Cotton Yield Loss Research Articles

Evaluation of the Genetic Diversity, Haplotype, and Virulence of Fusarium oxysporum f. sp. vasinfectum Field Isolates from Alabama.

The United States is the third largest producer of cotton and the largest exporter of cotton globally. Fusarium wilt, caused by the soilborne fungal pathogen Fusarium oxysporum f. sp. vasinfectum (Fov), was estimated to cause a $21 million cotton yield loss in 2022. Historically, Alabama was an important producer of cotton in the Southeastern United States and was the first state in which Fusarium wilt on cotton was described. To assess the genetic diversity of Fov field isolates in Alabama, 118 field isolates were collected from six counties across the state from 2014 to 2016. Phylogenetic analysis using TEF1 and RPB2 placed the Fov field isolates into 18 haplotypes. Upon profiling the Tfo1 transposon insertion in the NAT gene, it was determined that no race 4 isolates were recovered in Alabama. Representatives of all field isolate haplotypes caused disease on Upland cotton variety Rowden in a hydroponic test tube assay. Two haplotype A isolates were the most aggressive isolates recovered, and haplotype A isolate TF1 was more aggressive than the race 4 isolate 89-1A on Upland cotton and had similar symptom severity on Pima cotton. Karyotype profiling indicted an abundance of small chromosomes characteristic of karyotypes that include accessory chromosomes, with considerable variability between isolates. Collectively, our study indicates that Fov isolates from Alabama are genetically diverse, which may have been promoted by its persistence in cotton fields.

Compensation of cotton yield by nitrogen fertilizer in non-mulched fields with deficit drip irrigation

Irrigation and fertilization are the main measures used by farmers to maintain crop yield and quality, especially in arid and semi-arid regions. To investigate the compensation of cotton yield loss due to deficit drip irrigation in non-mulched fields by increasing nitrogen (N) fertilizer application, a split-plot experiment with irrigation amount as the main plot and N application as the subplot was carried out in southern Xinjiang, China. The experiment included two irrigation amounts of 450 mm (deficit irrigation, W1) and 540 mm (full irrigation, W2) and three N application rates of 150 kg ha−1 (F1), 225 kg ha−1 (F2), and 300 kg ha−1 (F3). From the cotton seedling stage, ET0 (reference evapotranspiration) was calculated from daily meteorological data and irrigation was applied when the cumulative ET0-P reached 45 ± 3 mm. The results showed that irrigation and fertilization significantly affected soil moisture and total N, which in turn affected cotton growth, biomass formation, and yield (P<0.05). Soil water storage (SWS) increased by 15.9% on average under W2 compared to W1. F2 and F3 decreased SWS by 5.0%-5.1% and 7.7%-10.9% respectively, compared to F1. Total soil nitrogen in W2 was on average 13.3% lower than W1, and F2 and F3 were 4.4%-11.1% and 11.3%-16.5% lower than F1, respectively. Aboveground biomass increased significantly with increasing irrigation and N amounts and was 47.9% higher in W2F3 than in W1F1. However, the increased biomass was not translated into yield and excessive accumulation of root and stem biomass reduced cotton yield. Under full irrigation (W2), cotton yield increased and then decreased with increasing N application. Cotton yield in F2 increased by 3.4% and 8.6% compared to F1 and F3, respectively. Under deficit irrigation (W1), N fertilizer compensation increased with increasing N application, and cotton yield in F3 increased by 10.3% and 15.0% compared to F1 and F2, respectively (P<0.05). TOPSIS analysis showed that W1F3 had the highest rank, followed by W1F2. The results indicated that W1F3, irrigation amount of 450 mm and N application rate of 300 kg ha−1, could be proposed for cotton fields under non-mulched drip irrigation in southern Xinjiang, China.

Performances of different yield-detrending methods in assessing the impacts of agricultural drought and flooding: A case study in the middle-and-lower reach of the Yangtze River, China

