Evaluating the impact of biogenic nanoparticles and pesticide application in controlling Cotton Leaf Curl Virus Disease (CLCuD) in Cotton (Gossypium hirsutum L.)

Evaluating the impact of biogenic nanoparticles and pesticide application in controlling Cotton Leaf Curl Virus Disease (CLCuD) in Cotton (Gossypium hirsutum L.) | 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 Evaluating the impact of biogenic nanoparticles and pesticide application in controlling Cotton Leaf Curl Virus Disease (CLCuD) in Cotton (Gossypium hirsutum L.) Usman Shafqat, Muhammad Ussama Yasin, Muhammad Shahid, Sabir Hussain, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4416740/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Cotton Leaf Curl Virus Disease (CLCuD) is one of the major concerns for cotton growers. The traditional approach to managing CLCuD involves the control of the vector (whitefly) population through the use of pesticides. In this study, the efficacy of nanoparticles was compared with pesticides. The present study was conducted to evaluate the comparative efficacy of zinc oxide nanoparticles (ZnO nanoparticles), iron oxide nanoparticles (FeO nanoparticles), copper nanoparticles (Cu nanoparticles) and silver nanoparticles (Ag nanoparticles). Optimized doses of zinc oxide nanoparticles (ZnO nanoparticles), iron oxide nanoparticles (FeO nanoparticles), copper nanoparticles (Cu nanoparticles) and silver nanoparticles (Ag nanoparticles) were applied in a field trial of cotton against Cotton Leaf Curl Virus Disease (CLCuD) in cotton. The study consisted of morphological parameters (height of stem, monopodial branches, sympodial branches, staple length, boll weight and number of bolls), yield parameters (seed cotton yield and ginning outturn), chlorophyll content (chlorophyll a, chlorophyll b, carotenoids and total chlorophyll), biochemical parameters (superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxidase (APX), catalase (CAT), hydrogen peroxide (H 2 O 2 ) and electrolyte leakage) and disease parameters (reduction infection, disease severity and disease incidence). Results Cotton Leaf Curl Virus (CLCuV) was detected by TAS-ELISA (Triple Antibody Sandwich-Enzyme-linked immunosorbent assay). Pesticide reduced the infection as 79.3%. Zinc oxide nanoparticles (ZnO nanoparticles), iron oxide nanoparticles (FeO nanoparticles), copper nanoparticles (Cu nanoparticles) and silver nanoparticles (Ag nanoparticles) reduced the infection as 42.33%, 41%, 34.7% and 44.8% respectively. The statistical design for field trial was randomized complete block design (RCBD). One-way ANOVA was performed. Conclusion Although treatment pesticide showed the least disease incidence compared to nanoparticles. Nanoparticles are eco-friendly and safe as compared to pesticides. It is concluded that nanocomposites and hybrid modes may be used for managing CLCuD efficiently in the future. Cotton Leaf Curl Virus Disease (CLCuD) Nanoparticles Plant viral Disease management Antioxidant defense Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Cotton is major chief cash crop and contributing in economy of many countries worldwide. The textile sector is heavily dependent on cotton. Cotton holds immense global significance as it is utilized in manufacturing various products such as textiles, clothing, and paper products. Cotton is a major income source and livelihood for many farmers around the world. The textile industry, which is heavily dependent on cotton, is a major global industry that employs millions of people and contributes significantly to the world economy. It plays an important role in the production of oil, animal feed, and other products Mathangadeera, Hequet, Kelly, Dever, Kelly and Products [ 1 ]. From last few decades various abiotic and biotic stresses reduce the yield of cotton crop. Furthermore, changes in climatic conditions also limitize the cotton yield. Pests are the major threats to cotton yield by impacting both the production and quality of crop. Boll weevil, cotton aphid, cotton bollworm, pink bollworm, spider mites, thrips and whiteflies are the major pests that damage the cotton field by reducing its yield upto 70%. Pests eat leaves, flowers, and bolls of cotton plants, which diminish plant's ability to produce cotton. Some pests also transmit diseases to cotton plants by acting as biological vector. Pests also induce stress to cotton plants, which reduces their ability to grow and produce cotton [ 2 ]. Another major threat to cotton yield is diseases. Cotton-Leaf-Curl-Virus-Disease-(CLCuD) is a destructive disease that can cause yield losses of up to 80%. The virus is transmitted by whiteflies and can lead to reduced growth, curling of leaves, and a decline in boll formation. CLCuV causes around 1 billion dollars to the Pakistani cotton industry annually. Cotton-leaf-curl-virus-disease-(CLCuD) is a viral disease that affects cotton crop, primarily in the South Asian region predominantly in Pakistan, some parts of Africa and China. The causative agent of cotton-leaf-curl-virus-disease is the Cotton leaf curl virus-(CLCuV), which is a member of the Geminiviridae family. The virus is transmitted biologically by means of whitefly ( Bemisia tabaci L.) and infect other plants in the same family, such as okra and hibiscus. The first report of the disease in Pakistan came from Multan in 1967, and it has since spread country wide cotton-growing regions. Despite designing new resistant varieties and management practices CLCuD retained as one of the major threats to yield and quality of cotton [ 3 ]. Actually, the cotton leaf curl virus-(CLCuV) is acquired by the whitefly when it feeds on an infected plant, and it is transmitted to a healthy plant when the whitefly feeds on it. Once a plant is affected by CLCuV infection, the virus replicates in the plant cells and causes characteristic symptoms such as curling and leaves yellowing, stunting of the plant, wilted and crumpled leaves and reduced yields [ 4 ]. Usually, CLCuD is managed by following the preventive measures such as controlling whitefly populations by pesticide and planting resistant varieties of cotton helps to reduce the spread of the disease. Other management approaches include removing infected plants and destroying them, as well as implementing good crop rotation and weed control practices to prevent the accumulation of virus reservoirs. Amongst all management practices, use of pesticides for reducing the whitefly population is common practice. Pesticides are the chemicals used to eliminate or control pests, including insects, weeds, and fungi, which possess a threat to crops, livestock, and human well-being. While pesticides are primarily intended to be toxic to pests, their improper or excessive use can potentially harm humans and non-target organisms, posing risks that make them potentially dangerous. Pesticides causes immediate and severe health effects, like vomiting, nausea, dizziness, seizures, and even death. Long-term health issues raised from exposure to even small amounts of pesticides, including cancer, neurological disorders, reproductive problems, and immune system dysfunction [ 5 ]. Some pesticides disrupt the body's hormonal balance, leading to reproductive and developmental problems, and other health effects [ 6 , 7 ]. Nanotechnology revolutionized the agriculture by improving crop productivity, reducing environmental impact, and enhancing food safety [ 8 ]. Nanoparticles have potential to improve soil fertility and reduce environmental negative impacts [ 9 ]. Nanoparticles improve plant health both directly and indirectly. In direct approach nanoparticles directly influence the causative agent of disease. Meanwhile, nanoparticles of micronutrients induce antioxidative defense in plants against plant bacterial, fungal and viral diseases. Actually, nanoparticles enhance bioavailability of nutrients for plants [ 7 ]. Zinc oxide nanoparticles gained significant importance in agriculture for fortification of crops. ZnO nanoparticles promote plant growth and metabolism by up regulating the growth hormones like auxin and gibberellin. ZnO nanoparticles have antimicrobial activities against plant pathogens, such as fungi and bacteria. ZnO nanoparticles enhance nutrient uptake of plants. ZnO nanoparticles improve soil health by retaining nutrients and minimizing the leaching. ZnO nanoparticles also remediate soil by immobilizing the heavy metals and reducing their toxicity in polluted soils. In agriculture, they also lower the usage of traditional chemical fertilizers and pesticides [ 10 ]. Iron oxide nanoparticles (FeO nanoparticles) is known for its role in improving soil quality. FeO nanoparticles enhance nutrient stability by coating and ultimately increase nutrient efficiency. FeO nanoparticles have insecticidal, fungicidal and as natural substitute to chemical pesticides [ 11 ]. Copper nanoparticles (Cu nanoparticles) is commonly used in agriculture for management of plant diseases. Copper is micronutrient and widely used for plant disease management. They are applied to seed coatings, which promotes the healthy germination and early growth. Cu nanoparticles improve crop yield and quality. Cu nanoparticles application increase the soil enzymatic activity that reduce soil borne pathogens and increase soil fertility [ 12 ]. Silver nanoparticles (Ag nanoparticles) are used in agriculture as pesticides and fertilizer. Ag nanoparticles used as alternate fertilizer. Ag nanoparticles improve nutrient uptake in plants, that increases crop yields. Ag nanoparticles remediate soil by managing the toxicants in contaminated soils. Ag nanoparticles promote the growth of plants by improving the efficiency of photosynthesis and enhancing the activity of plant enzymes. Ag nanoparticles also help in re-establishing the damage parts of plants [ 13 ]. It’s necessary to investigate the comparative efficacy of zinc oxide, iron oxide, copper and silver nanoparticles with pesticide against cotton leaf curl virus disease (CLCuD). The objective of the study is to compare the efficacy of zinc oxide, iron oxide, copper and silver nanoparticles with conventional pesticides against cotton leaf curl virus disease (CLCuD) in cotton. Material and method Field conditions Field experiment of cotton ( Gossypium hirsutum L.) was carried out to check the comparative efficacy of zinc oxide (ZnO nanoparticles), iron oxide (FeO nanoparticles), copper (Cu nanoparticles) and silver (Ag nanoparticles) at experiment field of Plant Pathology Research Institute, Ayub Agricultural Research Institute (AARI) in Faisalabad, Pakistan (31.40° N, 73.01° E). Seeds of cotton variety, FH-490 were taken from the Cotton Research Station located in Ayub Agricultural Research Institute, Faisalabad. The seeds were subjected to a treatment of 1200 ml of 60% diluted sulfuric acid for 15 minutes to eradicate fiber, then rinse with distilled water. The study was conducted in triplicates using a randomized complete block design (RCBD). Soil properties of site was given in Table 1 . The plants were spaced 35 cm apart from each other, and the rows were spaced 70 cm apart. The crop was sown in May 2022, with an average temperature ranging from 27.6–34.8°C and an average humidity of 28.9%. The nanoparticles used in this experiment for potential against cotton leaf curl virus disease (CLCuD), were previously characterized, optimized and reported in [ 14 ]. In previous pot experiment nanoparticles doses were optimized as zinc oxide nanoparticles (100ppm), iron oxide nanoparticles (50ppm), copper nanoparticle (50ppm) and silver nanoparticle as (25ppm). Table 1 Physiochemical properties of soil Sand % 61 Silt % 17 Clay % 22 pH 7.87 EC (dSm − 1 ) 1.2 Organic matter 0.83 Available N (%) 0.044 Available P (%) 6.89 Available K (%) 139 Cotton crop was fertilized with urea, triple super phosphate, and muriate of potash, with recommended dosages of 150 kg/ha, 60 kg/ha, and 60 kg/ha, respectively. Urea had a nitrogen content of 46.6%, triple super phosphate had 20% phosphorus, and muriate of potash had 60% potassium. During sowing, the crop received two-thirds of the urea and the entire doses of triple super phosphate and muriate of potash, while the remaining one-third of urea was applied after 3 weeks of sowing, with the irrigation. Disease induction The CLCuD in cotton was induced by infested whiteflies by following the method developed by Gautam et al. [ 15 ]. The Symptomatic and CLCuD-infested leaves were sterilized with 1% bleach and 70% ethanol, followed by distilled water. The whitefly population was fed on CLCuD-infected leaves for 36 hours to ensure virus infestation in the cage. Then, these whiteflies were released in wirehouses of respective treatments. After three weeks of whitefly transmission, the CLCuD symptoms started to appear. Treatments Experiment consisted of seven treatments. Healthy control was grown in control condition to keep it disease free. Infected control was the treatment in which no protection measures were applied to CLCuD infected plants. In treatment pesticide only imidacloprid was applied to CLCuD infected plants. Imidacloprid @200g/L is applied as insecticide to control the whitefly. Pesticide (Imidacloprid) was only applied in treatment pesticide. In other treatments optimized doses of ZnO (100 ppm) FeO (50 ppm) Cu (50 ppm) and Ag (25 ppm) nanoparticles were applied thrice during whole experiment to CLCuD infected plants. Two herbicides Pendimethaline and LS-metolachlor @1000ml and 800ml were applied to all treatments for weeds control. Crop harvest Crop was harvested after 23 weeks from experimental site. Morphological parameters, yield parameters and disease parameters were performed at experiment site. While biochemical parameters were performed at Environmental Microbiology and Biotechnology Lab, Department of Environmental Sciences, Government College University Faisalabad, Pakistan (31.4° N, 73.06° E). CLCuD detection Triple Antibody Sandwich-ELISA (TAS-ELISA) TAS-ELISA-HRP was performed by the method developed by [ 16 ]De Francesco., (2015). Total protein was extracted from cotton leaves with 1ml PBS buffer. Then wells were coated with polyclonal antiserum and then plates were incubated for 4 hours at 37°C. After three washings with PBS-T, protein was added with incubation at 4°C overnight. The procedure repeated with conjugated antibody. The samples were analyzed at 492nm. The analysis was performed by calculating the ratio between average OD492 nm of the sample and the average OD492 nm of the healthy control (I/H). Parameters Morphological parameters The height of each plant was measured in centimeters from the first node where cotyledons are present up to the top bud when it stopped growing, and then the average measurements were taken for each treatment. The number of sympodial and monopodial branches were determined for each plant individually, and then the values were averaged for the respective treatment. Staple length of cotton was measured by using the Digital Fiber graph model “530”. Cotton fiber was taken from each boll. Then tuft of cotton fiber and aligned parallel to each other. Average staple was calculated from each treatment. Representative samples were used to measure the staple length and uniformity ratio using the Digital Fiber graph model "530" at a 2.5% span length. The uniformity ratio was determined by dividing the 