Search Results (3,422)

Cotton, with a cultivated area of 31.92 × 10⁶ ha⁻¹ across 80 countries and an estimated annual turnover of USD 5.68 billion, is the world’s leading natural textile fiber. However, many cotton-producing countries have neglected to improve production practices, adversely affecting the environment and society. A systematic review of the sustainable cotton cultivation literature was performed for the first time to identify and suggest context-specific agricultural strategies that can be applied within different agroecosystems. The key aspects include (1) inoculation with arbuscular mycorrhizal species such as Gigaspora margarita, Funneliformis mosseae, and Acaulospora scrobiculata to enhance root exploration, biomass, and nutrient uptake; (2) using grass, legume, and brassica cover crops as a valid alternative to monoculture and fallow crop rotations to reduce resource depletion and increase the sustainability of cotton production; (3) adopting drip and mulched drip irrigation systems over traditional furrow and sprinkler systems for water conservation; (4) exploring the feasibility of prematurely terminating irrigation in humid subtropical and Mediterranean climates as an alternative to chemical defoliation without affecting cotton yield. This paper, which describes various farming practices adopted in different climates, provides farmers a guide for eco-friendly cotton agronomic management without sacrificing productivity. Full article

Cotton fiber consumption is higher than any other natural fiber due to its superior mechanical and physical properties. In the conventional reactive dyeing process, the dye undergoes hydrolysis due to hydrogen bonding and weak Van der Waals forces, yielding poor washing color fastness. Additionally, the post-dyeing wash-off process consumes significant amounts of water, energy, and time in order to remove the unreacted and hydrolyzed dye. Therefore, reactive dyes are predominantly utilized to color cotton fabrics as the covalent bond formed between dye and fiber results in excellent washing fastness for a wide range of colors, and improved ease of use. To support more efficient, economical, and sustainable reactive dyeing, polymers can be used as effective dye transfer inhibitors (DTIs) in the reactive wash-off process. In this study, poly(vinylpyrrolidone) (PVP) homopolymer and poly(vinylpyrrolidone)-co-poly(vinylimidazole) (PVP-co-PVI) were synthesized at different molecular weights. These polymers were then employed as DTIs to evaluate their performance during the wash-off process for dyed cotton fabrics treated with three reactive dyes (Reactive Red 195, Reactive Blue 221, and Reactive Yellow 145). It is noteworthy to mention that the alternative wash-off procedure involved only three stages, in contrast to the series of washing steps necessary in the conventional method. Evaluation of the DTIs was focused on parameters such as color strength, washing fastness, chemical oxygen demand (COD), as well as energy, water, and time consumption. The findings revealed that while the polymer DTIs generated minor differences in dyed fabric colors in comparison to conventional dyeing, they significantly improved washing fastness. The polymer DTIs studied display excellent potential to provide a much more sustainable dyeing process by saving 90% energy, 40% water, and 50% time versus conventional wash-off processing. The wastewater COD values from the polymer-assisted reactive dye wash-off process were higher for all reactive dyes tested than those from conventional processing. While the higher COD values are attributed to the lower water volumes, this finding highlights the need for further studies. In summary, PVP polymers successfully served as DTIs in the reactive dyeing washing-off process, providing massive water-energy-time efficiency improvements and, subsequently, a more sustainable process for cotton fabric dyeing. Full article

Fungi belonging to the genus Trichoderma have received high consideration in agriculture due to their beneficial effects on crops from their plant promotion effects and protection from disease. A role of Trichoderma fungi in triggering plant defense mechanisms against insect pests, either directly or by natural enemy attraction, has been proposed, even if the results in different studies are controversial. In this present study, using zucchini plants as a model species, we investigated the effects of Trichoderma afroharzianum strain T22 plant inoculation on the cotton aphid Aphis gossypii and its endoparasitoid Aphidius colemani. Our results showed that the inoculation with T. afroharzianum T22 promotes A. gossypii population growth and makes zucchini more attractive to the aphid. The higher abundance of aphids on Trichoderma-inoculated zucchini was compensated for by a higher presence of the mummies of Aphidius colemani. In this present study, we recorded a higher zucchini biomass, thereby confirming that Trichoderma can act as a plant growth inducer. Full article

