Cotton Crop Research Articles

Optimization of Power Tiller Operated Pneumatic Planter under Laboratory Conditions for Enhancing Cotton Planting Efficiency

Aim: The study aimed to optimize the operational parameters viz. orifice diameter, suction pressure and forward speed of operation of a power tiller operated pneumatic planter specifically for cotton crops in laboratory condition, focusing on improving planting operation. Study Design: A Central Composite Rotatable Design (CCRD) was employed to optimize the operational parameters. Place and Duration of Study: The study was conducted in a Soil test bin (sand bed test) in the Tillage and Traction Laboratory in the year 2023 to evaluate the effect of various operational parameters on seed uniformity. Methodology: Based on the study of engineering properties of cotton seed, the size of the orifice on the seed drum was determined. The effect of operational parameters was examined by evaluating mean seed spacing, precision in spacing (coefficient of variation), miss index, multiple index, and quality of feed index. Results: The study found that for achieving singulation of seeds, optimal parameters included a metering drum with an orifice size of 3 mm, a suction pressure of 2.78 kPa, and a forward speed of 1.36 km/h. Corresponding actual values of miss index, multiple index, quality feed index, and precision index were found 7.6±2.5, 6.8±1.6, 82.4±2.5 and 4.1±1.2 against predicted values of 4.40, 4.56, 90.1, and 3.90, respectively. Conclusion: The findings suggest that optimizing operational parameters such as orifice size, suction pressure, and forward speed can significantly improve the performance of power tiller operated pneumatic planters for cotton seed planting. These results contribute to enhancing planting accuracy and efficiency in cotton sown areas.

Micro level weather based agromet-advisories and economic impact assessment of cotton crop for Sirsa district, Haryana

Micro level weather based agromet-advisories and economic impact assessment of cotton crop for Sirsa district, Haryana

Soil property differences and irrigated‐cotton lint yield—Cause and effect? An on‐farm case study across three cotton‐growing regions in Australia

AbstractThe average lint yield of irrigated cotton in Australia ranges from 2270 to 3700 kg/ha, but yields vary substantially between farms and also between fields on the same farm. Differences in soil properties may cause these yield variations. Identifying which factors are causal and what management can be implemented to mitigate the impacts should help optimize inputs and improve profits. During the 2018–2019 summer cotton‐growing season, a paired‐field comparison approach was used to investigate and improve the understanding of soil property‐induced irrigated cotton yield differences within five farms across three regions of NSW, Australia. The paired fields at each farm recorded an average lint yield difference of >284 kg/ha (measured in 2018–2019 or 5‐year average lint yield). Several soil properties differed between the paired fields at each farm comparison. The soil organic carbon stocks were higher in the higher‐yielding fields at all the farm comparisons and the normalized lint yield percentage was positively correlated with soil organic carbon stocks. Soil sodicity was higher in the lower‐yielding fields at 3 of the 5 comparisons. Results for most soil nutrient tests were above the recommended critical concentrations for Australian cotton production. A stepwise linear regression excluding soil nutrients that were above soil test critical values for crop response and below crop toxicity levels indicated the lint yield was positively correlated with SOC stocks and negatively correlated with sodicity and bulk density. No earthworms were detected during visual soil assessment or soil sampling across all the sites. Visual soil assessment was not a sensitive predictor of cotton crop performance. Comparing soil properties using a paired field approach may assist cotton growers in understanding the factors behind yield differences. A similar strip comparison approach could be adopted for within‐field variability by dividing the fields into discrete performance zones and assessing the soil properties of each zone separately.

