Cotton Yield Research Articles

Cotton production in Mediterranean regions is increasingly constrained by limited water availability, making it essential to identify genotypes that can maintain yield under reduced irrigation. In this study, four partially interspecific cotton lines (Pa7) and the commercial cultivar Celia were evaluated under two nitrogen rates designed to test resource-use efficiency and three irrigation regimes across two growing seasons in Greece. A strip–split plot design with three replications was used, and field data were analyzed with ANOVA, stability indices, and multivariate tools (Additive Main Effects and Multiplicative Interaction—AMMI, and Genotype plus Genotype × Environment—GGE biplots). Results showed that moderate irrigation consistently ensured stable seed cotton yields, whereas a higher water supply increased the plant height without proportional yield benefits, while fertilizer supplied in the specific quantities showed a lower impact on yield stability. Genotype × environment interactions were highly significant: Celia confirmed its high stability, while line M3 combined good stability with favorable agronomic traits. Yield was strongly associated with boll weight and lint percentage, indicating their usefulness as indirect selection criteria. These findings highlight the agronomic potential of partially interspecific cotton lines and demonstrate that moderate irrigation can sustain productivity while reducing water inputs, contributing to a more efficient use of resources in cotton production under water-limited conditions. These results provide practical insights for breeding and water management strategies aiming to sustain cotton productivity under Mediterranean water-limited conditions.

Abstract Background Irrigation has been a strategy used to reduce losses due to drought, which combined with a good supply of nitrogen (N), can improve the protective system of cotton plants. The objective of this study was to investigate the effects of irrigated and rainfed cotton cultivation using different rates and sources of N. Cotton cultivation was carried out in Selvíria-MS field in the 2017/2018 harvest. The experiment was conducted in randomized blocks, which were designed in a 4 × 2 × 2 factorial scheme. The factors were composed of 0, 40, 80, and 150 kg·hm −2 level of N, using two sources of N under rainfed and irrigated systems. Results The provision of irrigation provided an increase in the levels of chlorophylls (Chl) a, Chl b, total Chl, carotenoids, pheophytin, leaf chlorophyll index (LCI), N content, nitrate (NO 3 − ), sucrose (SUC), the number of vegetative and reproductive branches, boll mass, and seed cotton productivity. There was no effect of N sources on any of the characteristics evaluated. Application of 150 kg·hm −2 level of N increased in 11%, 59%, 22%, 15%, 15% and 17% in LCI, NO 3 − , N, total amino acids (TA), SUC, and proline concentration in leaves, compared with 0 kg·hm −2 of N, respectively. Application of 150 kg·hm −2 level of N improved the leaf catalase activity (CAT) under the irrigation system; however, in a rainfed system, the highest CAT was observed at rates of 0 and 150 kg·hm −2 level of N. Irrigation increased in 55%, 117%, 68%, 46%, 8%, 36%, 24%, 118%, 48%, 10%, 11% and 72% in Chl a, Chl b, total Chl, CAR, LCI, pheophytins (Pheo), SUC, NO 3 − , the number of vegetative branches, the number of reproductive branches, mass of 20 bolls and seed cotton yield compared with rainfed system, respectively, however, the antioxidant system and the ammonium content of plants was stimulated by rainfed cultivation. Conclusions Antioxidant responses increased during droughts in cotton farming, which may be connected to oxidative stress-related losses. Better N metabolism, photosynthetic pigments, and manufacturing components were all made possible by irrigated cultivation. The delivery of 150 kg·hm −2 of N in topdressing in cotton agriculture promoted the N metabolism, sucrose, total amino acids, and the plant’s defense mechanism against oxidative stress.

Multi-task learning model driven by climate and remote sensing data collaboration for mid-season cotton yield prediction

Balancing mepiquat chloride and harvest aid applications for optimal cotton yield and defoliation in mechanical harvesting systems

Chlorophyll dynamic fusion based on high-throughput remote sensing and machine learning algorithms for cotton yield prediction

Optimal planting density enhances cotton yield by coordinating boll–leaf system photosynthesis under heat-limited conditions

Increasing the yield per unit area is crucial for achieving stable growth in China’s cotton production. Based on the transcendental logarithmic production function model and using panel data from eight major cotton-producing provinces in China from 1990 to 2022, this paper measures the elasticity of climate factors and factor inputs and calculates the contribution rate of each factor influencing cotton yield increase. The results show that accumulated temperature positively impacts cotton yield, while precipitation and sunshine duration have negative effects. Climate factors contribute 7.95% to yield growth. Among input factors, agricultural machinery and labor inputs positively affect yield, whereas fertilizer input negatively affects it. Factor inputs contribute 44.21% to yield improvement. Technological progress also plays a role in enhancing cotton yield. Finally, the paper suggests improving meteorological disaster forecasting, optimizing input structures, and promoting agricultural research and technology services based on local conditions.

