Abstract Background Cotton Verticillium wilt, causing by Verticillium dahliae, has seriously affected the yield and quality of cotton. The incidence of Verticillium wilt in cotton fields has been on the rise for many years, especially after straw has been returned to the fields. Intercropping can reduce the incidence of soil borne diseases and is often used to control crop diseases, but the relationship between the effects of intercropping on microbial communities and the occurrence of plant diseases is unclear. This research explored the relationship between soil microbial community structure and Cotton Verticillium wilt in interplanting of cotton-onion, cotton-garlic, cotton-wheat and cotton monocultures. Amplicon sequencing applied to the profile of bacterial and fungal communities. Results The results showed that the disease index of Cotton Verticillium wilt was significantly reduced after intercropping with cotton-garlic and cotton-onion. Chao1 and Sobs indices were not significantly different in the rhizosphere soil and pre-plant soils of the four planting patterns, but the pre-plant fungal shannon index was significantly lower in the cotton-onion intercropping plot than in the other three plots. PCoA analysis showed that the soil microbial communities changed to a certain extent after intercropping, with large differences in the microbial communities under different cropping patterns. The abundance of Chaetomium was highest in the cotton-garlic intercropping before planting; the abundance of Penicillium was significantly higher in the cotton-wheat intercropping than in the other three systems. Conclusion Cotton-garlic and cotton-onion interplanting can control Cotton Verticillium wilt by affecting the soil microbial community. Fungi of the genera Chaetomium and Penicillium may be associated with plant disease resistance.

Cotton is one of the essential cash crops; however, several factors, such as low yields and pest and disease infestations, affect the production. In South Africa, cotton production has increased among small-scale farmers since the late 1990s. Although the crop is not new to South African farmers, no recent information reflects the current status of cotton production practices. A study evaluated farmers' production practices, the incidence and management of pests and diseases, extension services, and factors limiting cotton production and quality in South Africa. One hundred and forty farmers, mainly smallholder farmers, were interviewed during the 2017/18 growing season. Most farmers planted genetically modified (GM) cotton on less than 5 ha of cotton, with 96% planting under dryland. Most farmers neither practised conservation agriculture (95%) nor conducted soil analyses (87%). A mean cottonseed yield of 700 kg ha-1 was reported on dryland cotton, and 5 000 kg ha-1 was obtained from irrigated cotton. Most of the farmers (99%) harvested their cotton by handpicking. Farmers' pest knowledge was higher than their knowledge of different diseases. Most participants were unaware of nematodes (88%) or disease-resistant cultivars (74%), while 91% were aware of insect-resistant cultivars. Extension officers only mentored and supported many respondents (82%). Most farmers (93%) relied on pesticides to control cotton pests, and the rest (7%) used biological control. Climatic conditions (98%), labour costs (88%), and insect infestations (42%) were identified as the main constraints in cotton production. Although this study had a limited number of surveyed farmers, it gives some insight into their knowledge and challenges.

Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at a higher accuracy. We selected a cotton field in Shihezi, Xinjiang in China to acquire multispectral images with an unmanned airborne vehicle (UAV); then, fifty-three 50 cm by 50 cm ground framed plots were set with defined coordinates, and a photo of its cotton canopy was taken of each and converted to the <i>L*a*b*</i> color space as either a training or a validation sample; finally, these two kinds of images were processed and combined to establish a cotton blight infection inversion model. Results show that the Red, Rededge, and NIR bands of multispectral UAV images were found to be most sensitive to changes in cotton leaf color caused by blight infection; NDVI and GNDVI were verified to be able to infer cotton blight infection information from the UAV images, of which the model calibration accuracy was 84%. Then, the cotton blight infection status was spatially identified with four severity levels. Finally, a cotton blight inversion model was constructed and validated with ground framed photos to be able to explain about 86% of the total variance. Evidently, multispectral UAV images coupled with ground framed cotton canopy photos can improve cotton blight infection identification accuracy and severity classification, and therefore provide a more reliable approach to effectively monitoring such cotton disease damage.

In the article, the scientific basis of the new stimulators on enzyme activity, biosynthesis of amino acids and proteins, and the physiological processes that occur during cotton development, the effect of new stimulators on seed and cotton during its growth period, on seed germination, cotton growth and development, and resistance to gommosis and wilt diseases, were determined. . Biodux stimulator provides 8.512.1% higher field fertility of seed, and the highest result was achieved at the rate of 3.0 ml/t. Also, when cotton was treated with Biodux at the rate of 2.0 ml/ha during the flowering period, the plant height was 7.7 cm higher, the yield branches were 1.3 pieces and the number of bolls was 1.8 more, and the degree of opening of bolls was 7.7-13.7 % has been found to be accelerated.