An Analytical Study of the Impact of Agricultural Policies on the Cotton Crop in Egypt (Field Study in Beni Suef Governorate)

An Analytical Study of the Impact of Agricultural Policies on the Cotton Crop in Egypt (Field Study in Beni Suef Governorate)

Assessing the Efficacy of Sodium Alginate and Polyacrylamide as Spray Adjuvants Combined with Bifenthrin and Imidacloprid against Lygus lineolaris and Piezodorus guildinii

The tarnished plant bug, Lygus lineolaris, and the red-banded stink bug, Piezodorus guildinii, pose significant economic threats to cotton and soybean crops in the mid-southern USA. However, the efficacy of insecticide spraying is comparatively low, and adjuvants play a crucial role in optimizing insecticide performance. This study evaluated the impact of two adjuvants, sodium alginate (SA) and polyacrylamide (PAM), on enhancing the efficacy of bifenthrin and imidacloprid via laboratory spray bioassays. Both SA and PAM demonstrated insignificant variation in LC50 values with formulated bifenthrin and imidacloprid. However, SA and PAM exhibited synergistic effects with two technical-grade insecticides. High concentrations of PAM increased the efficacy of bifenthrin by 1.50- and 1.70-fold for L. lineolaris and P. guildinii, respectively. Conversely, no enhancement effect was observed for the SA–technical-grade bifenthrin combination against either insect pests. Additionally, both SA and PAM enhanced the effectiveness of imidacloprid in P. guildinii by up to 2.68- and 2.73-fold, respectively. While a high concentration of PAM had a 1.45-fold synergistic effect on technical-grade imidacloprid, no enhancement effect was observed for the SA/imidacloprid combination in L. lineolaris. This study explored the synergistic impact of SA and PAM on the efficacy of technical-grade and formulated bifenthrin and imidacloprid, providing valuable insights into optimizing pest control strategies in agriculture.

Battling arid adversity: unveiling the resilience of cotton in the face of drought and innovative mitigation approaches

Climate change has had significant impacts on agriculture, particularly on cotton production, where drought has emerged as a major threat worldwide. Long and intense dry periods in cotton-growing regions have become more frequent and severe. Drought stress severely affects various aspects of cotton plants, including chlorophyll pigments, carbohydrate metabolism, and enzyme activities related to fiber development, such as vacuolar invertase and sucrose synthase. Furthermore, drought stress disrupts the movement of nutrients toward the reproductive tissues in cotton, resulting in compromised pollen function, propagative failure, and fiber characteristics. To tackle these issues, scientists have made advancements in creating drought-resistant cotton varieties through transgenic methods or molecular breeding techniques, genome editing, CRISPR/Cas9, utilizing quantitative trait loci (QTL). Moreover, the application of plant growth regulators and mineral elements has displayed the potential to improve cotton’s ability to endure drought stress while also enhancing fiber yield and quality. These approaches activate stress-responsive signaling pathways, which could contribute to mitigating reproductive failure and improving fiber characteristics. While the impact of drought stress on cotton plants has been extensively studied, the variations in fiber quality resulting from drought stress are not yet completely understood. Current research has been focused on unraveling the mechanisms underlying these changes, including the physiological, biochemical, and molecular alterations during the multiplicative growth phase that contribute to poor fiber development. Understanding these mechanisms will facilitate the development of novel strategies to alleviate the adverse impact of worldwide weather changes on cotton growth and fiber quality. This research focuses on the drought stress in cotton cultivation and explores its different effects on cotton morphology, physiology, crop yield, and fiber characteristics as well as mechanisms by which cotton exhibits drought tolerance and highlights innovative strategies to mitigate drought stress.

An Intelligent Approach for Cotton Plant Disease Detection using Convolutional Neural Networks: A Deep Learning Perspective

One of the most important crops for economic survival is cotton, and one of the biggest challenges it faces is early disease detection that affect productivity. The cotton business may suffer financial losses because of the frequently insufficient visual detection of these diseases by humans. This study presents an intelligent approach for the detection of cotton plant diseases using Convolutional Neural Networks (CNNs) with a focus on ResNet-152V2 architecture. Leveraging deep learning techniques, specifically ResNet-152V2, the model exhibits robust performance in identifying various diseases affecting cotton plants. The research involved training the model on a diverse dataset encompassing different cotton leaf diseases. Results demonstrate a better accuracy, with the proposed approach achieving an impressive precision in disease detection. The utilization of ResNet-152V2 enhances the model's capability to accurately classify and diagnose cotton plant diseases, showcasing its efficacy for real-world applications. The study contributes to the advancement of automated disease detection systems in agriculture, particularly in the context of cotton crops.

