Soil Moisture Research Articles

Quantifying Residual Soil Moisture through Empirical Orthogonal Functions Analysis to Support Legume-Based Cropping Systems

AbstractThis study investigates spatiotemporal variability of residual soil moisture during the OND (October-November-December) season in Ethiopia and its implications for crop productivity. Employing advanced statistical techniques, we analyze spatial and temporal distribution of soil moisture across Ethiopia from 1981 to 2020, focusing on selected crops including legumes: chickpea, field peas, common bean, soybean and alfalfa, to assess the potential of residual moisture to support post-rainy season cropping. Results indicate pronounced east-west moisture gradients, with eastern regions of Ethiopia exhibiting lower moisture levels (< 60 kg.m-2) compared to western regions (> 150 kg.m-2). The central highlands, which are pivotal for agricultural activities, demonstrate significant variability in moisture (standard deviations > 25 kg.m-2), with implications on agricultural sustainability. The northern and southeastern tips of the country are particularly vulnerable to prolonged drought, where climate change and frequent dry spells exacerbate moisture deficits, consequently impacting crop productivity. Despite these challenges, promising opportunities for future crop production emerge in the southeastern region, which is characterized by increasing moisture trend over time ($$\:\tau\:=0.59$$). Findings further indicate that residual moisture adequately meets and supports crop water requirements in the western, central, and southwestern Ethiopia. In these regions, residual moisture supports more than 90% of cropland water requirements of various crops during the initial and late-season growth stages, whereas water requirement coverage drops to less than 20% during the mid-season growth stage. Therefore, by utilizing residual soil moisture alongside supplemental irrigation, Ethiopian farmers can meet crop water needs for double cropping and enhance resilience to climate variability.

Effect of climate change on West Nile virus transmission in Italy: a systematic review

Abstract Background West Nile Virus (WNV) is a pathogen transmitted by mosquitoes of the Culex genus, affecting both humans and animals such as horses and birds. The prevalence of WNV infection has increased dramatically in recent years in Europe, especially in Italy, and has been related to climate and environmental changes linked to global warming. Our review aims to assess the relation between the spread of WNV in Italy and the different climate change-related factors. Methods We conducted a literature search in the PubMed, Web of Science, Embase and Scopus databases, actualised as of 20 February 2024, using as search terms WNV, its vectors and climate change, and limiting our analysis to studies conducted in to avoid an overly heterogeneous setting. We registered the review in PROSPERO (CRD42023430636). Results Of the 252 unique records retrieved, we eventually included 25 articles after full-text screening. These studies were published between 2011 and 2023 and evaluated the distribution of WNV or its vectors according to various climatic and environmental factors. Despite rather heterogeneous results, we found a consistently positive association with temperature, especially spring temperature, agricultural land use, demography, and soil moisture, and a negative one with evapotranspiration and wind, while the relationship with other environmental variables and water cycle-related factors was weak, null or conflicting. Conclusions Despite some inconsistencies in the results, likely related to differences in study methodologies as well as complex environmental interactions, we found unequivocal evidence that climate change-related factors, such as the lengthening of the warm season, extensive farming, and invasive human activities towards natural environments, favour WNV spread in Italy. Consequently, actions against global warming and preservation of natural environments may counteract the WNV epidemic. Key messages • WNV epidemic seems to be linked to specific climate change-related factors, such as the lengthening of the warm season, extensive farming, and invasive human activities towards natural environments. • Actions against global warming and preservation of natural environments may counteract WNV spread.

Water Stress Adaptations by Rhododendron arboreum Smith in The Himalayan Treeline Environment

Drought is amongst the most important climatic events which can severely impact natural ecosystems. The under-canopy Indian Himalayan tree species are less studied on water stress in harsh climatic conditions. Rhododendron arboreum is an ecologically and economically important tree species and distributed between 1500–3500 m asl in the Himalayan ecosystems. In this study, we have tried to assess the water stress adaptations by R. arboreum near the treeline ecotone located at 30°11´02˝N and 79°39´36˝E between 3100 and 3500 m asl elevation. Water potential (Ψ) was measured using a Pressure chamber and leaf conductance with the help of AP4 porometer. Soil moisture (Sm) across the depths was highly variable (p<0.05). The pre-dawn water potential (ΨPD) of R. arboreum tree varied between -0.12 and -0.91 MPa. ANOVA showed that the ΨPD of R. arboreum seedlings varied significantly across the years, seasons, and sites (p<0.05). The decline in osmotic potential at full turgor (Ψπ100) was -1.18 to -1.20 MPa in trees and -1.42 to -1.72 MPa in seedlings. The morning leaf conductance (gwAM) of R. arboreum trees ranged from 58.00 to 271.68 m mol m-2 sec-1. Correlation showed that the maximum leaf conductance in R. arboreum trees was observed when the ΨPD was -0.5 to -0.9 MPa. Water uptake function in plants is generally affected by winter frost which can change the Ψ, osmotic potential, and leaf conductance of plants. The ΨPD in the species was relatively high across all seasons in the treeline areas to disrupt physiological activities.

