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To meet the growing international demand for aromatic rice, this study, conducted at Uttar Banga Krishi Viswavidyalaya in Cooch Behar, West Bengal, aimed to enhance the yield and quality of the 'Tulaipanji' rice cultivar through advanced establishment methods and the use of organic nutrients over two years. The research tested three planting techniques: mechanical transplanting, wet direct seeding (using a drum seeder), and traditional methods, alongside four nutrient management strategies: vermicompost, farmyard manure, a mix of both, and conventional fertilizers. Findings revealed that mechanical transplanting significantly increased yield by over 31.98% and 71.05% compared to traditional methods and wet direct seeding, respectively. Using vermicompost alone as a nutrient source not only boosted yields by 21.31% over conventional fertilizers but also enhanced the rice's nutritional value and cooking quality. Moreover, soils treated with vermicompost showed higher dehydrogenase activity, indicating better soil health. Economically, mechanical transplanting with vermicompost was the most beneficial, yielding the highest net returns and benefit-cost ratios in both years studied. This approach presents a viable model for improving the sustainability of aromatic rice production globally, emphasizing the economic and environmental advantages of adopting mechanical planting techniques and organic fertilization methods.
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Fertilizantes , Valor Nutritivo , Oryza , Oryza/crecimiento & desarrollo , Oryza/metabolismo , Fertilizantes/análisis , Suelo/química , Agricultura/métodos , Producción de Cultivos/métodosRESUMEN
The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0-30 and 30-60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority.
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Ecosistema , Irán , Redes Neurales de la Computación , Suelo/química , Conservación de los Recursos Naturales , Modelos LogísticosRESUMEN
Effectively managing drought in the China-Pakistan Economic Corridor (CPEC) region requires a precise understanding of the three-dimensional characteristics of meteorological drought (MD) and agricultural drought (AD), as well as the factors that trigger their propagation. This study employed non-stationary drought indices (NSPEI and SSMI) to develop a cutting-edge 3-dimensional drought identification model. This model was used to detect MD and AD patterns from 1981 to 2022 in the CPEC region and was integrated with binomial logistic regression to identify the critical factors that drive drought propagation. This study's key findings include: 1) Between 1981 and 2022, droughts in Xinjiang, China, exhibited a discernible southward migration trend, while in Pakistan, droughts showed a northward migration pattern. Drought frequency and extent have increased over time, with affected regions becoming more widespread in CPEC. Notably, drought events with higher preceding drought contagion indices (DCI) were more likely to evolve into extreme, long-term droughts. 2) Drought area emerged as a significant positive triggering factor for drought propagation in the CPEC region. Conversely, snowmelt in Xinjiang and the leaf area index for low vegetation in Pakistan acted as triggering elements affecting negatively. 3) Various factors played a pivotal role during drought propagation process, including geographical coordinates of drought centroids, DCI, and temperature variations. Additionally, snowmelt and snow evaporation significantly impacted drought propagation in Xinjiang, while vegetation cover in Pakistan played a crucial role during the drought propagation process. By utilizing four regression models and conducting comprehensive attribution analysis, this study sheds light on the characteristics of drought propagation and the factors influencing it. These findings are valuable for enhancing early warning systems and implementing effective drought mitigation strategies in the CPEC region.
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Sequías , Pakistán , China , AgriculturaRESUMEN
Given the devastating effects and potential rising trends of compound drought and heatwave (CDH) events under the specter of global warming, this study embarks on a comprehensive examination of their spatial and temporal evolution, as well as the intrinsic drivers. This study identified CDH events based on the non-stationary standardized precipitation evapotranspiration index (NSPEI) and the relative threshold method. The study also quantified the spatial and temporal patterns of frequency, intensity, and duration of CDH events across different climatic sub-regions, quantifying the contribution of drought-heatwave interdependence to these events and assessing the impact of single extreme climate events on their proliferation. The study yielded several key findings: 1) The frequency, intensity, and duration of CDH events exhibited high spatial heterogeneity and a significant increasing trend over the study period. 2) A notable positive interdependence was observed between the occurrences of droughts and heatwaves, significantly contributing to the rise in CDH events. 3) Droughts exacerbated the intensity and duration of CDH events compared to heatwaves under non-drought conditions (NDCH). 4) The spatial distribution characteristics and the change indices of heatwaves and CDH events were strikingly similar, indicating a consistent evolution. Notably, the increase in heatwaves had a more pronounced influence on the escalation of CDH events compared to droughts. 5) The West Pacific Subtropical High (WPSH) and the South Asian High (SAH) have had significant impacts on CDH events in mainland China. This research provides vital insights into the dynamics of CDH events, emphasizing their growing frequency and severity in the context of climate change. It offers a crucial perspective for policymakers and disaster management authorities in developing targeted strategies for climate adaptation and mitigation.