Integrated Management of the Cotton Charcoal Rot Disease Using Biological Agents and Chemical Pesticides.

Charcoal rot disease (CRD), caused by the phytopathogenic fungus Macrophomina phaseolina, is a significant threat to cotton production in Israel and worldwide. The pathogen secretes toxins and degrading enzymes that disrupt the water and nutrient uptake, leading to death at the late stages of growth. While many control strategies were tested over the years to reduce CRD impact, reaching that goal remains a significant challenge. The current study aimed to establish, improve, and deepen our understanding of a new approach combining biological agents and chemical pesticides. Such intervention relies on reducing fungicides while providing stability and a head start to eco-friendly bio-protective Trichoderma species. The research design included sprouts in a growth room and commercial field plants receiving the same treatments. Under a controlled environment, comparing the bio-based coating treatments with their corresponding chemical coating partners resulted in similar outcomes in most measures. At 52 days, these practices gained up to 38% and 45% higher root and shoot weight and up to 78% decreased pathogen root infection (tracked by Real-Time PCR), compared to non-infected control plants. Yet, in the shoot weight assessment (day 29 post-sowing), the treatment with only biological seed coating outperformed (p < 0.05) all other biological-based treatments and all Azoxystrobin-based irrigation treatments. In contrast, adverse effects are observed in the chemical seed coating group, particularly in above ground plant parts, which are attributable to the addition of Azoxystrobin irrigation. In the field, the biological treatments had the same impact as the chemical intervention, increasing the cotton plants' yield (up to 17%), improving the health (up to 27%) and reducing M. phaseolina DNA in the roots (up to 37%). When considering all treatments within each approach, a significant benefit to plant health was observed with the bio-chemo integrated management compared to using only chemical interventions. Specific integrated treatments have shown potential in reducing CRD symptoms, such as applying bio-coating and sprinkling Azoxystrobin during sowing. Aerial remote sensing based on high-resolution visible-channel (RGB), green-red vegetation index (GRVI), and thermal imaging supported the above findings and proved its value for studying CRD control management. This research validates the combined biological and chemical intervention potential to shield cotton crops from CRD.

Soil carbon and aggregate stability are positively related and increased under combined soil amendment, tillage, and cover cropping practices

AbstractAgricultural practices alter the organic carbon dynamics in soil. An experiment was conducted to study the effect of carbon amendments, tillage, and cover cropping on permanganate oxidizable carbon (POXC), total organic carbon (TOC), and wet aggregate stability (WAS) in a 2‐year crop sequence (corn–cover crop–cotton–cover crop) at the Texas A&amp;M Research Farm. Two carbon amendments (biochar and composted biosolid) were applied at the rate of 500 kg C ha−1, along with a control. Two tillage practices were evaluated: conventional tillage (CT) and no‐tillage (No‐Till). A cover crop (CC) mixture of oat, mustard, and pea and no cover crop (No‐CC) were also evaluated. Treatments were arranged in a split‐split plot design with four replications. Amending the soil with carbon as composted biosolid or biochar affected POXC at both the 0‐ to 5‐ and 5‐ to 15‐cm depths. The POXC was significantly higher for the biochar treated plots for corn and CC after corn but significantly lower POXC was observed after cotton with biochar‐treated plots. The POXC increased under No‐Till compared with CT and CC plots relative to No‐CC plots. The TOC was not sensitive to soil management practices. The POXC and TOC both decreased with depth. The WAS greater under No‐Till and CC plots. The POXC and WAS were influenced by soil management practices and can be useful indicators to assess short‐term soil health improvements. The POXC and WAS were positively related, suggesting that one may be used to predict the other.

A novel mutation in mitochondrial cytochrome b conferring resistance to bifenazate in two-spotted spider mite Tetranychus urticae Koch (Acarina: Tetranychidae).