In the native Amazonian communities, there is a significant biodiversity of cotton varieties, where traditional agricultural practices are preserved, crucial for cultural identity and local economic livelihood. This study examines the socioeconomic and cultural impacts of native cotton cultivation in the Amazonian communities of Alto Urubamba, La Convención-Cusco, Peru. Through a structured survey encompassing eight dimensions—general data, family and household, housing and services, education, economy and work, perspectives and challenges, and community participation—data was collected from residents of the Koribeni, Poyentimari, and Chacopishiato communities, particularly artisans involved in cotton-related activities. The analysis revealed agriculture as the primary income source, with 94.1% of Chacopishiato, 100% of Koribeni, and 61.1% of Poyentimari respondents depending on it. Participation in native cotton activities varied, with 52.9% in Chacopishiato, 88.2% in Koribeni, and 33.3% in Poyentimari. Common challenges include limited access to quality seeds (68.8% in Chacopishiato) and a need for technical knowledge (100% in Koribeni and Poyentimari). Correlation analysis showed significant associations between the study’s dimensions. Variations in community perceptions and knowledge about cotton cultivation suggest the need for targeted interventions. This research underscores the importance of sustainable development strategies that integrate traditional agricultural practices, preserve biodiversity, and enhance community resilience in the region. Full article

Due to the difficulty in distinguishing transparent and white foreign fibers from seed cotton in RGB images and in order to improve the recognition ability of deep learning (DL) algorithms for white, transparent, and multi-class mixed foreign fibers with different sizes in seed cotton, this paper proposes a method of combining hyperspectral imaging technology with a multi-module joint hierarchical residue network (MJHResNet). Firstly, a series of preprocessing methods are performed on the hyperspectral image (HSI) to reduce the interference of noise. Secondly, a double-hierarchical residual (DHR) structure is designed, which can not only obtain multi-scale information, but also avoid gradient vanishing to some extent. After that, a squeeze-and-excitation network (SENet) is integrated to reduce redundant information, improve the expression of model features, and improve the accuracy of foreign fiber identification in seed cotton. Finally, by analyzing the experimental results with advanced classifiers, this method has significant advantages. The average accuracy is 98.71% and the overall accuracy is 99.28%. This method has great potential for application in the field of foreign fiber identification in seed cotton. Full article

As an essential component of the global economic crop, cotton is highly susceptible to the impact of diseases on its yield and quality. In recent years, artificial intelligence technology has been widely used in cotton crop disease recognition, but in complex backgrounds, existing technologies have certain limitations in accuracy and efficiency. To overcome these challenges, this study proposes an innovative cotton disease recognition method called CANnet, and we independently collected and constructed an image dataset containing multiple cotton diseases. Firstly, we introduced the innovatively designed Reception Field Space Channel (RFSC) module to replace traditional convolution kernels. This module combines dynamic receptive field features with traditional convolutional features to effectively utilize spatial channel attention, helping CANnet capture local and global features of images more comprehensively, thereby enhancing the expressive power of features. At the same time, the module also solves the problem of parameter sharing. To further optimize feature extraction and reduce the impact of spatial channel attention redundancy in the RFSC module, we connected a self-designed Precise Coordinate Attention (PCA) module after the RFSC module to achieve redundancy reduction. In the design of the classifier, CANnet abandoned the commonly used MLP in traditional models and instead adopted improved Kolmogorov Arnold Networks-s (KANs) for classification operations. KANs technology helps CANnet to more finely utilize extracted features for classification tasks through learnable activation functions. This is the first application of the KAN concept in crop disease recognition and has achieved excellent results. To comprehensively evaluate the performance of CANnet, we conducted extensive experiments on our cotton disease dataset and a publicly available cotton disease dataset. Numerous experimental results have shown that CANnet outperforms other advanced methods in the accuracy of cotton disease identification. Specifically, on the self-built dataset, the accuracy reached 96.3%; On the public dataset, the accuracy reached 98.6%. These results fully demonstrate the excellent performance of CANnet in cotton disease identification tasks. Full article