In evaluating the impacts of drought and flooding disasters on crop yields, accurately calculating meteorological yield (i.e., detrended yield) is an important procedure. The present work aimed to compare various yield-detrending methods in terms of characterizing the regression relationships between meteorological yield and the intensities of drought and flooding. Taking the middle-and-lower reach of the Yangtze River (MLRYR) as the study region, the intensities of drought and flooding during the growing seasons of four study crops (cotton, oilseed rape, wheat, and maize) were quantified using the standardized precipitation evapotranspiration index. Nine popular yield-detrending methods and the first-difference method were employed to determine meteorological yields. The results indicated that the examined methods consistently identified the cases with very significant yield-reducing impacts of drought and flooding. The first-difference method identified 58 significant regression relationships, outperforming the yield-detrending methods (identifying 39–44 significant regression relationships). The 20-yr moving average and the linear fitting methods performed best at the provincial level, while the cubic smoothing spline and the quadratic polynomial fitting methods performed best at the district level. Based on these best-performing methods, the average method was proposed and exhibited wider applicability and better performance than the individual methods. In terms of meteorological yield losses, cotton was the most affected crop (35% of all districts experienced severe cotton yield loss) and Anhui was the most affected region (with an average crop yield loss of 14.06%). The yield-reducing impact of flooding was significant in 41 districts; in comparison, the impact of drought was significant only in 7 districts. Additionally, oilseed rape was the crop most affected by drought and flooding. These results can provide guidance for assessing agricultural drought and flooding disasters under climate change.

Quantifying physiological contributions to yield loss in response to planting date in short-season cotton under a cotton[sbnd]wheat double-cropping system

Crop yield is primarily determined by physiological parameters, including light interception by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, little information is available about how these physiological parameters are impacted by planting date and what their contributions are to cotton yield loss. To evaluate the relative contribution of each physiological parameter to planting date-associated yield loss in cotton, an experiment was conducted during the 2019 to 2021 growing seasons at a field site in Anyang, Henan, China. Two contemporary cotton cultivars were grown in the field with four different planting dates (PDMAY10, PDMAY20, PDMAY30 and PDJUN10) for the growing seasons from 2019 to 2021. The measurements included climatic parameters, seed cotton yield, yield components, HI and biweekly leaf area index, light interception and biomass. First, seed cotton yield was significantly affected by the planting dates, with yields ranging from 1984 to 3388 kg ha−1, averaged across cultivars and years, while a significant difference between the first three planting dates was observed only in 2021. Second, the planting date significantly impacted the total light interception during the growing season (IPARtotal) and HI, while IPARtotal and HI decreased with the delayed planting date in all the years of the study. Compared to PDMAY10, IPARtotal was 0.34%, 13.2%, and 19.9% lower for PDMAY20, PDMAY30 and PDJUN10, respectively. Averaged across cultivars and years, PDMAY10 resulted in the highest HI value (0.28). The RUE gradually decreased as the planting date was delayed, but a significant difference in RUE between planting dates was observed only in 2021. Overall, among the three physiological yield-driving parameters, IPARtotal was a stronger contributor (46.9–47.0%) to the yield loss for PDJUN10 than PDMAY10 and PDMAY20. However, when compared to PDMAY30, HI was the greatest contributor, accounting for approximately 72.5% of the seed cotton yield loss for PDJUN10, followed by IPAR. We found that when cotton sowing in the Yellow River basin was postponed until June 10, the cotton yield was significantly reduced, and IPAR and HI were the main factors responsible for PDJUN10 yield loss on average. This study identified the most important functional traits for seed cotton yield response to planting date and has important reference value for stable cotton production.

Melatonin enhances waterlogging tolerance of field-grown cotton through quiescence adaptation and compensatory growth strategies

ContextWaterlogging stress inhibits plant growth and development, resulting in significant yield losses. Melatonin, a naturally occurring molecule in plants, has attracted attention in recent years for its potential role in aiding plants in response to abiotic stresses. However, it remains unclear whether melatonin can enhance cotton’s tolerance to waterlogging stress. MethodsTo fill this gap, field-grown cotton plants at early flowering were subjected to a 10-day waterlogging stress and were foliar sprayed with melatonin during waterlogging. The effects of melatonin on the endogenous melatonin concentration, plant biomass, and related physiological and molecular events, as well as the seedcotton yield and yield components, were evaluated over two consecutive years. ResultsIn waterlogged cotton without melatonin treatment (W), melatonin concentration increased by 10.2%, but it increased by 26.9% with melatonin treatment (W+M), compared to the non-waterlogged cotton (NW). Under waterlogging without the application of melatonin, there was a reduction of 27.1% in biological yield and 46.6% in seedcotton yield. However, these reductions were reduced to 21.8% and 39.0%, respectively, following melatonin application. Quantitative real-time PCR analysis revealed that melatonin up-regulated the melatonin biosynthesis genes GhSNAT1 and GhCOMT, as well as the gibberellin biosynthetic gene GhGA20ox1 by 2.1, 3.6 and 1.8 times, respectively, compared to non-waterlogged cotton. However, melatonin down-regulated the ABA synthesis gene GhNCED2, the H2O2 generation related gene GhRBOHC, and the glycolysis and fermentation gene GhADH2 by 61.9%, 65.0% and 40.0%, respectively. Additionally, melatonin increased the leaf chlorophyll content, photosynthetic rate, levels of auxin (IAA) and gibberellic acid (GA), as well as the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT). Conversely, melatonin reduced contents of hydrogen peroxide (H2O2), malonaldehyde (MDA) and ABA, as well as the activities of alcohol dehydrogenase (ADH) and pyruvate decarboxylase (PDC), compared to waterlogging without application of melatonin. ConclusionsThese findings demonstrate that foliar application of melatonin effectively improved the waterlogging tolerance of field-grown cotton, leading to a reduction in yield loss by 16.5% under waterlogged conditions. This outcome was achieved by a series of physiological, biological and molecular adjustments involved in quiescence adaption and compensatory growth strategies. Therefore, melatonin plays a crucial role in enhancing waterlogging tolerance and mitigating yield loss in waterlogged cotton through these strategies.