The two-spotted spider mite Tetranychus urticae causes significant damage to ornamental, cotton, sugarcane and horticultural crops in Australia. It has a long history of developing resistance to many acaricides including bifenazate. A mutation in the conserved cd1- and ef-helices of the Qo pocket of cytochrome b is recognized as the primary mechanism of bifenazate resistance. To investigate the resistance mechanisms against bifenazate in Australian two-spotted spider mite, we sequenced the complete mitochondrion genome of five mite strains including a susceptible and bifenazate-resistant strain. We identified a novel mutation D252N in the G126S background at cytochrome b being the cause of bifenazate resistance in a bifenazate-resistant strain, Bram. We validated the role of this mutation combination by reciprocal crosses between a bifenazate resistant and susceptible strain. By doing these crosses we confirmed the pattern of inheritance was maternal. Additionally, mitochondrial heteroplasmy was not observed by single mite genotyping of the mutations in cytb in a known bifenazate-resistant strain Bram. The phylogenetic analysis with the complete mitochondrion genome sequences revealed that Australian two-spotted spider mite strains are closely related to the green form of T. urticae found in China. The novel mutation D252N found in the cytochrome b in the G126S background was revealed to be the main cause of bifenazate resistance in the Australian T. urticae strain Bram. © 2024 Society of Chemical Industry.

Sensitivity of 40 Pythium species to the new fungicide Picarbutrazox

Picarbutrazox is the active ingredient of Vayantis®, a novel seed treatment fungicide developed by Syngenta and discovered by Nippon Soda. Vayantis® received registration from the US EPA in 2021. Picarbutrazox is a systemic fungicide and belongs to the tetrazolyloximes chemical group of fungicides, and its mode of action is still unknown (FRAC group U17). Picarbutrazox targets oomycete plant pathogens and controls diseases caused by Pythium, Phytophthora, and downy mildews. Picarbutrazox is in a new mode-of-action group with no known cross-resistance to other oomycete compounds. The sensitivity to Picarbutrazox of 528 isolates belonging to 40 different Pythium species was determined in vitro. Pythium isolates were collected from infected plants including corn, soybean, cotton, and other crops from 17 different states. Pythium isolates were also obtained from collections kept at universities. Isolates were identified at the species level using morphological and molecular tools. Mycelial growth inhibition was quantified using the following Picarbutrazox concentrations: 0, 0.00001, 0.0001, 0.001, 0.01, and 0.1 mg/L. All the species of Pythium tested were sensitive to Picarbutrazox. The sensitivity distribution (EC50 values) across all the Pythium species tested had a mean sensitivity of 0.000376 mg ai/L. The Picarbutrazox sensitivity of 40 species of Pythium species has been documented, and results demonstrate the high in vitro intrinsic activity of this fungicide to control Pythium species. The high potency of Picarbutrazox to control Pythium diseases in corn, soybeans, cotton, and other crops has also been demonstrated under field conditions when used as a seed treatment. Picarbutrazox, with its novel mode of action, will contribute to the management of potential fungicide resistance issues developed by other fungicide products controlling Pythium, including mefenoxam and ethaboxam.

Modified HDBSCAN based segmentation hyperspectral image segmentation for cotton crop classification

ABSTRACT The most crucial element in accurately monitoring and assessing cotton development is having effective cotton maps. In order to make decisions about governance, precision agriculture, and field management, the county-scale cotton remote sensing categorisation models must be evaluated. The main objective of this research is to propose novel hyperspectral image segmentation approach for cotton crops to monitor the crops and identify early signs of disease. The proposal for a hyperspectral image-based classification of cotton crops is made in this research. Using ‘Modified Hierarchical density-based spatial clustering of applications with noise (HDBSCAN),’ the procedure begins with the input image being segmented. Following this, features based on vegetation indices, hybrid vegetation indices, and statistical characteristics will be retrieved and trained with the classification model to ensure proper classification. Specifically, EVI, NDVI, and RVI are features that are based on vegetation indices. Using techniques like SVM, CNN, DBN, DT, and Improved Bidirectional Long Short-Term Memory (IBi-LSTM), this study replicates a stacked ensemble framework for classification. While the MHDBSCAN achieved the maximum accuracy value of 97.97%, the conventional techniques achieved limited accuracy. Thus, the MHDBSCAN far more effective at classifying the crop utilising hyperspectral image segmentation and the classification become more precise and accurate.

