Search Results (7)

The objective of this study is to assess the efficacy of walnut shell-derived activated carbon with phosphoric acid (WSAC) in the removal of ciprofloxacin (CIP), diclofenac (DC), and sulfamethoxazole (SMX) from aqueous solutions and real wastewater. WSAC was characterized using various analytical techniques such as specific surface area and pore size distribution determination, elemental analysis, SEM images, and FT-IR spectroscopy. The BET-specific surface area of WSAC was determined to be 1428 m² g⁻¹. The surface is characterized by the presence of irregular pits of varying dimensions and shapes. The adsorption of SMX, CIP, and DC from aqueous solutions using WSAC was tested under various parameters, including contact time, adsorbent dosage, initial concentration, pH, and temperature. The adsorption of SMX, CIP, and DC was found to be in accordance with the Langmuir isotherm model, which suggests that monomolecular adsorption is the predominant mechanism. The maximum adsorption capacities of WSAC towards SMX, CIP, and DC were calculated to be 476.2, 185.2, and 135.1 mg g⁻¹, respectively. The adsorption of SMX, CIP, and DC were found to be consistent with the pseudo-second-order model. Thermodynamic analyses demonstrated the spontaneous and endothermic nature of SMX, CIP, and DC adsorption onto WSAC. The adsorption performances of SMX, CIP, and DC on WSAC were found to be 60.2%, 77.4%, and 74.2%, respectively in the effluent from the municipal wastewater treatment plant. In conclusion, WSAC may be regarded as a readily available, eco-friendly, and efficient substance for the extraction of SMX, CIP, and DC from wastewater and aqueous solutions. Full article

The Leaf Area Index (LAI) is a key indicator for assessing fruit tree growth and productivity, and accurate estimation using hyperspectral technology is essential for monitoring plant health. This study aimed to improve LAI estimation accuracy in apricot, jujube, and walnut trees in Xinjiang, China. Canopy hyperspectral data were processed using fractional-order differentiation (FOD) from 0 to 2.0 orders to extract spectral features. Three feature selection methods—Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and their combination (CARS-SPA)—were applied to identify sensitive spectral bands. Various band combinations were used to construct three-band indices (TBIs) for optimal LAI estimation. Random forest (RF) models were developed and validated for LAI prediction. The results showed that (1) the reflectance spectra of jujube and walnut trees were similar, while apricot spectra differed. (2) The correlation between fractional-order differential spectra and LAI was highest at orders 1.4 and 1.7, outperforming integer-order spectra. (3) The CARS-SPA selected a smaller set of feature bands in the 1100~2500 nm, reducing collinearity and improving spectral index construction. (4) The RF model using TBI4 demonstrated high R², low RMSE, and an RPD value > 2, indicating optimal prediction accuracy. This approach holds promise for hyperspectral LAI monitoring in fruit trees. Full article

Biostimulants are an important alternative to improve and promote higher efficiency in cropping systems. Although the biostimulant industry has been developing for several years, there are still areas of opportunity for new sources of biostimulants as well as new ecofriendly extraction techniques that allow for a circular economy and the reuse of waste. Lignin is a heteropolymer that constitutes about 40% of the plant cell wall. A great source of lignin is agrowastes, giving it added value. Recently, its use has been tested in agronomy as a carrier of nutrients and pesticides. Walnuts are produced on a large scale in Northern Mexico, and the shell represents between 15 and 40% of its total weight. However, to obtain this biopolymer, to date, non-environmentally friendly techniques have been used; for this reason, it is necessary to find extraction alternatives to make this proposal sustainable. In this work, the obtaining and characterization of lignin through mild extraction conditions from nutshells and its evaluation as a biostimulant on the growth of tomato seedlings are reported. Lignin was extracted by hydrolysis with a mixture of acetic acid and distilled water (65:35 v/v). The results showed that it was possible to obtain 15% (w/w) lignin using mild solvents, evidenced by thermogravimetric analysis (TGA), proton magnetic nuclear resonance (H-RMN), and infrared (IR). Subsequently, lignin solutions were prepared at different concentrations, 0, 10, 50, and 100 ppm, and applied via foliar weekly to tomato seedlings. A greater fresh weight of the stem was found with 10 and 50 ppm, and the height and the fresh biomass increased with the three concentrations (10, 50, and 100 ppm), concluding that lignin extracted from nutshells using mild conditions can act as a plant biostimulant. Full article

The cultivation of walnuts (Juglans regia L.) has become increasingly popular worldwide due to the nutritional value of the nuts. Plant biostimulants (PBs) and nutrient-rich products have been increasingly used in agriculture to improve yield, quality, and abiotic stress tolerance. However, farmers need fast laboratory studies to determine the most suitable treatment per crop or ecosystem to take full advantage of these products. Evaluating nucleolar activity and protein content can provide clues about the most appropriate treatment. This study aimed to determine how five commercial products, four PBs based on seaweed extract and/or free amino acids and one boron-enriched fertiliser used as foliar sprays, affect walnut cv’s nucleolar activity and protein content. “Franquette” from an orchard located in NE Portugal was compared to untreated (control) plants. All treatments brought a low leaf mitotic index. The control showed the smallest nucleolar area, highest protein content, and highest frequency of nucleolar irregularities. Fitoalgas Green^®, Sprint Plus^®, and Tradebor^® showed the highest nucleolar area and lowest frequencies of nucleolar irregularities. The recruitment of proteins/enzymes for response against abiotic stresses may explain the high protein content in the control. Hence, the enhanced abiotic stress tolerance of the treated trees explains their lower protein content and frequency of nucleolar anomalies. Globally, the Fitoalgas Green^®, Sprint Plus^®, and Tradebor^® seem better suited for “Franquette” walnut trees under the edaphoclimatic conditions where trials were conducted. Full article

