Search Results (53)

Cotton (Gossypium barbadense L.) is a crop of great economic importance, as the superior quality of its fiber is highly valued worldwide. The objective of this research was to evaluate the agromorphological diversity of cotton germplasm using both qualitative and quantitative descriptors. A combination of univariate and multivariate statistical analyses was applied to identify promising accessions. The coefficients of variation (GCV and PCV) and high heritability estimates observed for descriptors such as the number of sympodial branches, fiber weight, and number of seeds per capsule confirm genetic control, ensuring the effectiveness of selection in future breeding programs. Correlation analysis revealed a strong positive relationship between lint yield and number of bolls per plant (0.893). Furthermore, principal component analysis indicated that accessions PER1010536, PER1010538, PER1010543, and PER1010547 were associated with high yield and early-maturity traits. Furthermore, multiple correspondence analysis and mixed data factor analysis demonstrated that the observed variability also depends on qualitative traits such as petal spot and bract color, supporting the concept of a complex genetic architecture. These findings provide a solid basis for the development of new cotton cultivars with improved productivity. Full article

Current saw- and pin-type lint-cleaning systems used by the ginning industry have challenges retaining lint quality. The objective of the research was to test a novel pneumatic fractionator for the lint cleaning of an Upland cotton variety that was both saw- and roller-ginned. The process variables tested were initial lint moisture content in the range of 5.5–15% w.b., line pressure in the range of 276–552 kPa, and residence time in the range of 15–45 s. Experiments were conducted based on a central composite design. Models based on response surface methodology (RSM) were developed for final lint moisture, total trash extracted during lint cleaning, and High-Volume Instrument (HVI) fiber quality. The RSM models adequately described the pneumatic fractionation process, as indicated by the coefficient of determination, predicted vs. observed plots, and residual values. The results indicated that the interactions among initial lint moisture content, residence time, and line pressure significantly affected lint quality. At the optimized pneumatic fractionator process conditions, the predicted lint quality attributes were better for both roller- and saw-ginned lint compared to lint cleaned with saw- and pin-type lint cleaners. The upper half mean length increased by 1 mm, the uniformity index was higher by 0.5–1 percentage points, the strength was 1–2 g/tex higher, and the short fiber content was reduced by more than one percentage point. Color grades were better for pneumatic fractionated lint compared to saw- and pin-type lint cleaning methods. Lint value was approximately 4 cents/kg higher for both saw- and roller-ginned pneumatic fractionated lint, compared to lint cleaned using saw- and pin-type lint cleaners. The novel pneumatic fractionator, when compared to industry-standard saw- and pin-type lint cleaners, effectively cleaned lint while retaining fiber quality and removing most of the motes and trash. Full article

Cotton fiber length and maturity, two critical fiber qualities, are commonly determined in the U.S. by Uster high volume instrument (HVI) and advanced fiber information system (AFIS). The main objectives of this investigation were to compare how HVI lengths agree with AFIS lengths and to examine whether the fiber length is linked with fiber maturity between the Universal HVI length calibration cotton standards and diverse upland lint samples. HVI micronaire (MIC) and AFIS fineness showed insignificant differences from HVI length calibration cotton standards to lint samples. Although there were strong and significant correlations between HVI upper-half mean length (UHML) and either AFIS UQL (w) or AFIS L5% (n), the relationship between UHML and L5% (n) was better suited than between UHML and UQL (w) in scrutinizing fiber lengths. Meanwhile, analysis revealed a moderate correlation between AFIS L5% (n) length and AFIS maturity ratio (MR), indicating the possibility of improving AFIS L5% (n) length by regulating fiber MR development. Further, AFIS MR values were positive and moderate correlated with algorithmic M_IR values of attenuated total reflection Fourier transform infrared (ATR FT-IR) spectra. The results suggested the feasibility of the ATR FT-IR method along with M_IR analysis in estimating AFIS MR rapidly away from fiber testing laboratories. Full article

