003eHere, similar to the method proposed by Beegum et al. (2024) for estimating cultivar-dependent parameters for newer cultivars, a band of \u0026plusmn;\u0026thinsp;2.5% width is determined around the simulated values (Beegum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024c\u003c/span\u003e). This facilitates setting a cultivar-specific parameter value of 1.0 for all cultivars that have their measured fiber quality values within this band (-2.5% to +\u0026thinsp;2.5%). We did not use\u0026thinsp;\u0026plusmn;\u0026thinsp;5%, \u0026plusmn;\u0026thinsp;10%, \u0026plusmn;\u0026thinsp;15%, etc., from the simulated values because this would result in a larger band (-5% to +\u0026thinsp;5%) close to the simulated values compared to subsequent bands. Starting from the \u0026plusmn;\u0026thinsp;2.5% band, an additional 5% is added on either side (-7.5%, +\u0026thinsp;7.5%) of the simulated values of fiber quality. The calibrated values for the cultivars within +\u0026thinsp;2.5% to +\u0026thinsp;7.5% were set to 1.05, and within \u0026minus;\u0026thinsp;2.5% to -7.5% were set to 0.95, which is based on the relative variation from a value of 1.0 for parameters for cultivars within \u0026minus;\u0026thinsp;2.5% to +\u0026thinsp;2.5%. Similarly, the calibrated values for the cultivars within +\u0026thinsp;7.5% to +\u0026thinsp;12.5% and within \u0026minus;\u0026thinsp;7.5% to -12.5% were set to 1.10 and 0.90 respectively. Following this procedure, cultivar parameters are estimated for the 40 cultivars. The cultivar-specific parameter values estimated using this approach are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Simulated and measured fiber quality after incorporating the cultivar-specific parameters\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOnce the cultivar-specific parameters are estimated based on the simulated and measured fiber quality (\u003cb\u003esection 3.2\u003c/b\u003e), the GOSSYM model is rerun by including the cultivar-specific parameters. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the simulated and measured fiber quality after incorporating the cultivar-specific parameters in the fiber quality functions in the GOSSYM model. The model simulated the fiber quality for all the cultivars with better accuracy, as shown by higher values of r (-0.06 versus 0.84) and IA (0.42 versus 0.88) and reduced RMSE compared to simulations without cultivar-specific parameters (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This highlights that the parameter estimation methodology efficiently improved the fiber quality simulations.\u003c/p\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\u003eCultivar-specific parameter values for fiber strength, length, micronaire, and uniformity for the 40 cotton cultivars estimated using the parameter estimation methodology adopted in this study.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMicronaire\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUniformity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR9371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e 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align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG3519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP1646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2012B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2115B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2127B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2143NRB3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2239B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFM958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFM966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHS26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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\u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST4595B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e 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\u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTNV4990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eProcess-based crop models are essential for simulating crop growth and development under varying management and climatic conditions, analyzing the effectiveness of different cropping systems, optimizing agricultural productivity, etc. (Oteng-Darko et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These models help assess interactions between cultivars, environmental factors, and management practices, aiding resource management and evaluating environmental impacts. Cultivar-specific parameters are employed in these models to represent different cultivars and reflect their phenological and physiological differences, thereby accurately simulating crop growth and development (Jones et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Identifying these parameters typically requires extensive experimental data across multiple environmental and management conditions, which is time-consuming and resource-intensive. With the rapid development of new cultivars, it becomes increasingly challenging to develop cultivar-specific parameters for each new cultivar (Mongiano et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite these challenges, identifying these parameters is crucial for effectively utilizing crop models.\u003c/p\u003e \u003cp\u003eIn crop models, cultivar-specific parameters can function as multipliers, modifiers of functional relationships, limits of variables, or arguments in equations. For example, in the GOSSYM model, parameters for potential cotton boll growth and stem growth act as limits, while parameters for the delay in fruiting node formation and cotton boll abscission act as arguments. For the fiber quality model in GOSSYM, all the functions are developed using the TM1 variety. The cultivar-specific parameters in the fiber quality module can act as multipliers, with the TM1 variety serving as a baseline for functional equations. By carrying out experiments with major cotton cultivars currently grown in the USA alongside the TM1 variety under the same environmental and management conditions, the present study was able to isolate the impact of cultivars on fiber quality variability as well as understand the relative variation in fiber quality with TM1.