EXPLORING CLIMATIC PATTERNS AMONG BRAZILIAN CAPITALS

This study aims to examine the patterns of climatic variables and delineate climatically homogeneous regions among Brazilian capitals using Principal Component Analysis (PCA) and clustering techniques. Annual climate data from 26 state capitals and the Federal District of Brazil were analyzed, covering the period from 1958 to 2022, representing different regions of the country. The PCA results revealed that the first three principal components accounted for 91.8% of the total data variability. Four climatically homogeneous regions were identified: the East region, the Northeast region, the North region and another region that comprises the rest of the country. Cluster Analysis (CA) also confirmed the formation of four homogeneous groups among municipalities based on climatic variables. The analysis of spatial distribution showed significant variations in annual climate variables between Brazilian capitals, with maximum values observed for maximum and minimum temperatures, precipitation, wind speed, relative humidity, evapotranspiration and soil moisture. These findings contribute to a deeper understanding of climate variability in Brazilian capitals and provide a basis for future studies on climate dynamics and regional impacts. Keywords: Principal Component Analysis, Cluster Analysis, Multivariate Analysis; Biplot

Differential effects of pH on cadmium accumulation in Artemisia argyi growing in low and moderately cadmium-contaminated paddy soils

BackgroundPhytoremediation is affected by physical and chemical properties of the soil such as soil pH, moisture, and nutrient content. Soil pH is a key element influencing Cd bioavailability and can be easily adjusted in agricultural practices. The soil pH level may relate to the effectiveness of phytoremediation; however, this has not been extensively investigated. In the current study, we evaluated the effect of Cd contamination level (0.56 and 0.92 mg/kg) and soil pH (5, 6, and 7) on Cd accumulation and allocation in Artemisia argyi, a fast-growing perennial crop.ResultsOur results indicated that higher soil Cd concentrations reduce A. argyi biomass, and the loss of the root mass was particularly significant. Higher soil pH decreased Cd content in stems and roots of A. argyi cultivated in moderately Cd-polluted soil, and increased Cd content in stems and roots of the plant grown in low Cd-polluted soil. Higher soil pH decreased the percentage of Cd distributed in the soluble fraction and cell walls and increased the percentage of Cd in the organelles of leaf cells for moderate soil Cd levels. The bioconcentration and translocation factor exceeded 4.0 and 1.0, respectively, across all tested treatments, indicating that A. argyi is a promising candidate for phytoremediation. Notably, the effects of soil pH on Cd accumulation and subcellular distribution in A. argyi differed between low and moderately Cd-contaminated soils.ConclusionAdjustments to soil pH based on the degree of Cd contamination can enhance Cd extraction by A. argyi, thereby reducing the overall remediation cycle of cadmium-polluted paddy fields of South China.Graphical

Отглеждане на соя при висока почвена влажност

The aim of this work is to study the biological and economic effect of growing soybeans at high soil moisture. The experiment is two-year and was conducted in the experimental field of AU - Plovdiv in 2009 and 2010. The variants are: 1) No irrigation, 2) Irrigation at 80% FC (field capacity), 3) Irrigation at 90% FC. When maintaining higher pre-irrigation moisture, the number of irrigation applications increases by an average of 3, and irrigation rates decrease by between 22 and 53%. The duration of the irrigation period increases by ten days. According to the results, the maintenance of high soil moisture for soybean is unjustified from a biological point of view, since the increase in the productive potential of the plants is not realized in reality. It can be considered that regardless of the irrigation method, the pre-irrigation humidity should be no higher than 80% FC for the soil layer 0-60 cm. When maintaining high soil moisture (above 90% FC), production costs increase by an average of 11%, without increasing the cost price and hardly affecting the net income, and the results for this indicator are not unidirectional.

The Impact of Nanobubble Gases in Enhancing Soil Moisture, Nutrient Uptake Efficiency and Plant Growth: A Review

Nanobubble (NB) technology in agriculture has received increased interest due to its potential to promote soil moisture retention and plant development. Therefore, this review investigates the impact of various types of NBs—such as oxygen, carbon dioxide, and air—on soil and plant systems. Various studies revealed that nanobubble-saturated water (NBSW) increases moisture retention, microbial activity, and nutrient absorption, which contribute to better plant development. However, there are still gaps in understanding the specific roles of different gases regarding their stability, interactions with soil, and long-term agricultural impacts. This review aims to combine previous research by focusing on various types of NBs impact on soil moisture, water quality, and nutrient retention. Challenges include the quick dissolution of particular gases, limited field studies, and scalability. The analysis showed that despite these challenges, NBs have potential for enhancing agriculture by improving soil structure and crop yield. More study is needed to maximize their application, particularly in determining the most effective gas types and concentrations for certain agricultural areas.