Quantifying physiological contributions to nitrogen-induced yield variation in field-grown cotton

The effect of nitrogen (N) application rate on lint yield and yield components have been studied extensively in cotton. However, reports quantifying contributions to N-induced yield loss and the response of intra-boll yield components to N deficiency are far less common. The objectives of this study were to 1) determine the effect of N application rate on lint yield biomass and fiber quality in cotton, 2) assess the contributions of intercepted photosynthetically active radiation (IPAR), radiation use efficiency (RUE), and harvest index (HI) to N-induced yield loss in cotton, and 3) characterize the effect of N application rate on yield component responses in cotton. An experiment was conducted at a field site near Tifton, Georgia, USA during the 2021 and 2022 growing seasons. The experiment was arranged as a randomized complete block design with five replications and five nitrogen application rates (0, 44, 89, 134, and 179 kg N ha−1). The studied parameters were lint yield, IPAR, RUE, HI, fiber quality, yield components, and yield loss contributions. Lint yield was significantly affected by N application rate, with yields ranging from 1452 to 1848 kg ha−1. Similarly, total IPAR was 23% lower for the 0 kg N ha−1 treatment than the 134 kg N ha−1 treatment, and IPAR contributed significantly to yield loss, with a 427 kg ha−1 loss attributable to IPAR under N deficiency. RUE was 37% higher for the 179 kg N ha−1 treatment than the 0 kg N ha−1 treatment and contributed positively to yield in the highest N rates (179 kg N ha−1). However, yield improvements due to RUE were mitigated by low HI values in the highest N application rate. Boll density was the most strongly and positively associated yield component parameter with N-induced variability in lint yield, whereas lint percent and lint production per seed were negatively associated with lint yield. Among the three physiological yield-driving parameters, light interception by the canopy was the primary contributor to N-induced yield loss. Boll density was the primary yield component governing N-induced variation in lint yield. Increased N application rate promotes greater dry matter partitioning to seeds than fiber, resulting in more seeds per boll and lower lint percent, lint weight per seed, and individual fiber weight. The results contained here identify the most important functional traits for determining lint yield response to N application rate.

Optimal choice of cotton cultivar for rainfed conditions in Sahelo-Sudanian climate with late planting: a case study in Senegal

AbstractLate planting due to erratic onset of the rainy season is becoming more frequent in the Sahelo-Sudanian climate where cotton is grown, causing seed cotton yield (SCY) loss and higher risk of drought at the end of the crop cycle. Therefore, cultivars should be adapted to late (from July 10) planting date (PD) in Senegal. The aim of this study was to analyse the interaction between genotypes and PD on SCY in Senegal under rainfed conditions. Field experiments were conducted in 2018 and 2019 using a split-plot design (two PDs, eight cultivars) at three experimental stations. Robust analysis of SCY was used to moderate the effect of potential outliers. The average SCY was 1404 kg/ha under early planting, and 714 kg/ha under late planting. The best SCY was obtained under early planting conditions, in environments with good rainfall. The loss due to late planting was significantly affected by cultivar choice. None of the cultivars performed best under both early and late PD. Under early PD, cultivar CS 50 gave the best SCY, while under late PD it was cultivar IRMA Q302. The best performing cultivar on average depended on the proportion of early plantings. A model was developed to identify the best overall cultivar based on the expected proportion of early planting, as a decision support tool for the cotton development company, if only one cultivar is released. The benefit of releasing a second cultivar for late-planted fields is considered.

