Field Samples Research Articles

Natural radioactivity level in Yemen: A systematic review of radiological studies

This paper aimed to conduct a systematic review of 26 published articles from 13 different regions in the Republic of Yemen related to the study of natural radioactivity (NORM) and enhanced artificial radioactivity (TENORM). The study relied on the analysis of various sample types, including air, groundwater, surface water, hot spring water, soil, sand, rocks, building materials, and oil field samples. It also analyzed the study areas, the types of detectors employed, and the study's timeframe. The analytical results raised significant concerns regarding the high levels of radioactivity observed in many of the studied regions. Moreover, some regions indicated the absence of any prior radiological study, despite apparent effects on the population and the environment, which suggest the presence of potential radionuclide concentration. Based on this study, it is strongly recommended that researchers conduct further radiological studies in regions previously studied over extended periods and in areas where no prior radiological studies have been conducted to assess potential radionuclide concentration.

Extension of the geographical range of the primary burrowing crayfish Cambarus strigosus Hobbs, 1981 (Decapoda: Astacidea: Cambaridae) in South Carolina

Abstract Knowledge of the geographical distribution for many primary burrowing crayfishes is often limited to information presented in species descriptions or field notes associated with epigean crayfish surveys. Cambarus strigosusHobbs, 1981, a rare primary burrowing crayfish, was collected as part of a survey of the Sumter National Forest, South Carolina (SC), USA. Prior to this discovery, C. strigosus appeared to be endemic to mid-reaches of the Broad River Drainage in Georgia (GA). We conducted additional field sampling in SC and GA and used mitochondrial DNA (mtDNA) to compare the relative genetic distance between individuals across all populations in an effort to determine the identity of the specimens collected. Genetic data indicated that the crayfish collected in SC was C. strigosus, with one shared haplotype between SC and GA populations. Our genetic results exhibited geographic structuring and indicated that SC populations have a specific haplotype unique to them, providing strong evidence that SC populations are not introduced. Two additional SC records of C. strigosus from 1983 housed in the US National Museum were examined and determined to be C. strigosus. With these records, we hypothesized that like in GA, the distribution of C. strigosus in SC is limited to riparian habitats associated with the Savannah River Drainage. While the range of this species has been extended, it is still limited to small portions of this drainage. Thus, further sampling to determine the true extent of C. strigosus distribution within SC along with conservation efforts are needed to protect this globally rare species.

Spatio-Temporal Analysis of Carbon Storage in Urban Areas After Wildfires: The Case of Marmaris Fire

Cities and urban areas are the primary source of CO2 worldwide by using around 70% of global energy and emitting more than 71% of CO2. Urban vegetation, referring to all trees and shrubs, are important components of urban environments. They provide many ecosystem services to human beings both directly and indirectly. Especially, they play a key role in reducing carbon emissions in urban areas by storing and capturing the carbon. However, recently, an increase in the number and intensity of wildfires that occur within urban areas has been observed. It resulted in losing stored carbon, releasing GHG to the atmosphere. Hence, quantifying above-ground carbon stored by urban trees and its distribution is essential to better understanding urban vegetation's role in urban environments and to better urban vegetation management. This study aimed to examine how forest fire affects the amount and distribution of stored carbon in the urban environment for the case of the Marmaris fire in the Summer of 2021 in Türkiye. For the study, urban forest carbon storage maps were generated before and after the Marmaris forest fire using remote sensing-based methodology with freely available remote sensing (RS) data. The results indicated that using the existing methodology could be rapid and cost-effective in monitoring the carbon storage change after an anthropogenic and natural disaster. However, for precise and reliable estimation of total carbon storage and the change in total urban carbon storage, the methodology needs to be developed at a local scale using field sampling along with RS data.

Control of Vibrio vulnificus proliferation in the Baltic Sea through eutrophication and algal bloom management

Due to climate change the pathogenic bacterium Vibrio vulnificus proliferates along brackish coastlines, posing risks to public health, tourism, and aquaculture. Here we investigated previously suggested regulation measures to reduce the prevalence of V. vulnificus, locally through seagrass and regionally through the reduction of eutrophication and consequential formation of algal blooms. Field samples collected in the summer of 2021 covered the salinity and eutrophication gradients of the Baltic Sea, one of the largest brackish areas worldwide. Physico-, biological- and hydrochemical parameters were measured and variables explaining V. vulnificus occurrence were identified by machine learning. The best V. vulnificus predictors were eutrophication-related features, such as particulate organic carbon and nitrogen, as well as occurrence of potential phytoplankton blooms and associated species. V. vulnificus abundance did not vary significantly between vegetated and non-vegetated areas. Thus, reducing nutrient inputs could be an effective method to control V. vulnificus populations in eutrophied brackish coasts.