Synthesis and Evaluation of Starch-Grafted-Poly[(Acrylic Acid)-Co-Acrylamide] Based Nanoclay Polymer Composite Fertilizers for Slow Release of Nitrogen in Soil.

Nitrogen (N) losses from conventional N fertilizers contribute to environmental degradation and low N use efficiency. Highlighting the need for slow-release fertilizers (SRFs) to mitigate these problems, this study aims to develop slow-release N fertilizers using starch-grafted-poly[(acrylic acid)-co-acrylamide] based nanoclay polymer composites (NCPCs) and investigate their efficacy for slow N delivery in soil. Three types of NCPCs, NCPC(A) (poly [(acrylic acid)-co-acrylamide]), NCPC(W) (wheat starch-grafted-poly[(acrylic acid)-co-acrylamide), and NCPC(M) (maize starch-grafted-poly[(acrylic acid)-co-acrylamide) were prepared and characterized using FTIR spectroscopy and X-ray diffraction techniques. N-release behaviour of the products was assessed under two distinct soils, i.e., Assam (Typic Hapludults, pH 4.2) and Delhi (Typic Haplustepts, pH 7.9) soils. Additionally, the effects of varying soil moisture and temperature levels on N release were studied in the Assam soil. The N-release kinetics of the synthesized fertilizers were assessed using zero-order, first-order, Higuchi, and Korsmeyer-Peppas models. Degradability of the NCPCs was evaluated by measuring evolved CO2-C under various soil conditions as an indicator of microbial degradation. The results indicated that NCPC fertilizers significantly slowed down the release of N compared to urea. According to the R2 values obtained, it was evident that the first-order kinetic model most accurately describes the N release from both urea and NCPC-based N fertilizers in the studied soils. Among the formulations, NCPC(A) exhibited the lowest N release (42.94-53.76%), followed by NCPC(M) (51.05-61.70%), NCPC(W) (54.86-67.75%), and urea (74.33-84.27%) after 21 days of incubation. The rate of N release was lower in the Assam soil compared to the Delhi soil, with higher soil moisture and temperature levels accelerating the release. Starch addition improved the biodegradability of the NCPCs, with NCPC(W) showing the highest cumulative CO2-C evolution (18.18-22.62 mg g-1), followed by NCPC(M) (15.54-20.97 mg g-1) and NCPC(A) (10.89-19.53 mg g-1). In conclusion, NCPC-based slow-release fertilizers demonstrated a more gradual N release compared to conventional urea and the inclusion of starch enhanced their degradability in the soil, which confirms their potential for sustainable agricultural applications. However, soil properties and environmental factors influenced the N release and degradation rates of NCPCs.

Integrated Anomaly Detection and Early Warning System for Forest Fires in the Odisha Region

The present study aims to develop a random forest algorithm-based classifier to predict the occurrence of fire events using observed meteorological parameters a day in advance. We considered the skin temperature, the air temperature close to the surface, the humidity close to the surface level, and soil moisture as important meteorological factors influencing forest fire occurrence. Twenty additional parameters were derived based on these four parameters that account for the energy exchanged in sensible and latent forms and the change in parameters in recent trends. We used the mutual information approach to identify critical meteorological parameters that carry significant information about fire occurrence the next day. The top nine parameters were then fed as input to the random forest algorithm to predict fire/no fire the next day. The weighted data sampling and SMOTE techniques were employed to address the class imbalance in the fire data class. Both techniques correctly classified fire incidents well, given the meteorological input from the previous days. This study also showed that as the class imbalance increases to 1:9, the performance based on the precision, recall, F1 score, and accuracy are maximum, showing the model’s ability to perform with class imbalance. Both techniques helped the random forest algorithm forecast fire instances as the data sample size increased.

Temporal variability and predictability predict alpine plant community composition and distribution patterns.