Reniform nematode impact on cotton growth and management strategies: A review

AbstractRotylenchulus reniformis Linford and Oliveira (reniform nematode) infestation has been a concerning issue in the Cotton Belt region of the United States for the past decade. Reniform nematode damage is more evident on cotton compared to other row crops in US Mid‐South because they share common edaphic conditions to sustain and develop. Annual cotton (Gossypium spp.) yield loss over the United States due to reniform nematode ranged from 1.14% to 2.37% in the past decade but exceeded 8% in the US Mid‐South States such as Mississippi. Yield losses due to nematode damage are mainly due to inconsistent control by cultural and chemical practices. In addition, there are location‐specific responses due to agronomic practices that result in its wide adaption. This review summarizes factors that influence reniform nematode infestations and the process of a plant infection, as well as management practices to mitigate nematode cotton production losses in the United States. The recent development of resistant cotton germplasm is a promising tool for reniform nematode management, although limited research has been conducted on the physiological mechanisms of resistance. In addition, understanding the role of host plant resistance and the interaction with other soilborne pathogens or abiotic stresses to control the nematode also is lacking. Future research must investigate best suited production practices specific to a region that could exploit the full potential of host plant resistance to minimize the risk of reniform nematode damage in cotton while protecting the environment.

Simulated effects of cover crops with no-tillage on soil and crop productivity in rainfed semi-arid cotton production systems

Cover cropping and no-tillage (NT) practices are promoted for soil health and environmental benefits. However, producing enough quantity of biomass to meet expected benefits can be a challenge in semi-arid cropping systems where water resources are limited, and producers have lingering questions regarding cover crop selection, termination timings, water use, and their subsequent effect on cash crop. The objective of this study was to assess the impacts of cover crops with NT on soil and crop productivity in a rainfed, continuous cotton (Gossypium hirsutum L.) system using the Denitrification Decomposition (DNDC) model. Treatments included: (a) conventional till (CT); (b) NT, and NT with the following cover crops: (c) Austrian winter pea (Pisum sativum L.) (Pea); (d) crimson clover (Trifolium incarnatum L.) (Clover); (e) hairy vetch (Vicia villosa L.) (Vetch); (f) a multi-species mixture (Mixture); and (g) winter wheat (Triticum aestivum L.) (Wheat). The DNDC model adequately simulated the seed cotton yield and cover crop biomass during calibration (root mean square error (RMSE) = 200 and 398 kg ha−1, respectively) and validation (RMSE = 277 and 270 kg ha−1, respectively). Stocks of soil organic carbon (SOC) and total nitrogen (TN) were simulated with Nash–Sutcliffe efficiency (NSE) of 0.91 and 0.78 during calibration, and with NSE of 0.88 and 0.75, respectively during validation. Long-term simulations revealed: (1) Vetch and Pea produced greater biomass and hence highest SOC and TN contents, which was followed by Mixture, Wheat, and Clover; (2) cotton yield in cover crop treatments was no different to that in non-cover crop treatments when cover crop was terminated about six weeks prior to cotton planting; (3) later termination of cover crops can result in greater cotton yield loss due to soil water depletion by cover crops. Overall, this study highlights the potential of conservation practices, such as cover crops and NT to improve soil health without significant loss in cotton yields.

GhGTG1 enhances cold stress tolerance by improving sensitivity to ABA in cotton and Arabidopsis

Cold stress is a potent abiotic stress resulting in cotton yield and fiber quality loss. We hypothesized that GhGTG1 could increase low temperature stress tolerance by improving the sensitivity to ABA. Genome-wide identification, biochemical traits and functional characterization of GhGTG1, were investigated under different levels of ABA and cold stress. GhGTG1 had two highly conserved functional domains (ABA_GPCR, GPHR_N) in all cotton species. Under ABA treatment, over expression of GhGTG1 in Arabidopsis had a lower germination rate and root elongation. While under 4 ℃ stress, transgenic plants had a higher germination rate and root elongation. This indicate that GhGTG1 improved sensitivity to ABA under cold stress. In transgenic plants, CAT and SOD activity was increased, while H2O2 and MDA content decreased. Less trypan blue and DAB staining area suggested that GhGTG1 enhances cold stress resistance. Under 4 ℃ treatment, silence cotton seedlings through VIGS method, resulted in a poor growth, lower relative leaf water content, chlorophyll content but a higher ion leakage and excised leaf water loss. The activity of CAT and SOD decreased, H2O2 and MDA content was increased. Two marker genes (OST1 and ABCG40) are associated with the stomatal movement, but these genes were substantially suppressed. This suggesting that GhGTG1 can induce cold stress tolerance by improving ABA sensitivity Via., maintaining stomatal movement. These data will provide new insight of cold resistance mechanism in cotton and further will help the breeder to develop new cotton cold stress tolerance germplasm.

