Cotton Yield Research Articles

Efficacy of Combi-Product Fipronil 40%+ Imidacloprid 40%WG against Leafhopper and Thrips in Cotton

The efficacy of lessenta 80 WG (fipronil 40%+ imidacloprid 40%WG) @ 125, 100 and 75 g ai/ ha was evaluated against sucking pests of cotton in comparison with the fipronil 5%SC @ 125 g ai/ ha, imidacloprid 17.8 SL @ 250 g ai/ ha, lambda-cyhalothrin 5%EC @ 600 g ai/ ha and monocrotophos 36%SL @ 437 g ai/ ha at the Agricultural Research Station, Dharwad, Karnataka, India. Fipronil 40%+ imidacloprid 40%WG @ 125 g ai/ ha recorded significantly minimum (8.25 and 3.01) Thrips tabaci (Lindeman) (Thysanoptera: Thripidae) and Amrasca biguttula biguttula (Ishida) (Hemiptera: Cicadellidae) incidence, respectively followed by lessenta 80 WG @ 100 g ai/ ha (8.68 and 3.42, respectivey) and fipronil 40%+ imidacloprid 40%WG @ 75 g ai/ ha (9.55 and 3.98, respectively). Further, significantly highest seed cotton yield of 13.99 q/ha, 13.58 q/ha and 12.86 q/ha, respectively were harvested from the fipronil 40%+ imidacloprid 40%WG treatments proving them better choice in effective management of both A. biguttula biguttula and T. tabaci incidence.

Modeling of cotton yield responses to different irrigation strategies in Southern Xinjiang Region,China

Southern Xinjiang region represents a prototypical oasis agricultural area, and cotton is the primary cash crop. The current cotton water productivity for this area remains room for improvement. In order to refine cotton drip irrigation strategy in Southern Xinjiang, this study combined field experiment with crop model simulation and prediction. In this experiment, two irrigation methods, furrow irrigation and drip irrigation, and different degrees of deficit irrigation were set up. The impacts of soil moisture on cotton yield and quality were assessed, with the additional utilization of the Root Zone Water Quality Model (RZWQM2) for simulating irrigation scenarios and predicting cotton yield field data showed that during the 2021–2022 growing season, drip irrigation significantly increased boll weight, yield, and Water Productivity compared to furrow irrigation. The deficit irrigation with 90 % of full irrigation during the flowering period resulted in the highest yield among all drip irrigation treatments, while simultaneously improving the fiber quality. The RZWQM2 model was calibrated and validated using 2021 and 2022 cotton field experimental data, respectively. The calibrated and verified RZWQM2 model shows good performance. In terms of soil water storage, cotton yield and evapotranspiration. The simulated value output by the model is close to the measured value in the field. Model prediction data suggest that there is still room for improvement in cotton yield, accompanied by an increase in evapotranspiration. But deficit irrigation below 80 % of the full irrigation quotas poses a notable risk of yield reduction. Targeting higher yields, the predicted scenario suggests an increase in water application (irrigation quota:314.8 mm). Additionally, this study proposes irrigation regimes with water savings of 10 % and 20 %, with irrigation quotas of 290.5 mm and 266.5 mm during the growth period, respectively. Based on this study, we provide a more detailed and reliable water-saving scheme for cotton drip irrigation in southern Xinjiang region.

SCREENING OF COTTON (GOSSYPIUM HIRSUTUM L.) GERMPLASM AGAINST COTTON LEAF CURL VIRUS (CLCUV)