\u003c/p\u003e \u003cp\u003eThe experiments revealed significant variability in fiber quality among different cultivars, with micronaire showing the highest variability, followed by fiber strength and length, and uniformity exhibiting the least variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings indicate that certain fiber quality traits, such as uniformity, are more stable across cultivars, while others vary greatly, necessitating cultivar-specific calibration for accurate fiber quality simulations. The study first analyzed the variability between simulated and measured cotton fiber quality without adding cultivar parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model's predictions closely aligned with the TM1 variety, reflecting the specificity of the functional relationships developed from experiments on this cultivar. However, significant deviations were observed when applying the model to other cultivars, highlighting the need for cultivar-specific parameter estimation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven that cultivar parameters for fiber quality act as multipliers and all governing functions in the fiber quality module of GOSSYM were developed based on the same cultivar (TM1), it was reasonable to group the cultivars based on their relative variability from the simulated values and identify the cultivar-specific parameters. Incorporating these parameters into the GOSSYM model significantly improved the accuracy of fiber quality predictions across all evaluated cultivars, as evidenced by increased Pearson r and IA values and reduced RMSE (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The methodology focuses on reasonably estimating parameters by accounting for the crop model structure and functions rather than finding the most precise value to match observed fiber quality closely.\u003c/p\u003e \u003cp\u003eThere are various existing calibration methods, such as genetic algorithms (Pabico et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), Sequential Uncertainty Fitting (Abbaspour et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), Generalized Likelihood Uncertainty Estimation (GLUE), Parameter Estimation and Sensitivity Testing (PEST), weighted least squares methods, optimization algorithms, evolutionary and bio-inspired algorithms (Zuniga et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Bayesian approaches, and trial-and-error searches (Seidel et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Studies have used these methods to estimate cultivar parameters. For example, Fukui et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) used data from the variety trial experiments involving 15 rice cultivars to optimize parameters using a genetic algorithm (Fukui et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Bannayan and Hoogenboom (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) employed a pattern recognition approach in the DSSAT crop model, using the k-nearest neighbor method to find the best-matching cultivar combination(Bannayan and Hoogenboom, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Most of these methods often treat the model as a black box, transforming inputs into outputs without considering the model structure or the functional relevance of the parameters being calibrated (Zhao et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese frequentist or Bayesian approaches can also be used to estimate the cultivar parameters for fiber quality. They could provide results similar to or better than the methodology adopted in this study. The choice of method depends on user preference. This study did not focus on comparing existing calibration methods, as that was not its primary aim. However, a genetic algorithm (GA)--based parameter optimization was performed to compare the methodology used. GA was chosen randomly for this purpose, as the study does not explicitly focus on comparing parameter estimation procedures. Details of the GA optimization are in the \u003cb\u003eSupplementary file.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the simulated and measured fiber quality after parameter calibration using the GA, and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the comparison of the observed and simulated values using the two methods. It can be observed that both approaches yield comparable results. The methodology adopted in this study achieved an RMSE of 0.62, while the GA-based approach resulted in an RMSE of 0.68. This does not mean that the methodology adopted in the presented study is superior to the GA. In GA, the convergence criteria (population size and iterations) or early stopping criteria can be adjusted for more accuracy. For example, in the GA-based optimization carried out for this study, the early stopping criterion is triggered if the absolute percentage error between the simulated and measured values falls below 2.5%, which can be varied by the user. The methodology adopted in this study facilitates simulating fiber quality within an error margin of \u0026plusmn;\u0026thinsp;2.5%. Similar to GA, the methodology for determining cultivar-specific parameters based on grouping used in this study is also flexible, allowing users to adjust the error margin to suit their specific needs, ranging from broad agricultural assessments to more precise applications. The error margin is a choice of the model user, depending on the precision and accuracy required for the model's purpose (Boote et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome general differences exist between the method adopted in this study and existing parameter calibration methods. Existing frequentist or Bayesian approaches do not inherently account for the model structure or functional equations where the parameters are used. Studies have shown that it is unwise to make adjustments without clearly understanding the parameters' relevance and the model structure, as it is essential to know how each cultivar parameter is used in the mathematical functions within crop models because individual parameters can be connected to the model structure and there can be interactions between parameters (Wallach et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast to existing calibration methods, the methodology adopted in this study groups the cultivars based on the relative variation from simulated values and estimates the cultivar-specific parameters for the groups. This grouping approach allows for the assignment of the same cultivar parameters to all cultivars within a group, enabling a structured crop database. For example, GOSSYM can identify a particular cultivar, determine its group, and assign the corresponding parameters for specific functions. Existing approaches do not perform this grouping during parameter estimation. In Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the calibrated parameter values using GA for each of the cultivars are presented. Each cultivar has different values as opposed to the group approach (parameter values presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the same in the graphical format. Even if the parameter variation would have only resulted in minimal variation between the observed and simulated values, each cultivar will have one parameter value, which the user can decide if they would like to group based on the similarity or have independent parameter values for each cultivar. Most calibration methods calibrate the cultivar parameters of a cultivar at a time; the methodology adopted in this study identified the cultivar parameters of all the cultivars currently growing simultaneously. Only some studies have looked into estimating the cultivar parameters collectively by accounting for the relative variability in the growth and development of the cultivars.