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Highly pathogenic H5N1 avian influenza (HPAI H5N1) viruses occasionally infect, but typically do not transmit, in mammals. In the spring of 2024, an unprecedented outbreak of HPAI H5N1 in bovine herds occurred in the USA, with virus spread within and between herds, infections in poultry and cats, and spillover into humans, collectively indicating an increased public health risk1-4. Here we characterize an HPAI H5N1 virus isolated from infected cow milk in mice and ferrets. Like other HPAI H5N1 viruses, the bovine H5N1 virus spread systemically, including to the mammary glands of both species, however, this tropism was also observed for an older HPAI H5N1 virus isolate. Bovine HPAI H5N1 virus bound to sialic acids expressed in human upper airways and inefficiently transmitted to exposed ferrets (one of four exposed ferrets seroconverted without virus detection). Bovine HPAI H5N1 virus thus possesses features that may facilitate infection and transmission in mammals.
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Sustainable soil resource management depends on reliable soil information, often derived from 'legacy soil data' or a combination of old and new soil data. However, the task of harmonizing soil data collected at different times remains a largely unexplored in the literature. Addressing this challenge requires incorporating the temporal dimension into mathematical and statistical models for spatio-temporal soil studies. This study aimed to create a comprehensive framework for harmonizing soil data across various time. We assessed the integration of historical and recent soil data, ranging from 4 to 48 years old data, using soil data recency analysis. To achieve this, we introduced an 'age of data' attribute, calculating the time difference between soil survey years and the present (e.g., 2022). We applied three machine learning models - Decision Trees (DT), Random Forest (RF), Gradient Boosting (GBM) - to a dataset containing 6339 sites and 28,149 depth-harmonized layers. The results consistently demonstrated robust performance across models, RF outperforming with an R-squared value of 0.99, RMSE of 1.41, and a concordance of 0.97. Similarly, DT and GBM also showed strong predictive power. Terrain-derived environmental covariates played a more important role than land use and land cover (LULC) change in predicting soil data recency. While LULC change showed soil organic carbon concentration variability across the different depths, it was a less important factor. Anthropogenic factors, such as LULC change and normalized difference vegetation index (NDVI), were not primary determinants of soil data recency. Variations in soil depth had no impact on predicting soil data recency. This study validated that terrain-derived covariates, especially elevation factors, effectively explain the quality of older soil data when predicting current soil attributes using the soil data recency concept. This approach has the potential to enhance real-time estimates, such as carbon budgets, and we emphasize its importance in global earth system models.
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Aprendizaje Automático , Suelo , Suelo/química , Monitoreo del Ambiente/métodosRESUMEN
Soil health plays a crucial role in crop production, both in terms of quality and quantity, highlighting the importance of effective methods for preserving soil quality to ensure global food security. Soil quality indices (SQIs) have been widely utilized as comprehensive measures of soil function by integrating multiple physical, chemical, and biological soil properties. Traditional SQI analysis involves laborious and costly laboratory analyses, which limits its practicality. To overcome this limitation, our study explores the use of visible near-infrared (vis-NIR) spectroscopy as a rapid and non-destructive alternative for predicting soil properties and SQIs. This study specifically focused on seven soil indicators that contribute to soil fertility, including pH, organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), available phosphorous (P), and total nitrogen (TN). These properties play key roles in nutrient availability, pH regulation, and soil structure, influencing soil fertility and overall soil health. By utilizing vis-NIR spectroscopy, we were able to accurately predict the soil indicators with good accuracy using the Cubist model (R2 = 0.35-0.93), offering a cost-effective and environmentally friendly alternative to traditional laboratory analyses. Using the seven soil indicators, we looked at three different approaches for calculating and predicting the SQI, including: (1) measured SQI (SQI_m), which is derived from laboratory-measured soil properties; (2) predicted SQI (SQI_p), which is calculated using predicted soil properties from spectral data; and (3) direct prediction of SQI (SQI_dp), The findings demonstrated that SQI_dp exhibited a higher accuracy (R2 = 0.90) in predicting soil quality compared to SQI_p (R2 = 0.23).