50% uniformity ratio by 2.5% and was based on five specific readings [ 17 ](Rodgers et al.) Bolls were picked from individual plants and then their average was calculated from each treatment. To determine the average weight of each boll in a particular genotype, the total seed cotton produced was divided by the number of bolls. This calculation was performed for each plant, and the results were averaged for each treatment [ 18 ](Syed et al.). Average boll weight = \(\frac{Yield of seed cotton per plant}{No. of bolls per plant}\) Yield parameters For seed cotton yield individual seed cotton weights were obtained from each plant and measured using a triple beam balance. The average seed cotton yield was then calculated for each treatment using the method described by [ 19 ]Robinson (1976). For ginning outturn, each plant's seed cotton was processed individually using a single roller electric gin, as described by Arshad et al. (2007). The weight of the resulting ginned lint was recorded, and the ginning outturn percentage was determined using a formula: Ginning outturn percentage = \(\frac{Weight of lint}{weight of seed cotton}*100\) Chlorophyll content To determine the chlorophyll a, chlorophyll b and carotenoid content, the method described by [ 20 ]Patla in 1990 was followed. About 0.5 g of ground leaves were placed in 10 ml of 80% (v/v) methanol and left for approximately 10 minutes before being centrifuged at 12,000 rpm to obtain the extract. The supernatant was separated from the extract after the particles had settled for 10 minutes and stored in small bottles in the refrigerator. The quantity of chlorophyll was determined by using a spectrophotometer at three distinct wavelengths, namely 663 nm, 645 nm, and 480 nm. The concentration of chlorophyll was calculated using the formula: Chlorophyll a = [12.7 (OD 663) − 2.69 (OD 645)] × v/1000 × w Chlorophyll b = [22.9 (OD 645) − 4.68 (OD 663)] × v/1000 × w Carotenoid (µg/g FW) = OD 480 + (0.114 x OD 663) × (0.638 × OD 645 Total Chlorophyll = [20.2 (OD 645) − 8.02(OD 663)] × v/w × 1/1000 Biochemical attributes Superoxide dismutase (SOD) activity (EC 1.15.1.1) was evaluated by the method developed by [ 21 ]Giannopolitis and Ries (1977). The reaction mixture consists of 50 µL of enzyme extract, 50 µM of NBT chloride, 1.3 µM of riboflavin, 13 mM of methionine, 75 mM of EDTA, and 50 mM of phosphate buffer at pH 7.8. Then reaction mixture was then exposed to fluorescent light with an intensity of 75 µM m − 2 s − 1 for 15 minutes. After exposure, the samples were placed in darkness, and measured the absorbance at 560 nm. A blank sample did not produce any color under light exposure was used for comparison. The inhibition of 50% of NBT photoreduction by the enzyme extract was used to determine one unit of SOD activity. The activity of the POD (Peroxidase) (EC1.11.1.7) enzyme was investigated following the method described by [ 22 ]Chance and Maehly (1955). To perform this analysis, a 1 mL sample of enzyme extract, which had been diluted 20 times, was mixed with 5 mL of enzyme reaction solution consisting of a phosphate buffer with a pH of 7.8, 50 M pyrogallol, and 50 mM H 2 O 2 . The mixture was then placed in an incubator at 25 ◦ C for 5 minutes. The given protocol describes a method for measuring the amount of purpurogallin produced during a reaction using a spectrophotometer, and defining one unit of POD (presumably an enzyme) as the amount of purpurogallin generated per milligram of protein per minute. The enzyme activity of APX (Ascorbate peroxidase) (EC 1.11.1.11) was evaluated by using procedure described by [ 23 ]Nakano and Asada (1987). The reaction mixture for APX activity was prepared by adding 100 µL of enzyme extract, 100 µL of ascorbate (7.5 mM), 100 µL of H 2 O 2 (300 mM), and 3 mL of 25 mM potassium phosphate buffer with 2 mM EDTA pH (7.8). The ascorbate oxidation was evaluated by measuring the absorbance at 290 nm. Catalase activity (EC 1.11.1.6) was evaluated by the protocol developed by [ 24 ]Woodbury et al. (1971). The reaction containing 100 µL of H 2 O 2 , 100 µL of enzyme extract (3000 mM), 2 mM EDTA, and 50 mM phosphate buffer pH (7.8). Catalase activity was evaluated by measuring the absorbance at 240 nm that indicated the H 2 O 2 disappearance. Hydrogen peroxide (H 2 O 2 ) content was evaluated by the method developed by [ 25 ]Patterson et al. The reaction mixture comprises 50% potassium iodide, 25% trichloroacetic acid (TCA) and 25% potassium phosphate buffer pH (7.6). Initially, 150 mg sample was homogenized with 5 ml solution containing 1.25 ml TCA, 2.5 ml KI and 1.25 ml potassium phosphate buffer. Then, centrifuged at 4°C for 8 minutes at 10,000 rpm. Change of color was measure at 410 nm. The method of [ 26 ] Murray et al. was used to obtain measurement of electrolyte leakage. After harvesting of crop 5 mm sample of the leaf was collected and subsequently taken into 8 mL of deionized water. A 5 mm sample of the leaf was collected once the crop was harvested and placed into 8 mL of deionized water. This mixture was then incubated in a water bath at a temperature of 32°C for a duration of 2 hours. The initial electrical conductance of the medium (EC1) was then recorded, and the samples were subsequently autoclaved at a temperature of 121°C for 20 minutes to extract all electrolytes. After the mixture was allowed to cool to a temperature of 25°C. On cooling second electrical conductance of the medium (EC2) was determined. The total electrolyte leakage was determined using the following formula: Electrolyte leakage % = \(\left(\frac{EC1}{EC2}\right)\) × 100. Disease parameters Disease incidence (DI) was calculated by dividing number of affected plants from an individual treatment and total plants of treatment to find the percentage [ 27 ](Madden and Hughes, 1995). The following formula was used to calculate disease incidence (%): Disease incidence (%) = \(\frac{Number of infected plants}{Total number of plants}\) *100 Disease severity refers to the extent or intensity of the damage caused by a disease on the host plant. It was measured by assessing the symptoms exhibited by the plant, such as leaf yellowing, wilting, necrosis, or stunting. Disease severity expressed numerically as a percentage or on a rating scale, such as the one used for Cotton Leaf Curl Virus Disease (CLCuD) severity [ 28 ](Shaul et al.). Disease scale given in Table 2 was generated by using the method developed by Large 1(996). Table 2 Cotton Leaf Curl Virus Disease (CLCuD) rating scale Symptoms Disease Severity Disease rating index Disease response Plants in which symptoms are absent 0 0 Immune Plants having few scattered venation or leaf enation 1 0.1–10 High resistance A small cluster of thickened veins was observed on 10 or less than 10 leaves per plant 2 10.1–20 Resistant Plants having thick venation on leaves but leaves curling is absent 3 20.1–30 Moderately resistant Plants exhibited thick venation and leaf curling on leaves 4 30.1–40 Moderately susceptible Severe leaf curling and vein thickening developed on half of the plant 5 40.1–50 Susceptible The plant exhibits stunting, severe leaf curling, vein thickening, and little or no fruiting 6 > 50 Highly susceptible Formula of disease severity (DS)% is as follow: Disease severity (DS)% = \(\frac{disease grade*number of plants in each grade}{total number of plants*highest disease grade}*100\) Disease grade mentioned in above formula was obtained from disease rating scale. Reduction of virus infestation (%) of cotton leaf curl virus disease in cotton was measured by comparing the disease incidence (%) of a particular treatment with disease incidence (%) of infected control [ 29 ](Killick, 1979). Reduction of virus infestation (%) was calculated by following formula: Reduction of virus infestation (%) = \(\frac{a-b}{a}\) *100 Where a = Disease incidence (%) of treatment b = Disease incidence (%) of infected control Statistical analysis The statistical analysis was performed by using the Statistix 8.1 to perform analysis of variance (ANOVA). Statistical design was Randomized Complete Block Design (RCBD) for field trial, which was followed by a one-way ANOVA analysis according to [ 30 ]Steel et al. Results Morphological parameters Figure 1 showed that maximum height of stem was observed 124 cm in treatment healthy control while minimum 65 cm in treatment infected control. Height of stem decreased 47% in treatment infected control as compared to healthy control. Pesticide treatment showed increase in height of stem 36.8% as compared to infected control. Height of stem increased by ZnO, FeO, Cu and Ag nanoparticles 36%, 38.2%, 39% and 31.2% respectively as compared to infected control. Maximum mean of monopodial branches was (3.01) observed in treatment FeO nanoparticles. While minimum mean of monopodial branches was (2.6) observed in healthy control. Statistical data revealed that mean of monopodial branches were at par with treatment ZnO nanoparticles and FeO nanoparticles. Maximum mean of sympodial branches was observed (17.5) in healthy control. While minimum mean of sympodial branches was (11.7) observed in infected control. While treatment pesticide showed significant increase in mean of sympodial branches followed by ZnO, Cu, FeO and Ag nanoparticles. Treatment ZnO nanoparticles and Cu nanoparticles were at par with each other. While FeO nanoparticles and Ag nanoparticles were also at par with each other. Infected control showed decrease 17.7% as compared to healthy control. Pesticide showed increased 26.4% as compared to infected control. ZnO, FeO, Cu and Ag nanoparticles showed increment as 15.8%, 10.2%, 15% and 12% respectively. Maximum staple length was (30.4 cm) observed in treatment healthy control. Minimum staple length was (26.8 cm) observed in treatment infected control. In infected control staple length was decreased 12% as compared to healthy control. Pesticide application enhanced the staple length 8.9% as compared to infected control. Application of ZnO nanoparticles increased the staple length of cotton up-to 8.31% as compared to infected control. Compared to the infected control, the staple length of cotton increased by 5.3% with FeO nanoparticles, while Cu nanoparticles and Ag nanoparticles resulted in a 4.9% and 4.81% increase, respectively. Maximum boll weight was observed (3.65g) in healthy control. Minimum boll weight was observed (2.59g) in infected control. In infected control boll weight significantly decreased as 31.48% due to cotton leaf curl virus (CLCuD) as compared to healthy control. But pesticide and application of ZnO nanoparticles significantly enhanced the boll weight of cotton as compared to infected control. Pesticide and ZnO nanoparticles are at par with each other. Maximum boll weight showed by pesticide and ZnO nanoparticles followed by Cu nanoparticles, FeO nanoparticles and Ag nanoparticles. Pesticide increased the boll weight by 26.1%, while ZnO nanoparticles enhanced it by 24.9%, as compared to the infected control compared to the infected control, the application of FeO nanoparticles resulted in a 19.4% increase in boll weight, while Cu nanoparticles and Ag nanoparticles showed increases of 24% and 11.6% respectively. Number of bolls significantly decreased as 42.3% in infected control as compared to healthy control. Maximum number of bolls were observed (33.8) in healthy control and minimum (19.5) in infected control. Pesticide application showed increase in number of bolls as 34.3% as compared to infected control. ZnO nanoparticles showed increase in number of bolls as 29.34% as compared to infected control. While FeO nanoparticles, Cu nanoparticles and Ag nanoparticles increased as 10.2%, 15% and 12.34% as compared to infected control. Yield parameters Figure 2 showed that seed cotton yield of infected control is significantly low as compared to healthy control. Maximum seed cotton yield was observed (10.8-ton ha − 1 ) in healthy control. While minimum seed cotton yield was observed (3.9-ton ha − 1 ) in infected control. Seed cotton yield decreased in infected control 63% as compared to healthy control. Treatment in which pesticide applied showed increased in seed cotton yield 53%. Similarly, ZnO nanoparticles also showed increase in seed cotton yield 50.12% as compared to infected control. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles showed increase in seed cotton yield as 46.7%, 48.1% and 45.02% respectively. Ginning outturn significantly decreased in infected control as compared to healthy control. But application of pesticide significantly increased the ginning outturn of cotton as compared to infected control. ZnO nanoparticles followed by Cu, FeO and Ag nanoparticles respectively. Maximum ginning outturn was observed (43%) in healthy control while minimum in healthy control (27%). Ginning outturn decreased in infected control as 37% due to cotton leaf curl virus disease (CLCuD) as compared to healthy control. Application of pesticide increased the ginning outturn 29.7% as compared to healthy control. Application of ZnO, FeO, Cu and Ag nanoparticles increased the ginning outturn of seed as 22.8%, 21.02%, 24.9% and 18.9% respectively as compared to infected control. Chlorophyll content Figure 3 revealed that chlorophyll a was significantly decreased in infected control as compared to healthy control. In the healthy control, the maximum chlorophyll a content of (1.86 mg g- 1 FW) was observed, while the infected control showed the minimum chlorophyll content of (1.22 mg g- 1 ) FW. Chlorophyll a was decreased in infected control up-to 34.2% as compared to healthy control. ZnO nanoparticles significantly enhanced the chlorophyll a content of cotton up-to 28.6%. Pesticide increased the chlorophyll a 26.1% as compared to infected control. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles increased the chlorophyll a 20.7%, 28% and 18.6% respectively as compared to infected control. Chlorophyll b was significantly decreased in infected control as compared to healthy control. Maximum chlorophyll b was (0.91 mg g − 1 FW) in healthy control. Minimum chlorophyll was (0.60 mg g − 1 FW) observed in infected control. Chlorophyll b was decreased in infected control up-to 34.7% as compared to healthy control. ZnO nanoparticles significantly enhanced the chlorophyll a content of cotton up-to 28.3%. Pesticide increased the chlorophyll a 29.3% as compared to infected control. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles increased the chlorophyll a 21.2%, 25.9% and 14.2% respectively as compared to infected control. Maximum carotenoid content was (0.29 mg g − 1 FW) in healthy control. Minimum carotenoid content was (0.173 mg g − 1 FW) observed in infected control. Carotenoid content was decreased in infected control up-to 40.3% as compared to healthy control. Pesticide increased the chlorophyll a 27.9% as compared to infected control. ZnO nanoparticles significantly enhanced the chlorophyll a content of cotton up-to 35.9%. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles increased the chlorophyll a 24.7%, 33.4% and 21.3% respectively as compared to infected control. Total chlorophyll was significantly decreased in infected control as compared to healthy control. Maximum total chlorophyll content was (2.77 mg g − 1 FW) in healthy control. Minimum total chlorophyll content was (1.81 mg g − 1 FW) observed in infected control. Total chlorophyll was decreased in infected control up-to 34.6% as compared to healthy control. ZnO nanoparticles significantly enhanced the total chlorophyll content of cotton up-to 29%. Pesticide increased the total chlorophyll 27.3% as compared to infected control. FeO, Cu and Ag nanoparticles increased the total chlorophyll 21%, 29.5% and 17.7% respectively as compared to infected control. Biochemical parameters Figure 4 showed significant decreased in antioxidants due to stress induced by cotton leaf curl virus disease (CLCuD). Maximum SOD was observed (93 mg − 1 protein) in healthy control. Minimum SOD was observed (46 mg − 1 protein) in infected control. SOD value significantly decreased in infected control up-to 50.6% as compared to healthy control. Pesticide application significantly increased the value of SOD up-to 39.7%. Application of ZnO nanoparticles also significantly enhanced 45.4% as compared to infected control. FeO, Cu and Ag nanoparticles enhanced the SOD 37.3%, 54.2% and 22.3% respectively. Maximum POD was observed (80.3 mg − 1 protein) in healthy control. Minimum POD was observed (38.6 mg − 1 protein) in infected control. POD value significantly decreased in infected control up-to 51.9% as compared to healthy control. Pesticide application significantly increased the value of POD up-to 48.7%. Application of ZnO nanoparticles also significantly enhanced 50.43% as compared to infected control. FeO, Cu and Ag nanoparticles enhanced SOD 38%, 46.3% and 34.3% respectively. Maximum APX was observed (56.6 mg − 1 protein) in healthy control. Minimum APX was observed (41.7 mg − 1 protein) in infected control. APX value significantly decreased in infected control up-to 48.7% as compared to healthy control. Pesticide application significantly increased the value of APX up-to 41.8%. Application of ZnO nanoparticles also significantly enhanced 32.7% as compared to infected control. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles enhanced SOD 26.4%, 32.6% and 17.7% respectively. Maximum CAT value was observed (168 mg − 1 protein) in ZnO nanoparticles. Minimum CAT value was observed (110 mg − 1 protein) in infected control. CAT value significantly decreased in infected control up-to 34.5% as compared to healthy control. Pesticide application significantly increase the value of CAT up-to 32.9%. Application of ZnO nanoparticles also significantly enhanced 36% as compared to infected control. FeO, Cu and Ag nanoparticles enhance SOD 29.9%, 33.1% and 23.7% respectively. Maximum H 2 O 2 content (221 nmol g − 1 FW) was observed in infected control while minimum (78.6 nmol g − 1 FW). H 2 O 2 content significantly increased in infected control up-to 64.8% as compared to healthy control. Pesticide application showed 40% decrease in H 2 O 2 content as compared to infected control. Application of ZnO, FeO, Cu and Ag nanoparticles decreased the H 2 O 2 content up-to 23.4%, 14.8%, 18% and 29.7% respectively as compared to infected control. Maximum electrolyte leakage value (53.3%) was observed in infected control and minimum (19.3%) in healthy control. Electrolyte leakage significantly increased in infected control up-to 64% as compared to healthy control. Pesticide application showed 41.3% decrease in electrolyte leakage as compared to infected control. Application of ZnO, FeO, Cu and Ag nanoparticles decreased the electrolyte leakage up-to 32%, 28.13%, 21.4% and 13.7% respectively as compared to infected control. Disease parameters Figure 5 revealed that reduction infection was significantly increased in pesticide. Maximum value of reduction infection was (79.3%) in pesticide. While, minimum reduction infection was (0%). ZnO nanoparticles reduce infection up-to 42.33%. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles application reduced the infection 41%, 34.66% and 44.87% respectively. Maximum reduction of infection was observed in pesticide treatment followed by ZnO nanoparticles, Ag nanoparticles, FeO nanoparticles and Cu nanoparticles respectively. The disease severity increased by up to 73.6% in the infected control group compared to the healthy control group. The maximum disease incidence of 73.66% was observed in the infected control group, while the healthy control group had a minimum disease incidence of 0%. Application of pesticide reduced the disease incidence up-to 67% as compared to infected control. Meanwhile, ZnO, FeO, Cu and Ag nanoparticles application reduced the disease incidence (46.1%), (20.6%), (28%) and (39.8%) respectively as compared to infected control. Disease incidence increased up-to 89.2% in infected control as compared to healthy control. Maximum disease incidence was observed (73.6%) in infected control. However, minimum disease incidence was (7%) observed in healthy control. Application of pesticide reduced the disease incidence up-to 57% as compared to infected control. Meanwhile, ZnO, FeO, Cu and Ag nanoparticles application reduced the disease incidence (29.14%), (31.6%), (28.8%) and (44.2%) respectively as compared to infected control. Correlation Figure 6 depicted that morphological parameters (sympodial branches, number of bolls, staple length and height of stem) except monopodial branches have positive correlation with chlorophyll content (chlorophyll a, b, carotenoids and total chlorophyll), yield parameters (ginning outturn and seed cotton yield) and negatively correlated with disease parameters (disease incidence, disease severity and reduction infection). While, in biochemical parameters (enzymatic antioxidants (APX, CAT, SOD and POD)) have positive correlation with morphological and chlorophyll content. Oxidative stress parameters (electrolyte leakage and H 2 O 2 ) have negative correlation with morphological parameters and chlorophyll content and positive correlation with disease parameters. Principle Component Analysis (PCA) The loading plots of PCA to illustrate the effect of ZnO, FeO, Cu and Ag NPS along with pesticide against cotton leaf curl virus disease (CLCuD) in cotton are presented in Fig. 7 . In the whole database, Dim1 and Dim2 exhibited maximum contribution and occupy more than 91.8% of all databases, among which Dim1 exhibits 81.7% and Dim2 exhibits 10.1%. All studied parameters were distributed successfully in the database, which is giving a clear indication that CLCuD caused a significant effect on the growth, photosynthetic, and antioxidant defense mechanisms, of cotton plants. It can be indicated that reduction infection, antioxidant, photosynthetic pigments, and lipid peroxidation are positively correlated with other studied attributes in the whole database. Heat map Histogram correlation analysis was carried out to depict relationships among morpho-physio-biochemical attributes and disease parameters of cotton for amelioration of cotton leaf curl virus disease (CLCuD) by foliar application of zinc oxide, iron oxide, copper and silver nanoparticles along with pesticide (Fig. 8 ). Significant variations were observed in antioxidative defense mechanism, plant growth, photosynthetic parameters, and disease parameters, while the rest of the heat map shows significant results with all other parameters under CLCuD by foliar application of ZnO, FeO, Cu and Ag nanoparticles. Although blue color is showing nonsignificant differences within the treatments, purple, green, and red colors depict a significant difference in the histogram study. This histogram is showing a clear difference between CLCuD treatments of nanoparticles. Discussion In this experiment, a comprehensive field trial was conducted to evaluate the efficacy of optimized doses of ZnO nanoparticles, FeO nanoparticles, Cu nanoparticles, and Ag nanoparticles in combatting cotton leaf curl virus disease (CLCuD). This trial is designed to assess the potential of these nanoparticles as agents for disease management in the real-world agricultural field. The impact of cotton leaf curl virus disease (CLCuD) on plant health was evident in the infected control group. The infected plants exhibited severely diminished physical growth parameters, chlorophyll content, yield parameters, and antioxidant levels, with reductions ranging from 12–63%. These findings explore the devastating effects of CLCuD on plant growth and metabolic processes, highlighted its potential to impair cotton plants' overall growth and productivity significantly. Moreover, the appearance of elevated oxidative stress parameters and disease-related indicators further confirm incidence of CLCuD. The 64–89% increase in oxidative stress parameters and disease-related parameters indicates a significant level of stress induced by the CLCuV. These findings collectively show the drastic impact of CLCuD on cotton plants based on physicochemical parameters that include physiological and biochemical parameters. [ 31 ] (Ahmed et al.) also reported 87.3% yield loss in cotton due to CLCuD. Cotton fibre quality was significantly decreased by cotton leaf curl virus disease (CLCuD) incidence [ 32 ]. Pesticide application significantly improves physical growth parameters, chlorophyll content, yield parameters and antioxidants by 36–67%. Meanwhile, pesticides deregulate oxidative stress and disease parameters by 29–43%. Using pesticides to manage cotton leaf curl virus disease (CLCuD) is very common. Vector whitefly Bemisia tabaci L usually spread CLCuD. [ 31 ]. Through application of pesticide whitefly population can be controlled and ultimately the disease incidence [ 33 ]. Applying ZnO nanoparticles has proven to be highly beneficial, leading to significant improvements in plant health and productivity. The physical growth parameters, chlorophyll content, yield parameters, and antioxidant levels exhibited remarkable enhancements, with increases ranging from 29–53%. This finding reveals the potential of ZnO nanoparticles to stimulate plant growth, enhance photosynthetic efficiency, elevate yields, and improve antioxidative defense mechanisms, collectively contributing to the overall health of the plants. [ 34 ] (Faizan et al.) also studied the impact of ZnO nanoparticles on cotton and concluded that it can improve the antioxidant defense of cotton. [ 35 ] (Sofy et al.) reported the activity of ZnO nanoparticles against tomato mosaic virus in tomato. [ 36 ] (Abdelkhalek et al.) also reported the antiviral activity of ZnO nanoparticles. FeO nanoparticle application also significantly improves the physical growth parameters, chlorophyll content, yield parameters and antioxidants by 26–49%. Meanwhile, pesticides deregulate oxidative stress and disease parameters by 36–49%. [ 37 ] (Cai et al.) reported that FeO nanoparticles also induced plant antioxidant defense in various plants. [ 38 ] (Bhat et al.) reported that application of FeO nanoparticles improved plant growth in Soyabean. [ 39 ] (Hussain et al.) described the activity of FeO nanoparticles for promoted growth under abiotic stress in wheat. The presence of iron can trigger the activation of genes responsible for defense responses in plants, which in turn can stimulate their immune system to protect against viral diseases. [ 40 ] (Liu et al.) stated that iron plays a crucial role in preventing or minimizing viral infections in plants. Cu nanoparticle application significantly improves physical growth parameters, chlorophyll content, yield parameters and antioxidants by 25–46%. Meanwhile, pesticides deregulate oxidative stress and disease parameters by 37–51%. [ 41 ] (Zhang et al.) reported that Cu nanoparticles promoted wheat growth by increasing antioxidant defense. [ 42 ] (Van Nguyen et al.) stated that Cu nanoparticle application also promoted plant growth and yield under drought stress. Copper is crucial for plant growth and development and promotes plant growth when faced with biotic stress-like diseases. Copper functions as a cofactor for various enzymes involved in plant metabolism, such as photosynthesis, respiration, and nitrogen fixation. In the presence of plant viral disease, the plant's ability to perform photosynthesis and other metabolic processes may be hampered, resulting in stunted growth and reduced yield. Copper helped the plant to alleviate these negative impacts by enhancing enzyme activity and restoring some of the plant's metabolic functions. However, using copper carefully and appropriately is important, as excessive amounts can have toxic effects on plants [ 43 ](Liu et al.). Ag nanoparticle application significantly improves physical growth parameters, chlorophyll content, yield parameters and antioxidants by 21–37%. Meanwhile, pesticides deregulate oxidative stress and disease parameters by 36–49%. [ 44 ] (Yousaf et al.) reported the activity of Ag nanoparticles on the antioxidant system of wheat. [ 45 ] (Kim et al.) described that Ag nanoparticles have shown activity against various diseases of plants. [ 46 ] (Lamsa et al.) reported Ag nanoparticles powdery mildew activity on pumpkin and cucumber. Studies have indicated that silver nanoparticles exhibit antiviral effects on plants by impeding the propagation and transmission of viruses, reducing viral disease severity. [ 47 ] (Sharma et al.) stated that Ag nanoparticles are applied for antiviral activities through various mechanisms, such as attaching to viral surface proteins to prevent infection of plant cells and interfering with viral replication by disrupting the function of viral nucleic acids like RNA or DNA. Usually, Cotton Leaf Curl Virus (CLCuD) disease in cotton crops is usually managed by pesticides. This strategy focuses on applying pesticides, predominantly insecticides, to control the whitefly (Bemisia tabaci) vector population. This vector plays a life-threatening role in the distribution of CLCuD in cotton-growing regions[ 48 ]. The main goal of using pesticides is to interrupt the life cycle of the whitefly. Pesticide helps stop the virus from spreading and reduces the chance of the disease ocuuring. Imidacloprid is a neonicotinoid insecticide and is considered one of the pesticides applied in cotton to control white fly (Bemisia tabaci) to mitigate CLCuD in cotton [ 49 ]. When imidacloprid is applied to plants, it undergoes systemic absorption, distributed throughout the plant tissues, i.e., leaves, stems, and roots. This systemic absorption of imidacloprid indicated significant residual effects, allowing it to remain within the plant for an extended duration [ 50 ]. As whiteflies and other targeted pests feed on the treated plant, they ingest imidacloprid, which disrupts their nervous systems by binding to nicotinic acetylcholine receptors. This interference induces paralysis and eventual demise in these pests. Imidacloprid lasts over several weeks to months, reducing the necessity for frequent pesticide applications [ 51 ]. Pesticides, without any doubt, effectively manage the distribution of CLCuD. However, this strategy has disadvantages regarding its prevailing environmental consequences and threats [ 52 ]. While pesticides like imidacloprid lower the disease incidence by decreasing the population of the whitefly vector, the consequences extend beyond their intended target. The application of pesticides leads to a reduction in the population of not only whiteflies but also other organisms in the ecosystem, including those that are non-targeted and beneficial for the Cotton [ 53 ]. This shows application of pesticides is complex because of ecological concerns. In