Cotton seeds, as the main by-product of cotton, are not only an important raw material for edible oil and feed but also a source of biofuel. The quality of cotton seeds directly affects cotton planting and is closely related to the yield and fiber quality. However, the molecular mechanism governing cotton seed size remains largely unexplored. This study investigates the regulatory mechanisms of cotton seed size by focusing on two cotton genotypes, N10 and N12, which exhibit notable phenotypic variations across multiple environments. Developing seeds were sampled at various stages (5, 20, 30, and 35 DPA) and subjected to RNA-seq. Temporal pattern clustering and WGCNA on differentially expressed genes identified 413 candidate genes, including these related to sugar metabolism that were significantly enriched in transcriptional regulation. A genetic transformation experiment indicated that the overexpression of the GhUXS5 gene encoding UDP-glucuronate decarboxylase 5 significantly increased seed size, suggesting an important role of GhUXS5 in regulating cotton seed size. This discovery provides crucial insights into the molecular mechanisms controlling cotton seed size, helping to unravel the complex regulatory network and offering new strategies and targets for cotton breeding to enhance the economic value of cotton seeds and overall cotton yield. Full article

Monitoring Tetranychus urticae Koch in cotton crops is challenging due to the vast crop areas and clustered mite attacks, hindering early infestation detection. Hyperspectral imaging offers a solution to such a challenge by capturing detailed spectral information for more accurate pest detection. This study evaluated machine learning models for classifying T. urticae infestation levels in cotton using proximal hyperspectral remote sensing. Leaf reflection data were collected over 21 days, covering various infestation levels: no infestation (0 mites/leaf), low (1–10), medium (11–30), and high (>30). Data were preprocessed, and spectral bands were selected to train six machine learning models, including Random Forest (RF), Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), Feedforward Neural Network (FNN), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Partial Least Squares (PLS). Our analysis identified 31 out of 281 wavelengths in the near-infrared (NIR) region (817–941 nm) that achieved accuracies between 80% and 100% across 21 assessment days using Random Forest and Feedforward Neural Network models to distinguish infestation levels. The PCA loadings highlighted 907.69 nm as the most significant wavelength for differentiating levels of two-spotted mite infestation. These findings are significant for developing novel monitoring methodologies for T. urticae in cotton, offering insights for early detection, potential cost savings in cotton production, and the validation of the spectral signature of T. urticae damage, thus enabling more efficient monitoring methods. Full article

We present a review of current research on promising flame retardants using specific methods of applying metal–organic frameworks (MOFs) to the highly flammable fibrous surface of cotton fabric. In this review, we initially address the reasons why the search for new flame retardants has becomes critically important in textile finishing, the area responsible for adhering new functionalities to substrates. This addition of characteristics is closely linked to the nature of the fibers, so the reason for the improvement in cotton fabric in relation to flame retardancy is discussed. Furthermore, the development of highly porous nanomaterials that can generate composites with specific functions is described, as well as their application and methods of integration into textile surfaces. Finally, the main candidates for flame retardant functionality in cellulosic materials are identified. It is also hoped that this work will facilitate researchers to develop and formulate new methods of applying nanomaterials to textile substrates, with a view to becoming a reference for new research into the development of adhesion of emerging materials to traditional materials. Full article