Genome Resource for the Verticillium Wilt Resistant Gossypium hirsutum Cultivar Zhongzhimian No. 2.

Verticillium wilt, caused by the fungal pathogen Verticillium dahliae, is the major cause of disease-related yield losses in cotton (Gossypium hirsutum). Despite these losses, the major cultivars of G. hirsutum remain highly susceptible to Verticillium wilt. The lack of understanding on the genetic basis for Verticillium wilt resistance may further hinder progress in deploying elite cultivars with proven resistance, such as the wilt resistant G. hirsutum cultivar Zhongzhimian No. 2. To help remedy this knowledge gap, we sequenced the whole genome of Zhongzhimian No. 2 and assembled it from a combination of PacBio long reads, Illumina short reads, and high-throughput chromosome conformation capture technologies. The final assembly of the genome was 2.33 Gb, encoding 95,327 predicted coding sequences. The GC content was 34.39% with 99.2% of the bases anchored to 26 pseudo-chromosomes that ranged from 53.8 to 127.7 Mb. This resource will help gain a detailed understanding of the genomic features governing high yield and Verticillium wilt resistance in this cultivar. Comparative genomics will be particularly helpful, since there are several published genomes of other Gossypium species. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Nitrogen fertilization can mitigate cotton yield loss by temporary shading at early flowering

Cotton (Gossypium hirsutum L.) yield is decreased under low radiation, but there are few reports on possible interactions of N fertilizer application and light limitation on cotton production. The objective of this work was to evaluate growth, yield components, and boll distribution in cotton as affected by N rates under light restriction. Three experiments were conducted for two years in Central (Primavera do Leste and Chapadão do Sul) and Southeastern (Itapeva) Brazil. Cotton plants were shaded at early flowering to a 42 % reduction in global radiation for 16–25 days. Four N rates were used in Primavera do Leste and Itapeva; and three N rates and two genotypes, an early- (FM 906GLT) and a late-maturing (FM 983GLT) cultivar, were used in Chapadão do Sul. Shading decreased lint yield by 10 % in Primavera do Leste and by 12 % in Chapadão due to 24 % and 32 % reduction in boll number at the lower third of the plant, respectively. In Itapeva, the number of bolls at the lower fruiting branches was decreased 19% by shading, but a 10% lint yield reduction was observed only with 35 kg ha−1 of N. As to the main effects, lint yield and number of bolls increased with N rates up to 155, 142 and 105 kg ha−1, depending on the site. Lint yield of shaded plants was more responsive to higher N rates, which also increased plant height, number of bolls and the main stem node number of the last fruiting branch. Cotton cultivars had similar lint yields despite differences in boll distribution. At lower fruiting branches, the early-maturing cultivar showed an average boll number 59 % higher, but the late one produced 81 % and 63 % more bolls on the upper branches at 105 and 185 kg ha−1 of N, respectively. We concluded that N-deficiency inhibited the recovery of cotton from losses caused by shading, whereas a high-N availability minimizes damages after shading during blooming.

Genome wide identification and characterization of light-harvesting Chloro a/b binding (LHC) genes reveals their potential role in enhancing drought tolerance in Gossypium hirsutum

BackgroundCotton is an important commercial crop for being a valuable source of natural fiber. Its production has undergone a sharp decline because of abiotic stresses, etc. Drought is one of the major abiotic stress causing significant yield losses in cotton. However, plants have evolved self-defense mechanisms to cope abiotic factors like drought, salt, cold, etc. The evolution of stress responsive transcription factors such as the trihelix, a nodule-inception-like protein (NLP), and the late embryogenesis abundant proteins have shown positive response in the resistance improvement to several abiotic stresses.ResultsGenome wide identification and characterization of the effects of Light-Harvesting Chloro a/b binding (LHC) genes were carried out in cotton under drought stress conditions. A hundred and nine proteins encoded by the LHC genes were found in the cotton genome, with 55, 27, and 27 genes found to be distributed in Gossypium hirsutum, G. arboreum, and G. raimondii, respectively. The proteins encoded by the genes were unevenly distributed on various chromosomes. The Ka/Ks (Non-synonymous substitution rate/Synonymous substitution rate) values were less than one, an indication of negative selection of the gene family. Differential expressions of genes showed that majority of the genes are being highly upregulated in the roots as compared with leaves and stem tissues. Most genes were found to be highly expressed in MR-85, a relative drought tolerant germplasm.ConclusionThe results provide proofs of the possible role of the LHC genes in improving drought stress tolerance, and can be explored by cotton breeders in releasing a more drought tolerant cotton varieties.