The protein phosphatase 2C (PP2C) gene family plays vital roles in plant growth and stress responses, yet remains inadequately characterized in cotton, particularly in Gossypium barbadense renowned for its superior fiber quality. Here, we identified 152 GbPP2Cs in Gossypium barbadense through genome-wide analysis and comparative genomics with three related cotton species (G. arboreum, G. raimondii, and G. hirsutum), identifying 105, 105, and 204 GbPP2Cs, respectively. GbPP2Cs show uneven chromosomal distribution with notable clustering on A05, evolutionary conservation in gene structure and motif composition, and predominant nuclear/chloroplast localization. Phylogenetic analysis classified them into 15 subfamilies showing conserved evolution. Protein enrichment revealed 15 GbPP2Cs involved in mitogen-activated protein kinase (MAPK) and hormone signaling pathways. Expression profiling revealed distinct members responsive to biotic/abiotic stresses, fiber development stages, and maturity. Notably, we discovered potential pleiotropic regulators including two genes (Gbar_D13G012000 and Gbar_A13G012360) co-regulating lint percentage and disease resistance, GbAIP1 coordinating fiber length-strength trade-off, and GbPP2C59 as a maturity negative candidate. These findings provide valuable genetic resources for cotton improvement. Full article

Freshwater scarcity severely limits sustainable cotton production in arid regions. This study aimed to establish the optimal salinity threshold for staged saline water supplemental irrigation (SWSI) and elucidate its canopy-level mechanisms in optimizing within-boll yield components and fiber quality. A two-year field trial (2023–2024) was conducted in Awati County, Xinjiang, using mulched drip irrigation at five SWSI levels (3.5–9.5 g L⁻¹) and a freshwater control (CK). Compared with CK, 3.5 g L⁻¹ treatment significantly increased lint yield by 31.4%, boll number per plant by 22.45%, and fibers per seed by 6.01–10.59%, while fiber length and strength rose by 6.98–10.38% and 2.69–6.00%, respectively. When salinity reached 8.0 g L⁻¹, yield declined by 8.5%, and a salinity of 9.5 g L⁻¹ reduced yield by 24.52%. Spatially, mid-fruiting branches (nodes 4–6) remained stable, maintaining high lint mass per seed even under high salinity, whereas upper branches (≥node 7) were most sensitive; at 9.5 g L⁻¹, the boll number (0.36) was 56.6% lower than at 3.5 g L⁻¹ (0.83), and the Q-score decreased by 6.7%. These results demonstrate that SWSI with ≤5.0 g L⁻¹ salinity (optimum 3.5 g L⁻¹) simultaneously enhances lint yield and fiber quality, providing a practical strategy for efficient saline water use in arid cotton regions. Full article

This study pioneers the integration of the water sensitivity coefficient (Ky) with cotton yield performance under varying water regimes in the Brazilian Cerrado. The objective was to evaluate the productive performance and fiber yield of cotton cultivars under different water regimes during the tropical dry season. The experiment followed a randomized block design in a 5 × 4 factorial scheme with four replications, totaling 80 plots. Treatments consisted of five irrigation levels based on crop evapotranspiration (25%, 50%, 75%, 100% and 125% of ETc) and four cultivars (TMG44B2RF, FM944GL, IMA5801B2RF and IMA709B2RF). Increasing water supply enhanced cotton lint yield, reaching 3209.4 kg ha⁻¹ at the highest regime. Water regimes between 25 and 125% of the ETc significantly improved yield components, leading to an increase of up to 221% in lint yield. Fiber quality remained stable across irrigation levels and was mainly genotype-dependent. Among the cultivars, FM944GL showed high productivity and fiber yield, while IMA5801B2RF demonstrated greater water resilience (Ky = 0.73), making it suitable for water-limited environments. The findings reflect the specific conditions of the evaluated growing season. Thus, long-term studies under diverse environmental conditions are recommended to confirm these trends and enhance understanding of cotton responses to water regimes in the Cerrado. Full article

Chemical defoliation is an important management practice in cotton to facilitate mechanical harvesting and leaf removal and maintain lint quality. Recent advances in precision agriculture have enabled the development of autonomous robotic platforms with a targeted side-spraying system that can achieve good canopy penetration while preventing soil compaction and crop mechanical damage. A side-wise spraying system allows for application of defoliant at different canopy heights. However, information on the effects of staggered defoliation on cotton fiber quality is limited. Thus, field research was conducted to evaluate the effects of various staggered application timing intervals (15, 10, 8, 5, and 3 days) on fiber quality and compare them with standard over-the-top broadcast applications. Staggered defoliation affected fiber length, with significant differences observed for upper half mean length, fiber length based on weight, and upper quartile length. Fiber maturity was also influenced by staggered defoliation timing, with a 15-day interval resulting in the lowest micronaire and higher immature fiber content. The effects of staggered defoliation on other parameters, such as strength, uniformity, and trash characteristics, varied across locations. The findings highlight the potential of robotic systems for chemical spraying and emphasize the need for further research on more precise and targeted application of defoliants to improve fiber quality. Full article