Cotton (Gossypium hirsutum) is one of the most important crops and is the main source of fiber for the textile industry, but its productivity is still hampered by several challenges, such as pests, diseases and abiotic stresses. This scenario has increased interest in achieving cotton events with greater productivity and sustainability through biotechnological approaches. An essential component of these strategies is the controlled expression of the gene of interest, suggesting that promoters are a key element. These promoters are generally divided into three types: constitutive, spatiotemporal, and inducible. However, to date, this diversity of promoter activity has not been as widely explored in cotton improvement. In this review, we provide an overview of cotton promoters that can be used to achieve fine-tuning of expression, facilitating decision-making and improving the ability to develop desirable traits in cotton plants. In addition, we present new approaches to identify promoters that may be useful for the development of new tools for cotton improvement.How to cite: Atella AL, Grossi-de-Sá MF, Alves-Ferreira M. Cotton promoters for controlled gene expression. Electron J Biotechnol 2023;62. https://doi.org/10.1016/j.ejbt.2022.12.002.

Seed coating is the most important type of pretreatment. Since cotton is an important economic crop, the cost of its cultivation and the resulting economic benefits are undoubtedly important aspects to be considered. In recent years, the high cost of coating materials and complex production processes have prevented the widespread application of cotton seed coating. Moreover, cotton plants emerge from cotyledons, and the coating material on the seed coat does not play a role after the seed emerges. Given the above shortcomings, to adapt to the mechanized direct seeding method and to include a large number of fertilizers and fungicides, insecticides can be used together with the seed direct seeding into the soil; at the same time, this will improve the cotton seedling emergence rate, the physiological qualities of cotton seedlings after the emergence of cotton seedlings, and the resilience of cotton seedlings in the early stage of resistance ability. In this study, we devised a technique for balling cotton seeds employing components such as cassava starch, bentonite, diatomite, attapulgite, and seedling substrate. The compositional ratios of the method were determined via a growth chamber trial, and we evaluated its effect throughout the cotton reproductive period using field trials. The results showed that the emergence and emergence hole rates of the balled cotton seeds increased by 34.42% and 28.84%, respectively, compared with the uncoated control. In terms of cotton yield, the seed balling treatment increased the number of bolls per plant and the overall cotton yield. Seed balling technology is different from traditional seed pelleting or seed coating techniques. It gathers one or more seeds in seed balls, enabling the simultaneous sowing of multiple seeds of the same variety or different varieties in the same crop. Additionally, seed balls can encourage seeds to carry fertilizer and pesticides into the soil, further weakening soil-borne diseases and abiotic stresses, form a relatively stable internal environment in the soil, and ensure the germination of cotton seeds. Our findings provide a reference point to improve cotton seedling emergence through the utilization of this novel technology.

Verticillium wilt is one of the most crucial diseases caused by Verticillium dahliae that threatens the cotton industry. Statistical results showed that the return of cotton plants infected with V. dahliae to the field might be an essential cause of the continuous aggravation of cotton Verticillium wilt. The correlation among the cotton plants infected with V. dahliae returning to the field, the occurrence of Verticillium wilt, and the number of microsclerotia in rhizosphere soil need further investigation. A potted experiment was carried out to explore the effects of the direct return of cotton plants infected with Verticillium dahliae to the field on the subsequent growth and Verticillium wilt occurrence in cotton. As a risk response plan, we investigated the feasibility of returning dung-sand (i.e., insect excreta) to the field, the dung-sand was from the larvae of Protaetia brevitarsis (Coleoptera: Cetoniidea) that were fed with the V. dahliae–infected cotton plants. The results demonstrated that the return of the entire cotton plants to the field presented a promotional effect on the growth and development of cotton, whereas the return of a single root stubble or cotton stalks had an inhibitive effect. The return of cotton stalks and root stubble infected with V. dahliae increased the risk and degree of Verticillium wilt occurrence. The disease index of Verticillium wilt occurrence in cotton was positively correlated with the number of microsclerotia in the rhizosphere soil. The disease index increased by 20.00%, and the number of soil microsclerotia increased by 8.37 fold in the treatment of returning root stubble infected with V. dahliae to the field. No Verticillium wilt microsclerotia were detected in the feed prepared from cotton stalks and root stubble fermented for more than 5 days or in the transformed dung-sand. There was no risk of inoculation with Verticillium wilt microsclerotia when the dung-sand was returned to the field. The indirect return of cotton plants infected with V. dahliae to the field by microorganism–insect systems is worthy of further exploration plan of the green prevention and control for Verticillium wilt and the sustainable development of the cotton industry.