Cotton Leaf Curl Virus (CLCuV) is a devastating disease affecting cotton production worldwide, particularly in South Asia and Africa transmitted by the whitefly (Bemisia tabaci). CLCuV infection leads to severe symptoms, including leaf curling, vein thickening, stunted growth, and significant yield losses. Developing resistant cotton varieties is the most suitable strategy for managing CLCuV, given the limitations and environmental concerns associated with chemical controls. This study aimed to evaluate the resistance and susceptibility of different cotton varieties to CLCuV under natural field conditions and artificially infected condition using a disease severity scale ranging from 0 (highly resistant) to 5 (susceptible). A total of five cotton varieties, were assessed at three critical growth stages: vegetative, flowering, and boll formation. Disease severity, and yield components were recorded to determine extent of resistance of each variety. The results indicated that genotypes and treatments had significant variation in control compared to T1. According to DSI, NIAB-852 was scored as resistant with 10% infected leaves. FH-142 and CIM-496 were scored as tolerant according to DSI with 10-25% infection in leaves. NIAB Karishma and CIM-448 were scored as moderately susceptible with 50-75% infection. All genotypes had significant reduction in number of bolls, boll weight and seed cotton yield in T1 than control. These findings provide valuable insights into the effectiveness of existing cotton germplasm against CLCuV and highlight the need for continuous evaluation and development of resistant varieties. The study highlights the importance of generating resistant cultivars, thereby ensuring sustainable cotton production.

Regenerative Practices Show Promise in Dryland Cotton Systems

AbstractRegenerative practices, such as no‐till and cover crops, are advocated for their positive impacts on soil health and the environment. Nevertheless, there are challenges associated with implementing these practices. The objective of this simulation study was to assess the potential for soil carbon sequestration, soil water storage, and cotton yield response when implementing regenerative practices in the water‐limited environment of the Texas Rolling Plains region. Earn 1 CEU in Sustainability by reading this article and taking the quiz.

Effects of planting date and density on cotton cultivars in sub‐Saharan Africa rainfed conditions: A case study in Mali

AbstractMali is among Africa's three biggest cotton (Gossypium hirsutum L.)‐producing countries, and cotton growing is the principal driving force behind Mali's agricultural sector. Cotton production is rainfed on small‐scale family farms as a commercial crop alongside staple crops grown for subsistence. Cultivar choice, planting date, and planting density are critical elements for seed cotton yield that should be optimized. This study aimed to understand the interactions between planting dates and planting densities for the optimal production of four cotton cultivars in Mali. Two trials were set up in two seasons at the Finkolo and N'Tarla research stations. A split‐plot design with four replications was used, with planting dates (early and delayed by 3 weeks) as the main plots and planting density (41,666; 83,333; and 166,666 plants/ha) and cultivar (Malian NTA MS334, Togolese STAM 129A, Australian SIOKRA L23, and Brazilian BRS 293) as the subplots. In 2021, seed cotton yield was 1263 kg/ha for early planting versus 361 kg/ha for late planting. Medium and high planting densities produced the same yield level, higher than the low planting density. Regardless of the planting density, early plantings' average capsular weight and seed index were higher than those of late plantings. The African cultivars (STAM 129A and Malian cultivar NTA MS334) were the most productive. Due to significant interactions on fiber percentage and to optimize cotton yields in Mali, planting should be early, with planting densities higher than 41,666 plants/ha, and either of the African cultivars tested should be used.

Residual swin transformer for classifying the types of cotton pests in complex background.

Cotton pests have a major impact on cotton quality and yield during cotton production and cultivation. With the rapid development of agricultural intelligence, the accurate classification of cotton pests is a key factor in realizing the precise application of medicines by utilize unmanned aerial vehicles (UAVs), large application devices and other equipment. In this study, a cotton insect pest classification model based on improved Swin Transformer is proposed. The model introduces the residual module, skip connection, into Swin Transformer to improve the problem that pest features are easily confused in complex backgrounds leading to poor classification accuracy, and to enhance the recognition of cotton pests. In this study, 2705 leaf images of cotton insect pests (including three insect pests, cotton aphids, cotton mirids and cotton leaf mites) were collected in the field, and after image preprocessing and data augmentation operations, model training was performed. The test results proved that the accuracy of the improved model compared to the original model increased from 94.6% to 97.4%, and the prediction time for a single image was 0.00434s. The improved Swin Transformer model was compared with seven kinds of classification models (VGG11, VGG11-bn, Resnet18, MobilenetV2, VIT, Swin Transformer small, and Swin Transformer base), and the model accuracy was increased respectively by 0.5%, 4.7%, 2.2%, 2.5%, 6.3%, 7.9%, 8.0%. Therefore, this study demonstrates that the improved Swin Transformer model significantly improves the accuracy and efficiency of cotton pest detection compared with other classification models, and can be deployed on edge devices such as utilize unmanned aerial vehicles (UAVs), thus providing an important technological support and theoretical basis for cotton pest control and precision drug application.