This experiment was conducted to determine the nutrient composition of three agricultural by-products, namely garlic peel, sweet potato vine, and cotton straw, calculate their relative feeding value, effective energy value, and other indexes, and comprehensively evaluate their nutrient value by combining with rumen in vitro fermentation technology, with the aim of providing data references for the development and utilization of non-conventional feed resources for ruminants. The results showed that: 1) the dry matter (DM), ash, ether extract (EE), and crude protein (CP) contents of cotton straw were significantly higher than the other two feeds (p < 0.05), while the acid detergent fiber (ADF) and neutral detergent fiber (NDF) contents of garlic peel were highly significantly higher than the others (p < 0.05); 2) the relative feed value (DMI, DDM, TDN, RFV, and RFQ) and effective energy value (GE, DE, ME, NE_m, NE_g, and NE_L) indexes of cotton straw were significantly higher than garlic peel and sweet potato vine (p < 0.01); 3) after 48 h of in vitro fermentation, the dry matter degradation rate (IVDMD) of sweet potato vine was significantly higher than the other two feeds (p < 0.01), and the cumulative gas productions (mL) and estimated gas parameters (a, b, a + b, and c) of sweet potato vine were significantly (p < 0.01) higher than those of garlic peel and cotton straw; 4) the sweet potato vine had lower pH but higher NH₃-N compared to garlic peel and cotton straw (p < 0.05). The sweet potato vine had higher propionate, iso-butyrate, butyrate, iso-valerate, and total VFA than the other two roughages, which also had the lowest acetate-to-propionate ratio. Garlic peel produced the lowest acetate, while it produced the highest valerate (p < 0.05). These findings demonstrate that all three by-products have high potential as livestock feed based on their nutritive value parameters. Comparatively, sweet potato vines exhibit higher feeding value due to their relatively moderate NDF content and superior rumen fermentation performance. Full article

The problem of soil salinization has seriously hindered agricultural development. Cotton is a pioneering salinity-tolerant crop, so harvesting its key salinity-tolerant genes is important for improving crop salt tolerance. In this study, we analyzed changes in the transcriptome expression profiles of the salt-tolerant cultivar Lu Mian 28 (LM) and the salt-sensitive cultivar Zhong Mian Suo 12 (ZMS) after applying salt stress, and we constructed weighted gene co-expression networks (WGCNA). The results indicated that photosynthesis, amino acid biosynthesis, membrane lipid remodeling, autophagy, and ROS scavenging are key pathways in the salt stress response. Plant–pathogen interactions, plant hormone signal transduction, the mitogen-activated protein kinase (MAPK) signaling pathway, and carotenoid biosynthesis are the regulatory networks associated with these metabolic pathways that confer cotton salt tolerance. The gene-weighted co-expression network was used to screen four modules closely related to traits, identifying 114 transcription factors, including WRKYs, ERFs, NACs, bHLHs, bZIPs, and MYBs, and 11 hub genes. This study provides a reference for acquiring salt-tolerant cotton and abundant genetic resources for molecular breeding. Full article

Kenaf (Hibiscus cannabinu) is a grass bast fiber crop that has the ability to tolerate and accumulate heavy metals, and it has been considered as a potential heavy metal accumulator and remediation plant. Nramp is a natural resistance-related macrophage, which plays an important role in the transport of divalent metal ions, plant growth and development, and abiotic stress. In this study, the Nramp gene family of kenaf was analyzed at the whole genome level. A total of 15 HcNramp genes were identified. They are distributed unevenly on chromosomes. Phylogenetic analysis classified 15 HcNramp proteins into 3 different subfamilies. All proteins share specific motif 4 and motif 6, and the genes belonging to the same subfamily are similar in structure and motif. The promoters are rich in hormone response, meristem expression, and environmental stress response elements. Under different treatments, the expression levels of HcNramp genes vary in different tissues, and most of them are expressed in roots first. These findings can provide a basis for understanding the potential role of the Nramp gene family in kenaf in response to cadmium (Cd) stress, and are of great significance for screening related Cd tolerance genes in kenaf. Full article