Cotton is a vital economic crop, and cotton fiber serves as the primary raw material for the textile industry. Lignin in cotton fiber is closely associated with fiber quality. Lignin is synthesized through the phenylpropanoid metabolic pathway, where the cinnamyl alcohol dehydrogenase gene CAD6 plays a significant role. In this study, we obtained successfully transformed overexpression plants by constructing an overexpression vector and performing genetic transformation and tissue culture. To verify the function of the GhCAD6 gene in upland cotton, we analyzed the agronomic traits, fiber quality, cell wall structure, and lignin content of GhCAD6-overexpressing plants. Our results indicate that the GhCAD6 gene is predominantly expressed during the stages of fiber elongation and secondary wall synthesis. Overexpression of the GhCAD6 gene resulted in increased plant lignin content and fiber upper half mean length, boll number per plant, fiber uniformity index, strength, and lint were improved. The fiber surface was smoother, and the fiber cell wall was more compact. These findings demonstrate that the GhCAD6 gene positively regulates lignin synthesis and fiber quality formation, contributing to the enhancement of cotton fiber quality. Full article

Background: The regulation of oil and protein contents in cottonseed is governed by a complex genetic network. Gaining insight into the mechanisms controlling these traits is necessary for dissecting the formation patterns of cottonseed quality. Method: In this study, Xinluzhong 37 (P₁) and Xinluzhong 51 (P₂) were selected as parental lines for two reciprocal crosses: P₁ × P₂ (F₁) and its reciprocal P₂ × P₁ (F₁′). Each F1 was selfed and backcrossed to both parents to generate the F₂ (F₂′), B₁ (B₁′), and B₂ (B₂′) generations. To assess nutritional traits in hairy (non-delinted) and lint-free (delinted) seeds, two indicators, oil content and protein content, were measured in both seed types. Joint segregation analysis was employed to analyze the inheritance of these traits, based on a major gene plus polygene model. Results: In the orthogonal crosses, the CVs for the four nutritional traits ranged at 2.710–7.879%, 4.086–11.070%, 2.724–6.727%, and 3.717–9.602%. In the reciprocal crosses, CVs ranged at 2.710–8.053%, 4.086–9.572%, 2.724–6.376%, and 3.717–8.845%. All traits exhibited normal or skewed-normal distributions. For oil content in undelinted/delinted seeds, polygenic heritabilities in the orthogonal cross were 0.64/0.52, and 0.40/0.36 in the reciprocal cross. For protein content, major-gene heritabilities in the orthogonal cross were 0.79 (undelinted) and 0.78 (delinted), while those in the reciprocal cross were both 0.62. Conclusions: Oil and protein contents in cottonseeds are quantitative traits. In both orthogonal and reciprocal crosses, oil content is controlled by multiple genes and is shaped by additive, dominance, and epistatic effects. Protein content, in contrast, is largely controlled by two major genes along with minor genes. In the P₁ × P₂ combination, major genes act through additive, dominance, and epistatic effects, while in the P₂ × P₁ combination, their effects are additive only. In both combinations, minor genes contribute through additive and dominance effects. In summary, the oil content in cottonseed is mainly regulated by polygenes, whereas the protein content is primarily determined by major genes. These genetic features in both linted, and lint-free seeds may offer a theoretical foundation for molecular breeding aimed at improving cottonseed oil and protein quality. Full article

Understanding the influence of environmental factors on cotton performance is crucial for enhancing yield and fiber quality in the context of climate change. This study investigates genotype-by-environment (G×E) interactions in cotton, using data from 250 recombinant inbred lines (CCRI70 RILs) cultivated across 14 diverse environments in China’s major cotton cultivation areas. Our findings reveal that environmental effects predominantly influenced yield-related traits (boll weight, lint percentage, and the seed index), contributing to 34.7% to 55.7% of their variance. In contrast fiber quality traits showed lower environmental sensitivity (12.3–27.0%), with notable phenotypic plasticity observed in the boll weight, lint percentage, and fiber micronaire. Employing six machine learning models, Random Forest demonstrated superior predictive ability (R² = 0.40–0.72; predictive Pearson correlation = 0.63–0.86). Through SHAP-based interpretation and sliding-window regression, we identified key environmental drivers primarily active during mid-to-late growth stages. This approach effectively reduced the number of influential input variables to just 0.1–2.4% of the original dataset, spanning 2–9 critical time windows per trait. Incorporating these identified drivers significantly improved cross-environment predictions, enhancing Random Forest accuracy by 0.02–0.15. These results underscore the strong potential of machine learning to uncover critical temporal environmental factors underlying G×E interactions and to substantially improve predictive modeling in cotton breeding programs, ultimately contributing to more resilient and productive cotton cultivation. Full article

