Search Results (99)

The necrotrophic pathogen Sclerotinia sclerotiorum compromises the physiological and anatomical integrity of cotton, leading to substantial economic losses due to rapid tissue necrosis, stem blight, boll rot, and leaf wilting. In this context, the use of endophytic microorganisms emerges as a promising strategy for the biocontrol of white mold. This study tested the hypothesis that endophytic fungal strains isolated from the roots of Butia purpurascens, a palm tree endemic to the Cerrado biome, could mitigate disease symptoms in Gossypium hirsutum L. To evaluate this, cotton plants were subjected to biotic stress imposed by S. sclerotiorum to assess the effectiveness of seven fungal strains in attenuating disease. The impact of the pathogen was monitored through growth variables, gas exchange, leaf temperature, chlorophyll a fluorescence, antioxidant enzyme activity, proline and malondialdehyde (MDA) levels, and the incidence of rot in petioles, leaves, and flower buds. Overall, inoculation with endophytic fungi significantly alleviated the effects of the phytopathogen, promoting vegetative growth and optimizing physiological performance. Treated plants exhibited alleviated stress in primary photochemistry, reduced non-photochemical energy dissipation, and stable carbon fixation. Additionally, efficient modulation of the antioxidant system and preservation of anatomical structures were observed, minimizing the severe symptoms of white mold. Notably, the non-pathogenic strains BP10EF (Gibberella moniliformis), BP16EF (Penicillium purpurogenum), and BP33EF (Hamigera insecticola) acted as potent physiological modulators, yielding responses similar to those of healthy plants. These results highlight the biotechnological potential of these endophytic strains, which can be explored as both growth promoters and resistance inducers in cotton against white mold. Full article

This article analyzes the scale, fluctuations and geographical distribution of olive (Olea europaea) cultivation in Palestine over 550 years, from the Late Mamluk period (1300–1517), through the Ottoman era (1517–1917), until the end of the British Mandate in 1947. Although olive oil played a dominant role in the diet and the local economy, there is currently no research that measures and quantifies the number of olive trees or the number of villages and towns that cultivated olive trees and produced olive oil. We reconstruct the agricultural landscape with its vast olive groves and examine the cultural history of olive tree farming, the growth of the olive oil industries and their economic role and importance. The earliest figures we have, that are from the year 1596, show that 400 villages cultivated 1,400,794 olive trees. By 1943, there were 6,053,367 olive trees that were cultivated by 644 villages. We found a strong correlation (R² = 0.96, p < 0.01) between the number of olive trees and the number of villages, indicating that olive oil demand and the olive oil industry align with population size. The research data derives from a variety of medieval local chroniclers, as well as diaries by European, North African and Middle Eastern travelers who provide descriptions of olive groves and the olive oil industry. Among the most important sources are the 1596 Ottoman tax registers. The tax registers are the first document that present clear-cut figures on the numbers of olive trees, olive presses and the names of the villages that cultivated olive groves. The main sources for the last period dealt with in this study are the British Mandate maps (1943), which display the acreage of the different crops across Palestine. The data from the maps is supplemented by two modern works on olive cultivation written by agronomists Assaf Goor (b. 1894) and Ali Nasouh (b. 1906) who were born in Palestine and employed by the British department of agriculture. The analysis of data shows that demands of local and oversea markets; the olive oil soap industry, which was based on the local olive oil; as well as competing agricultural crops like sugarcane, cotton and citrus, contributed to a complex economic structure. Olive tree cultivation did not depend on government investment. Olive groves in Palestine were rain fed, and, except for the harvest, they required relatively few working days a year. Hence, moderate policies (low taxation during periods of drought and low yields) adopted by enterprising local rulers and the central British government created a unique and relatively balanced relationship between rulers and farmers, which encouraged olive cultivation and led to a constant increase in the number of olive trees and the development of the olive oil industry. Full article