Tillage and cover crops effects on crop yield in wheat double‐cropping systems

AbstractThe United States is experiencing longer crop growing season in most states, which could afford producers the opportunity to diversify into double‐cropping (DC) and cover crop systems rather than the predominant summer and winter fallow systems. Thus, this study evaluated DC and cover crops effects on wheat (Triticum aestivum L.), cotton (Gossypium hirsutum L.), and soybean (Glycine max) yield under conventional tillage (CT) and no‐tillage (NT). Summer cover crops (SCCs) were sunn hemp (Crotolaria juncea L.) and sorghum sudangrass (Sorghum bicolor), while winter cover crops (WCCs) were Austrian winter pea (Pisum sativum) and wheat. Cropping systems were wheat‐fallow (W‐F), wheat‐cotton (W‐C), wheat‐soybean (W‐S), W‐SCC, WCC‐C, F‐C, WCC‐S, and F‐S. Tillage effect on crop yields varied across years. In 2021, wheat yield in CT of W‐C, W‐F, and W‐SCC (2831, 2689, and 2646 kg ha−1) significantly differed from NT of W‐S (1720 kg ha−1). No significant tillage effect was observed on cotton lint yield between W‐C and WCC‐C. For soybean, in 2020, the CT of W‐S and WCC‐S significantly outyielded the NT of W‐S and WCC‐S. Cropping system effect on wheat yield between W‐S and W‐SCC (1419 and 1987 kg ha−1) was significant in 2020 due to low stand counts in W‐SCC arising from the thick SCC biomass. Cotton lint yield in WCC‐C outyielded W‐C in all 3 years but was not significant. Soybean grain yield in W‐S was consistently higher than in WCC‐S, though not significant. Cotton lint and soybean grain yield in the fallow systems were the least. Overall, in a short term, crop yield in DC and cover crop systems were similar.

Enhancing cotton seedling recognition: a method for high-resolution UAV remote sensing images

ABSTRACT Timely and accurate acquisition of cotton seedling information is crucial for seedling replenishment, yield increase, and effective cotton field management. This study proposes a cotton seedling recognition model and weed removal method for high-resolution UAV (Unmanned Aerial Vehicle) remote sensing images. First, the image information of cotton seedlings is enhanced by calculating the remote sensing index and using threshold segmentation. Second, the recognition model is constructed based on the spatial morphological characteristics of cotton seedlings, with weed interference removed using the straight-line method. Finally, the growth condition of cotton seedlings in farmland is comprehensively evaluated by calculating the seedling emergence rate and coverage rate. The experimental results show that: (1) the combination of the GLI (Green Leaf Index) and Otsu threshold segmentation algorithm effectively distinguishes cotton seedlings from the soil background and accurately portrays cotton seedling contours, further enhancing image information; (2) the proposed recognition model achieves an average overall accuracy of 95.75%, making it a practical method for cotton seedling recognition; and (3) the overall growth condition of cotton seedlings in the study area is good, enabling farmers to apply appropriate seedling replenishment and fertilizer based on the comprehensive cotton evaluation map to further improve the final cotton yield. This study aims to provide new research ideas for crop seedling identification and serve as a reference for the practical application of UAVs in crop monitoring.