recent years, a complementary approach to combating CLCuD has emerged through the utilization of nanoparticles. These nanoscale materials have shown promise in reducing disease incidence by enhancing the antioxidant defense system of cotton plants [ 54 ]. Unlike pesticides, which primarily act on the vector population, nanoparticles intervene at the molecular level within the plant, fortifying its ability to resist the virus. It's worth noting that while pesticides generally exhibit more immediate and tangible results than nanoparticles, both strategies employ different modes of action to reduce disease incidence [ 55 ]. A distinctive characteristic shared by nanoparticles and pesticides is their favourable impact when introduced before the Cotton Leaf Curl Virus outbreak. In this preliminary stage, nanoparticles and pesticides positively influence the cotton plants' defense mechanisms against potential viral diseases. However, once the visible symptoms of CLCuD become apparent, managing and restraining the disease's spread becomes exceedingly challenging [ 56 ]. This shift in dynamics highlights the importance of early intervention and preventative measures to minimize the impact of the disease. In addition to their role in disease control, pesticides have been observed to indirectly enhance the overall physical growth, chlorophyll content, and antioxidant levels of cotton plants [ 57 ]. This suggests a more comprehensive influence on the plant's physiological improvement beyond disease management. In contrast, nanoparticles exert a more direct influence by elevating enzymatic activity within the plant, which in turn enhances metabolic processes and overall plant health [ 58 ]. It can be concluded that multifaceted approach of combined use of pesticides and nanoparticles underscores the balance between disease control efficacy and environment. The combination of nanoparticles and other pest management strategies could be used in future at molecular level for better understanding and development of certain strategies to mitigate CLCuD. Conclusion The study demonstrated the potential of zinc oxide, iron oxide, copper and silver nanoparticles in comparison to pesticide for mitigating cotton leaf curl virus disease (CLCuD). Morphological parameters (height of stem, monopodial branches, sympodial branches, staple length, boll weight and number of bolls), yield parameters (seed cotton yield and ginning outturn), chlorophyll content (chlorophyll a, chlorophyll b, carotenoids and total chlorophyll), biochemical parameters (superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxidase (APX), catalase (CAT), hydrogen peroxide (H2O2) and electrolyte leakage) and disease parameters (reduction infection, disease severity and disease incidence) were studied Field experiment was conducted as randomized complete block design (RCBD). One-way ANOVA was performed to analyze the results. Cotton Leaf Curl Virus (CLCuV) was detected by TAS-ELISA (Triple Antibody Sandwich-Enzyme-linked immunosorbent assay). Results showed that use of pesticide has reduced the disease infection by79.3% as compared to control treatment. However, ZnO nanoparticles, FeO nanoparticles, Cu nanoparticles and Ag nanoparticles have reduced the infection by 42.33%, 41%, 34.7% and 44.8% respectively. It was concluded that application of pesticide showed the least disease incidence compared to nanoparticles. Declarations Acknowledgments The authors would like to extend their sincere appreciation to the Researchers Supporting Project number (RSP2024R123), King Saud University, Riyadh, Saudi Arabia. Author contributions US, MUY, MS, TS, FM and SH designed the experiment. AI, MN, ANS, HMA and WAAA prepared the samples, SE, AZ, US, FM, AI, SH and MS performed the experiments, analyzed data and wrote the paper. US, TS, FM, AI and ANS reviewed and checked all the details. All authors read and approved the final manuscript. Funding This work was funded by the Researchers Supporting Project number (RSP2024R123), King Saud University, Riyadh, Saudi Arabia. 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Supplementary Files floatimage1.png Graphical abstract Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Jun, 2024 Reviews received at journal 06 Jun, 2024 Reviewers agreed at journal 06 Jun, 2024 Reviews received at journal 31 May, 2024 Reviewers agreed at journal 30 May, 2024 Reviewers agreed at journal 26 May, 2024 Reviewers invited by journal 25 May, 2024 Submission checks completed at journal 21 May, 2024 Editor assigned by journal 21 May, 2024 First submitted to journal 14 May, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4416740","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308935731,"identity":"f0a7ae05-f90e-4779-b111-3ea3d9983f0c","order_by":0,"name":"Usman Shafqat","email":"","orcid":"","institution":"Government College University","correspondingAuthor":false,"prefix":"","firstName":"Usman","middleName":"","lastName":"Shafqat","suffix":""},{"id":308935733,"identity":"3f80408c-92e9-47d8-99ee-d8def6665c59","order_by":1,"name":"Muhammad Ussama Yasin","email":"","orcid":"","institution":"Ayub Agricultural Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Ussama","lastName":"Yasin","suffix":""},{"id":308935737,"identity":"e4394a7a-0603-4ddc-ba31-62d1c7a06892","order_by":2,"name":"Muhammad Shahid","email":"","orcid":"","institution":"Government College University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Shahid","suffix":""},{"id":308935739,"identity":"efd023c1-08cc-478a-9100-26ea182d2baa","order_by":3,"name":"Sabir Hussain","email":"","orcid":"","institution":"Government College University","correspondingAuthor":false,"prefix":"","firstName":"Sabir","middleName":"","lastName":"Hussain","suffix":""},{"id":308935741,"identity":"3af75818-a56d-469b-aace-6cd5a9162414","order_by":4,"name":"Tanvir Shahzad","email":"","orcid":"","institution":"Government College University","correspondingAuthor":false,"prefix":"","firstName":"Tanvir","middleName":"","lastName":"Shahzad","suffix":""},{"id":308935742,"identity":"9df0f4a2-7fa6-4d82-a5bd-7ab1f218b72e","order_by":5,"name":"Faisal Mahmood","email":"data:image/png;base64,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","orcid":"","institution":"Government College University","correspondingAuthor":true,"prefix":"","firstName":"Faisal","middleName":"","lastName":"Mahmood","suffix":""},{"id":308935743,"identity":"d33db785-7417-4c34-b06d-5f87225f5d9b","order_by":6,"name":"Aneeza Ishfaq","email":"","orcid":"","institution":"Government College University","correspondingAuthor":false,"prefix":"","firstName":"Aneeza","middleName":"","lastName":"Ishfaq","suffix":""},{"id":308935744,"identity":"f6a5f7f4-a15a-458c-8075-d744e779a421","order_by":7,"name":"Muhammad Nawaz","email":"","orcid":"","institution":"Khwaja Fareed University of Engineering and Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Nawaz","suffix":""},{"id":308935747,"identity":"4984259a-de22-4595-8244-95b5383e1ca7","order_by":8,"name":"Adnan Noor Shah","email":"","orcid":"","institution":"Khwaja Fareed University of Engineering and Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Adnan","middleName":"Noor","lastName":"Shah","suffix":""},{"id":308935748,"identity":"5957e14e-e166-4c4b-be95-da6c1c946da4","order_by":9,"name":"Hayssam M. Ali","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Hayssam","middleName":"M.","lastName":"Ali","suffix":""},{"id":308935750,"identity":"27271567-95c5-4344-a6ec-efa4ee2d2519","order_by":10,"name":"Waleed A. A. Alsakkaf","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Waleed","middleName":"A. A.","lastName":"Alsakkaf","suffix":""},{"id":308935752,"identity":"17337cba-b3a6-4117-a730-0e10b5bebd2b","order_by":11,"name":"Sezai Ercisli","email":"","orcid":"","institution":"Ataturk University","correspondingAuthor":false,"prefix":"","firstName":"Sezai","middleName":"","lastName":"Ercisli","suffix":""},{"id":308935755,"identity":"766c00e1-43ec-4101-a9b0-6f9b86982426","order_by":12,"name":"Ahmed Zeid","email":"","orcid":"","institution":"Alexandria University","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Zeid","suffix":""}],"badges":[],"createdAt":"2024-05-14 05:52:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4416740/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4416740/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57613121,"identity":"cacd0c08-a0cc-4dfe-bed1-8544c176385d","added_by":"auto","created_at":"2024-06-03 10:44:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":631225,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of pesticide, ZnO (100ppm), FeO (50ppm), Cu (50ppm) and Ag (25ppm) nanoparticles on height of stem (A), monopodial branches (B), sympodial branches (C), staple length (D), boll weight (E) and number of bolls (F) of Gossypium herbaceum L. against Cotton Leaf Curl Virus Disease (CLCuD)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/267228321357f382ec9e962b.png"},{"id":57613123,"identity":"ff606dcd-41b5-4e04-bdc8-d845b2accd35","added_by":"auto","created_at":"2024-06-03 10:44:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":586607,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of pesticide, ZnO (100ppm), FeO (50ppm), Cu (50ppm) and Ag (25ppm) nanoparticles on seed cotton yield (A) and ginning outturn (B) of Gossypium herbaceum L. against Cotton Leaf Curl Virus Disease (CLCuD)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/5fe27deb748479a38a51a4fc.png"},{"id":57612505,"identity":"45172c07-46ea-4d94-b5ef-83976336d2b2","added_by":"auto","created_at":"2024-06-03 10:36:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":724687,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of pesticide, ZnO (100ppm), FeO (50ppm), Cu (50ppm) and Ag (25ppm) nanoparticles on chlorophyll a (A), chlorophyll b (B), carotenoids (C) and total chlorophyll (D) of Gossypium herbaceum L. against Cotton Leaf Curl Virus Disease (CLCuD)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/ca4c94c9bcd12a408b3bd1e1.png"},{"id":57612499,"identity":"7fe10f90-1abc-4986-b559-7223b22358e6","added_by":"auto","created_at":"2024-06-03 10:36:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":691625,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of pesticide, ZnO (100ppm), FeO (50ppm), Cu (50ppm) and Ag (25ppm) nanoparticles on superoxide dismutase (SOD) (A), peroxidase (POD) (B), ascorbate peroxidase (APX) (C), catalase (CAT) (D), hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) (E) and electrolyte leakage (F) of Gossypium herbaceum L. against Cotton Leaf Curl Virus Disease (CLCuD)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/8745a71166ca6331f97719a0.png"},{"id":57613124,"identity":"15873eb5-05a1-4f62-ab09-c69529acaec5","added_by":"auto","created_at":"2024-06-03 10:44:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":435444,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of pesticide, ZnO (100ppm), FeO (50ppm), Cu (50ppm) and Ag (25ppm) nanoparticles on reduction infection (A), disease severity (B) and disease incidence (C) of Gossypium herbaceum L. against Cotton Leaf Curl Virus Disease (CLCuD)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/1aef1e910a7b757168f90753.png"},{"id":57612503,"identity":"6855433e-ef51-4a8c-84d9-da515da36868","added_by":"auto","created_at":"2024-06-03 10:36:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":363138,"visible":true,"origin":"","legend":"\u003cp\u003ePearson’s correlation matrix between morphological parameters (height of stem, monopodial branches, sympodial branches, staple length, boll weight and number of bolls), yield parameters (seed cotton yield and ginning outturn), chlorophyll content (chlorophyll a, chlorophyll b, carotenoids and total chlorophyll), biochemical parameters (superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxidase (APX), catalase (CAT), hydrogen peroxide (H2O2) and electrolyte leakage) and disease parameters (reduction infection, disease severity and disease incidence)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/6f3fe0513d10900c2750e16a.png"},{"id":57612498,"identity":"1743976f-99ce-49e4-9d3b-a356f388bbd1","added_by":"auto","created_at":"2024-06-03 10:36:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":220524,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis showing association among morphological parameters (height of stem, monopodial branches, sympodial branches, staple length, boll weight and number of bolls), yield parameters (seed cotton yield and ginning outturn), chlorophyll content (chlorophyll a, chlorophyll b, carotenoids and total chlorophyll), biochemical parameters (superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxidase (APX), catalase (CAT), hydrogen peroxide (H2O2) and electrolyte leakage) and disease parameters (reduction infection, disease severity and disease incidence)\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/740333a2d9117f440edbf03f.png"},{"id":57612506,"identity":"a13302d6-f3d6-4fa3-92a3-51e72fe77857","added_by":"auto","created_at":"2024-06-03 10:36:26","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":131198,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map histogram correlation between different studied parameters morphological parameters (height of stem, monopodial branches, sympodial branches, staple length, boll weight and number of bolls), yield parameters (seed cotton yield and ginning outturn), chlorophyll content (chlorophyll a, chlorophyll b, carotenoids and total chlorophyll), biochemical parameters (superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxidase (APX), catalase (CAT), hydrogen peroxide (H2O2) and electrolyte leakage) and disease parameters (reduction infection, disease severity and disease incidence) and treatments Imidacloprid, zinc oxide nanoparticles (100ppm), iron oxide nanoparticles (50ppm), copper nanoparticles (50 ppm) and silver nanoparticles (25ppm)\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/58dfa454d0ead9a848795349.png"},{"id":57613608,"identity":"c3dac199-a254-4c8e-ada5-b015562978a8","added_by":"auto","created_at":"2024-06-03 10:52:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4103784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/064ea055-462b-49f0-a6a4-7ba54ca3f72b.pdf"},{"id":57613122,"identity":"70a1d631-abdd-4856-bd3f-f554222cddfe","added_by":"auto","created_at":"2024-06-03 10:44:25","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":517407,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical abstract\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4416740/v1/469a25c3fc7c72a03b3bab17.