Cotton leaf mites are pests that cause irreparable damage to cotton and pose a severe threat to the cotton yield, and the application of unmanned aerial vehicles (UAVs) to monitor the incidence of cotton leaf mites across a vast region is important for cotton leaf mite prevention. In this work, 52 vegetation indices were calculated based on the original five bands of spliced UAV multispectral images, and six featured indices were screened using Shapley value theory. To classify and identify cotton leaf mite infestation classes, seven machine learning classification models were used: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), K-Nearest Neighbors (KNN), decision tree (DT), and gradient boosting decision tree (GBDT) models. The base model and metamodel used in stacked models were built based on a combination of four models, namely, the XGB, GBDT, KNN, and DT models, which were selected in accordance with the heterogeneity principle. The experimental results showed that the stacked classification models based on the XGB, KNN base model, and DT metamodel were the best performers, outperforming other integrated and single individual models, with an overall accuracy of 85.7% (precision: 93.3%, recall: 72.6%, and F1-score: 78.2% in the macro_avg case; precision: 88.6%, recall: 85.7%, and F1 score: 84.7% in the weighted_avg case). This approach provides support for using UAVs to monitor the cotton leaf mite prevalence over vast regions.

Abstract Potassium (K) deficiency is a common yield‐limiting factor in cotton ( Gossypium hirsutum L.) production, requiring effective management to minimize yield losses and maintain fiber quality. We evaluated how K availability influences cotton lint yield and fiber quality. Ten fertilizer‐K rate (0–187 kg K ha −1 ) trials were conducted on silt loam soils with soil‐test K (STK) ranging from very low to above optimum during the 2023 and 2024 growing seasons. Cotton was planted in raised beds and furrow‐irrigated, and lint yield, turnout, and fiber quality (i.e., fiber length, micronaire, uniformity, strength, and elongation) were measured at maturity. Cotton lint yield was positively affected by fertilizer‐K rates ( p ≤ 0.10) at STK ≤ 114 mg K kg −1 . Yields were maximized at responsive sites with applications of 56 kg K ha −1 in long‐term trials and 37, 75, or 112 kg K ha −1 in single‐site‐year trials, showing yield increases of 20%, 53%, 47%, and 70% compared to the no‐K control, respectively. Lint turnout and fiber quality were affected by K availability. Overall, at yield‐maximizing fertilizer‐K rates, lint turnout was 2.4% greater across cultivars in relation to the control. Similarly, fiber elongation increased by 0.35%. At sites with Very Low STK, as little as 37 kg K ha −1 increased lint uniformity and strength up to 0.67% and 1.84 g tex −1 . Micronaire increased on average by 0.50, with greatest values occurring with 112 kg K ha −1 application. These findings suggest adequate K management is key to maximizing both cotton yield potential and fiber quality.

Introduction Straw return is a widely endorsed sustainable agronomic practice. However, a systematic understanding of its carbon–nitrogen coupling mechanisms and their consequent impacts on the soil–microbe–plant continuum across the entire cotton growth cycle is critically lacking. Methods We conducted a field experiment with five treatments: CK (no straw return), T1 (one-third shredded straw), T2 (two-thirds shredded straw), T3 (full shredded straw), and T4 (full straw left intact as surface mulch). This design enabled us to decipher how the amount and fragmentation of straw residues synchronize the soil-microbe-plant system to enhance sustainability. Results Our findings reveal distinct mechanistic pathways. The T3 treatment (full shredding) triggered an early-season microbial "relay," where Gammaproteobacteria expansion was succeeded by Actinobacteria , elevating soil pH from 4.82 to 5.73 and boosting alkaline-hydrolysable N by 113.01% at the flower and boll stage. This enhanced nitrate reductase activity by 74.1% and increased bolls per plant by 35.0%. In contrast, the T4 treatment (surface mulch) provided a more gradual nitrogen release (+28.4% alkaline-hydrolysable N during boll opening), which prolonged the secondary cell wall deposition phase in fibers. This strategy achieved a lint yield of 2055.63 kg ha⁻¹ (+63.8%) and a 2.6% increase in fiber strength. Furthermore, T4 fostered a "microbial sanctuary" at boll opening, evidenced by a 130.5% explosion in OTU richness and an 18.7% suppression of pathogen populations. Discussion We demonstrate that surface mulching (T4) is the superior strategy, as it optimally balances high yield with superior fiber quality by creating a resilient and suppressive soil microbiome. This work provides a novel carbon–nitrogen synergy framework for the resource-efficient utilization of crop residues in sustainable cotton production.