Integrating genetic assortment and molecular insights for climate-resilient breeding to unravel drought tolerance in cotton

This study addresses the challenges posed by rainfall variability, leading to water deficits during critical stages of crop growth, resulting in a drastic reduction of cotton yield. In a comprehensive evaluation, thirty cotton genotypes, including five Gossypium arboreum (wild) and twenty-five Gossypium hirsutum (cultivated), were grown under rainfed and irrigated conditions. Drought tolerance indices (DTI) were evaluated, categorizing genotypes based on their resilience. Further, in-vitro screening at the seedling stage (20 days) under PEG-induced drought identified tolerant genotypes exhibiting elevated levels of free proline (19.07±5.31 mg.g−100fr.wt.), amino acids (34.59±6.51 mg.g−100fr.wt.), soluble proteins (13.73±2.65 mg.g−1fr.wt.), and glycine betaine (10.95±3.62 mg.g−100fr.wt.), in their leaves, positively correlating (p<0.001) with relative water content (87.70±3.45 %). Molecular analysis using drought-specific simple sequence repeats (SSR) markers revealed significant genetic variability in a cotton genotypes, with lower observed and higher expected heterozygosity. F statistics exposed a higher level of gene flow corresponding to low differentiation among populations. Among the genotypes group, wild GAM-67 and cultivated Deviraj emerged as the most potent, exhibiting the higher DTI and diverse gene pools. Study exhibited higher total gene diversity in drought-tolerant wild GAM-67 (0.8501) and greater expected heterozygosity (0.626) and gene flow (0.6731) in cultivated Deviraj, underlining their robust genetic adaptability to drought conditions. The integrated approach of field evaluations, in-vitro screening, and molecular analyses explained substantial genetic diversity, with the identification of promising genotypes displaying higher drought tolerance indices, elevated levels of stress-related biochemical osmolytes, and pronounced genetic adaptability, thereby contributing to the advancement of breeding initiatives for enhanced drought resilience in cotton.

Suppressing a β-1,3-glucanase gene expression increases the seed and fibre yield in cotton.

Seeds are initiated from the carpel margin meristem (CMM) and high seed yield is top one of breeding objectives for many crops. β-1,3-glucanases play various roles in plant growth and developmental processes; however, whether it participates in CMM development and seed formation remains largely unknown. Here, we identified a β-1,3-glucanase gene (GLU19) as a determinant of CMM callose deposition and seed yield in cotton. GLU19 was differentially expressed in carpel tissues between Gossypium barbadense (Gb) and Gossypium hirsutum (Gh). Based on resequencing data, one interspecies-specific InDel in the promoter of GLU19 was further detected. The InDel was involved in the binding site of the CRABS CLAW (CRC) transcription factor, a regulator of carpel development. We found that the CRC binding affinity to the GLU19 promoter of G. barbadense was higher than that of G. hirsutum. Since G. barbadense yields fewer seeds than G. hirsutum, we speculated that stronger CRC binding to the GLU19 promoter activated higher expression of GLU19 which in turn suppressed seed production. Consistent with this hypothesis was that the overexpression of GhGLU19 caused reduced seed number, boll weight and less callose formation in CMM. Conversely, GhGLU19-knockdown (GhGLU19-KD) cotton led to the opposite phenotypes. By crossing GhGLU19-KD lines with several G. hirsutum and G. barbadense cotton accessions, all F1 and F2 plants carrying GhGLU19-KD transgenic loci exhibited higher seed yield than control plants without the locus. The increased seed effect was also found in the down-regulation of Arabidopsis orthologs lines, indicating that this engineering strategy may improve the seed yield in other crops.

Phosphorus High-rate application through band placement improved cotton productivity under arid climate

The plant-available soil phosphorus rate and methods for applying phosphatic fertilizer and soil P-fixation capacity are critical factors for lower cotton productivity in Southern Punjab, Pakistan. Hence, a two-year study was conducted in Central Cotton Research Institute (CCRI), Multan, Pakistan, to examine the effects of various P rates and application methods on cotton crop output during the growing seasons of 2014 and 2015. Phosphorus was applied in four rates (0, 40, 80, and 120 kg ha−1 P2O5) using broadcast, band application, and fertigation methods. Results indicated that the impact of P rates was statistically significant on plant height, the number of nodes, monopodial and sympodial branches, leaf area index, harvest index, and seed cotton yield. The greater P application (120 kg P2O5 ha−1) had a better effect on cotton productivity than the lower application rates (0, 40, and 80 kg P2O5 ha−1). The band application responded better on nodes plant−1, sympodial branches plant−1, boll weight, leaf area index, lint yield, and harvest during the growing season 2015. Therefore, by adopting the band application coupled with 120 kg P2O5 ha−1 rather than the conventional method of broadcast, productivity of cotton crops could be increased.