Evolution of Crop Planting Structure in Traditional Agricultural Areas and Its Influence Factors: A Case Study in Alar Reclamation

This research provides a comprehensive analysis of the spatiotemporal evolution of the regional cropping structure and its influencing factors. Using Landsat satellite images, field surveys, and yearbook data, we developed a planting structure extraction model employing the classification regression tree algorithm to obtain data on the major crop cultivation and structural characteristics of Alar reclamation from 1990 to 2023. A dynamic model and transfer matrix were used to analyze temporal changes, and a centroid migration model was used to study spatial changes in the cropping structure. Nonparametric mutation tests and through-traffic coefficient analysis were utilized to quantify the main driving factors influencing the cropping structure. During the period of 1990–2023, the cotton area in the Alar reclamation region expanded by 722.08 km2, while the jujube exhibited an initial increase followed by a decrease in the same period. The primary reasons are linked to the cost of purchase, agricultural mechanization, and crop compatibility. In the Alar reclamation area, cotton, chili, and jujube are the primary cultivated crops. Cotton is mainly grown on the southern side of the Tarim River, while chili cultivation is concentrated on the northern bank of the river. Over the years, there has been a noticeable spatial complementarity in the distribution and density of rice and cotton crops in this region. In the Alar reclamation, the main factors influencing the change in cultivated land area are cotton price, agricultural machinery gross power, and population. Consequently, implementing measures such as providing planting subsidies and other policy incentives to enhance planting income can effectively stimulate farmers’ willingness to engage in planting activities.

Residual phytotoxicity effects of different integrated herbicides management in soils from field-treated plots and post-harvest field

Soil residual phytotoxicity of commonly used herbicides in cotton crop in India were investigated through bioassay. Pendimethalin at 900 g a.i. ha-1, oxyfluorfen at 180 g a.i. ha-1, oxadiargyl at 90 g a.i. ha-1, quizalofop-ethyl at 40 g a.i. ha-1, imazethapyr at 75 g a.i. ha-1, propaquizafop at 50 g a.i. ha-1 and glyphosate at 960 g a.i. ha-1 were applied to field plots of 6.0 x 6.0 m2. Sorghum and cucumber were used in the bioassay and succeeding crops groundnut, green gram &amp; pearl millet to test the residual effect of herbicides in the soil of the treated plots and post-harvest field. Soil, sampled at 30 day after treatment (DAT) for pre emergence herbicide and until 2 weeks (WAT) for post emergence herbicide later, show significant affect the seed germination and seedling development stage of sorghum and cucumber. There was no residual toxicity of pre and post-emergence herbicides as observed through field bioassay after harvesting cotton crop and it was found safe to grow groundnut, green gram and pearl millet as succeeding crop.

Sine tangent search algorithm enabled LeNet for cotton crop classification using satellite image

Economic growth of country largely depends on crop production quantity and quality. Among various crops, cotton is one of the major crops in India, where 23 percent of cotton gets exported to various other countries. To classify these cotton crops, farmers consume much time, and this remains inaccurate most probably. Hence, to eradicate this issue, cotton crops are classified using deep learning model, named LeNet in this research paper. Novelty of this paper lies in utilization of hybrid optimization algorithm, named proposed sine tangent search algorithm for training LeNet. Initially, hyperspectral image is pre-processed by anisotropic diffusion, and then allowed for further processing. Also, SegNet is deep learning model that is used for segmenting pre-processed image. For perfect and clear details of pre-processed image, feature extraction is carried out, wherein vegetation index and spectral spatial features of image are found accurately. Finally, cotton crop is classified from segmented image and features extracted, using LeNet that is trained by sine tangent search algorithm. Here, sine tangent search algorithm is formed by hybridization of sine cosine algorithm and tangent search algorithm. Then, performance of sine tangent search algorithm enabled LeNet is assessed with evaluation metrics along with Receiver Operating Characteristic (ROC) curve. These metrics showed that sine tangent search algorithm enabled LeNet is highly effective for cotton crop classification with superior values of accuracy of 91.7%, true negative rate of 92%, and true positive rate of 92%.