Cotton enjoys a pre-eminent status among all cash crops in the country. It is one of the most important economic products of the group of fibers due to volume and value of production. Like any other crop, cotton is also in infested by many diseases which are&nbsp; usually managed chemically. Here in this experiment, different bioagents were evaluated against the cotton diseases. This experiment engaged total nine treatments including control from which, treatment T, (17.33 PDI) (Seed and soil application PfCICR i.¢., Seed application: 10°cfu/g @10g per kg of seed+ Soil application: 2.5 kg/ha at 30 &amp; 60 DAS) followed by T, (23.22 PDI) recorded significantly&nbsp; low Bacterial leaf blight infection in comparison to the treatment T, i.e. control (45.17 PDI) in RCH 2 BGII hybrid in the pooled results&nbsp; for the three years i.e., 2020, 2021 and 2022. For Alternaria leaf spot disease in the pooled data of two years 2020 and 2021, treatment&nbsp; T, (7.83 PDI) (Seed treatment with Pseudomonas fluorescens CICR (2 x 10®cfu/g) @ 10 g/kg seed + soil application of Trichoderma&nbsp; viride TNAU1 (2x 106 cfu/g) @ 2.5 kg/ha in 250 kg of vermicompost and sprays of Kresoxim methyl (0.0443%) followed by Captan 70% +&nbsp; Hexaconazole 5% WP @ 1.5 g/l) recorded significantly minimum disease as compared to the T, i.e. control (19.79 PDI) followed by T, (9.25 PDI). The highest seed cotton yield was recorded in the treatment T, (2542.88 kg/ha) followed by treatment T, (2299.55 kg/ha),&nbsp; respectively.

Cotton the ‘King of fibres’ is the important commercial crop of Telangana state occupying an area of 23.48 lakh hectares with 30.42 lakh MT of production during the year 2020-21. The crop is mainly grown under rainfed condition in a wide range of soilslike red loamy, deep black, chalka soil and dubba. Considering the different soil types and the irrigation availability, the costs andreturns were computed for different farming situations viz., FS-1 (Irrigated red soil), FS-2 (Rainfed red soil), FS-3 (Chalka soil), FS-4 (Irrigated black soil) and FS-5 (Rainfed black soil). Further, the constraints were analysed based on the primary data collectedfrom 350 cotton growing farmers from 16 villages belonging to eight mandals of districts viz., Nalgonda, Nagarkurnool, Adilabad and Sangareddy during the year 2020-21. The comparison done using tabular analysis inferred that, cotton cultivation was highlyprofitable in FS-4 with net returns of ( 64773.67) followed by FS-1 ( 48680.16), FS-5 ( 33483.70) and FS-2 ( 24441.86) while, in FS-3 it was a loss with negative net returns ( -1310.70). Economically unviable cotton cultivation in FS-3 recommends for cultivation of other suitable crop viz., millets, pulses and ground nut. Further, constraint analysis inferred that, the crop suffered from pests and diseases infestation, inadequate /excess rainfall, labour scarcity, lack of infrastructure leading to low prices at the time of harvest and poor technical know-how. Hence, the study recommends developing resistant cotton hybrids. Adoption of mulching practices in rainfed cotton, timely and adequate irrigation via water saving techniques in irrigated cotton, better infrastructure and training programmes for dissemination of new technologies is much needed.&nbsp;

Verticillium wilt (VW) is often referred to as the cancer of cotton and it has a detrimental effect on cotton yield and quality. Since the root system is the first to be infested, it is feasible to detect VW by root analysis in the early stages of the disease. In recent years, with the update of computing equipment and the emergence of large-scale high-quality data sets, deep learning has achieved remarkable results in computer vision tasks. However, in some specific areas, such as cotton root MRI image task processing, it will bring some challenges. For example, the data imbalance problem (there is a serious imbalance between the cotton root and the background in the segmentation task) makes it difficult for existing algorithms to segment the target. In this paper, we proposed two new methods to solve these problems. The effectiveness of the algorithms was verified by experimental results. The results showed that the new segmentation model improved the Dice and mIoU by 46% and 44% compared with the original model. And this model could segment MRI images of rapeseed root cross-sections well with good robustness and scalability. The new classification model improved the accuracy by 34.9% over the original model. The recall score and F1 score increased by 59% and 42%, respectively. The results of this paper indicate that MRI and deep learning have the potential for non-destructive early detection of VW diseases in cotton.