Assessing teratogenic risks of gadolinium in freshwater environments: Implications for environmental health

Gadolinium (Gd) is among the rare earth elements extensively utilized in both industrial and medical applications. The latter application appears to contribute to the rise in Gd levels in aquatic ecosystems, as it is excreted via urine from patients undergoing MRI scans and often not captured by wastewater treatment systems. The potential environmental and biological hazards posed by gadolinium exposure are still under investigation. This study aimed to assess the teratogenic risk posed by a gadolinium chelate on the freshwater cnidarian Hydra vulgaris. The experimental design evaluated the impact of pure Gadodiamide (25 μg/l, 50 μg/l, 100 μg/l, 500 μg/l) and its commercial counterpart compound (Omniscan®; 100 μg/l, 500 μg/l, 782.7 mg/l) at varying concentrations using the Teratogenic Risk Index (TRI). Here we showed a moderate risk (Class III of TRI) following exposure to both tested formulations at concentrations ≥ 100 μg/l. Given the potential for similar concentrations in aquatic environments, particularly near wastewater discharge points, a teratogenic risk assessment using the Hydra regeneration assay was conducted on environmental samples collected from three rivers (Tiber, Almone, and Sacco) in Central Italy. Additionally, chemical analysis of field samples was performed using ICP-MS. Analysis of freshwater samples revealed low Gd concentrations (≤ 0.1 μg/l), despite localized increases near domestic and/or industrial wastewater discharge sites. Although teratogenic risk in environmental samples ranged from high (Class IV of TRI) to negligible (Class I of TRI), the low Gd concentrations, particularly when compared to higher levels of other contaminants like arsenic and heavy metals, preclude establishing a direct cause-effect relationship between Gd and observed teratogenic risks in environmental samples. Nevertheless, the teratogenic risks observed in laboratory tests warrant further investigation.

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages.

Unmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice. To investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT). The results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R 2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R 2 = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%-13.5%), demonstrating excellent practical applicability. Model evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages.

Integrating few-shot personalized thermal comfort model and reinforcement learning for HVAC demand response optimization

Heating, ventilation, and air conditioning (HVAC) is the most common equipment to maintain personalized thermal comfort (PTC), and a valuable demand response (DR) resource. The challenges to be addressed in HVAC DR optimization include uncertainties of environment, PTC varying by users and rapid decision-making. This paper proposed a fast HVAC DR optimization method that integrates deep reinforcement learning (DRL) and few-shot PTC model. Firstly, we established an HVAC DR optimization framework integrating few-shot PTC model and DRL, in which the few-shot PTC model was utilized to generate user's PTC temperature range served as the constraint and state variable of DRL. Secondly, we proposed a few-shot PTC model, which only required several field samples and had excellent engineering usability. Thirdly, we designed the HVAC DR optimization algorithm considering user's PTC based on proximal policy optimization, which automatically dealt with the uncertainties and made rapid decisions for setting HVAC setpoints of temperature dynamically. Simulation results show that the few-shot PTC model only requires 9 field samples, which is less than 1 % of existing advanced methods. Furthermore, the proposed method converges within 12.6 min under uncertainties on an i7-12700 processor, which is only about 1/2 of the standard deep deterministic policy gradient.

Divergent controlling factors of freeze–thaw-induced changes in dissolved organic carbon and microbial biomass carbon between topsoil and subsoil of cold alpine grasslands