Occurrence of Amaranthus palmeri in Israeli agriculture: Status of spread and response to glyphosate and trifloxysulfuron

AbstractAmaranthus palmeri (Palmer Amaranth) is a dioecious annual weed species, originating from the Southern USA, spreading rapidly beyond its original range into Europe and the Mediterranean region. In Israel, A. palmeri distribution has expanded quickly in recent years, with farmers reporting on weed control failure using acetolactate synthase (ALS) inhibitors. Furthermore, recent studies have documented glyphosate‐resistant cases from other countries in the region, such as Spain, Greece and Turkey. We conducted a survey in order to understand A. palmeri distribution and study the occurrence of herbicide resistance to both glyphosate and trifloxysulfuron in different fields across the country. According to our data, A. palmeri population locations are aligned with the major agricultural areas for summer field crops, including the Hula Valley, Jezreel Valley and the Southern Coastal Plain. Regarding herbicide responses, while several populations showed a reduced response to glyphosate, dose–response assays did not show resistance to the recommended labelled field rate. For the ALS inhibitor trifloxysulfuron, the survival percentage was very high, especially in the southern coastline region (77%). Four populations used for dose–response studies were highly resistant, surviving at four times the recommended labelled field rate of trifloxysulfuron (30 g a.i. ha−1). Sequencing of the ALS gene, Trp574 to Leu alteration in resistant populations was recorded in all populations. The high level of resistance observed in this study, alongside the target‐site mutation found in populations of A. palmeri, endangers the future use of ALS inhibitors in corn, cotton, and other summer crops grown in Israel.

A comparative study on area, production, productivity, cost of cultivation and production of cotton crop in Telangana and India

A comparative study on area, production, productivity, cost of cultivation and production of cotton crop in Telangana and India

Resistance risk assessment, cross-resistance potential and realized heritability of resistance to methomyl in Musca domestica Linnaeus.

The use of insecticides in agricultural settings often exerts negative effects on nontarget species. Methomyl, a broad-spectrum carbamate insecticide, is recommended to manage a number of insect pests of the cotton crop. Recently, Musca domestica, which is a nontarget insect species in cotton fields, has shown resistance to methomyl in Pakistan. The present study tried to assess resistance-risk assessment, rapidity of resistance development to methomyl, cross-resistance potential to other insecticides, resistance heritability and to forecast the projected rate of resistance development under field conditions. For this purpose, a field strain of M. domestica with 186 fold resistance to methomyl was re-selected in the laboratory for eight consecutive generations. Consequently, LD50 values increased rapidly (126.64 ng/fly to 3112.79 ng/fly) compared to those before selection experiments. Similarly, RR values increased from 186 to 3113 fold as a result of the selection process. However, resistance to methomyl did not remain stable when the selected strain (Meth-SEL) reared for the next five generations in a pesticide free environment. The Meth-SEL strain also developed cross-resistance to permethrin. The realized heritability (h2) value for the Meth-SEL strain was 0.39 with 27% average mortality of M. domestica. Assuming the standard deviation (σp) value 0.27 and the h2 value 0.39 for eight generations of continuous exposure to methomyl, then five, seven, eight, ten and twelve generations at 90, 80, 70, 60 and 50% selection intensity, respectively, would be required for a tenfold increase in the LD50 value of methomyl. In conclusion, the Meth-SEL strain of M. domestica exhibited a high risk of resistance development to methomyl under continuous selection pressure. Resistance increased rapidly during selection experiments that reflect the probability of resistance development under field conditions if M. domestica receive exposures to methomyl during its applications for the management of target pest species.