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the impact of biogenic nanoparticles and pesticide application in controlling Cotton Leaf Curl Virus Disease (CLCuD) in Cotton (Gossypium hirsutum L.)","fulltext":[{"header":"Background","content":"\u003cp\u003eCotton is major chief cash crop and contributing in economy of many countries worldwide. The textile sector is heavily dependent on cotton. Cotton holds immense global significance as it is utilized in manufacturing various products such as textiles, clothing, and paper products. Cotton is a major income source and livelihood for many farmers around the world. The textile industry, which is heavily dependent on cotton, is a major global industry that employs millions of people and contributes significantly to the world economy. It plays an important role in the production of oil, animal feed, and other products Mathangadeera, Hequet, Kelly, Dever, Kelly and Products [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom last few decades various abiotic and biotic stresses reduce the yield of cotton crop. Furthermore, changes in climatic conditions also limitize the cotton yield. Pests are the major threats to cotton yield by impacting both the production and quality of crop. Boll weevil, cotton aphid, cotton bollworm, pink bollworm, spider mites, thrips and whiteflies are the major pests that damage the cotton field by reducing its yield upto 70%. Pests eat leaves, flowers, and bolls of cotton plants, which diminish plant's ability to produce cotton. Some pests also transmit diseases to cotton plants by acting as biological vector. Pests also induce stress to cotton plants, which reduces their ability to grow and produce cotton [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother major threat to cotton yield is diseases. Cotton-Leaf-Curl-Virus-Disease-(CLCuD) is a destructive disease that can cause yield losses of up to 80%. The virus is transmitted by whiteflies and can lead to reduced growth, curling of leaves, and a decline in boll formation. CLCuV causes around 1\u0026nbsp;billion dollars to the Pakistani cotton industry annually. Cotton-leaf-curl-virus-disease-(CLCuD) is a viral disease that affects cotton crop, primarily in the South Asian region predominantly in Pakistan, some parts of Africa and China. The causative agent of cotton-leaf-curl-virus-disease is the Cotton leaf curl virus-(CLCuV), which is a member of the Geminiviridae family. The virus is transmitted biologically by means of whitefly (\u003cem\u003eBemisia tabaci\u003c/em\u003e L.) and infect other plants in the same family, such as okra and hibiscus. The first report of the disease in Pakistan came from Multan in 1967, and it has since spread country wide cotton-growing regions. Despite designing new resistant varieties and management practices CLCuD retained as one of the major threats to yield and quality of cotton [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Actually, the cotton leaf curl virus-(CLCuV) is acquired by the whitefly when it feeds on an infected plant, and it is transmitted to a healthy plant when the whitefly feeds on it. Once a plant is affected by CLCuV infection, the virus replicates in the plant cells and causes characteristic symptoms such as curling and leaves yellowing, stunting of the plant, wilted and crumpled leaves and reduced yields [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUsually, CLCuD is managed by following the preventive measures such as controlling whitefly populations by pesticide and planting resistant varieties of cotton helps to reduce the spread of the disease. Other management approaches include removing infected plants and destroying them, as well as implementing good crop rotation and weed control practices to prevent the accumulation of virus reservoirs. Amongst all management practices, use of pesticides for reducing the whitefly population is common practice. Pesticides are the chemicals used to eliminate or control pests, including insects, weeds, and fungi, which possess a threat to crops, livestock, and human well-being. While pesticides are primarily intended to be toxic to pests, their improper or excessive use can potentially harm humans and non-target organisms, posing risks that make them potentially dangerous. Pesticides causes immediate and severe health effects, like vomiting, nausea, dizziness, seizures, and even death. Long-term health issues raised from exposure to even small amounts of pesticides, including cancer, neurological disorders, reproductive problems, and immune system dysfunction [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Some pesticides disrupt the body's hormonal balance, leading to reproductive and developmental problems, and other health effects [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNanotechnology revolutionized the agriculture by improving crop productivity, reducing environmental impact, and enhancing food safety [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nanoparticles have potential to improve soil fertility and reduce environmental negative impacts [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Nanoparticles improve plant health both directly and indirectly. In direct approach nanoparticles directly influence the causative agent of disease. Meanwhile, nanoparticles of micronutrients induce antioxidative defense in plants against plant bacterial, fungal and viral diseases. Actually, nanoparticles enhance bioavailability of nutrients for plants [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eZinc oxide nanoparticles gained significant importance in agriculture for fortification of crops. ZnO nanoparticles promote plant growth and metabolism by up regulating the growth hormones like auxin and gibberellin. ZnO nanoparticles have antimicrobial activities against plant pathogens, such as fungi and bacteria. ZnO nanoparticles enhance nutrient uptake of plants. ZnO nanoparticles improve soil health by retaining nutrients and minimizing the leaching. ZnO nanoparticles also remediate soil by immobilizing the heavy metals and reducing their toxicity in polluted soils. In agriculture, they also lower the usage of traditional chemical fertilizers and pesticides [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIron oxide nanoparticles (FeO nanoparticles) is known for its role in improving soil quality. FeO nanoparticles enhance nutrient stability by coating and ultimately increase nutrient efficiency. FeO nanoparticles have insecticidal, fungicidal and as natural substitute to chemical pesticides [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Copper nanoparticles (Cu nanoparticles) is commonly used in agriculture for management of plant diseases. Copper is micronutrient and widely used for plant disease management. They are applied to seed coatings, which promotes the healthy germination and early growth. Cu nanoparticles improve crop yield and quality. Cu nanoparticles application increase the soil enzymatic activity that reduce soil borne pathogens and increase soil fertility [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSilver nanoparticles (Ag nanoparticles) are used in agriculture as pesticides and fertilizer. Ag nanoparticles used as alternate fertilizer. Ag nanoparticles improve nutrient uptake in plants, that increases crop yields. Ag nanoparticles remediate soil by managing the toxicants in contaminated soils. Ag nanoparticles promote the growth of plants by improving the efficiency of photosynthesis and enhancing the activity of plant enzymes. Ag nanoparticles also help in re-establishing the damage parts of plants [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It\u0026rsquo;s necessary to investigate the comparative efficacy of zinc oxide, iron oxide, copper and silver nanoparticles with pesticide against cotton leaf curl virus disease (CLCuD). The objective of the study is to compare the efficacy of zinc oxide, iron oxide, copper and silver nanoparticles with conventional pesticides against cotton leaf curl virus disease (CLCuD) in cotton.\u003c/p\u003e"},{"header":"Material and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eField conditions\u003c/h2\u003e \u003cp\u003eField experiment of cotton (\u003cem\u003eGossypium hirsutum\u003c/em\u003e L.) was carried out to check the comparative efficacy of zinc oxide (ZnO nanoparticles), iron oxide (FeO nanoparticles), copper (Cu nanoparticles) and silver (Ag nanoparticles) at experiment field of Plant Pathology Research Institute, Ayub Agricultural Research Institute (AARI) in Faisalabad, Pakistan (31.40\u0026deg; N, 73.01\u0026deg; E). Seeds of cotton variety, FH-490 were taken from the Cotton Research Station located in Ayub Agricultural Research Institute, Faisalabad. The seeds were subjected to a treatment of 1200 ml of 60% diluted sulfuric acid for 15 minutes to eradicate fiber, then rinse with distilled water. The study was conducted in triplicates using a randomized complete block design (RCBD). Soil properties of site was given in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The plants were spaced 35 cm apart from each other, and the rows were spaced 70 cm apart. The crop was sown in May 2022, with an average temperature ranging from 27.6\u0026ndash;34.8\u0026deg;C and an average humidity of 28.9%. The nanoparticles used in this experiment for potential against cotton leaf curl virus disease (CLCuD), were previously characterized, optimized and reported in [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In previous pot experiment nanoparticles doses were optimized as zinc oxide nanoparticles (100ppm), iron oxide nanoparticles (50ppm), copper nanoparticle (50ppm) and silver nanoparticle as (25ppm).\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\u003ePhysiochemical properties of soil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSand %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSilt %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClay %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC (dSm\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOrganic matter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvailable N (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvailable P (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvailable K (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139\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\u003eCotton crop was fertilized with urea, triple super phosphate, and muriate of potash, with recommended dosages of 150 kg/ha, 60 kg/ha, and 60 kg/ha, respectively. Urea had a nitrogen content of 46.6%, triple super phosphate had 20% phosphorus, and muriate of potash had 60% potassium. During sowing, the crop received two-thirds of the urea and the entire doses of triple super phosphate and muriate of potash, while the remaining one-third of urea was applied after 3 weeks of sowing, with the irrigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDisease induction\u003c/h2\u003e \u003cp\u003eThe CLCuD in cotton was induced by infested whiteflies by following the method developed by Gautam et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The Symptomatic and CLCuD-infested leaves were sterilized with 1% bleach and 70% ethanol, followed by distilled water. The whitefly population was fed on CLCuD-infected leaves for 36 hours to ensure virus infestation in the cage. Then, these whiteflies were released in wirehouses of respective treatments. After three weeks of whitefly transmission, the CLCuD symptoms started to appear.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eTreatments\u003c/h2\u003e \u003cp\u003eExperiment consisted of seven treatments. Healthy control was grown in control condition to keep it disease free. Infected control was the treatment in which no protection measures were applied to CLCuD infected plants. In treatment pesticide only imidacloprid was applied to CLCuD infected plants. Imidacloprid @200g/L is applied as insecticide to control the whitefly. Pesticide (Imidacloprid) was only applied in treatment pesticide. In other treatments optimized doses of ZnO (100 ppm) FeO (50 ppm) Cu (50 ppm) and Ag (25 ppm) nanoparticles were applied thrice during whole experiment to CLCuD infected plants. Two herbicides Pendimethaline and LS-metolachlor @1000ml and 800ml were applied to all treatments for weeds control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCrop harvest\u003c/h2\u003e \u003cp\u003eCrop was harvested after 23 weeks from experimental site. Morphological parameters, yield parameters and disease parameters were performed at experiment site. While biochemical parameters were performed at Environmental Microbiology and Biotechnology Lab, Department of Environmental Sciences, Government College University Faisalabad, Pakistan (31.4\u0026deg; N, 73.06\u0026deg; E).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCLCuD detection\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eTriple Antibody Sandwich-ELISA (TAS-ELISA)\u003c/h2\u003e \u003cp\u003eTAS-ELISA-HRP was performed by the method developed by [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]De Francesco., (2015). Total protein was extracted from cotton leaves with 1ml PBS buffer. Then wells were coated with polyclonal antiserum and then plates were incubated for 4 hours at 37\u0026deg;C. After three washings with PBS-T, protein was added with incubation at 4\u0026deg;C overnight. The procedure repeated with conjugated antibody. The samples were analyzed at 492nm. The analysis was performed by calculating the ratio between average OD492 nm of the sample and the average OD492 nm of the healthy control (I/H).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eParameters\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eMorphological parameters\u003c/h2\u003e \u003cp\u003eThe height of each plant was measured in centimeters from the first node where cotyledons are present up to the top bud when it stopped growing, and then the average measurements were taken for each treatment. The number of sympodial and monopodial branches were determined for each plant individually, and then the values were averaged for the respective treatment. Staple length of cotton was measured by using the Digital Fiber graph model \u0026ldquo;530\u0026rdquo;. Cotton fiber was taken from each boll. Then tuft of cotton fiber and aligned parallel to each other. Average staple was calculated from each treatment. Representative samples were used to measure the staple length and uniformity ratio using the Digital Fiber graph model \"530\" at a 2.5% span length. The uniformity ratio was determined by dividing the 50% uniformity ratio by 2.5% and was based on five specific readings [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e](Rodgers et al.)\u003c/p\u003e \u003cp\u003eBolls were picked from individual plants and then their average was calculated from each treatment. To determine the average weight of each boll in a particular genotype, the total seed cotton produced was divided by the number of bolls. This calculation was performed for each plant, and the results were averaged for each treatment [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e](Syed et al.).\u003c/p\u003e \u003cp\u003eAverage boll weight = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{Yield of seed cotton per plant}{No. of bolls per plant}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eYield parameters\u003c/h2\u003e \u003cp\u003eFor seed cotton yield individual seed cotton weights were obtained from each plant and measured using a triple beam balance. The average seed cotton yield was then calculated for each treatment using the method described by [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]Robinson (1976).