Freeze-thaw (FT) processes in cold regions significantly influence the mineralization and sequestration of soil organic carbon (SOC), particularly in its active pools, such as dissolved organic carbon (DOC) and microbial biomass carbon (MBC). However, manipulation experiments often amplify the effects of natural FT processes and overlook soils below 20 cm depth. To investigate carbon responses to seasonal FT events across soil depths (0–80 cm) and identify controlling factors, field sampling was conducted before freezing and after thawing in alpine grassland soils on the Qinghai-Tibet Plateau. Results are as follows: 1) After soil thawing, the entire soil profile showed insignificant changes in SOC (−4.46%) and MBC (3.21%), but DOC (45.27%) and DOC/SOC (160.08%) increased significantly, and strong associations were observed in DOC changes between adjacent soil layers. 2) FT indicators (number and temperature amplitude of daily FT cycles, frozen temperature, and frozen days) influenced SOC changes differently in the topsoil (0–10 cm) and subsoil (20–30 cm). Contrary to the impact of frozen temperature, other FT indicators largely facilitated the production of DOC0–10 and DOC20–30. The effects of FT indicators on MBC were nonlinear. 3) Structural equation models indicated that average soil water governed FT-induced changes in both DOC0–10 and DOC20–30. The varying effects of porosity and pH from topsoil to subsoil, along with the weakening relationship between changes in DOC and MBC, favored a stronger increase in DOC20–30. Increases in soil water and DOC after thawing stimulated the increment of MBC0–10, while porosity (positively) and sand content (negatively) regulated the MBC20–30 change. It indicates that, influenced by soil structure and biogeochemical environment, DOC and MBC in grassland soils are more sensitive to natural FT processes than SOC. Nevertheless, changes in FT patterns due to long-term climate change may cumulatively affect SOC.

Effect of pooled tracheal sample testing on the probability of Mycoplasma hyopneumoniae detection

Tracheal pooling for Mycoplasma hyopneumoniae (M. hyopneumoniae) DNA detection allows for decreased diagnostic cost, one of the main constraints in surveillance programs. The objectives of this study were to estimate the sensitivity of pooled-sample testing for the detection of M. hyopneumoniae in tracheal samples and to develop probability of M. hyopneumoniae detection estimates for tracheal samples pooled by 3, 5, and 10. A total of 48 M. hyopneumoniae PCR-positive field samples were pooled 3-, 5-, and 10-times using field M. hyopneumoniae DNA-negative samples and tested in triplicate. The sensitivity was estimated at 0.96 (95% credible interval [Cred. Int.]: 0.93, 0.98) for pools of 3, 0.95 (95% Cred. Int: 0.92, 0.98) for pools of 5, and 0.93 (95% Cred. Int.: 0.89, 0.96) for pools of 10. All pool sizes resulted in PCR-positive if the individual tracheal sample Ct value was < 33. Additionally, there was no significant decrease in the probability of detecting at least one M. hyopneumoniae-infected pig given any pool size (3, 5, or 10) of tracheal swabs. Furthermore, this manuscript applies the probability of detection estimates to various real-life diagnostic testing scenarios. Combining increased total animals sampled with pooling can be a cost-effective tool to maximize the performance of M. hyopneumoniae surveillance programs.

Field-based soil-plant uptake measurements of natural radionuclides for key vegetables and ghaf leaves in Abu Dhabi

With the thriving fossil fuel and nuclear based industries in the nation, radioecology has become necessary for the radiation safety and emergency-preparedness for the United Arab Emirates (UAE). Environmental radiation transport modelling in the UAE and the Arabian Peninsula are severely limited, as we discuss in this paper, due to lack of experiments specific to arid desert climates. To fill the missing gaps in the baseline arid region radioecological database, especially for the soil-plant uptake studies, rigorous field works have been conducted for the first time on the soil and plant in the farms and open fields of the UAE. We present Abu Dhabi based measurements of activity concentrations of radionuclides of natural origins, in soils, key vegetables (cucumber, tomato, and bell pepper), and leaves of ghaf - a prominent native tree. The empirical data are utilized to get the first published estimates of UAE-specific plant-soil concentration ratios (CR), measuring root uptake of radionuclides in nonleafy vegetables and native trees. Such systematic studies are very rare for arid sandy soils. For the 27 samples analyzed, the activity concentrations' (unit Bq kg−1) ranges are: 169–1746 for 40K, 12–19.5 for 226Ra, and 2.7–23.1 for 228Ra. Likewise, wide variability is seen in the averages of concentration ratios also, ranging in 1.05–4.94 for 40K, 0.14–1.82 for 226Ra, and 0.53–2.78 for 228Ra. A net bioaccumulation (concentration ratio >1) of some of these natural radionuclides is found in many samples, but no significant doses or hazard indices are found due to these three radionuclides in the UAE's soils and vegetations. The paper discusses the careful work through tens of field sampling exercises, well controlled sample processing, high resolution gamma spectrometry, and treatment of data from gamma counting rates to accumulated dose rate estimations.