Detection of cotton waterlogging stress based on hyperspectral images and convolutional neural network

Waterlogging in the early stage of cotton will reduce the number of bolls and do harm to yield. Early detection of waterlogging will help farmers to adjust cotton management and save the loss. To evaluate the application of deep learning for the detection of early waterlogging, this study applied a convolutional neural network (CNN) to classify different durations of waterlogging stress (0, 2, 4, 6, 8, 10 d) based on hyperspectral images (HSIs) of cotton leaves. An experiment was designed to simulate the situation of cotton under waterlogging stress and collect HSIs of visible and near-infrared (VNIR 450-950 nm) spectra with 126 bands 66 d after cotton sowing (66 DAS). It was found the spectral curve reflectance of waterlogging cotton was higher than that of non-waterlogging cotton. Especially near 550 nm and 750 nm, and the spectral curve increased with durations of waterlogging stress and there were ‘blue shift’ phenomena for the position of the red edge of the spectra. The first principal components of HSIs after band randomly discarding and principal component analysis (PCA) were used to build a dataset. GoogLeNet Inception-v3 (GLNI-v3) and VGG-16 models were selected to detect cotton waterlogging stress with the dataset. The results showed that the average time for a round training for GLNI-v3 was 13.337 s, with a classification accuracy of 96.95% and a loss value of 0.09431. The average time for a round training for VGG-16 was 21.470 s, with a classification accuracy of 97.00% and a loss value of 0.17912. Though these two models had similar classification accuracy and loss value, GLNI-v3 achieved a high accuracy with fewer training iterations. The durations of waterlogging stress of cotton in a short-term can be detected by HSIs of cotton leaves and CNN models are suitable for the classification of HSIs, and this method can provide support for cotton yield estimation and loss assessment after waterlogging. Keywords: cotton, waterlogging, hyperspectral image, convolutional neural network DOI: 10.25165/j.ijabe.20211402.6023 Citation: Zhao J, Pan F J, Li Z M, Lan Y B, Lu L Q, Yang D J, et al. Detection of cotton waterlogging stress based on hyperspectral images and convolutional neural network. Int J Agric & Biol Eng, 2021; 14(2): 167–174.

Impact of Chemical Defoliants on Chlorophyll Fluorescence, Biochemical Parameters, Yield and Fiber Quality of High Density Cotton

Background: The Cotton growing farmers in India are facing major problem at the time of harvest due to labor shortages, asynchronized boll opening, leaf trash in the cotton, which is reducing the boll picking efficiency and fiber quality. Presently, the Indian cotton industry is moving towards mechanical harvesting by cotton harvester. Defoliation or leaf abscission is induced in cotton as a natural physiological process which usually is inadequate for a complete mechanical harvest of cotton. Defoliation before harvest is often induced by managing the plants so that senescence, abscission (separation) layer development and leaf drop are encouraged. Chemical defoliants induce leaf abscission, hasten mature boll dehiscence and inhibit re­ growth. Selection of appropriate defoliants is one of the critical decisions in cotton production. Their use can result in increased manual as well as machine harvest efficiency, reducing boll rot and the trash in seed cotton. The physiological basis of defoliation in cotton is essential to understand the role of appropriate defoliant with time of application. Hence, the objective of the study was to evaluate the defoliation efficiency, boll opening percentage and to know the physiological basis of defoliation in response to different defoliants and time of application in high density Cotton cultivation which eases the mechanical harvesting.Methods: In this field investigation during 2018 - 2019 at Department of Crop Physiology, Tamil Nadu Agricultural University, Coimbatore was carried with seven different defoliants and three different time of defoliants application were fixed based on crop maturity and duration of the crop. In this study, high density cotton variety CO 17 was used as experimental material. A series of lab and field analysis were determined on defoliation process, physiological attributes, boll opening percentage and seed yield and quality. The collected data were subjected to statistical analysis.Result: Our investigation exploring the effect of different defoliants on physiological, biochemical, yield and quality parameters of high density Cotton. Among the different defoliants and time of applications, two defoliants and two times of applications works better in terms of better defoliation, boll opening rate, physiology, yield and quality. The physiological and biochemical effects of defoliants on defoliation process were discussed. The present work will be useful for mechanized picking with these effective defoliants without loss of yield and quality loss in cotton.

Transcriptome Analysis Revealed GhWOX4 Intercedes Myriad Regulatory Pathways to Modulate Drought Tolerance and Vascular Growth in Cotton.