Terpene synthases (TPSs), key gatekeepers in the biosynthesis of herbivore-induced terpenes, are pivotal in the diversity of terpene chemotypes across and within plant species. Here, we constructed a gene-based pangenome of the Gossypium genus by integrating the genomes of 17 diploid and 10 tetraploid species. Within this pangenome, 208 TPS syntelog groups (SGs) were identified, comprising 2 core SGs (TPS5 and TPS42) present in all 27 analyzed genomes, 6 softcore SGs (TPS11, TPS12, TPS13, TPS35, TPS37, and TPS47) found in 25 to 26 genomes, 131 dispensable SGs identified in 2 to 24 genomes, and 69 private SGs exclusive to a single genome. The mutational load analysis of these identified TPS genes across 216 cotton accessions revealed a great number of splicing variants and complex splicing patterns. The nonsynonymous/synonymous Ka/Ks value for all 52 analyzed TPS SGs was less than one, indicating that these genes were subject to purifying selection. Of 208 TPS SGs encompassing 1795 genes, 362 genes derived from 102 SGs were identified as atypical and truncated. The structural analysis of TPS genes revealed that gene truncation is a major mechanism contributing to the formation of atypical genes. An integrated analysis of three RNA-seq datasets from cotton plants subjected to herbivore infestation highlighted nine upregulated TPSs, which included six previously characterized TPSs in G. hirsutum (AD1_TPS10, AD1_TPS12, AD1_TPS40, AD1_TPS42, AD1_TPS89, and AD1_TPS104), two private TPSs (AD1_TPS100 and AD2_TPS125), and one atypical TPS (AD2_TPS41). Also, a TPS-associated coexpression module of eight genes involved in the terpenoid biosynthesis pathway was identified in the transcriptomic data of herbivore-infested G. hirsutum. These findings will help us understand the contributions of TPS family members to interspecific terpene chemotypes within Gossypium and offer valuable resources for breeding insect-resistant cotton cultivars. Full article

Phenacoccus solenopsis Tinsley is a pest that poses a significant threat to agricultural crops, especially cotton, and is now widely distributed across many regions worldwide. In this study, we performed a meta-analysis on the collected experimental data and found that within the suitable temperature range, the survival rate of P. solenopsis increases with rising temperatures, indicating that climate plays a decisive role in its distribution. Using the MaxEnt model this study predicted that under three future climate scenarios (SSP1–2.6, SSP3–7.0, and SSP5–8.5), the distribution of P. solenopsis will expand and move towards higher latitudes. Climate change is the primary factor influencing changes in pest distribution. We conducted a meta-analysis of P. solenopsis, including seven independent studies covering 221 observation results, and examined the impact of temperature ranging from 18 °C to 39 °C on the developmental cycle of P. solenopsis. As the temperature rises, the development cycle of P. solenopsis gradually decreases. Additionally, by combining the MaxEnt model, we predicted the current and potential future distribution range of P. solenopsis. The results show that under future climate warming, the distribution area of P. solenopsis in China will expand. This research provides a theoretical basis for early monitoring and control of this pest’s occurrence and spread. Therefore, the predictive results of this study will provide important information for managers in monitoring P. solenopsis and help them formulate relevant control strategies. Full article

A narrow genetic basis limits further the improvement of modern Gossypium hirsutum cultivar. The abundant genetic diversity of wild species provides available resources to solve this dilemma. In the present study, a chromosome segment substitution line (CSSL) population including 553 individuals was established using G. darwinii accession 5-7 as the donor parent and G. hirsutum cultivar CCRI35 as the recipient parent. After constructing a high-density genetic map with the BC₁ population, the genotype and phenotype of the CSSL population were investigated. A total of 235 QTLs, including 104 QTLs for fiber-related traits and 132 QTLs for seed-related traits, were identified from four environments. Among these QTLs, twenty-seven QTLs were identified in two or more environments, and twenty-five QTL clusters consisted of 114 QTLs. Moreover, we identified three candidate genes for three stable QTLs, including GH_A01G1096 (ARF5) and GH_A10G0141 (PDF2) for lint percentage, and GH_D01G0047 (KCS4) for seed index or oil content. These results pave way for understanding the molecular regulatory mechanism of fiber and seed development and would provide valuable information for marker-assisted genetic improvement in cotton. Full article