Exploring unseen 3D scenarios of physics variables using machine learning-based synthetic data: An application to wave energy converters

This work uses machine learning to produce synthetic data of wave energy converters from time-expensive 3D simulations based on computational fluid dynamics models. The simulations to analyse the response of these systems to incoming waves are lengthy and computationally expensive to obtain. Here, we explore the use of a beta-VAE and a Principal Components-based adversarial autoencoder for generating new synthetic data. The compression plus the generation of synthetic data introduces an exceptionally fast surrogate model of the original simulation and delivers more samples of either dynamic viscosity and velocity fields, enlarging the design space. The new generated synthetic samples can have a speed up from 5 to 6 orders of magnitude. The new design space can be used to improve the prediction of dynamic viscosity given the velocity fields. The generative model has the potential to capture the transition and the new physical phenomena under extreme initial conditions.

Systematic evaluation of the influence of Taq enzyme choice and amplification conditions on targeted environmental DNA assay performance and detection in field samples

AbstractTargeted environmental DNA (eDNA) studies mainly rely on quantitative real‐time polymerase chain reaction (qPCR) to amplify extremely low concentrations of DNA present in environmental samples. Understanding factors that influence targeted eDNA assay performance and detection in field samples, such as Taq DNA polymerase enzyme type and thermocycle protocol, is critical for the interpretation of qPCR results. We completed a systematic performance evaluation of five distinct targeted eDNA assays (eANFI6, eFISH1, eANBO5, eGLIN1, and eLICA3 targeting sablefish, general fish, Boreal toad, wood turtle, and the American bullfrog) by subjecting the same samples to analysis by five different Taq enzyme reagent mixes (Immolase, Environmental Master Mix (EMM), Amplitaq, QIAcuity, and QuantiNova) and the commonly used 2‐step (95°C, 60°C) and 3‐step (95°C, 64°C, 72°C) thermocycle protocols. We evaluated assay performance using a standardized dilution series of synthetic dsDNA target sequences and calculated limits of detection (LOD) and quantification (LOQ) and 95% confidence intervals for each combination of Taq enzyme and thermocycle protocol. All assays performed within acceptable performance criteria as defined by the Canadian national standard for targeted eDNA assays. Based on data generated by synthetic dsDNA fragments, the eDNA assays performed comparably regardless of the enzyme reagent mix and protocol used, except for eANFI6 and eGLIN1 using EMM and the 3‐step protocol, where no amplification was observed. On freshwater field samples, EMM and Immolase performed best. On marine field samples, Immolase, EMM, Qiacuity, and QuantiNova performed equally well, although EMM failed to amplify some samples. The work reveals that an enzyme reaction mix or thermocycle protocol can affect the result of an eDNA assay, but the appropriate choice also depends on the nature of the field sample. It is therefore imperative that these are considered when selecting appropriate reaction conditions and that they are clearly reported.

Estimating the Aboveground Fresh Weight of Sugarcane Using Multispectral Images and Light Detection and Ranging (LiDAR)

This study aimed to develop a remote sensing method for estimating the aboveground fresh weight (AGFW) of sugarcane using multispectral images and light detection and ranging (LiDAR). Remotely sensed data were acquired from an unmanned aerial vehicle (drone). Sample plots were harvested and the AGFW of each plot was measured. Sugarcane crown heights and volumes were obtained by isolating individual tree crowns using a LiDAR-derived digital surface model of the area. Multiple linear regression (MLR) and partial least-squares regression (PLSR) models were tested for the field-sampled AGFWs (dependent variable) and individual canopy heights and volumes, and spectral indices were used as independent variables or predictors. The PLSR model showed more promising results than the MLR model when predicting the AGFW over the study area. Although PLSR is well-suited to a large number of collinear predictor variables and a limited number of field samples, this study showed moderate results (R2 = 0.5). The visual appearance of the spatial distribution of the AGFW map is satisfactory. The limited no. of field samples overfitted the MLR prediction results. Overall, this research highlights the potential of integrating remote sensing technologies in the sugarcane industry, thereby improving yield estimation and effective crop management.

The First International eDNA Workshop in Hong Kong: A beginner's guide for the next‐generation eDNA researcher