Correction: Optimal row configuration in jujube-cotton intercropping systems increases cotton yield by enhancing growth characteristics and photosynthetically active radiation in arid region

A high yielding herbaceum cotton variety Gujarat Anand Desi Cotton 3 (GADC 3: WagadGaurav) tested as GVhv 767 was developed by Regional Cotton Research Station, Anand Agricultural University, Viramgam and released in the year of 2019. It was notified during the year 2020 for the North West Agroclimatic Zone - V and Bhal and Coastal Agroclimatic Zone-VIII of Gujarat. It is a variety derivative of the intraspecific varietal cross between GVhv 104 and GVhv 504. GADC 3 is semi open boll type in nature with determinant growth habit and smooth boll surface. Pedigree method of selection was used for development of high yielding genotypes under rainfed condition. Existing G. herbaceum cultivars show limited adaptability and productivity under the erratic rainfall and marginal soil conditions prevailing in the North West (Zone V) and Bhal & Coastal (Zone VIII) regions of Gujarat. A new cultivar with wider adaptability is essential to ensure stable yield performance under rainfed conditions. The variety GADC 3 (Wagad Gaurav) had produced average seed cotton yield of 2150 kg/ha. This variety gave 34.02, 15.01 and 27.54% higher seed cotton yield and 34.87, 26.67 and 27.28% higher lint yield over check varieties G. Cot.21, ADC 1 and GADC 2, respectively in the rainfed condition of Gujarat state.The ginning percentage (%) of GADC 3 is 44.8% which is equivalent with check variety G. Cot. 21 (44.2%) and higher than ADC 1(42.2%) and GADC 2 (43.8%). Seeds contain 14.9 % oil. GADC 3has recorded fibre length (upper half mean) of 22.7 mm, fibre fineness of 5.1µg/inch and bundle strength of 22.5 g/tex in HVI mode of fibre quality testing. The genotype GADC 3 was moderate tolerant to root rot, bacterial leaf blight, alternaria leaf blight and wilt as well as major pests including sucking pests and boll worm complex under field conditions. These results indicate that GADC 3 is well-suited for cultivation in Agro-climatic Zones V and VIII of Gujarat to achieve maximum yield and profit.

The field experiment was conducted during the 2024–2025 cropping season at the Barley and Wheat Research Institute, Agricultural Research Tandojam, Sindh, Pakistan, to evaluate the performance of eleven wheat (Triticum aestivum L.) varieties for growth and yield traits under local agro-climatic conditions. The tested varieties included Wafaq-23, Borlaug-16, NARC-Super, Anmol-91, Abadgar, SKD-1, Moomal, Benazir-13, IV-2, Sindhu-16, and Auqab-2000. The experiment was laid out in a randomized complete block design (RCBD) with three replications. Observations were recorded on plant height, number of tillers, spike length, number of grains spike-1, 1000-grain weight, and grain yield hectare-1. Significant variation was observed among the varieties for all parameters studied. The variety Wafaq-23 exhibited superior performance with the highest grain yield (48.38 mds ha⁻¹), followed by NARC-Super (37.49 mds ha⁻¹) and Borlaug-16 (36.47 mds ha⁻¹). In contrast, Sindhu-16 recorded the lowest yield (19.97 mds ha⁻¹). The findings indicate that Wafaq-23 is well adapted to the semi-arid conditions of Tandojam and can be recommended for higher productivity in similar environments. The results provide valuable insights for wheat growers and breeding programs aiming to enhance yield potential under local conditions.

Cotton leafroll dwarf virus (CLRDV) is a polerovirus transmitted by Aphis gossypii Glover. Factors contributing to cotton (Gossypium hirsutum L.) yield losses caused by CLRDV-infection remain unclear, and results from previous studies indicate the environmental component of the disease triangle may significantly influence yield loss outcomes. This three-year study was conducted to compare yield and yield components, fiber quality, root weight and other morphological parameters of CLRDV-infected and non-infected plants grown under high heat conditions. Potted plants were grown in an insect-proof screen house covered with plastic, and data was collected on a per-plant basis. Plants did not exhibit obvious symptoms any year of the study. CLRDV infection significantly reduced lint yield, number of seeds, number of bolls in the first fruiting position, seed index, and root dry weight. Fiber quality analysis showed there was a reduction in content of short fibers in CLRDV-infected plants. Possible interference of CLRDV on translocation of photoassimilates in the phloem, along with changes in net photosynthesis and other physiological processes may explain how this virus affected some of the parameters evaluated, especially lint yield, yield components, and root dry weight.