\u003c/p\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\u003eCultivar-specific parameter values for fiber strength, length, micronaire, and uniformity for the 40 cotton cultivars estimated using Genetic Algorithm-based parameter optimization.\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\u003eCultivar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMicronaire\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUniformity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR9371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARMOR9831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e 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\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG3519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDG3615B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e 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align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2012B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2020B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP20R733B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2115B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2127B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2143NRB3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP2239B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDP90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFM958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFM966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHS26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNG3195B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNG3299B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNG4190B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHY332W3FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHY360W3FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHY390W3FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHY400W3FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHY411W3FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHY443W3FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSC355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST4595B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST5091B3XF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTNV4990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom a practical standpoint, accurately predicting fiber quality for different cultivars using the GOSSYM model has significant implications for the cotton industry. This study facilitates less effort in parameter estimation for recent cultivars and results in more accurate quality estimation. The model can help decide management conditions that could improve cotton fiber quality.\u003c/p\u003e \u003cp\u003eWhen adding newer cultivars beyond the 40 included in the study, experiments can be conducted in the same way (carrying out the experiments under the same environmental and management conditions), and the parameters can be estimated in comparison with the GOSSYM simulated values using a parameter value of 1.0. Another approach could be to include a few of the cultivars from the original 40 cultivars along with the new cultivars and find the cultivar parameter values in relation to the parameters of the cultivars already in the 40-cultivar group.\u003c/p\u003e \u003cp\u003eWhile this study demonstrates the improved accuracy of the fiber quality simulation in the GOSSYM model with the developed cultivar-specific parameters, future studies should include additional validations by comparing the fiber quality of the cultivars in varying environmental and management conditions to validate further the cultivar parameters estimated in this study.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe study examined the variability of four major fiber quality indices among the 40 cotton cultivars currently grown in the US Cotton Belt. Considering the importance of accurate simulation of fiber quality, as well as the variability in fiber quality even when cultivars are grown under the same environmental and management conditions, the study focused on estimating the fiber quality-related cultivar-specific parameters in the cotton crop growth and development model, GOSSYM. The methodology adopted considered the GOSSYM model structure and the fiber quality functional equations used in the model. The parameter estimation methodology adopted and the estimated cultivar-specific parameters improved the simulation capabilities of the model. The model with cultivar-specific parameters for fiber quality for the existing cultivars will be helpful for model users, requiring less calibration effort and providing more accurate quality simulations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have approved the manuscript and agree to its submission to the Journal of Cotton Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirm that the manuscript has not been published and is not under consideration for publication elsewhere. All authors have given their consent for publication.\u003c/p\u003e\n\u003ch2\u003eCompeting interests:\u003c/h2\u003e\n\u003cp\u003eWe have no conflicts of interest to disclose.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; information:\u003c/h2\u003e\n\u003cp\u003eSahila Beegum\u003csup\u003eab*\u003c/sup\u003e, Muhammad Adeel Hassan\u003csup\u003eac\u003c/sup\u003e, Krishna N. Reddy\u003csup\u003ed\u003c/sup\u003e, Vangimalla Reddy\u003csup\u003ea\u003c/sup\u003e, Kambham Raja Reddy\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD 20705, USA\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Nebraska Water Center, Robert B. Daugherty Water for Food Global Institute, 2021 Transformation Drive, University of Nebraska, Lincoln, NE 68588, USA\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee 37830, USA\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e USDA-ARS, Crop Production Systems Research Unit, 141 Experiment Station Road, P.O. Box 350, Stoneville, MS 38776, USA\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003e Department of Plant and Soil Sciences, Mississippi State University, Mississippi, Mississippi State, MS 39762, USA\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eUnited States Department of Agriculture, Agricultural Research Service (under Agreement No. 58-8042-9-072), USDA NIFA 2019\u0026ndash;34263 30552 and MIS 043050, and USDA-ARS NACA 58-6066-2-030\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions:\u003c/h2\u003e\n\u003cp\u003eSahila Beegum: Conceptualization, Methodology, Software, formal analysis, Writing- Original draft; Muhammad Adeel Hassan: Software, formal analysis, Writing- Original draft; Krishna N. Reddy: Supervision, review, and editing; Vangimalla Reddy: Supervision, review, and editing; Kambham Raja Reddy: Conceptualization, Methodology, Software, Experiments, Data acquisition, Writing- Reviewing and Editing preparation.