\u003c/p\u003e \u003cp\u003eFor ginning outturn, each plant's seed cotton was processed individually using a single roller electric gin, as described by Arshad et al. (2007). The weight of the resulting ginned lint was recorded, and the ginning outturn percentage was determined using a formula:\u003c/p\u003e \u003cp\u003eGinning outturn percentage = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{Weight of lint}{weight of seed cotton}*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eChlorophyll content\u003c/h2\u003e \u003cp\u003eTo determine the chlorophyll a, chlorophyll b and carotenoid content, the method described by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]Patla in 1990 was followed. About 0.5 g of ground leaves were placed in 10 ml of 80% (v/v) methanol and left for approximately 10 minutes before being centrifuged at 12,000 rpm to obtain the extract. The supernatant was separated from the extract after the particles had settled for 10 minutes and stored in small bottles in the refrigerator. The quantity of chlorophyll was determined by using a spectrophotometer at three distinct wavelengths, namely 663 nm, 645 nm, and 480 nm.\u003c/p\u003e \u003cp\u003eThe concentration of chlorophyll was calculated using the formula:\u003c/p\u003e \u003cp\u003eChlorophyll a = [12.7 (OD 663) \u0026minus;\u0026thinsp;2.69 (OD 645)] \u0026times; v/1000 \u0026times; w\u003c/p\u003e \u003cp\u003eChlorophyll b = [22.9 (OD 645) \u0026minus;\u0026thinsp;4.68 (OD 663)] \u0026times; v/1000 \u0026times; w\u003c/p\u003e \u003cp\u003eCarotenoid (\u0026micro;g/g FW)\u0026thinsp;=\u0026thinsp;OD 480 + (0.114 x OD 663) \u0026times; (0.638 \u0026times; OD 645\u003c/p\u003e \u003cp\u003eTotal Chlorophyll = [20.2 (OD 645) \u0026minus;\u0026thinsp;8.02(OD 663)] \u0026times; v/w \u0026times; 1/1000\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical attributes\u003c/h2\u003e \u003cp\u003eSuperoxide dismutase (SOD) activity (EC 1.15.1.1) was evaluated by the method developed by [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]Giannopolitis and Ries (1977). The reaction mixture consists of 50 \u0026micro;L of enzyme extract, 50 \u0026micro;M of NBT chloride, 1.3 \u0026micro;M of riboflavin, 13 mM of methionine, 75 mM of EDTA, and 50 mM of phosphate buffer at pH 7.8. Then reaction mixture was then exposed to fluorescent light with an intensity of 75 \u0026micro;M m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for 15 minutes. After exposure, the samples were placed in darkness, and measured the absorbance at 560 nm. A blank sample did not produce any color under light exposure was used for comparison. The inhibition of 50% of NBT photoreduction by the enzyme extract was used to determine one unit of SOD activity.\u003c/p\u003e \u003cp\u003eThe activity of the POD (Peroxidase) (EC1.11.1.7) enzyme was investigated following the method described by [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]Chance and Maehly (1955). To perform this analysis, a 1 mL sample of enzyme extract, which had been diluted 20 times, was mixed with 5 mL of enzyme reaction solution consisting of a phosphate buffer with a pH of 7.8, 50 M pyrogallol, and 50 mM H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. The mixture was then placed in an incubator at 25\u003csup\u003e◦\u003c/sup\u003eC for 5 minutes. The given protocol describes a method for measuring the amount of purpurogallin produced during a reaction using a spectrophotometer, and defining one unit of POD (presumably an enzyme) as the amount of purpurogallin generated per milligram of protein per minute.\u003c/p\u003e \u003cp\u003eThe enzyme activity of APX (Ascorbate peroxidase) (EC 1.11.1.11) was evaluated by using procedure described by [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]Nakano and Asada (1987). The reaction mixture for APX activity was prepared by adding 100 \u0026micro;L of enzyme extract, 100 \u0026micro;L of ascorbate (7.5 mM), 100 \u0026micro;L of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (300 mM), and 3 mL of 25 mM potassium phosphate buffer with 2 mM EDTA pH (7.8). The ascorbate oxidation was evaluated by measuring the absorbance at 290 nm.\u003c/p\u003e \u003cp\u003eCatalase activity (EC 1.11.1.6) was evaluated by the protocol developed by [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]Woodbury et al. (1971). The reaction containing 100 \u0026micro;L of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, 100 \u0026micro;L of enzyme extract (3000 mM), 2 mM EDTA, and 50 mM phosphate buffer pH (7.8). Catalase activity was evaluated by measuring the absorbance at 240 nm that indicated the H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e disappearance.\u003c/p\u003e \u003cp\u003eHydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) content was evaluated by the method developed by [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]Patterson et al. The reaction mixture comprises 50% potassium iodide, 25% trichloroacetic acid (TCA) and 25% potassium phosphate buffer pH (7.6). Initially, 150 mg sample was homogenized with 5 ml solution containing 1.25 ml TCA, 2.5 ml KI and 1.25 ml potassium phosphate buffer. Then, centrifuged at 4\u0026deg;C for 8 minutes at 10,000 rpm. Change of color was measure at 410 nm.\u003c/p\u003e \u003cp\u003eThe method of [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] Murray et al. was used to obtain measurement of electrolyte leakage. After harvesting of crop 5 mm sample of the leaf was collected and subsequently taken into 8 mL of deionized water. A 5 mm sample of the leaf was collected once the crop was harvested and placed into 8 mL of deionized water. This mixture was then incubated in a water bath at a temperature of 32\u0026deg;C for a duration of 2 hours. The initial electrical conductance of the medium (EC1) was then recorded, and the samples were subsequently autoclaved at a temperature of 121\u0026deg;C for 20 minutes to extract all electrolytes. After the mixture was allowed to cool to a temperature of 25\u0026deg;C. On cooling second electrical conductance of the medium (EC2) was determined. The total electrolyte leakage was determined using the following formula:\u003c/p\u003e \u003cp\u003eElectrolyte leakage % = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(\\frac{EC1}{EC2}\\right)\\)\u003c/span\u003e\u003c/span\u003e \u0026times; 100.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDisease parameters\u003c/h2\u003e \u003cp\u003eDisease incidence (DI) was calculated by dividing number of affected plants from an individual treatment and total plants of treatment to find the percentage [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e](Madden and Hughes, 1995).\u003c/p\u003e \u003cp\u003eThe following formula was used to calculate disease incidence (%):\u003c/p\u003e \u003cp\u003eDisease incidence (%) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{Number of infected plants}{Total number of plants}\\)\u003c/span\u003e\u003c/span\u003e *100\u003c/p\u003e \u003cp\u003eDisease severity refers to the extent or intensity of the damage caused by a disease on the host plant. It was measured by assessing the symptoms exhibited by the plant, such as leaf yellowing, wilting, necrosis, or stunting. Disease severity expressed numerically as a percentage or on a rating scale, such as the one used for Cotton Leaf Curl Virus Disease (CLCuD) severity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e](Shaul et al.).\u003c/p\u003e \u003cp\u003eDisease scale given in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was generated by using the method developed by Large 1(996).\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\u003eCotton Leaf Curl Virus Disease (CLCuD) rating scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003cp\u003eSeverity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003cp\u003erating index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisease response\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlants in which symptoms are absent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImmune\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlants having few scattered venation or leaf enation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh resistance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA small cluster of thickened veins was observed on 10 or less than 10 leaves per plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eResistant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlants having thick venation on leaves but leaves curling is absent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerately\u003c/p\u003e \u003cp\u003eresistant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlants exhibited thick venation and leaf curling on leaves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.1\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerately\u003c/p\u003e \u003cp\u003esusceptible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere leaf curling and vein thickening\u003c/p\u003e \u003cp\u003edeveloped on half of the plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.1\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSusceptible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe plant exhibits stunting, severe leaf curling, vein thickening, and little or no fruiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighly\u003c/p\u003e \u003cp\u003esusceptible\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\u003eFormula of disease severity (DS)% is as follow:\u003c/p\u003e \u003cp\u003eDisease severity (DS)% = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{disease grade*number of plants in each grade}{total number of plants*highest disease grade}*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eDisease grade mentioned in above formula was obtained from disease rating scale.\u003c/p\u003e \u003cp\u003eReduction of virus infestation (%) of cotton leaf curl virus disease in cotton was measured by comparing the disease incidence (%) of a particular treatment with disease incidence (%) of infected control [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e](Killick, 1979). Reduction of virus infestation (%) was calculated by following formula:\u003c/p\u003e \u003cp\u003eReduction of virus infestation (%) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{a-b}{a}\\)\u003c/span\u003e\u003c/span\u003e *100\u003c/p\u003e \u003cp\u003eWhere a\u0026thinsp;=\u0026thinsp;Disease incidence (%) of treatment\u003c/p\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;Disease incidence (%) of infected control\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed by using the Statistix 8.1 to perform analysis of variance (ANOVA). Statistical design was Randomized Complete Block Design (RCBD) for field trial, which was followed by a one-way ANOVA analysis according to [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]Steel et al.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMorphological parameters\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed that maximum height of stem was observed 124 cm in treatment healthy control while minimum 65 cm in treatment infected control. Height of stem decreased 47% in treatment infected control as compared to healthy control. Pesticide treatment showed increase in height of stem 36.8% as compared to infected control. Height of stem increased by ZnO, FeO, Cu and Ag nanoparticles 36%, 38.2%, 39% and 31.2% respectively as compared to infected control. Maximum mean of monopodial branches was (3.01) observed in treatment FeO nanoparticles. While minimum mean of monopodial branches was (2.6) observed in healthy control. Statistical data revealed that mean of monopodial branches were at par with treatment ZnO nanoparticles and FeO nanoparticles. Maximum mean of sympodial branches was observed (17.5) in healthy control. While minimum mean of sympodial branches was (11.7) observed in infected control. While treatment pesticide showed significant increase in mean of sympodial branches followed by ZnO, Cu, FeO and Ag nanoparticles. Treatment ZnO nanoparticles and Cu nanoparticles were at par with each other. While FeO nanoparticles and Ag nanoparticles were also at par with each other. Infected control showed decrease 17.7% as compared to healthy control. Pesticide showed increased 26.4% as compared to infected control. ZnO, FeO, Cu and Ag nanoparticles showed increment as 15.8%, 10.2%, 15% and 12% respectively. Maximum staple length was (30.4 cm) observed in treatment healthy control. Minimum staple length was (26.8 cm) observed in treatment infected control. In infected control staple length was decreased 12% as compared to healthy control.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePesticide application enhanced the staple length 8.9% as compared to infected control. Application of ZnO nanoparticles increased the staple length of cotton up-to 8.31% as compared to infected control. Compared to the infected control, the staple length of cotton increased by 5.3% with FeO nanoparticles, while Cu nanoparticles and Ag nanoparticles resulted in a 4.9% and 4.81% increase, respectively. Maximum boll weight was observed (3.65g) in healthy control. Minimum boll weight was observed (2.59g) in infected control. In infected control boll weight significantly decreased as 31.48% due to cotton leaf curl virus (CLCuD) as compared to healthy control. But pesticide and application of ZnO nanoparticles significantly enhanced the boll weight of cotton as compared to infected control. Pesticide and ZnO nanoparticles are at par with each other. Maximum boll weight showed by pesticide and ZnO nanoparticles followed by Cu nanoparticles, FeO nanoparticles and Ag nanoparticles. Pesticide increased the boll weight by 26.1%, while ZnO nanoparticles enhanced it by 24.9%, as compared to the infected control compared to the infected control, the application of FeO nanoparticles resulted in a 19.4% increase in boll weight, while Cu nanoparticles and Ag nanoparticles showed increases of 24% and 11.6% respectively. Number of bolls significantly decreased as 42.3% in infected control as compared to healthy control. Maximum number of bolls were observed (33.8) in healthy control and minimum (19.5) in infected control. Pesticide application showed increase in number of bolls as 34.3% as compared to infected control. ZnO nanoparticles showed increase in number of bolls as 29.34% as compared to infected control. While FeO nanoparticles, Cu nanoparticles and Ag nanoparticles increased as 10.2%, 15% and 12.34% as compared to infected control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eYield parameters\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed that seed cotton yield of infected control is significantly low as compared to healthy control. Maximum seed cotton yield was observed (10.8-ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in healthy control. While minimum seed cotton yield was observed (3.9-ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in infected control. Seed cotton yield decreased in infected control 63% as compared to healthy control. Treatment in which pesticide applied showed increased in seed cotton yield 53%. Similarly, ZnO nanoparticles also showed increase in seed cotton yield 50.12% as compared to infected control. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles showed increase in seed cotton yield as 46.7%, 48.1% and 45.02% respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGinning outturn significantly decreased in infected control as compared to healthy control. But application of pesticide significantly increased the ginning outturn of cotton as compared to infected control. ZnO nanoparticles followed by Cu, FeO and Ag nanoparticles respectively. Maximum ginning outturn was observed (43%) in healthy control while minimum in healthy control (27%). Ginning outturn decreased in infected control as 37% due to cotton leaf curl virus disease (CLCuD) as compared to healthy control. Application of pesticide increased the ginning outturn 29.7% as compared to healthy control. Application of ZnO, FeO, Cu and Ag nanoparticles increased the ginning outturn of seed as 22.8%, 21.02%, 24.9% and 18.9% respectively as compared to infected control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eChlorophyll content\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e revealed that chlorophyll \u003cem\u003ea\u003c/em\u003e was significantly decreased in infected control as compared to healthy control. In the healthy control, the maximum chlorophyll a content of (1.86 mg g-\u003csup\u003e1\u003c/sup\u003e FW) was observed, while the infected control showed the minimum chlorophyll content of (1.22 mg g-\u003csup\u003e1\u003c/sup\u003e) FW. Chlorophyll a was decreased in infected control up-to 34.2% as compared to healthy control. ZnO nanoparticles significantly enhanced the chlorophyll a content of cotton up-to 28.6%. Pesticide increased the chlorophyll a 26.1% as compared to infected control. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles increased the chlorophyll a 20.7%, 28% and 18.6% respectively as compared to infected control. Chlorophyll \u003cem\u003eb\u003c/em\u003e was significantly decreased in infected control as compared to healthy control. Maximum chlorophyll b was (0.91 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW) in healthy control. Minimum chlorophyll was (0.60 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW) observed in infected control. Chlorophyll b was decreased in infected control up-to 34.7% as compared to healthy control. ZnO nanoparticles significantly enhanced the chlorophyll a content of cotton up-to 28.3%. Pesticide increased the chlorophyll a 29.3% as compared to infected control. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles increased the chlorophyll a 21.2%, 25.9% and 14.2% respectively as compared to infected control.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMaximum carotenoid content was (0.29 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW) in healthy control. Minimum carotenoid content was (0.173 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW) observed in infected control. Carotenoid content was decreased in infected control up-to 40.3% as compared to healthy control. Pesticide increased the chlorophyll a 27.9% as compared to infected control. ZnO nanoparticles significantly enhanced the chlorophyll a content of cotton up-to 35.9%. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles increased the chlorophyll a 24.7%, 33.4% and 21.3% respectively as compared to infected control. Total chlorophyll was significantly decreased in infected control as compared to healthy control. Maximum total chlorophyll content was (2.77 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW) in healthy control. Minimum total chlorophyll content was (1.81 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW) observed in infected control. Total chlorophyll was decreased in infected control up-to 34.6% as compared to healthy control. ZnO nanoparticles significantly enhanced the total chlorophyll content of cotton up-to 29%. Pesticide increased the total chlorophyll 27.3% as compared to infected control. FeO, Cu and Ag nanoparticles increased the total chlorophyll 21%, 29.5% and 17.7% respectively as compared to infected control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical parameters\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed significant decreased in antioxidants due to stress induced by cotton leaf curl virus disease (CLCuD). Maximum SOD was observed (93 mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e protein) in healthy control. Minimum SOD was observed (46 mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e protein) in infected control. SOD value significantly decreased in infected control up-to 50.6% as compared to healthy control. Pesticide application significantly increased the value of SOD up-to 39.7%. Application of ZnO nanoparticles also significantly enhanced 45.4% as compared to infected control. FeO, Cu and Ag nanoparticles enhanced the SOD 37.3%, 54.2% and 22.3% respectively. Maximum POD was observed (80.3 mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e protein) in healthy control. Minimum POD was observed (38.6 mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e protein) in infected control. POD value significantly decreased in infected control up-to 51.9% as compared to healthy control. Pesticide application significantly increased the value of POD up-to 48.7%. Application of ZnO nanoparticles also significantly enhanced 50.43% as compared to infected control. FeO, Cu and Ag nanoparticles enhanced SOD 38%, 46.3% and 34.3% respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMaximum APX was observed (56.6 mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e protein) in healthy control. Minimum APX was observed (41.7 mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e protein) in infected control. APX value significantly decreased in infected control up-to 48.7% as compared to healthy control. Pesticide application significantly increased the value of APX up-to 41.8%. Application of ZnO nanoparticles also significantly enhanced 32.7% as compared to infected control. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles enhanced SOD 26.4%, 32.6% and 17.7% respectively. Maximum CAT value was observed (168 mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e protein) in ZnO nanoparticles. Minimum CAT value was observed (110 mg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e protein) in infected control. CAT value significantly decreased in infected control up-to 34.5% as compared to healthy control. Pesticide application significantly increase the value of CAT up-to 32.9%. Application of ZnO nanoparticles also significantly enhanced 36% as compared to infected control. FeO, Cu and Ag nanoparticles enhance SOD 29.9%, 33.1% and 23.7% respectively.\u003c/p\u003e \u003cp\u003eMaximum H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content (221 nmol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW) was observed in infected control while minimum (78.6 nmol g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e FW). H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content significantly increased in infected control up-to 64.8% as compared to healthy control. Pesticide application showed 40% decrease in H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content as compared to infected control. Application of ZnO, FeO, Cu and Ag nanoparticles decreased the H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e content up-to 23.4%, 14.8%, 18% and 29.7% respectively as compared to infected control. Maximum electrolyte leakage value (53.3%) was observed in infected control and minimum (19.3%) in healthy control. Electrolyte leakage significantly increased in infected control up-to 64% as compared to healthy control. Pesticide application showed 41.3% decrease in electrolyte leakage as compared to infected control. Application of ZnO, FeO, Cu and Ag nanoparticles decreased the electrolyte leakage up-to 32%, 28.13%, 21.4% and 13.7% respectively as compared to infected control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDisease parameters\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e revealed that reduction infection was significantly increased in pesticide. Maximum value of reduction infection was (79.3%) in pesticide. While, minimum reduction infection was (0%). ZnO nanoparticles reduce infection up-to 42.33%. FeO nanoparticles, Cu nanoparticles and Ag nanoparticles application reduced the infection 41%, 34.66% and 44.87% respectively. Maximum reduction of infection was observed in pesticide treatment followed by ZnO nanoparticles, Ag nanoparticles, FeO nanoparticles and Cu nanoparticles respectively. The disease severity increased by up to 73.6% in the infected control group compared to the healthy control group. The maximum disease incidence of 73.66% was observed in the infected control group, while the healthy control group had a minimum disease incidence of 0%. Application of pesticide reduced the disease incidence up-to 67% as compared to infected control. Meanwhile, ZnO, FeO, Cu and Ag nanoparticles application reduced the disease incidence (46.1%), (20.6%), (28%) and (39.8%) respectively as compared to infected control.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDisease incidence increased up-to 89.2% in infected control as compared to healthy control. Maximum disease incidence was observed (73.6%) in infected control. However, minimum disease incidence was (7%) observed in healthy control. Application of pesticide reduced the disease incidence up-to 57% as compared to infected control. Meanwhile, ZnO, FeO, Cu and Ag nanoparticles application reduced the disease incidence (29.14%), (31.6%), (28.8%) and (44.2%) respectively as compared to infected control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e depicted that morphological parameters (sympodial branches, number of bolls, staple length and height of stem) except monopodial branches have positive correlation with chlorophyll content (chlorophyll a, b, carotenoids and total chlorophyll), yield parameters (ginning outturn and seed cotton yield) and negatively correlated with disease parameters (disease incidence, disease severity and reduction infection). While, in biochemical parameters (enzymatic antioxidants (APX, CAT, SOD and POD)) have positive correlation with morphological and chlorophyll content. Oxidative stress parameters (electrolyte leakage and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) have negative correlation with morphological parameters and chlorophyll content and positive correlation with disease parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePrinciple Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003eThe loading plots of PCA to illustrate the effect of ZnO, FeO, Cu and Ag NPS along with pesticide against cotton leaf curl virus disease (CLCuD) in cotton are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. In the whole database, Dim1 and Dim2 exhibited maximum contribution and occupy more than 91.8% of all databases, among which Dim1 exhibits 81.7% and Dim2 exhibits 10.1%. All studied parameters were distributed successfully in the database, which is giving a clear indication that CLCuD caused a significant effect on the growth, photosynthetic, and antioxidant defense mechanisms, of cotton plants. It can be indicated that reduction infection, antioxidant, photosynthetic pigments, and lipid peroxidation are positively correlated with other studied attributes in the whole database.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eHeat map\u003c/h2\u003e \u003cp\u003eHistogram correlation analysis was carried out to depict relationships among morpho-physio-biochemical attributes and disease parameters of cotton for amelioration of cotton leaf curl virus disease (CLCuD) by foliar application of zinc oxide, iron oxide, copper and silver nanoparticles along with pesticide (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Significant variations were observed in antioxidative defense mechanism, plant growth, photosynthetic parameters, and disease parameters, while the rest of the heat map shows significant results with all other parameters under CLCuD by foliar application of ZnO, FeO, Cu and Ag nanoparticles. Although blue color is showing nonsignificant differences within the treatments, purple, green, and red colors depict a significant difference in the histogram study. This histogram is showing a clear difference between CLCuD treatments of nanoparticles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this experiment, a comprehensive field trial was conducted to evaluate the efficacy of optimized doses of ZnO nanoparticles, FeO nanoparticles, Cu nanoparticles, and Ag nanoparticles in combatting cotton leaf curl virus disease (CLCuD). This trial is designed to assess the potential of these nanoparticles as agents for disease management in the real-world agricultural field. The impact of cotton leaf curl virus disease (CLCuD) on plant health was evident in the infected control group. The infected plants exhibited severely diminished physical growth parameters, chlorophyll content, yield parameters, and antioxidant levels, with reductions ranging from 12\u0026ndash;63%. These findings explore the devastating effects of CLCuD on plant growth and metabolic processes, highlighted its potential to impair cotton plants' overall growth and productivity significantly.\u003c/p\u003e \u003cp\u003eMoreover, the appearance of elevated oxidative stress parameters and disease-related indicators further confirm incidence of CLCuD. The 64\u0026ndash;89% increase in oxidative stress parameters and disease-related parameters indicates a significant level of stress induced by the CLCuV. These findings collectively show the drastic impact of CLCuD on cotton plants based on physicochemical parameters that include physiological and biochemical parameters. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] (Ahmed et al.) also reported 87.3% yield loss in cotton due to CLCuD. Cotton fibre quality was significantly decreased by cotton leaf curl virus disease (CLCuD) incidence [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Pesticide application significantly improves physical growth parameters, chlorophyll content, yield parameters and antioxidants by 36\u0026ndash;67%. Meanwhile, pesticides deregulate oxidative stress and disease parameters by 29\u0026ndash;43%. Using pesticides to manage cotton leaf curl virus disease (CLCuD) is very common. Vector whitefly Bemisia tabaci L usually spread CLCuD. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Through application of pesticide whitefly population can be controlled and ultimately the disease incidence [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eApplying ZnO nanoparticles has proven to be highly beneficial, leading to significant improvements in plant health and productivity. The physical growth parameters, chlorophyll content, yield parameters, and antioxidant levels exhibited remarkable enhancements, with increases ranging from 29\u0026ndash;53%. This finding reveals the potential of ZnO nanoparticles to stimulate plant growth, enhance photosynthetic efficiency, elevate yields, and improve antioxidative defense mechanisms, collectively contributing to the overall health of the plants. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (Faizan et al.) also studied the impact of ZnO nanoparticles on cotton and concluded that it can improve the antioxidant defense of cotton. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] (Sofy et al.) reported the activity of ZnO nanoparticles against tomato mosaic virus in tomato. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] (Abdelkhalek et al.) also reported the antiviral activity of ZnO nanoparticles.