With China’s fruit tree industry becoming the largest in the world, accurately understanding the spatial distribution of fruit tree growing areas is crucial for promoting socio-economic development and rural revitalization. Remote sensing offers unprecedented opportunities for fruit tree monitoring. However, previous research has mainly focused on UAV and near-ground remote sensing, with limited accuracy in obtaining fruit tree distribution information through satellite remote sensing. In this study, we utilized the Google Earth Engine (GEE) remote sensing cloud platform and integrated data from Sentinel-1, Sentinel-2, and SRTM sources. We constructed a feature space by extracting original band features, vegetation index features, polarization features, terrain features, and texture features. The sequential forward selection (SFS) algorithm was employed for feature optimization, and a combined machine learning and object-oriented classification model was used to accurately extract fruit tree crop distributions by comparing key temporal phases of fruit trees. The results revealed that the backscatter coefficient features from Sentinel-1 had the highest contribution to the classification, followed by the original band features and vegetation index features from Sentinel-2, while the terrain features had a relatively smaller contribution. The highest classification accuracy for jujube plantation areas was observed in November (99.1% for user accuracy and 96.6% for producer accuracy), whereas the lowest accuracy was found for pear tree plantation areas in the same month (93.4% for user accuracy and 89.0% for producer accuracy). Among the four different classification methods, the combined random forest and object-oriented (RF + OO) model exhibited the highest accuracy (OA = 0.94, Kappa = 0.92), while the support vector machine (SVM) classification method had the lowest accuracy (OA = 0.52, Kappa = 0.31). The total fruit tree plantation area in Aksu City in 2022 was estimated to be 64,000 hectares, with walnut, jujube, pear, and apple trees accounting for 42.5%, 20.6%, 19.3%, and 17.5% of the total fruit tree area, respectively (27,200 hectares, 13,200 hectares, 12,400 hectares, and 11,200 hectares, respectively). The SFS feature optimization and RF + OO-combined classification model algorithm selected in this study effectively mapped the fruit tree planting areas, enabling the estimation of fruit tree planting areas based on remote sensing satellite image data. This approach facilitates accurate fruit tree industry and real-time crop monitoring and provides valuable support for fruit tree planting management by the relevant departments. Full article

Walnut (Juglans regia) planting is the main economic pillar industry in southern Xinjiang. Based on the Google Earth Engine (GEE) cloud platform, the NDVI maximum synthesis method was used to estimate changes in the walnut cultivation area in Ganquan Town, South Xinjiang, from 2017 to 2021. The simultaneous difference between NDVI and meteorological conditions was also used to monitor the growth and correlation analysis of walnuts from April to September 2021. To improve the classification accuracy of the extracted walnut plantation area, Sentinel-2 image data were selected, and features were trained using the random forest algorithm, and by combining topographic features, texture features, NDVI, and EVI. The results show that, compared with Statistical Yearbook data, the average error of the extracted walnut planted area is less than 10%, the overall classification accuracy is 92.828%, the average kappa coefficient is 90.344%, and the average walnut classification accuracy is 94.4%. The accuracy of the data was significantly improved by adding vegetation indices EVI and NDVI compared with the single vegetation index. An analysis of the results from monitoring comparative growth shows that the growth of walnuts in Ganquan was better during the hardcore and oil transformation stages compared with 2020, and in the fruit development stage, the growth was the same as in 2020, and overall, the growth of walnuts in 2021 was better than in previous years. Full article

Juglans regia Linn. is a valuable medicinal plant that possesses the therapeutic potential to treat a wide range of diseases in humans. It has been known to have significant nutritional and curative properties since ancient times, and almost all parts of this plant have been utilized to cure numerous fungal and bacterial disorders. The separation and identification of the active ingredients in J. regia as well as the testing of those active compounds for pharmacological properties are currently of great interest. Recently, the naphthoquinones extracted from walnut have been observed to inhibit the enzymes essential for viral protein synthesis in the SARS-CoV-2. Anticancer characteristics have been observed in the synthetic triazole analogue derivatives of juglone, and the unique modifications in the parent derivative of juglone have paved the way for further synthetic research in this area. Though there are some research articles available on the pharmacological importance of J. regia, a comprehensive review article to summarize these findings is still required. The current review, therefore, abridges the most recent scientific findings about antimicrobial, antioxidant, anti-fungal, and anticancer properties of various discovered and separated chemical compounds from different solvents and different parts of J. regia. Full article