Rational optimization of irrigation regimes for drip-irrigated cotton fields without mulch can alleviate the problem of residual film contamination in arid zones

The serious problem of residual film pollution in the arid region of northwestern China has a severe negative impact on both the farmland environment and the benefits of cotton planting. Drip irrigation without mulch (DIWM) has the potential to serve as an effective alternative to mulch drip irrigation and address the issue of residual film pollution. However, there are few studies on how to formulate a rational irrigation regime under DIWM conditions and achieve water savings and productivity gains. Consequently, from 2020 to 2021, we established three DIWM treatments: W4, W6, and W8. None of the treatments were irrigated at the seedling stage, and the irrigation regime was the same for all the treatments at the squaring stage (2 irrigations at an irrigation quota of 45 mm). From the initial flowering stage, the frequency of irrigation was once every 6 d, 8 d, and 12 d for W4, W6, and W8, respectively, and the corresponding total numbers of watering times were 8, 6, and 4, respectively. The irrigation amount was 69 mm at each time point during the flowering and boll-forming stages in all the treatments, and the final irrigation amount was 52.2 mm in W8. The total irrigation amounts were 366 mm, 504 mm and 625.2 mm for W4, W6 and W8, respectively. This study explored the spatiotemporal characteristics of soil moisture via sensors combined with the spatial grid method. Additionally, the growth indices, biomass accumulation, yield components, and water use efficiency (WUE) of cotton were assessed across various treatments. The results revealed that, in 2020 and 2021, the W8 treatment resulted in the highest soil water content (SWC) in the 70–110 cm soil layer and soil water consumption (WC) in the 10–110 cm layer, with the WC in the 10 cm layer being 20 % and 44 % greater than that in W6, respectively. The excessive total irrigation amount (IA) in the W8 treatment led to high vegetative growth of cotton, diminishing the positive impact on yield. Across both years, the WUE observed in the W6 treatment significantly exceeded that of W8, while the seed cotton yield demonstrated only marginal decreases of 5 % and 0.9 % compared with that of W8. The irrigation amount and average WC in the 10–40 cm soil layer were 19 % and 29 % lower in W6 than in W8, respectively, but the economic benefits were only 2 % lower than those in W8. There was a significant positive correlation between the SWC and WC in all the different soil layers, and the effect of the SWC on the WC gradually decreased with increasing soil depth. Overall, the irrigation regime employed in the W6 treatment within the arid zone, devoid of mulch cover, proves to be an effective water-saving strategy, ensuring a consistent cotton yield and enhancing WUE. This study serves as a reference for developing an efficient irrigation system tailored to the DIWM cotton industry in arid zones, contributing to the promotion of green and sustainable agricultural development in this region.

Optimizing type, date, and dose of compost fertilization of organic cotton under climate change in Mali: A modeling study.

Adapting organic farming to climate change is a major issue. Cotton yields in Mali are declining due to deteriorating climatic conditions, soil fertility, and poor management. This study aimed to improve organic cotton yield in Mali in the future climate with the optimal choice of compost type, date, and dose of application. Experimental data collected in 2021 from the Sotuba research station in Mali was used for calibration and evaluation of the crop model DSSAT CSM-CROPGRO-Cotton model using phenology, leaf area index, and seed cotton yield. Climate data from the RCP4.5 and RCP8.5 scenarios of the GFDL-ESM2M model were used for future weather datasets for 2020-2039, 2040-2059, and 2060-2079. The model was able to simulate anthesis and maturity with excellent results, with nRMSE < 4%, and seed cotton yields moderately well, an nRMSE of 26% during calibration and 20.3% in evaluation. The scenario RCP8.5 from 2060 to 2079 gave the best seed cotton yields. Seed cotton yields with RCP4.5 and RCP8.5 were all better with the mid-May application period of small ruminant silo compost at 7.5 t/ha. In such conditions, more than 75% of the cases would produce more than 2000 kg/ha of seed cotton.