A deep understanding of the associations between characteristics is crucial in determining selection criteria for the development of cotton genotypes with desirable traits such as high yield and fiber quality. For this purpose, the correlation, stepwise, and path analysis results were examined in two separate sets consisting of parents + F1 and F2. The relationships between seed cotton yield and boll number, days to first squaring, and days to first flowering were parallel in both parents + F1 and F2, whereas all other relationships were different in both data sets. Boll number significantly and positively correlated with seed cotton yield and ginning out-turn in both populations. Boll weight positively increased the seed cotton yield but negatively affected the ginning out-turn. As a result of stepwise regression analysis, seed cotton yield = -77.674 + 14.234 boll weight + 5.466 boll number in parents + F1 and seed cotton yield = -47.367 + 11.627 boll weight + 5.163 boll number + 2.933 seed index + 1.508 ginning out-turn -3.362 fiber length in F2. In F2 generation, a single plant with a high boll number per plant should be selected to increase seed cotton yield and ginning out-turn without negatively affecting fiber quality characteristics.

Boll abscission in cotton (Gossypium spp.) is a key factor that limits yield; however, the molecular mechanisms underlying this process remain poorly understood. In this study, boll abscission characteristics were uncovered in four cotton varieties that exhibited extreme differences in boll abscission rates via tissue sectioning. Transcriptome analysis was performed on the four cotton varieties. Using weighted gene co-expression network analysis (WGCNA) of the transcriptome data, we identified a stearoyl-(acyl-carrier-protein) desaturase (SACPD) as a potential key regulator of boll abscission. We also performed evolutionary analyses on the SACPD gene family across five cotton species and identified 63 members that were classified into four evolutionary clades, with duplication-polyploidization events being a major driver of gene expansion. Tissue-specific expression profiling revealed that Gossypium hirsutum GhSACPD19 is highly expressed in the abscission zone. Our findings suggest a role of GhSACPD19 in regulating boll abscission, likely through metabolism of jasmonate, a well-known positive regulator of abscission. Our work offers new insights into the regulation of organ abscission at cellular and molecular levels and presents a valuable resource for cotton yield improvement.

The 66 stable QTLs related to fiber quality and yield components were identified using Gh-Gb introgression population. A keratin-associated gene GbKAP from qFU-D04-1b is verified to modulate cell elongation and seed yield. Chromosome segment substitution lines (CSSLs) are a powerful tool for genetic dissection of complex quantitative traits. Here, we re-sequenced 117 CSSLs introgressed from Gossypium barbadense acc. Hai7124 into G. hirsutum acc. TM-1. A total of substitution segment length of 4572Mb, with a total genome coverage of 68.78%, were identified. Utilizing both regression stepwise-likelihood ratio test (RSTEP-LRT) and composite interval mapping (CIM) methods, 145 common QTLs (co-QTLs) were detected simultaneously across four environments/years, including 65 associated with fiber quality traits and 80 related to yield components. Among these, 66 co-QTLs were detected in two or more environments/years as stable QTLs. We further verified a stable fiber uniformity (FU)-related QTL qFU-D04-1b using F2 and F2:3 secondary segregating populations derived from a cross between the elite introgression line CSSL25 and TM-1 and fine-mapped the QTL to the region of 762kb on D04 chromosome. Within this interval, GB_D04G0512 named as GbKAP, encoding a keratin-associated protein, exhibited higher transcript level during fiber elongation stages in CSSL25 than in TM-1. There was a non-synonymous SNP in the coding region and three specific transcription factor binding sites in the promoter of GbKAP in CSSL25 compared to its homolog in TM-1. Heterologous expression of GbKAP in Arabidopsis resulted in increased root length, root cell length, hypocotyl length, rosette leaf growth, plant height, and seed size and weight, indicating GbKAP plays an important role in cell elongation and seed yield. This study provides valuable resources for the improvement of upland cotton fiber quality and yield traits.