Apolygus lucorum, a phytozoophagous mirid bug, plays an important role in the species interactions within fruit tree and cotton ecosystems. Previous research has mainly focused on the phytophagous damage that it causes to crops, while its role as a predator of arthropods remains poorly understood. In this study, we systematically investigated the functional responses of A. lucorum to three crop pests: eggs of Helicoverpa armigera, nymphs of Aphis gossypii, and nymphs of Bemisia tabaci. The results show that the predatory behavior of A. lucorum towards all three prey species followed a Holling type II functional response model. Predatory performance varied significantly depending on prey species, developmental stage, and sex of the mirid. The theoretical maximum predation rate was highest for A. gossypii (833.33 individuals/day) and lowest for B. tabaci nymphs. Adult mirids and older nymphs (4th instar) exhibited higher predation rates than younger nymphs. Field-collected A. lucorum from Bt cotton fields were analyzed using molecular diagnostics, and the result confirmed natural predation on A. gossypii, which was consistent with observed pest occurrence patterns in the field. Overall, this study clarifies the prey selectivity and stage-dependent predatory strategies of A. lucorum, providing insights into its trophic flexibility as a facultative predator. These findings contribute to a more comprehensive understanding of its ecological role in agricultural ecosystems, but do not support its use as a biological control agent given its predominantly phytophagous nature and documented pest status. Full article

Advances in Industry 4.0 manufacturing have accelerated the adoption of machine learning (ML) for automated classification. Polyester (PES), a widely used synthetic fiber, competes with natural fibers like cotton and other synthetics, highlighting the need for continuous research and improvement. In the textile sector, distinguishing recycled polyester (rPES) from virgin polyester (vPES) remains challenging due to overlapping chemical signatures and material variability. A combination of Fourier transform infrared (FTIR) spectroscopy and ML has not been explored for this purpose. In this study, we evaluated ML models to discriminate three PES fiber types (45 vPES, 65 rPES, and 55 mixed PES) using 165 FTIR spectra across four spectral regions, R1, R2, R3, and R4, as well as their combined representation. Six ML approaches were tested on data reduced with fast independent component analysis (FastICA) (1–30 components) using an 80/20 train–test dataset split. The Decision Tree classifier achieved the highest Accuracy in four of the five spectral evaluations, with classification accuracies ranging from 66.67% to 77.78% for region R4, which also had a balanced classification profile with an area-under-the-curve (AUC) value of 0.81. Notably, despite the moderate overall Accuracy, the model achieved 100% discrimination of rPES when distinguishing it from both mixed and vPES. Mixed fibers remained the most difficult to classify, highlighting the need for improved feature representation. Full article

Water scarcity and ecological degradation driven by the expansion of irrigated agriculture in arid regions urgently necessitate a rigorous assessment of the combined impacts of climate change and crop-structure adjustments on irrigation water requirements (IWR). Taking the Qarqan River Basin as a case study, this study establishes an integrated framework that incorporates remote sensing (Landsat/MODIS), the AquaCrop-OS crop model, and a CNN-LSTM deep learning architecture to simulate historical IWR (2000–2024) and project future trajectories under CMIP6 climate scenarios. The results indicate that: (1) from 2000 to 2024, fruit tree area expanded from 120.3 to 320.3 km², cotton stabilized at approximately 165.3 km² after peaking at 187.9 km² in 2014, wheat recovered to 113.1 km², and maize varied between 23.7 and 85.0 km², indicating that fruit trees have become the dominant crop type. (2) Over the same period, total basin-wide IWR increased by 91% (3.7 × 10⁸ to 7.1 × 10⁸ m³), with fruit trees accounting for 44–68% of this growth. Logarithmic mean Divisia index (LMDI) decomposition further shows that meteorological factors and human activities jointly drove the increase in IWR, with cultivated-area expansion and cropping-structure change contributing most, while improvements in agricultural water-use efficiency partially offset the rise. (3) Projections for 2025–2100 suggest stronger structural dominance of fruit trees and cotton; the growing share of water-intensive cash crops may further elevate irrigation pressure. Under SSP5-8.5, a 30% reduction in fruit tree area in the late century could save 4.3% of irrigation water (0.33 × 10⁸ m³). Overall, this study provides dynamic projections and decision support for adaptive regulation of agricultural water resources in arid regions. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing. Full article