AbstractThe field of environmental DNA (eDNA) has rapidly advanced in recent years, providing a non‐invasive and time‐saving method for assessing biodiversity. The First International Environmental DNA (eDNA) Workshop in Hong Kong was held from 16 to 27 October 2023 and provided early career professionals with hands‐on training and collaboration opportunities in eDNA research. With support from The Croucher Foundation Limited (Hong Kong), the workshop covered all stages of an eDNA‐based research project, including study design, field sampling, eDNA extraction, library preparation, bioinformatics, statistical data analysis, and ethics in scientific research. Participants gained insights into the principles and practical applications of eDNA technology, emphasizing the importance of careful experimental design and data interpretation. The workshop also highlighted the need for standardized protocols, comprehensive and local DNA reference databases, and careful selection of primer sets to overcome current issues and limitations. Workshop participants expressed enthusiasm for the potential of eDNA metabarcoding as a valuable tool for ecological monitoring, biodiversity assessment, and conservation decision‐making. The future of eDNA research looks promising overall, with continued advancements in technology, collaboration among researchers, and the integration of eDNA into large‐scale ecological monitoring. Future iterations of the Hong Kong International eDNA workshop will continue to provide hands‐on training and collaboration opportunities for early career professionals interested in eDNA research, focusing on addressing current limitations and challenges in the field.

Comparing shallow landslide susceptibility maps in Northeastern Türkiye (Beşikdüzü, Trabzon): a multivariate statistical, machine learning, and physical data-based analysis

This study endeavors to assess and compare the efficacy of various modeling approaches, including statistical, machine learning, and physical-based models, in the creation of shallow landslide susceptibility maps within the Besikduzu district of Trabzon province, situated in the Black Sea Region of Türkiye. The landslide inventory data, spanning from 2000 to 2018, was acquired through meticulous field surveys and analysis of Google Earth satellite imagery. Key topographic and geologic input parameters, such as slope, aspect, topographic wetness index, stream power index, plan and profile curvature, and geologic units, were extracted from a high-resolution 10 m spatial DEM (Digital Elevation Model) and a 1:25,000 scaled digital geology map, respectively. Additionally, soil unit weight and shear strength parameters, critical for the physical-based model, were determined through field samples. To evaluate landslide susceptibility, logistic regression, random forest, and Shalstab were employed as the chosen methods. The accuracy of susceptibility maps generated by each method was assessed using the area under the curve method, yielding impressive values of 0.99 for the random forest model, 0.97 for the logistic regression model, and 0.93 for the Shalstab model. These results underscore the robust performance of all three methods, suggesting their applicability for generating shallow landslide susceptibility maps not only in the Black Sea Region but also in analogous areas with similar geological characteristics.

Sample entropy-based quantitative assessment of the arc magnetic field spectrum for improved arc welding quality

Arc magnetic field analysis is a valuable approach for assessing the stability of the arc welding process, yet existing methods lack the ability to effectively quantify the disorder within the process. Through an investigation into the characteristics of the arc magnetic field signal, it was observed that the occurrence of low-frequency random fluctuations in arc magnetic field power, induced by unstable factors such as bubbles or short circuits, contributed to increased complexity and randomness in the arc magnetic field signals. To visualise the arc magnetic field signals in a time-frequency domain, a spectrogram was employed, revealing a strong correlation between the distribution of maximum power spectral density (PSD) in the spectrogram and the stability of the arc welding process. Furthermore, a novel method based on sample entropy was introduced to provide a quantitative measure of this relationship. A comprehensive quantitative assessment indicator called arc magnetic field sample entropy (AMFSE) was proposed. This indicator effectively mitigates the influence of varying parameters on the quantitative results, enabling a more accurate and consistent representation of the stability of the arc welding process. The proposed method was validated through testing, yielding an accuracy rate exceeding 90%.

Simulation of Bed Change in Al-Musayyab Canal using HEC-RAS Software

This study focuses on the simulation of the bed change and total sediment concentration in the Al-Musayyab Canal, which is a vital irrigation system located in the central region of Iraq, using HEC-RAS V 6.3 software. The 49.5 km canal, with 13 branches, faces challenges from sediment influx originating from the Euphrates River. The primary objective is to analyze sedimentation, erosion patterns, and sediment discharge variations to support effective canal management. Field sampling collected suspended and bed load samples for laboratory tests, providing input data for the sediment transport model. Simulation results show steady flow water surface elevations ranging from 26.32 to 33.16 m.a.s.l, with velocities from 0.24 to 0.98 m/s. Significant sediment deposition and erosion variations are observed as data for 2019 and 2020 are analyzed. Notable changes in the canal's invert levels occurred in both years, with maximum deposition depths reaching 14 cm in 2019 and 21 cm in 2020. Erosion depths ranged from 3 to 15 cm in 2019 and 5 to 25 cm in 2020. The total sediment discharge for 2019 in the Al-Musayyab Canal amounted to 17 tons/day, while 22 tons/day in 2020.

Inter-clonal competition over queen succession imposes a cost of parthenogenesis on termite colonies.