To identify optimal strategies for high-yield and high-efficiency cultivation under a “short-season direct-seeded cotton with once-only irrigation” regime, we conducted two-year field experiments (2022 and 2023) using a split-plot factorial design with three planting densities (30,000 (D1), 45,000 (D2), and 60,000 (D3) plants·ha⁻¹) and three nitrogen application rates (150 (N1), 180 (N2), and 210 (N3) kg·ha⁻¹). Our study systematically examined how these treatment combinations influenced canopy architecture, physiological traits, yield components, and fiber quality. The results showed that increased planting density significantly enhanced plant height, the leaf area index (LAI), and the number of fruiting branches, with the highest density (D3) contributing to a more compact and efficient canopy. Moderate nitrogen input (N2) significantly increased peroxidase (POD) activity, reduced malondialdehyde (MDA) accumulation, delayed functional leaf senescence, and prolonged the canopy’s photosynthetic performance. A significant interaction between planting density and nitrogen application was observed. The D3N2 treatment (high density with moderate nitrogen) consistently achieved the highest fruiting branch count, boll number per plant, and yields of both seed cotton and lint in both years, while maintaining stable fiber quality. This indicates its strong capacity to balance high yield with quality and maintain physiological resilience. By contrast, the D1N1 treatment (low density and low nitrogen) exhibited a loose canopy, premature photosynthetic decline, and the lowest yield. The D3N3 treatment (high density and high nitrogen) promoted vigorous early growth but reduced stress tolerance during later growth stages, leading to yield instability. These findings demonstrate that moderately increasing planting density while maintaining appropriate nitrogen levels can effectively optimize canopy structure, improve stress resilience, and enhance yield under short-season direct-seeded cotton systems with once-only irrigation. This provides both theoretical underpinning and practical guidance for achieving stable and efficient cotton production under such systems. Full article

The continuous provision of nitrogen (N) to the crop is critical for optimal cotton production; however, the constant and excessive application of synthetic fertilizers causes adverse impacts on soil, plants, animals, and human health. The current study focused on the short-term effects (one-year study) of adding different rates of clinoptilolite zeolite, as part of an integrated nutrient management plan, and different rates of inorganic N fertilizer to improve soil and crop performance of cotton in three locations (ATH, MES, and KAR) in Greece. Each experiment was set up according to a split-plot design with three replications, three main plots (zeolite application at rates of 0, 5, and 7.5 t ha⁻¹), and four sub-plots (N fertilization regimes at rates of 0, 100, 150, and 200 kg N ha⁻¹). The results of this study indicated that increasing rates of the examined factors increased cotton yields (seed cotton yield, lint yield, and lint percentage), with the greatest lint yield recorded under the highest rates of zeolite (7.5 t ha⁻¹: 1808, 1723, and 1847 kg ha⁻¹ in ATH, MES, and KAR, respectively) and N fertilization (200 kg N ha⁻¹: 1804, 1768, and 1911 kg ha⁻¹ in ATH, MES, and KAR, respectively). From the evaluated parameters, most soil parameters (soil organic matter, soil total nitrogen, and total porosity), root and shoot development (root length density, plant height, leaf area index, and dry weight), fiber maturity traits (micronaire, maturity, fiber strength, and elongation), fiber length traits (upper half mean length, uniformity index, and short fiber index), as well as color (reflectance and spinning consistency index) and trash traits (trash area and trash grade), were positively impacted by the increasing rates of the evaluated factors. In conclusion, the results of the present research suggest that increasing zeolite and N fertilization rates to 7.5 t ha⁻¹ and 200 kg N ha⁻¹, respectively, improved soil properties (except mean weight diameter), stimulated crop development, and enhanced cotton and lint yield, as well as improved the fiber maturity, length, and color parameters of cotton grown in clay-loam soils in the Mediterranean region. Full article