Genetic diversity and kinship information of cotton germplasm resources are fundamental to breeding, providing a theoretical basis for the rational selection of hybrid parents and further breeding of new varieties with high yield, high quality, and multi-resistance. This study utilized cotton varieties that have been used for variety improvement or are widely planted in the Northern Xinjiang cotton region as materials. Genotyping was performed using the ZJU CottonSNP40K chip to analyze genetic diversity and kinship relationships. A total of 26,852 high-quality SNP markers were obtained, including 15,222 SNPs in subgenome A and 11,630 SNPs in subgenome D. The number of SNPs per chromosome ranged from 547 (A04) to 2168 (A08). Based on phylogenetic tree and principal component analysis, the 83 materials were clustered into 3 major subgroups. Group I contained varieties introduced from the former Soviet Union and the United States, which have become important parents for cotton breeding in Northern Xinjiang. Among them, as many as 27 varieties were derived and selected from the introduced US variety ‘Beiersinuo’ as a parent. While playing an important role in cotton breeding in Northern Xinjiang, this has also led to the current situation where the genetic base of Northern Xinjiang varieties is relatively narrow (average kinship coefficient 0.72). It clarifies the significant role of introduced American variety ‘Beiersinuo’ in the breeding of Northern Xinjiang cultivars and provides theoretical guidance for broadening the genetic base of Northern Xinjiang cotton varieties. Full article

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. Full article

In this study, the effects of composting phase and bulking agent on macronutrient extraction and the chemical, physicochemical, and biological properties of 20 compost teas from poultry waste composting mixtures were evaluated. Phosphorus (P) extraction was more efficient during stabilization after the thermophilic phase; however, water-soluble P declined as composting progressed. K was more amenable to extraction, with yields ranging from 30% to 70%, followed by N (2% to 12%) and P (1% to 7%). Compost tea quality was clearly affected by both the bulking agent and the composting stage. Bulking agents that accelerate the process, such as cotton waste (CW) and Napier grass (NG), contributed to nutrient mineralization, increasing availability in the compost tea but also raising salt contents responsible for phytotoxicity. In contrast, tree trimmings (TT), sawdust (S), and sugarcane bagasse (SCB) showed better results, striking a balance between nutrient availability and salt content. The period between the thermophilic phase and cooling was the most suitable for extraction, providing the greatest contribution of water-soluble nutrients. This study highlights the influence of bulking agents and composting phases on nutrient extraction and phytotoxicity of compost teas and provides new insights into the role of electrical conductivity as a threshold indicator for safe agricultural application. Full article

Verticillium wilt, caused by Verticillium dahliae, severely threatens various crops and trees worldwide. This study aimed to characterize the function of a CUE (coupling of ubiquitin conjugation to endoplasmic reticulum (ER) degradation)-domain-containing protein, VdCUE, in V. dahliae, which exhibits sequence divergence between the defoliating strain XJ592 and the non-defoliating strain XJ511. We generated ∆VdCUE-knockout mutants and evaluated their phenotypes in growth and virulence. Functional analyses included verifying the signal peptide activity of VdCUE, testing its ability to induce cell death or inhibit BAX-induced cell death in Nicotiana benthamiana leaves, and identifying host targets via yeast two-hybrid screening. The ∆VdCUE mutants showed reduced formation of melanized microsclerotia but no other obvious growth defects. Cotton plants infected with the ∆VdCUE mutants exhibited a significantly lower disease index and defoliation rate. VdCUE was confirmed to be secreted via a functional signal peptide, but it neither triggered cell death nor inhibited BAX-induced cell death. Three putative host targets were identified and supported by AI-based three-dimensional structural modeling, including tRNA-specific 2-thiouridylase, peptidyl-prolyl cis-trans isomerase, and 40S ribosomal protein, which may mediate VdCUE-dependent virulence regulation. These findings reveal VdCUE as a key virulence factor in V. dahliae, contributing to our understanding of its pathogenic mechanism. Full article

Background: Cotton, a key global economic crop, suffers yield and quality losses due to salt stress. This study aims to analyze the cotton STARD gene family and its role in salt stress responses. Methods: We conducted a genome-wide analysis of the STARD gene family in four cotton species, using phylogenetic trees, chromosomal mapping, and collinearity analyses to explore their evolutionary relationships and expansion mechanisms. We also examined gene structures, conserved motifs, and promoter cis-elements. Results: STARD genes are evenly distributed across the four cotton species. Segmental duplication was found to be the main driver of gene expansion, with most pairs undergoing purifying selection. Distinct structural features and potential roles in plant growth and stress responses were identified. Notably, 11 GhSTARD genes showed significant expression changes under salt stress, especially GhSTARD45 in root tissues. Conclusions: This study provides new insights into the function and salt stress response mechanisms of the cotton STARD gene family, suggesting GhSTARD45 plays a key role in root-mediated salt tolerance and highlighting the potential of STARD genes in enhancing cotton’s salt tolerance. Full article