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Differential regulation of the 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), which is considered the rate-limiting enzyme of the cholesterol biosynthesis pathway, has been reported in case of infection with many viruses. In our study, we have found that influenza virus infection decreases total cellular cholesterol level which is directly related to the downregulation of HMGCR protein. We found that HMGCR is degraded through ubiquitination and proteasomal-mediated pathway upon viral infection. Upregulation of Autocrine Motility Factor Receptor (AMFR), which is an E3-ubiquitin ligase of HMGCR, was also observed. Furthermore, knockdown of AMFR inhibits ubiquitination of HMGCR and also leads to inactivation of the innate immunity components TANK-binding kinase 1 (TBK1) and Interferon regulatory factor 3 (IRF3). Our study is the first to show the role of HMGCR and AMFR in influenza virus infection and reveals that AMFR plays a crucial role in the downregulation of HMGCR and the activation of innate immunity following influenza virus infection.
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Drought and the impacts of climate change have led to an escalation in soil salinity and alkalinity across various regions worldwide, including Iran. The Chahardowli Plain in western Iran, in particular, has witnessed a significant intensification of this phenomenon over the past decade. Consequently, modeling of soil attributes that serve as indicators of soil salinity and alkalinity became a priority in this region. To date, only a limited number of studies have been conducted to assess indicators of salinity and alkalinity through spectrometry across diverse spectral ranges. The spectral ranges encompassing mid-infrared (mid-IR), visible, and near-infrared (vis-NIR) spectroscopy were employed to estimate soil properties including sodium adsorption ratio (SAR), exchangeable sodium ratio (ESR), exchangeable sodium percentage (ESP), pH, and electrical conductivity (EC). Five distinct models were employed: Partial Least Squares Regression (PLSR), bootstrapping aggregation PLSR (BgPLSR), Memory-Based Learning (MBL), Random Forest (RF), and Cubist. The calibration and assessment of model performance were carried out using several key metrics including Ratio of Performance to Deviation (RPD) and the coefficient of determination (R2). Analysis of the outcomes indicates that the accuracy and precision of the mid-IR spectra surpassed that of vis-NIR spectra, except for pH, which exhibited a superior RPD compared to other properties. Notably, in the prediction of pH utilizing vis-NIR reflectance spectra, the BgPLSR model exhibited the highest accuracy and precision, boasting an RPD value of 2.56. In the domain of EC prediction, the PLSR model yielded an RPD of 2.64. For SAR, the MBL model achieved an RPD of 2.70, while ESR prediction benefited from the MBL model with an impressive RPD of 4.36. Likewise, the MBL model demonstrated remarkable precision and accuracy in ESP prediction, garnering an RPD of 4.41. The MBL model's efficacy in forecasting with limited datasets was notably pronounced among the models considered. This study underscores the valuable role of spectral predictions in facilitating the work of soil surveyors in gauging salinity and alkalinity indicators. It is recommended that the integration of spectrometry-based salinity and alkalinity predictions be incorporated into forthcoming soil mapping endeavors within semi-arid and arid regions.
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Cambio Climático , Salinidad , Espectrofotometría Infrarroja , Adsorción , SueloRESUMEN
China has the second-largest grassland area in the world. Soil organic carbon storage (SOCS) in grasslands plays a critical role in maintaining carbon balance and mitigating climate change, both nationally and globally. Soil organic carbon density (SOCD) is an important indicator of SOCS. Exploring the spatiotemporal dynamics of SOCD enables policymakers to develop strategies to reduce carbon emissions, thus meeting the goals of "emission peak" in 2030 and "carbon neutrality" in 2060 proposed by the Chinese government. The objective of this study was to quantify the dynamics of SOCD (0-100 cm) in Chinese grasslands from 1982 to 2020 and identify the dominant drivers of SOCD change using a random forest model. The results showed that the mean SOCD in Chinese grasslands was 7.791 kg C m-2 in 1982 and 8.525 kg C m-2 in 2020, with a net increase of 0.734 kg C m-2 across China. The areas with increased SOCD were mainly distributed in the southern (0.411 kg C m-2), northwestern (1.439 kg C m-2), and Qinghai-Tibetan (0.915 kg C m-2) regions, while those with decreased SOCD were mainly found in the northern (0.172 kg C m-2) region. Temperature, normalized difference vegetation index, elevation, and wind speed were the dominant factors driving grassland SOCD change, explaining 73.23% of total variation in SOCD. During the study period, grassland SOCS increased in the northwestern region but decreased in the other three regions. Overall, SOCS of Chinese grasslands in 2020 was 22.623 Pg, with a net decrease of 1.158 Pg since 1982. Over the past few decades, the reduction in SOCS caused by grassland degradation may have contributed to soil organic carbon loss and created a negative impact on climate. The results highlight the urgency of strengthening soil carbon management in these grasslands and improving SOCS towards a positive climate impact.