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eThis study is based on work supported by Mississippi State University, Mississippi, the United States Department of Agriculture, Agricultural Research Service (under Agreement No. 58-8042-9-072), and the USDA NIFA 2019\u0026ndash;34263 30552 and MIS 043050, and USDA-ARS NACA 58-6066-2-030. The authors also received support from the University of Nebraska, Lincoln. The authors also received support from the University of Nebraska, Lincoln, and the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA). We thank David Brand, S. Poudel, R.R. Vennam, and P. Ramamoorthy for their help during the experiment.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials:\u003c/h2\u003e\n\u003cp\u003eData used in this study are available within the article and the supplementary materials. The latest version of the GOSSYM source code with the fiber quality module can be accessed from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/USDA-ARS-ACSL/GOSSYM-2DSOIL\u003c/span\u003e\u003c/span\u003e. There are no restrictions for accessing the source code.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbaspour KC, Johnson CA, Van Genuchten MT. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J. 2004;3:1340\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakhsh A, Rehman M, Salman S, Ullah R. (2019). 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Appl Soft Comput. 2014;23:474\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.asoc.2014.06.023\u003c/span\u003e\u003cspan address=\"10.1016/j.asoc.2014.06.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cotton-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cotn","sideBox":"Learn more about [Journal of Cotton Research](https://jcottonres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cotn/default.aspx","title":"Journal of Cotton Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cotton, GOSSYM, Crop modeling, Fiber quality, Cultivar parameters","lastPublishedDoi":"10.21203/rs.3.rs-5198065/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5198065/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eA fiber quality module developed in 2023 and integrated into the process-based mechanistic cotton crop growth and development model, GOSSYM is the first of its kind. In this fiber quality module, the functional relationships between fiber quality and the major factors influencing it (temperature, water, and nutrient status) are established based on experiments spanned four years conducted in the sunlit Soil Plant Atmospheric Research chambers. All these experiments were conducted only on the Texas Marker-1 cotton variety. Therefore, there is a possibility that the functional equations will be more aligned with this specific cultivar. Consequently, it's essential to assess how the model performs for other cotton cultivars and address any variability that arises. In this study, data from experiments conducted on 40 major cultivars currently grown in the USA, including the Texas Marker-1 variety, under the same environmental and management conditions is used to analyze the variability in fiber quality among the varieties. The measured fiber quality is then compared with the GOSSYM model-simulated fiber quality. Based on the relative variation between measured and simulated fiber quality, cultivar-dependent parameters were developed for the fiber quality model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on the relative variation between measured and simulated fiber quality, cultivar-dependent parameters were developed for the fiber quality model. The GOSSYM model, after incorporating the developed cultivar-dependent parameters, simulated the fiber quality (fiber length, strength, micronaire, and uniformity) with an average Pearson correlation coefficient value of 0.84 and index of agreement of 0.88.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study aims to analyze the fiber quality variability among modern cotton cultivars and establish the cultivar-dependent parameters for cotton fiber quality simulation in the GOSSYM model. The parameter estimation methodology adopted and the estimated cultivar-specific parameters improved the simulation capabilities of the model. The model with cultivar-specific parameters for fiber quality will be helpful for model users, requiring less calibration effort and providing more accurate quality simulations.\u003c/p\u003e","manuscriptTitle":"Assessing Fiber Quality Variability Among Modern Cotton Cultivars and Integrating it into the GOSSYM-based Fiber Quality Simulation Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 18:19:10","doi":"10.21203/rs.3.rs-5198065/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-11-12T12:32:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-12T09:20:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-09T06:12:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cotton Research","date":"2024-10-06T11:17:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cotton-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cotn","sideBox":"Learn more about [Journal of Cotton Research](https://jcottonres.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cotn/default.aspx","title":"Journal of Cotton Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18eb4583-0f09-4a8d-ac21-53856e332733","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-19T16:02:09+00:00","versionOfRecord":{"articleIdentity":"rs-5198065","link":"https://doi.org/10.1186/s42397-025-00221-5","journal":{"identity":"journal-of-cotton-research","isVorOnly":false,"title":"Journal of Cotton Research"},"publishedOn":"2025-05-13 15:57:53","publishedOnDateReadable":"May 13th, 2025"},"versionCreatedAt":"2024-11-29 18:19:10","video":"","vorDoi":"10.1186/s42397-025-00221-5","vorDoiUrl":"https://doi.org/10.1186/s42397-025-00221-5","workflowStages":[]},"version":"v1","identity":"rs-5198065","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5198065","identity":"rs-5198065","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}