(1) Background: This study aims to analyze the defoliation and boll opening performance of 123 upland cotton germplasm resources after spraying defoliant, using multispectral data to select relevant vegetation indices and identify germplasm resources sensitive to defoliants, providing methods for cotton variety improvement and high-quality parental resources. (2) Methods: 123 historical upland cotton germplasm resources from Xinjiang were selected, and the defoliation and boll opening of cotton leaves were investigated at 0, 4, 8, 12, 16, and 20 days after defoliant application. Simultaneously, multispectral digital images were collected using drones to obtain 12 vegetation indices. Based on defoliation rate, the optimal vegetation index was selected, and defoliant-sensitive germplasm resources were identified. (3) Results: The most significant difference in defoliation rate of cotton germplasm resources occurred 16 days after application. Cluster analysis grouped the 123 breeding materials into three categories, with Class I showing the best defoliation effect. Among the 12 vegetation indices, the Plant Senescence Reflectance Index (PSRI) has the highest correlation coefficient with the defoliation rate; and when the PSRI value is higher, the defoliation effect of the material is better. By comparing the traditional investigation method with the unmanned aerial vehicle multispectral technology, 15 cotton materials sensitive to defoliants were determined, with a defoliation rate of over 85%, a lint percentage ranging from 76.67% to 98.04%, and a PSRI value ranging from 0.1607 to 0.1984. (4) Conclusions: The study found that the vegetation index with sensitive response can be used as an effective indicator to evaluate the sensitivity of cotton breeding materials to defoliants. Using an unmanned aerial vehicle (UAV) equipped with vegetation indices for screening shows a high consistency with the manual investigation and screening method in screening excellent defoliation materials; it proves that it is feasible to screen cotton breeding materials with excellent defoliation effects using UAV multispectral technology. Full article

Cotton micronaire (MIC) is an essential fiber quality index that characterizes both fiber maturity and fineness components. This study compared how MIC affects the fiber high volume instrument (HVI) and advanced fiber information system (AFIS) qualities between Deltapine^® and PhytoGen upland varieties. There were noticeable differences among HVI and AFIS qualities from Deltapine^® fiber samples and PhytoGen samples, with significant differences om HVI strength and elongation. MIC development benefited fiber HVI strength enhancement and also HVI short fiber index (SFI), AFIS neps, AFIS short fiber contents, and AFIS immature fiber content (IFC) reduction, all of which were desired. Adversely, MIC evolution could cause undesired HVI Rd lowering, HVI +b boosting, and AFIS UQL(w), and a decrease in L5%(n) in fiber. Further, MIC values were not related with lint turnout, but they were positively and greatly correlated with algorithmic M_IR values of the attenuated total reflection in Fourier transform infrared (ATR FT-IR) spectra. The results demonstrated the applicability of the ATR FT-IR technique combined with the M_IR approach for rapid laboratory MIC assessment at early MIC testing in remote/breeding locations. Full article

Estimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. Thus, this study aims to explore the potential of using UAV-based multispectral images to estimate important in-season parameters, such as intercepted photosynthetically active radiation (IPAR), cotton height, the number of mainstem nodes, leaf area index (LAI), and end-of-the-season yield and cotton fiber quality parameters. Research trials were carried out in 2018 and 2020 in two experimental fields. In both years, a randomized complete block design was used with three cotton cultivars (2018), three plant growth regulators (2020), and three different irrigation levels to promote variability (both years). Cotton growth parameters were collected throughout the season on the same dates as UAV flights. Yield and fiber quality data were collected during harvest. The VI-based models used in this study were mostly sensitive to differences in cotton growth and final yield but less sensitive in detecting variation in cotton fiber quality indicators, such as length, strength, and micronaire, early in the season. The best performing regression model among the three fiber quality indicators was achieved in 2020, using a combination of four VIs, which explained 68% of the micronaire variability at 71 DAP. Results from this study also showed that multispectral-based VIs can be applied as early as the squaring stage at around 44 DAP to estimate most cotton growth indicators and final lint yield. Multiple linear regression validation models for height using NDVI, GNDVI, and RDVI obtained an R² of 0.62, and for LAI using MSR and NDVI an R² of 0.60. For lint yield, the best regression model combined four VIs and explained 66% of the yield variability. The ability to capture the variability in important growth and yield parameters early in the season can provide useful insights on potential crop performance and aid in in-season decisions. Full article