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Inorganic carbon is the largest source of carbon in terrestrial surface, particularly in arid and semiarid regions, including the Chahardowli Plain in western Iran. Inorganic carbon plays an equal or greater role than organic soil carbon in these areas, although less attention has been paid in quantifying their variability. The objective of this study was to model and map calcium carbonate equivalent (CCE) presenting inorganic carbon in soil using machine learning and digital soil mapping techniques. Chahardowli Plain in foothills of the Zagros Mountains in the southeast of Kurdistan Province in Iran was taken as a case study area. CCE was measured at 0-5, 5-15, 15-30, 30-60, and 60-100 cm depths following GloalSoilMap.net project specifications. A total of 145 samples were collected from 30 soil profiles using the conditional Latin hypercube (cLHS) method of sampling. Relationships between CCE and environmental predictors were modeled using random forest (RF) and decision tree (DT) models. In general, the RF model performed slightly superior than the DT model. The mean value of CCE increased with soil depth, from 3.5% (0-5 cm) to 63.8% (30-60 cm). Remote sensing (RS) variables and terrestrial variables were equally important. The importance of RS variables was higher at the surface than terrestrial variables, and vice versa. The most significant variables were Channel Network Base Level (CNBL) variable and Difference Vegetation Index (DVI) with the same variable importance value (21.1%). In areas affected by river activities, the use of the CNBL and vertical distance to channel networks (VDCN) as variables in digital soil mapping (DSM) could increase the accuracy of soil property prediction maps. The VDCN played a principal role in soil distribution in the study area by affecting the rate of discharge and, thus, erosion and sedimentation. A high percentage of carbonate in parts of the region could exacerbate nutrient deficiencies for most crops and provide information for sustainably managing agricultural activity.
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Carbonato de Calcio , Monitoreo del Ambiente , Monitoreo del Ambiente/métodos , Suelo , Carbono/análisis , Aprendizaje AutomáticoRESUMEN
Bacterial infections continue to jeopardize human and animal health, impacting millions of lives by causing significant deaths every year. The use of antibiotics remains the primary choice of therapy and has only been partly successful in reducing the disease burden due to the evolving nature of resistant microbes. Widespread and inappropriate use of antibiotics resulted in the development of antibiotic-resistant microbial species provoking substantial economic burdens. The most promising way to resolve the issue of antibiotic resistance is the use of bacterial viruses called bacteriophages to treat microbial infections. Earlier reports on experimental bacteriophage therapy showed successful patient outcomes, and many clinical trials of such clinical bacteriophages have already been investigated in many western countries. In this review, we are focusing on the advantages as well as drawbacks of bacteriophage therapy to use it as an alternative to antibiotics for microbial infections, together with its current success status. There is also a need to extensively study the past, present, and future outlook of phage therapy in comparison to presently available antimicrobial agents and especially immunological response by the host after phage administration. Our aim is to highlight the fast-promoting field of bacteriophage therapy and provocations that lie ahead as the world is gradually moving aside from complete dependence on antimicrobial agents.