In social insect colonies, selfish behaviour due to intracolonial conflict among members can result in colony-level costs despite close relatedness. In certain termite species, queens use asexual reproduction for within-colony queen succession but rely on sexual reproduction for worker and alate production, resulting in multiple half-clones of a single primary queen competing for personal reproduction. Our study demonstrates that competition over asexual queen succession among different clone types leads to the overproduction of parthenogenetic offspring, resulting in the production of dysfunctional parthenogenetic alates. By genotyping the queens of 23 field colonies of Reticulitermes speratus, we found that clone variation in the queen population reduces as colonies develop. Field sampling of alates and primary reproductives of incipient colonies showed that overproduced parthenogenetic offspring develop into alates that have significantly smaller body sizes and much lower survivorship than sexually produced alates. Our results indicate that while the production of earlier and more parthenogenetic eggs is advantageous for winning the competition for personal reproduction, it comes at a great cost to the colony. Thus, this study highlights the evolutionary interplay between individual-level and colony-level selection on parthenogenesis by queens.

The dimensions of deep-layer soil desiccation and its impact on xylem hydraulic conductivity in dryland tree plantations

Abstract. In water-limited areas, planted trees can extract substantial amounts of soil water from deep layers (&gt; 200 cm) to meet their high water demand, resulting in deep soil desiccation, which influences not only regional water cycling but also the sustainability of trees per se in drylands. However, the specific dimensions of deep soil desiccation in relation to both the soil moisture limitation and the maximum root water uptake (RWU) depth are still not well determined. Whether the dimensions depend on tree species and how they will affect trees' xylem hydraulic conductivity are also unclear, restricting our ability to predict the fate of dryland tree plantations. Therefore, we studied the spatiotemporal distribution of deep soil moisture deficit (DSMD) for two typical planted trees, apple (Malus pumila Mill.) and black locust (Robinia pseudoacacia L.), based on published data and multiple field samplings on China's Loess Plateau. The results indicated that the lowest deep soil moisture (DSM; units of gravimetric percent, i.e., grav-%) occurred under the planted trees aged 24–28 years at all sites. The lowest DSMD varied around −0.6, which was close to the DSMD at the permanent wilting point (PWP, grav-%), regardless of tree species and site, although shallow (&lt; 200 cm) soil moisture was not reduced to the point of limitation. This suggests that PWP is a reliable indicator of the moisture limitation of deep-layer soil desiccation for the tree species examined. The corresponding depth of soil moisture use reached 18.0–22.0 m for these old planted trees at different sites, while it was more than 25 m for R. pseudoacacia in the drier site of Mizhi. Furthermore, the mean values of native percentage loss of hydraulic conductivity of planted trees' branches xylem reached 74.9 %–96.5 % in the plantations sampled in this study, indicating that tree mortality may occur. The findings help predict the sustainability of planted trees in semi-arid regions with a thick vadose zone.

Assessing the Severity of Verticillium Wilt in Cotton Fields and Constructing Pesticide Application Prescription Maps Using Unmanned Aerial Vehicle (UAV) Multispectral Images

Cotton Verticillium wilt is a common fungal disease during the growth of cotton, leading to the yellowing of leaves, stem dryness, and root rot, severely affecting the yield and quality of cotton. Current monitoring methods for Verticillium wilt mainly rely on manual inspection and field investigation, which are inefficient and costly, and the methods of applying pesticides in cotton fields are singular, with issues of low pesticide efficiency and uneven application. This study aims to combine UAV remote sensing monitoring of cotton Verticillium wilt with the precision spraying characteristics of agricultural drones, to provide a methodological reference for monitoring and precision application of pesticides for cotton diseases. Taking the cotton fields of Shihezi City, Xinjiang as the research subject, high-resolution multispectral images were collected using drones. Simultaneously, 150 sets of field samples with varying degrees of Verticillium wilt were collected through ground data collection, utilizing data analysis methods such as partial least squares regression (PLSR) and neural network models; additionally, a cotton Verticillium wilt monitoring model based on drone remote sensing images was constructed. The results showed that the estimation accuracy R2 of the PLSR and BP neural network models based on EVI, RENDVI, SAVI, MSAVI, and RDVI vegetation indices were 0.778 and 0.817, respectively, with RMSE of 0.126 and 0.117, respectively. Based on this, an analysis of the condition of the areas to be treated was performed, combining the operational parameters of agricultural drones, resulting in a prescription map for spraying against cotton Verticillium wilt.