Drone application of herbicides is an emerging concept in weed management. For drone spraying, spray volume needs to be optimized to improve its efficacy. The present study was conducted at Tamil Nadu Agricultural University, Coimbatore, during summer, 2024 to optimize spray volumes for drone spraying of herbicides in high-density cotton. The study was designed in a strip-plot with 3 horizontal plots (herbicides: H1 - PE application of pendimethalin 1kg/ha followed by hand weeding at 25DAS, H2 - PE application of pendimethalin 1kg/ha followed by EPoE application of quizalafop-ethyl 50 g/ha + pyrithiobac-sodium 62.5 g/ha and H3 - EPoE application of quizalafop-ethyl 50 g/ha + pyrithiobac-sodium 62.5 g/ha), 5 vertical plots (spray volumes: S1 - 30 L/ha, S2 - 35 L/ha, S3 - 40 L/ha, S4 - 45 L/ha, S5 - 50 L/ha) and replicated thrice. A weed-free check and an unweeded check were maintained separately. Regarding herbicides, pendimethalin followed by quizalafop-ethyl + pyrithiobac-sodium and pendimethalin followed by hand weeding, recorded lower total weed density and dry weight and higher drymatter production of cotton and seed cotton yield. Application of herbicides with 45 L/ha and 50 L/ha recorded lower total weed density and dry weight, higher drymatter production of cotton and seed cotton yield. The results revealed that the drone spraying of pendimethalin 1.0 kg ha-1 as pre-emergence followed by EPoE application of quizalafop ethyl 50g/ha + pyrithiobac sodium 62.5 g/ha with spray volume of 45 L/ha was found to be effective in combating the weeds in high-density cotton.

Cotton (Gossypium hirsutum L.) is a versatile crop with multiple applications. The evaluation of 30 upland cotton genotypes was conducted to measure variability and divergence in seed cotton yield, yield-related attributes and fiber quality traits using multivariate analyses. The investigation, conducted during the Kharif season of 2022, was laid out in a randomized block design (RBD) with three replications at the Main Cotton Research Station (MCRS), Navsari Agricultural University (NAU), Surat. Analysis of variance (ANOVA) showed significant genetic variability between the genotypes for all traits. Morphological observations identified GISV-400 as the genotype with the highest seed cotton yield (148.00 g/plant). Fiber quality analysis highlighted GISV-391 as superior in upper half mean length (28.03 mm), fiber strength (28.37 g/tex) and fiber fineness (4.80 µg/inch). Seed cotton yield per plant displayed a significant positive relationship with the number of bolls per plant and boll weight. Principal component analysis (PCA) identified five components with eigenvalues greater than one, collectively explaining 78.66 % of the total variation. The biplot revealed GISV-399, GISV-398, GISV-391, GISV-322, GISV-323, GISV-313 and GISV-402 as the most genetically diverse genotypes. Hierarchical clustering further classified the 30 genotypes into five distinct groups. To identify the best-performing genotypes, the Multi-Trait Genotype-Ideotype Distance Index (MGIDI) was employed for the ranking of genotypes at 10 % selection intensity, four genotypes (GISV-389, GISV-395, GISV-394 and GISV-391) were identified as superior. Overall, seven genotypes (GISV-389, GISV-395, GISV-394, GISV-391, GISV-398, GISV-399 and GISV-400) were consistently identified as high-performing in terms of both yield and fiber quality based on PCA, hierarchical clustering and MGIDI. Overall, the study indicated that the existing variability in tetraploid cotton can be effectively utilized through hybridization and development of mapping populations.

Cotton production is a crucial agricultural industry, a raw material source for the textiles sector and a major source of livelihood for more than 30 million farmers globally. The yield and quality of cotton (Gossypium) are influenced by different types of stress and diseases. Deep Learning as a solution for disease prevention, detection, and management can increase the yield, reduce the cost and improve the quality of crop. This study presents a robust method using 10-fold cross-validation with the YOLOv8 DL model for precise cotton leaf disease recognition. The k-fold cross-validation mitigates overfitting by training the model on diverse data subsets, which leads to enhanced generalizability while ensuring reliable performance. The proposed method achieved 99.60% and 100% as Top_1 and Top_5 accuracy, respectively. The method also achieved a recall of 99.53%, a precision of 99.53%, and an F1 score of 99.60%. During 10 trials, the method consistently performed with an average. Top_1 and Top_5 accuracy of 98.41% and 100% respectively, recall 98.53%, precision 98.39% and F1 score 98.42%.This study is among the first to apply YOLOv8 classification with 10-fold cross-validation for multi-class cotton leaf disease identification using field-captured images.