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Infecciones Bacterianas , Bacteriófagos , Terapia de Fagos , Animales , Humanos , Bacterias , Infecciones Bacterianas/tratamiento farmacológico , Antibacterianos/farmacología , Antibacterianos/uso terapéuticoRESUMEN
Tile-back type slopes comprise ephemeral gullies (EGs) and hillslopes; they are a unique and widely distributed micro-landform in the Loess Plateau region of China. Gully erosion from these landforms is a serious issue, but the micro-landform makes the erosion process and its estimation complex. Quantifying soil erosion processes and their distribution characteristics at different positions on tile-back type slopes will provide a clearer picture for ecological restoration to control further soil degradation. This study investigated the erosion process of tile-back type slope with non-uniform slopes using a 3D photo-reconstruction method during eight successive simulated rainfall events. The results showed that EG erosion began with a chain of intermittent headcuts. When the accumulated rainfall reached 76 mm, serious collapses dramatically increased the amount of sediment by 216% after the first rainfall (cumulative rainfall was about 15 mm). We quantified the sediment contribution of EG erosion (46.20%), rill erosion (35.62%), and inter-rill erosion (18.18%) to total soil loss. The erosion area of the steep slope section and extremely steep slope section accounted for 33.26% and 66.74% of the total erosion area, respectively. Moreover, sediment amounts significantly correlated with morphological parameters, particularly the amount of EG erosion and maximum gully depth, with a correlation coefficient of 0.98. Cumulative gully length and erosion area had the greatest effect on rill erosion, with a correlation coefficient of 0.97. These results provide insight into the qualitative and quantitative understanding of EG erosion process on Loess Plateau of China and an important reference for the rational arrangement of EG control measures.
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Imagenología Tridimensional , Suelo , ChinaRESUMEN
Soil tests for plant-available phosphorus (P) are suggested to provide offsite P analysis required to monitor P fertilizer application and reduce P losses to downstream water. However, procedural and cost limitations of current soil phosphate tests have restricted their widespread use and have made them accessible only in laboratories. This study proposes a novel paper-based reagentless electrochemical soil phosphate sensor to extract and detect soil phosphate using an inexpensive and simple approach. In this test, concentrated Mehlich-3 and molybdate ions were impregnated in filter paper, which served as the phosphate extraction and reaction zone, and was followed by electrochemical detection using cyclic voltammetry signals. Soil samples from 22 sampling sites were used to validate this method against inductively coupled plasma optical emission spectroscopy (ICP) soil phosphate tests. Regression and correlation analyses showed a significant relationship between phosphate determinations by ICP and the proposed method, delivering a correlation coefficient, r, of 0.98 and a correlation slope of 1.02. The proposed approach provided a fast, portable, low-cost, accessible, reliable, and single-step test to extract and detect phosphate simultaneously with minimum waste (0.5 mL per sample), which made phosphate characterization possible in the field.
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Contaminantes del Suelo , Suelo , Suelo/química , Fosfatos/análisis , Fósforo/análisis , Fertilizantes/análisis , Contaminantes del Suelo/análisisRESUMEN
The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features' capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.
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Algoritmos , Máquina de Vectores de Soporte , Productos Agrícolas , Redes Neurales de la ComputaciónRESUMEN
Plants adapt to changes in elevation by regulating their leaf ecological stoichiometry. Potentilla anserina L. that grows rapidly under poor or even bare soil conditions has become an important ground cover plant for ecological restoration. However, its leaf ecological stoichiometry has been given little attention, resulting in an insufficient understanding of its environmental adaptability and growth strategies. The objective of this study was to compare the leaf stoichiometry of P. anserina at different elevations (2,400, 2,600, 2,800, 3,000, 3,200, 3,500, and 3,800 m) in the middle eastern part of Qilian Mountains. With an increase in elevation, leaf carbon concentration [(C)leaf] significantly decreased, with the maximum value of 446.04 g·kg-1 (2,400 m) and the minimum value of 396.78 g·kg-1 (3,500 m). Leaf nitrogen concentration [(N)leaf] also increased with an increase in elevation, and its maximum and minimum values were 37.57 g·kg-1 (3,500 m) and 23.71 g·kg-1 (2,800 m), respectively. Leaf phosphorus concentration [(P)leaf] was the highest (2.79 g·kg-1) at 2,400 m and the lowest (0.91 g·kg-1) at 2,800 m. The [C]leaf/[N]leaf decreased with an increase in elevation, while [N]leaf/[P]leaf showed an opposite trend. The mean annual temperature, mean annual precipitation, soil pH, organic carbon, nitrogen, and phosphorus at different elevations mainly affected [C]leaf, [N]leaf, and [P]leaf. The growth of P. anserina in the study area was mainly limited by P, and this limitation was stronger with increased elevation. Progressively reducing P loss at high elevation is of great significance to the survival of P. anserina in this specific region.