A Comparative Study of Detrending Methods on Crop Yield Time Series for Drought Studies

Detrending is a statistical technique that removes systematic variations or trends from time series data, allowing analysts to focus on the underlying patterns or fluctuations. While multiple detrending approaches have been applied but rarely discussed their consistency of outcomes and effectiveness in accurately capturing better yield trends. The validation of drought occurrences has proven to be a challenging task due to the non-stationary characteristics of time series data related to crop yield. This research utilizes time series of cotton yield data from the Marathwada region covering the period from 1998 to 2021. Three traditional trend models, including simple linear regression, second-order polynomial regression and central moving average were applied. Additionally, two machine learning models (random forest and support vector regression) were tested with a novel approach. Moreover, two decomposition models (additive and multiplicative) were used to remove non-linear trends in crop yield time series data. The performance of the chosen models was evaluated based on metrics such as root mean square error, mean absolute error, Nash-Sutcliffe efficiency, and index of agreement. The results suggest that the most effective detrending approach involves combining a random forest machine learning model with an additive decomposition model.

Identification of QTNs and Their Candidate Genes for Boll Number and Boll Weight in Upland Cotton.

Genome-wide association study (GWAS) has identified numerous significant loci for boll number (BN) and boll weight (BW), which play an essential role in cotton (Gossypium spp.) yield. The North Carolina design II (NC II) genetic mating population exhibits a greater number of genetic variations than other populations, which may facilitate the identification of additional genes. Accordingly, the 3VmrMLM method was employed for the analysis of upland cotton (Gossypium hirsutum L.) in an incomplete NC II genetic mating population across three environments. A total of 204 quantitative trait nucleotides (QTNs) were identified, of which 25 (24.75%) BN and 30 (29.13%) BW QTNs were of small effect (<1%) and 24 (23.76%) BN and 20 (19.42%) BW QTNs were rare (<10%). In the vicinity of these QTNs, two BN-related genes and two BW-related genes reported in previous studies were identified, in addition to five BN candidate genes and six BW candidate genes, which were obtained using differential expression analysis, gene function annotation, and haplotype analysis. Among these, six candidate genes were identified as homologs of Arabidopsis genes. The present study addresses the limitation of heritability missing and uncovers several new candidate genes. The findings of this study can provide a basis for further research and marker-assisted selection in upland cotton.

Exploring Spatial Variability and Yield-Determining Factors in Cotton Production: A Multivariate Analysis

ABSTRACT The yield of cotton crops is severely affected by the soil’s chemical characteristics. This research probes the influence of soil properties on cotton yield, with a focus on assessing within-field spatial variability for advanced site-specific management in cotton production. Geostatistical analysis and semivariograms were conducted to examine the spatial variability of soil chemical properties. While multivariate analysis was employed to identify key yield-determining factors. Principal Component Analysis (PCA) was employed to explore leading soil indicators, as opposed to using a pre-defined set of indicators, and to analyze the spatial variability of soil properties. The soil characteristics encompassed saturation, soil pH, electrical conductivity (EC), organic matter (OM), phosphorous (P), potassium (K), calcium carbonate (CC), and micronutrients such as zinc (Zn), copper (Cu), iron (Fe), manganese (Mn), and boron (B). For this study, one thousand soil samples were meticulously collected from the Multan district, facilitated by Global Positioning System (GPS) devices. The PCA revealed that the first four PCs accounted for 57% of the total variation. The first PC, Cu, B, Mn, OM, and Fe emerged as the most influential variables, while the second PC exhibited a high correlation with CC, pH, and saturation. The third PC highlighted the dominant role of K and P in its construction, whereas the fourth PC was primarily shaped by Zn. The Principal Component Regression (PCR) analysis further revealed that EC, K, P, and Zn had positive impacts on cotton yield, while pH, Cu, Fe, Mn, and B had negative impacts.