The GaKAN2, a KANADI transcription factor, modulates stem trichomes in Gossypium arboreum.

GaKAN2, a member of the KANADI family, was found to be widely expressed in the cotton tissues and regulates trichome development through complex pathways. Cotton trichomes are believed to be the defense barrier against insect pests. Cotton fiber and trichomes are single-cell epidermal extensions with shared regulatory mechanisms. Despite several studies underlying mechanism of trichome development remains elusive. The KANADI is one of the key transcription factors (TFs) family, regulating Arabidopsis trichomes growth. However, the function of KANADI genes in cotton remains unknown. In the current study genome-wide scanning, transcriptomic analysis, gene silencing, subcellular localization, and yeast two-hybrid techniques were employed to decipher the function of KANADI TFs family genes in cotton crop. A total of 7 GaKAN genes were found in the Gossypium arboreum. Transcriptomic data revealed that these genes were significantly expressed in stem and root. Moreover, GaKAN2 was widely expressed in other tissues also. Subsequently, we selected GaKAN2 to validate the function of KANADI genes. Silencing of GaKAN2 resulted in a 24.99% decrease in single-cell trichomes and an 11.33% reduction in internodal distance, indicating its potential role in regulating trichomes and plant growth. RNA-Seq analysis elucidated that GaSuS and GaERS were the downstream genes of GaKAN2. The transcriptional activation and similarity in silencing phenotype between GaKAN2 and GaERS suggested that GaKAN2 regulates trichomes development through GaERS. Moreover, KEGG analysis revealed that a significant number of genes were enriched in the biosynthesis of secondary metabolites and plant hormone signal transduction pathways, thereby suggesting that GaKAN2 regulates the stem trichomes and plant growth. The GFP subcellular localization and yeast transcriptional activation analysis elucidated that GaKAN2 was located in the nucleus and capable of regulating the transcription of downstream genes. This study elucidated the function and characteristics of the KANADI gene family in cotton, providing a fundamental basis for further research on GaKAN2 gene in cotton plant trichomes and plant developmental processes.

Soil water movement may regulate soil water consumption and improve cotton yields under different cotton cropping systems

By quantifying the soil water movement (SWM) in crop planting systems, we can better understand the soil water consumption (SWC) and crop yield relationship; this finding is significant for determining the field water cycle and reducing agricultural water waste. In this paper, a case study was conducted on cotton production. Soil moisture sensors were set at depths of 10–110 cm under three cotton cropping systems (monoculture cotton (MC), wheat/delayed intercropped cotton (WIC), and wheat/direct-seeded cotton (WDC)) based on spatial grid methods; a geostatistical grid calculus was used to calculate SWM; and the crop and meteorological influence mechanisms on cotton lint yield were comprehensively analyzed. At the squaring stage, SWC and vertical SWM were significantly correlated with light, temperature and water conditions. At the flowering and boll development stage, SWC and vertical SWM were collectively affected by meteorological conditions and crops, and they were positively correlated with lint yield. The aboveground and belowground biomass accumulation at the flowering and boll development stage positively affected vertical SWM in and between cotton rows. Vertical SWM in cotton rows increased SWC in cotton rows. SWC in cotton rows and aboveground biomass positively impacted lint yield formation; SWC between rows negatively impacted lint yield. The SWC and vertical SWM between rows in the MC seedling stage exceeded those in cotton rows, and more precise irrigation at the seedling stage reduced water waste. The WIC horizontal SWC at the squaring and flowering and boll opening stages was relatively high, moving from the row midline to cotton row. A better SWC distribution in and between cotton rows promoted water utilization in the cotton rows; this method was feasible for improving cotton yield in diverse planting systems. The results could optimize precision irrigation management at different cotton growth stages and provide a theoretical reference for promoting sustainable agricultural production and climate adaptation.