This study aimed to clarify the effects of jujube–cotton intercropping on cotton yield and photosynthetic characteristics, providing a theoretical basis for its application in the oasis irrigation areas of southern Xinjiang and offering practical recommendations to local farmers for increasing economic benefits. The effects were investigated from 2020 to 2023 using Zhongmian 619 cotton and juvenile jujube trees. Changes in leaf area index (LAI), transpiration rate (Tr), stomatal conductance (Gs), net photosynthetic rate (Pn), intercellular CO₂ concentration (Ci), yield, and economic benefits were evaluated over the years. The results showed that (1) a positive correlation was observed between LAI and the photosynthetic characteristics of cotton. Compared to monoculture cotton, intercropped cotton exhibited lower Pn, Gs, and Tr, and at the peak boll stage, monoculture cotton had significantly higher photosynthetic characteristics, indicating that intercropping affected cotton photosynthesis. (2) From 2020 to 2023, the land equivalent ratio (LER) of jujube–cotton intercropping remained above 1, with overall yield and economic benefit surpassing those of monoculture cotton and jujube, particularly in 2023 when the yield increased by 55.35%. (3) A significant positive correlation was found between cotton yield and LAI. In conclusion, jujube–cotton intercropping enhances photosynthesis, improving yield, economic benefits, and land use efficiency. Full article

This paper is focused on the modification of commercial resin by using biobased fillers during stereolithography (SLA) 3D printing. This research aims to create a composite material with a matrix made of commercially available photosensitive resin modified with a filler based on secondary raw materials and materials formed as by-products in the processing of biological materials. The research determines the effect of different fillers on the tensile properties and hardness of samples printed using SLA 3D printing, and it also investigates their integrity using SEM analysis. This study aims to evaluate the feasibility of using these fillers for producing 3D-printed parts with SLA technology. The results of this study open up new possibilities for designing modified composite materials based on additive SLA 3D-printing technology using biological fillers. Within the framework of research activities, a positive effect on tensile properties and an improved interfacial interface between the matrix and the filler was demonstrated for several tested fillers. Significant increases in tensile strength of up to 22% occurred in composite systems filled with cotton flakes (CF), miscanthus (MS), walnut (WN), spruce tree (SB), wheat (WT) and eggshells (ES). Significant potential for further research activities and added value was shown by most of the tested bio-fillers. A significant contribution of the current research is the demonstration of the improved mechanical performance of photosensitive resin modified with natural fillers. Full article

Seed quality traits, such as seed size, oil and protein content, mineral accumulation, and morphological characteristics, are crucial for enhancing crop productivity, nutritional value, and marketability. Traditional quantitative trait loci (QTL) mapping methods, such as linkage analysis and genome-wide association studies (GWAS), have played fundamental role in identifying loci associated with these complex traits. However, these approaches often struggle with high-dimensional genomic data, polygenic inheritance, and genotype-by-environment (GXE) interactions. Recent advances in artificial intelligence (AI) and machine learning (ML) provide powerful alternatives that enable more accurate trait prediction, robust marker-trait associations, and efficient feature selection. This review presents an integrated overview of AI/ML applications in QTL mapping and seed trait prediction, highlighting key methodologies such as LASSO regression, Random Forest, Gradient Boosting, ElasticNet, and deep learning techniques including convolutional neural networks (CNNs) and graph neural networks (GNNs). A case study on soybean seed mineral nutrients accumulation illustrates the effectiveness of ML models in identifying significant SNPs on chromosomes 8, 9, and 14. LASSO and ElasticNet consistently achieved superior predictive accuracy compared to tree-based models. Beyond soybean, AI/ML methods have enhanced QTL detection in wheat, lettuce, rice, and cotton, supporting trait dissection across diverse crop species. I also explored AI-driven integration of multi-omics data—genomics, transcriptomics, metabolomics, and phenomics—to improve resolution in QTL mapping. While challenges remain in terms of model interpretability, biological validation, and computational scalability, ongoing developments in explainable AI, multi-view learning, and high-throughput phenotyping offer promising avenues. This review underscores the transformative potential of AI in accelerating genomic-assisted breeding and developing high-quality, climate-resilient crop varieties. Full article