Cotton is a paramount cash crop around the globe. Among all abiotic stresses, drought is a leading cause of cotton growth and yield loss. However, the molecular link between drought stress and vascular growth and development is relatively uncharted. Here, we validated a crucial role of GhWOX4, a transcription factor, modulating drought stress with that of vasculature growth in cotton. Knock-down of GhWOX4 decreased the stem width and severely compromised vascular growth and drought tolerance. Conversely, ectopic expression of GhWOX4 in Arabidopsis enhanced the tolerance to drought stress. Comparative RNAseq analysis revealed auxin responsive protein (AUX/IAA), abscisic acid (ABA), and ethylene were significantly induced. Additionally, MYC-bHLH, WRKY, MYB, homeodomain, and heat-shock transcription factors (HSF) were differentially expressed in control plants as compared to GhWOX4-silenced plants. The promotor zone of GhWOX4 was found congested with plant growth, light, and stress response related cis-elements. differentially expressed genes (DEGs) related to stress, water deprivation, and desiccation response were repressed in drought treated GhWOX4-virus-induced gene silencing (VIGS) plants as compared to control. Gene ontology (GO) functions related to cell proliferation, light response, fluid transport, and flavonoid biosynthesis were over-induced in TRV: 156-0 h/TRV: 156-1 h (control) in comparison to TRV: VIGS-0 h/TRV: VIGS-1 h (GhWOX4-silenced) plants. This study improves our context for elucidating the pivotal role of GhWOX4 transcription factors (TF), which mediates drought tolerance, plays a decisive role in plant growth and development, and is likely involved in different regulatory pathways in cotton.

Sowing Dates and Cultivars Mediated Changes in Phenology and Yield Traits of Cotton-Sunflower Cropping System in the Arid Environment

Cotton-sunflower cropping system is a unique oilseed-based rotation. The real problem is overlapping sunflower-maturity with cotton-sowing. Investigations aim to tackle cotton-late-sowing through sowing-time-adjustment and cultivar-selection at cropping-system-level. Cotton (May 10th to June 24th) and sunflower (December 20th to February 03rd) sowing-dates were maintained biweekly. Maturity-time based cultivars were selected (early, medium, late) and variations in ambient-temperature through sowing-dates from 33.2 to 33.9 °C, 32.2 to 33.6 °C and 29.2 to 32.6 °C, length of emergence-squaring, squaring-flowering and flowering-maturity differed by 3.1, 1.5 and 5.1 days, respectively. Likewise, sunflower-sowing-dates based ambient-temperature ranged 13.7–18.1 °C, 16.3–17.5 °C and 23.8–28.5 °C at emergence-budding, budding-anthesis and anthesis-maturity, resulted in a difference of 13.5, 4.8 and 1.0 days. Results revealed that cotton late-sowing (May 10th to June 24th) and sunflower (December 20th to January 19th) resulted in reduces seedcotton, lint and achene yield by 19.9, 8.2 and 8.8 kg ha−1 day−1. Oil productivity was the highest in cotton vide June 24th and in sunflower 04th January. In this cropping system, cotton is highly sensitive to sowing-dates for yield losses (35%) and sunflower less sensitive (14.4%). Meanwhile, yield variations in cotton-cultivars (12.5%) and sunflower-hybrids (10.0%). It was realized that sunflower December 20th hold great importance to assure minimum cotton yield-losses than looking for hybrids.

Nitric oxide increases the biomass and lint yield of field-grown cotton under temporary waterlogging through physiological and molecular regulation