\u003c/p\u003e \u003cp\u003eFeO nanoparticle application also significantly improves the physical growth parameters, chlorophyll content, yield parameters and antioxidants by 26\u0026ndash;49%. Meanwhile, pesticides deregulate oxidative stress and disease parameters by 36\u0026ndash;49%. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] (Cai et al.) reported that FeO nanoparticles also induced plant antioxidant defense in various plants. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (Bhat et al.) reported that application of FeO nanoparticles improved plant growth in Soyabean. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (Hussain et al.) described the activity of FeO nanoparticles for promoted growth under abiotic stress in wheat. The presence of iron can trigger the activation of genes responsible for defense responses in plants, which in turn can stimulate their immune system to protect against viral diseases. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] (Liu et al.) stated that iron plays a crucial role in preventing or minimizing viral infections in plants.\u003c/p\u003e \u003cp\u003eCu nanoparticle application significantly improves physical growth parameters, chlorophyll content, yield parameters and antioxidants by 25\u0026ndash;46%. Meanwhile, pesticides deregulate oxidative stress and disease parameters by 37\u0026ndash;51%. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] (Zhang et al.) reported that Cu nanoparticles promoted wheat growth by increasing antioxidant defense. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] (Van Nguyen et al.) stated that Cu nanoparticle application also promoted plant growth and yield under drought stress. Copper is crucial for plant growth and development and promotes plant growth when faced with biotic stress-like diseases. Copper functions as a cofactor for various enzymes involved in plant metabolism, such as photosynthesis, respiration, and nitrogen fixation. In the presence of plant viral disease, the plant's ability to perform photosynthesis and other metabolic processes may be hampered, resulting in stunted growth and reduced yield. Copper helped the plant to alleviate these negative impacts by enhancing enzyme activity and restoring some of the plant's metabolic functions. However, using copper carefully and appropriately is important, as excessive amounts can have toxic effects on plants [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e](Liu et al.).\u003c/p\u003e \u003cp\u003eAg nanoparticle application significantly improves physical growth parameters, chlorophyll content, yield parameters and antioxidants by 21\u0026ndash;37%. Meanwhile, pesticides deregulate oxidative stress and disease parameters by 36\u0026ndash;49%. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] (Yousaf et al.) reported the activity of Ag nanoparticles on the antioxidant system of wheat. [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] (Kim et al.) described that Ag nanoparticles have shown activity against various diseases of plants. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] (Lamsa et al.) reported Ag nanoparticles powdery mildew activity on pumpkin and cucumber. Studies have indicated that silver nanoparticles exhibit antiviral effects on plants by impeding the propagation and transmission of viruses, reducing viral disease severity. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] (Sharma et al.) stated that Ag nanoparticles are applied for antiviral activities through various mechanisms, such as attaching to viral surface proteins to prevent infection of plant cells and interfering with viral replication by disrupting the function of viral nucleic acids like RNA or DNA.\u003c/p\u003e \u003cp\u003eUsually, Cotton Leaf Curl Virus (CLCuD) disease in cotton crops is usually managed by pesticides. This strategy focuses on applying pesticides, predominantly insecticides, to control the whitefly (Bemisia tabaci) vector population. This vector plays a life-threatening role in the distribution of CLCuD in cotton-growing regions[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The main goal of using pesticides is to interrupt the life cycle of the whitefly. Pesticide helps stop the virus from spreading and reduces the chance of the disease ocuuring. Imidacloprid is a neonicotinoid insecticide and is considered one of the pesticides applied in cotton to control white fly (Bemisia tabaci) to mitigate CLCuD in cotton [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. When imidacloprid is applied to plants, it undergoes systemic absorption, distributed throughout the plant tissues, i.e., leaves, stems, and roots. This systemic absorption of imidacloprid indicated significant residual effects, allowing it to remain within the plant for an extended duration [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. As whiteflies and other targeted pests feed on the treated plant, they ingest imidacloprid, which disrupts their nervous systems by binding to nicotinic acetylcholine receptors. This interference induces paralysis and eventual demise in these pests. Imidacloprid lasts over several weeks to months, reducing the necessity for frequent pesticide applications [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePesticides, without any doubt, effectively manage the distribution of CLCuD. However, this strategy has disadvantages regarding its prevailing environmental consequences and threats [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. While pesticides like imidacloprid lower the disease incidence by decreasing the population of the whitefly vector, the consequences extend beyond their intended target. The application of pesticides leads to a reduction in the population of not only whiteflies but also other organisms in the ecosystem, including those that are non-targeted and beneficial for the Cotton [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This shows application of pesticides is complex because of ecological concerns.\u003c/p\u003e \u003cp\u003eIn recent years, a complementary approach to combating CLCuD has emerged through the utilization of nanoparticles. These nanoscale materials have shown promise in reducing disease incidence by enhancing the antioxidant defense system of cotton plants [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Unlike pesticides, which primarily act on the vector population, nanoparticles intervene at the molecular level within the plant, fortifying its ability to resist the virus. It's worth noting that while pesticides generally exhibit more immediate and tangible results than nanoparticles, both strategies employ different modes of action to reduce disease incidence [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. A distinctive characteristic shared by nanoparticles and pesticides is their favourable impact when introduced before the Cotton Leaf Curl Virus outbreak. In this preliminary stage, nanoparticles and pesticides positively influence the cotton plants' defense mechanisms against potential viral diseases. However, once the visible symptoms of CLCuD become apparent, managing and restraining the disease's spread becomes exceedingly challenging [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This shift in dynamics highlights the importance of early intervention and preventative measures to minimize the impact of the disease.\u003c/p\u003e \u003cp\u003eIn addition to their role in disease control, pesticides have been observed to indirectly enhance the overall physical growth, chlorophyll content, and antioxidant levels of cotton plants [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This suggests a more comprehensive influence on the plant's physiological improvement beyond disease management. In contrast, nanoparticles exert a more direct influence by elevating enzymatic activity within the plant, which in turn enhances metabolic processes and overall plant health [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. It can be concluded that multifaceted approach of combined use of pesticides and nanoparticles underscores the balance between disease control efficacy and environment. The combination of nanoparticles and other pest management strategies could be used in future at molecular level for better understanding and development of certain strategies to mitigate CLCuD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study demonstrated the potential of zinc oxide, iron oxide, copper and silver nanoparticles in comparison to pesticide for mitigating cotton leaf curl virus disease (CLCuD). Morphological parameters (height of stem, monopodial branches, sympodial branches, staple length, boll weight and number of bolls), yield parameters (seed cotton yield and ginning outturn), chlorophyll content (chlorophyll a, chlorophyll b, carotenoids and total chlorophyll), biochemical parameters (superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxidase (APX), catalase (CAT), hydrogen peroxide (H2O2) and electrolyte leakage) and disease parameters (reduction infection, disease severity and disease incidence) were studied Field experiment was conducted as randomized complete block design (RCBD). One-way ANOVA was performed to analyze the results. Cotton Leaf Curl Virus (CLCuV) was detected by TAS-ELISA (Triple Antibody Sandwich-Enzyme-linked immunosorbent assay). Results showed that use of pesticide has reduced the disease infection by79.3% as compared to control treatment. However, ZnO nanoparticles, FeO nanoparticles, Cu nanoparticles and Ag nanoparticles have reduced the infection by 42.33%, 41%, 34.7% and 44.8% respectively. It was concluded that application of pesticide showed the least disease incidence compared to nanoparticles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to extend their sincere appreciation to the Researchers Supporting Project number (RSP2024R123), King Saud University, Riyadh, Saudi Arabia.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUS, MUY, MS, TS, FM and SH designed the experiment. AI, MN, ANS, HMA and WAAA prepared the samples, SE, AZ, US, FM, AI, SH and MS performed the experiments, analyzed data and wrote the paper. US, TS, FM, AI and ANS reviewed and checked all the details. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Researchers Supporting Project number (RSP2024R123), King Saud University, Riyadh, Saudi Arabia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOther data could be made available upon request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been confirmed for publication in the journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMathangadeera RW, Hequet EF, Kelly B, Dever JK, Kelly CMJIC, Products. 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J Nanomaterials plant potential. 2019:427\u0026ndash;39.\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cotton Leaf Curl Virus Disease (CLCuD), Nanoparticles, Plant viral Disease management, Antioxidant defense","lastPublishedDoi":"10.21203/rs.3.rs-4416740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4416740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCotton Leaf Curl Virus Disease (CLCuD) is one of the major concerns for cotton growers. The traditional approach to managing CLCuD involves the control of the vector (whitefly) population through the use of pesticides. In this study, the efficacy of nanoparticles was compared with pesticides. The present study was conducted to evaluate the comparative efficacy of zinc oxide nanoparticles (ZnO nanoparticles), iron oxide nanoparticles (FeO nanoparticles), copper nanoparticles (Cu nanoparticles) and silver nanoparticles (Ag nanoparticles). Optimized doses of zinc oxide nanoparticles (ZnO nanoparticles), iron oxide nanoparticles (FeO nanoparticles), copper nanoparticles (Cu nanoparticles) and silver nanoparticles (Ag nanoparticles) were applied in a field trial of cotton against Cotton Leaf Curl Virus Disease (CLCuD) in cotton. The study consisted of morphological parameters (height of stem, monopodial branches, sympodial branches, staple length, boll weight and number of bolls), yield parameters (seed cotton yield and ginning outturn), chlorophyll content (chlorophyll a, chlorophyll b, carotenoids and total chlorophyll), biochemical parameters (superoxide dismutase (SOD), peroxidase (POD), ascorbate peroxidase (APX), catalase (CAT), hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) and electrolyte leakage) and disease parameters (reduction infection, disease severity and disease incidence).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCotton Leaf Curl Virus (CLCuV) was detected by TAS-ELISA (Triple Antibody Sandwich-Enzyme-linked immunosorbent assay). Pesticide reduced the infection as 79.3%. Zinc oxide nanoparticles (ZnO nanoparticles), iron oxide nanoparticles (FeO nanoparticles), copper nanoparticles (Cu nanoparticles) and silver nanoparticles (Ag nanoparticles) reduced the infection as 42.33%, 41%, 34.7% and 44.8% respectively. The statistical design for field trial was randomized complete block design (RCBD). One-way ANOVA was performed.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAlthough treatment pesticide showed the least disease incidence compared to nanoparticles. Nanoparticles are eco-friendly and safe as compared to pesticides. It is concluded that nanocomposites and hybrid modes may be used for managing CLCuD efficiently in the future.\u003c/p\u003e","manuscriptTitle":"Evaluating the impact of biogenic nanoparticles and pesticide application in controlling Cotton Leaf Curl Virus Disease (CLCuD) in Cotton (Gossypium hirsutum L.)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-03 10:36:20","doi":"10.21203/rs.3.rs-4416740/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-23T10:52:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-06T15:11:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335981874264692237608034760490443561056","date":"2024-06-06T05:46:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-31T06:28:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59498041879584322530952177221145951950","date":"2024-05-31T01:57:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162569742700302559831377066484065323942","date":"2024-05-26T04:23:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-25T18:55:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-21T11:40:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-21T11:40:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chemical and Biological Technologies in Agriculture","date":"2024-05-14T05:51:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chemical-and-biological-technologies-in-agriculture","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Chemical and Biological Technologies in Agriculture](https://chembioagro.springeropen.com/)","snPcode":"40538","submissionUrl":"https://submission.nature.com/new-submission/40538/3","title":"Chemical and Biological Technologies in Agriculture","twitterHandle":"@SpringerPlants","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed2e186b-fc61-49ee-a9cd-949624dcc7b5","owner":[],"postedDate":"June 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-08-16T08:50:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-03 10:36:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4416740","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4416740","identity":"rs-4416740","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}