Effects of Irrigation Amount and Nitrogen Rate on Cotton Yield, Nitrogen Use Efficiency, and Soil Nitrogen Balance under Drip Irrigation

Water and nitrogen (N) are major constraints for cotton growth and yield formation in arid regions. Irrigation and N application have been widely investigated to improve crop yield and water and N use efficiency (NUE). However, further optimization of water and N management is needed because the effects of N application on soil N balance and N loss under different irrigation levels remain unclear. In this study, a field experiment was conducted in 2020 and 2021 to investigate the effects of different irrigation amounts (full irrigation (100% ETc), moderate irrigation (80% ETc), low irrigation (60% ETc)) and N application rates (control (without N application, N0), low N (150 kg ha−1, N150), medium N (225 kg ha−1 and 300 kg ha−1, N225 and N300), and high N (375 kg ha−1, N375)) on cotton yield, NUE, and soil N balance. The 2-year results showed that under the 60% ETc treatment, cotton dry matter accumulation, N uptake, and yield were significantly enhanced by increasing N application. Under the 80% and 100% ETc treatments, these parameters peaked with the N300 treatment; the N375 treatment showed no significant difference or decrease compared to the N300 treatment. The 80% ETc N300 treatment had the highest cotton yield and NUE, which increased by 17.49–106.57% and 12.28–88.78% compared with other treatments, respectively. Residual soil N accumulation (RSNmin), apparent N loss (ANL), and apparent N surplus (ANS) increased as the N application rate increased under the 60% and 100% ETc treatments. While under the 80% ETc treatment, the ANS did not significantly differ between the N225 and N300 treatments, ANL significantly decreased by 43.51–88.56% when the N application rate increased from 150 to 225 and 300 kg ha−1. The ANL of the 80% ETc N300 treatment was the lowest, but ANS did not significantly increase. The regression analysis and spatial analysis results showed that under irrigation of 336–348 mm and N application of 254–327 kg ha−1, cotton yield, NUE, and WPI reached more than 80% of the maximum value, with less apparent N loss, thus maintaining the soil N balance in drip-irrigated cotton fields. This study helps to improve the utilization of water and N resources in cotton production. Future research on optimizing water and N management needs to more fully consider environmental pollution to achieve sustainable development of agricultural ecosystems.

Effects of Different Irrigation Water Volumes with 1,1-Dimethyl-piperidinium Chloride (DPC) on Cotton Growth and Yield

Effects of Different Irrigation Water Volumes with 1,1-Dimethyl-piperidinium Chloride (DPC) on Cotton Growth and Yield

An accuracy assessment of satellite-based cotton yield estimation using panel data regression: a case study of Uzbekistan

An accuracy assessment of satellite-based cotton yield estimation using panel data regression: a case study of Uzbekistan

GhSBI1, a CUP-SHAPED COTYLEDON 2 homologue, modulates branch internode elongation in cotton.

Branch length is an important plant architecture trait in cotton (Gossypium) breeding. Development of cultivars with short branch has been proposed as a main object to enhance cotton yield potential, because they are suitable for high planting density. Here, we report the molecular cloning and characterization of a semi-dominant quantitative trait locus, Short Branch Internode 1(GhSBI1), which encodes a NAC transcription factor homologous to CUP-SHAPED COTYLEDON 2 (CUC2) and is regulated by microRNA ghr-miR164. We demonstrate that a point mutation found in sbi1 mutants perturbs ghr-miR164-directed regulation of GhSBI1, resulting in an increased expression level of GhSBI1. The sbi1 mutant was sensitive to exogenous gibberellic acid (GA) treatments. Overexpression of GhSBI1 inhibited branch internode elongation and led to the decreased levels of bioactive GAs. In addition, gene knockout analysis showed that GhSBI1 is required for the maintenance of the boundaries of multiple tissues in cotton. Transcriptome analysis revealed that overexpression of GhSBI1 affects the expression of plant hormone signalling-, axillary meristems initiation-, and abiotic stress response-related genes. GhSBI1 interacted with GAIs, the DELLA repressors of GA signalling. GhSBI1 represses expression of GA signalling- and cell elongation-related genes by directly targeting their promoters. Our work thus provides new insights into the molecular mechanisms for branch length and paves the way for the development of elite cultivars with suitable plant architecture in cotton.