• Foliar spray of exogenous nitric oxide increased its concentration in waterlogged cotton. • Increased nitric oxide concentration triggered a series of physiological and molecular events. • Foliar spray of nitric oxide ultimately reduced yield loss under waterlogging. Waterlogging is a major abiotic stress that reduces crop growth and productivity. Nitric oxide (NO) is an essential signaling molecule involved in abiotic stress responses in many plant species. However, it is unclear if and how NO mitigates waterlogging stress in field-grown cotton. In this study, the NO donor sodium nitroprusside (SNP) or its scavenger 2-(4-carboxyphenyl)-4,4,5,5-tetramethyl -imidazoline-1-1-oxyl-3-oxide (cPTIO) was foliar-applied to waterlogged and non-waterlogged cotton plants. The NO concentrations in different plant parts, plant growth, yield and yield components, as well as related physiological and molecular events, were examined over three consecutive years under field conditions. Foliar application of SNP increased NO concentration in leaves by 83.6 and roots by 80.2 % under waterlogging, while cPTIO decreased the NO concentration by 18.9 and 5.3 %, respectively. Biomass and lint yield were reduced by 17.2 and 36.0 % under waterlogging without the application of SNP or cPTIO, but the reductions were 8.8 and 28.4 % following SNP application and 29.5 and 43.4 % respectively, following cPTIO application. Foliar applied SNP increased biomass by 10.2 % and lint yield by 12.0 % under waterlogging, whereas cPTIO caused 5.0 and 11.5 % reductions in biomass and lint yield. Quantitative real-time PCR analysis showed that the SNP up-regulated the NO synthesis gene (Gh NIR ), but down-regulated the genes involved in glycolysis and fermentation (Gh ADH 2 and Gh PDC ), ethylene production (Gh ACO and Gh ACS 8), and abscisic acid (ABA) synthesis (Gh NCED 2). It also reduced hydrogen peroxide (H 2 O 2 ) production and malonaldehyde (MDA) content in the leaves, but increased the leaf chlorophyll content and photosynthetic rate. The levels of auxin (IAA) and gibberellic acid (GA) were increased by SNP but those of ABA and ethylene contents as well as the activities of alcohol dehydrogenase (ADH) and pyruvate decarboxylase (PDC) in leaves were decreased. The cPTIO showed an opposite effect to SNP in almost all physiological and molecular responses to waterlogging. The overall results indicated that increasing the concentrations of NO in plant tissues via the foliar application of SNP mitigates waterlogging stress through a series of physiological and molecular events, ultimately reducing biological and lint yield loss under waterlogging. Therefore, increasing the concentration of NO in plant tissues by genetic breeding or agronomic measures might be a potential strategy for improving waterlogging tolerance and reducing yield loss in cotton.

Pseudomonas species isolated via high-throughput screening significantly protect cotton plants against verticillium wilt

Verticillium wilt (VW) caused by Verticillium dahliae is a devastating soil-borne disease that causes severe yield losses in cotton and other major crops worldwide. Here we conducted a high-throughput screening of isolates recovered from 886 plant rhizosphere samples taken from the three main cotton-producing areas of China. Fifteen isolates distributed in different genera of bacteria that showed inhibitory activity against V. dahliae were screened out. Of these, two Pseudomonas strains, P. protegens XY2F4 and P. donghuensis 22G5, showed significant inhibitory action against V. dahliae. Additional comparative genomic analyses and phenotypical assays confirmed that P. protegens XY2F4 and P. donghuensis 22G5 were the strains most efficient at protecting cotton plants against VW due to specific biological control products they produced. Importantly, we identified a significant efficacy of the natural tropolone compound 7-hydroxytropolone (7-HT) against VW. By phenotypical assay using the wild-type 22G5 and its mutant strain in 7-HT production, we revealed that the 7-HT produced by P. donghuensis is the major substance protecting cotton against VW. This study reveals that Pseudomonas specifically has gene clusters that allow the production of effective antipathogenic metabolites that can now be used as new agents in the biocontrol of VW.

Constitutive expression of Asparaginase in Gossypium hirsutum triggers insecticidal activity against Bemisia tabaci

Whitefly infestation of cotton crop imparts enormous damage to cotton yield by severely affecting plant health, vigour and transmitting Cotton Leaf Curl Virus (CLCuV). Genetic modification of cotton helps to overcome both the direct whitefly infestation as well as CLCuV based cotton yield losses. We have constitutively overexpressed asparaginase (ZmASN) gene in Gossypium hirsutum to overcome the cotton yield losses imparted by whitefly infestation. We achieved 2.54% transformation efficiency in CIM-482 by Agrobacterium-mediated shoot apex transformation method. The relative qRT-PCR revealed 40-fold higher transcripts of asparaginase in transgenic cotton line vs. non-transgenic cotton lines. Metabolic analysis showed higher contents of aspartic acid and glutamic acid in seeds and phloem sap of the transgenic cotton lines. Phenotypically, the transgenic cotton lines showed vigorous growth and height, greater number of bolls, and yield. Among six representative transgenic cotton lines, line 14 had higher photosynthetic rate, stomatal conductance, smooth fiber surface, increased fiber convolutions (SEM analysis) and 95% whitefly mortality as compared to non-transgenic cotton line. The gene integration analysis by fluorescence in situ hybridization showed single copy gene integration at chromosome number 1. Collectively, asparaginase gene demonstrated potential to control whitefly infestation, post-infestation damages and improve cotton plant health and yield: a pre-requisite for farmer’s community.