One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species-environment associations. In this study, we evaluated how two components of temporal environmental variation-variability and predictability-impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long-Term Ecological Research site, we used in situ, high-resolution temporal measurements of soil moisture and temperature from 13 locations ("nodes") distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within-day) and seasonal (2- to 4-month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Comparison of transformer, LSTM and coupled algorithms for soil moisture prediction in shallow-groundwater-level areas with interpretability analysis

Accurate quantification of soil moisture is essential for understanding water and energy exchanges between the atmosphere and the Earth’s surface, as well as for agricultural applications. Predicting soil moisture content is vital for efficient water management, irrigation scheduling, and drought monitoring. Traditional forecasting methods, such as numerical regression models, often struggle due to various influencing factors and poor observation data quality. In contrast, deep learning algorithms, particularly recurrent and convolutional neural networks, show promise in predicting nonlinear data like soil moisture. This study focuses on shallow groundwater regions, using groundwater levels and meteorological data as features while coupling the Transformer model with other neural network structures. We investigate the potential of attention-based neural networks for soil moisture time series prediction. Our findings demonstrate that the Transformer model achieves an average R2 of 0.523 across different time lags, outperforming the LSTM model with an R2 of 0.485. The introduction of LSTM enhances the Transformer’s stability in handling temporal changes. Additionally, we verified the importance of groundwater for soil moisture prediction. This study introduces new methods for soil moisture prediction and offers new insights and recommendations for the development of artificial intelligence technology for soil moisture prediction.

Modelling net primary productivity using near real-time land cover data and soil moisture information

ABSTRACT The objective of this study is to develop a methodological approach to assess ecosystem functioning in terms of carbon uptake and sequestration. To achieve this, a model was developed to forecast Net Primary Productivity (NPP) and tested in a rural area, the Thrace region (NE Greece). Monthly net photosynthesis data from July 2015 to February 2024 was estimated using the MODerate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD17A2H v061 dataset for the study area. This dataset was used as the dependent variable in an Autoregressive Integrated Moving Average (ARIMA) model. The predictive ability of monthly Land Cover (LC) and Soil Moisture (SM) data at various time lags was examined, and variables found to be significant predictors were introduced into the model as external predictors. The study found that crop areas and SM conditions exert the greatest influence on NPP, with areas containing trees exerting a lesser impact. Additionally, NPP was estimated for seven different LC and SM scenarios from March 2023 to February 2024.

Temperate Soils Exposed to Drought—Key Processes, Impacts, Indicators, and Unknowns

The summer drought in the United Kingdom (UK) in 2022 produced significant speculation concerning how its termination may impact and interact with the soil resource. Whilst knowledge regarding soils and droughts exists in the scientific literature, a coherent understanding of the wider range of impacts on soil properties and functions has not been compiled for temperate soils. Here, we draw together knowledge from studies in the UK and other temperate countries to understand how soils respond to drought, and importantly what and where our knowledge gaps are. First, we define the different types of droughts and their frequency in the UK and provide a brief overview on the likely societal impacts that droughts place on the soil and related ecosystems. Our focus is on ‘agricultural and ecosystem drought’, as this is when soils experience dry periods affecting crops and ecosystem function, followed by rewetting. The behaviour of moisture in soils and the key processes that contribute to its storage and transport are examined. The principal changes in the physical, chemical, and biological properties of soils resulting from drought, and rewetting (i.e., drought termination) are discussed and their extensive interactions are demonstrated. Processes that are involved in the rewetting of soils are explored for soil and catchment-scale soil responses. Lastly, soils’ recovery after drought is considered, knowledge gaps are identified, and areas to improve understanding are highlighted.

Effect of layered fertilizer strategies on rapeseed (Brassica napus L.) productivity and soil macropore characteristics under mechanical direct-sowing

Layered fertilization parameters affects crop root system configuration and growth distribution, which in turn affects soil pore properties and aggregate structure. Therefore, understanding the spatial distribution of the root system and soil pore space is helpful in choosing a reasonable fertilization ratio in production practice, which ensures high and stable crop yields and at the same time improves fertilizer utilization and soil health. Albeit the impact of layered fertilization ratio at varied rates on soil pore characteristics in the soil profile at a microscale level remains limited. This study quantifies the impacts of layered fertilization ratio under on rapeseed root distribution and soil pore characteristics using the root analysis system and X-ray computed tomography (XCT). A new fertilization pattern that shallow-deep layer synchronized mechanical sowing was using, rotary tillage mixed fertilization in the shallow layer and 10 cm side-deep band fertilization in the deeper layer. It set five ratios of fertilizer amounts between shallow and deep layers as 0:4 (FD), 1:3 (FL), 2:2 (FM), 3:1 (FH), and 4:0 (F0), with non-fertilization (CK) as the control treatment. The results indicated that layered fertilization effectively improved the rape root conformation and spatial distribution, significantly increased total soil porosity and moisture content, and reduced soil bulk density and resistance (P < 0.05). Additionally, root configuration is closely related to soil pore structure and crop yield. Notably, compared with other treatments, the macroscopic porosity, equivalent pore diameter, hydraulic radius and roundness of FM treatment are significantly improved, and it effectively improves the yield and quality of rapeseed. Correlation analysis showed that the root system configuration parameters were negatively correlated with soil bulk density and resistance but positively correlated with soil moisture content, and macropore morphology parameters and rapeseed yield. Our study demonstrate that layered fertilization can improved rape growth and soil macropore porosity, but its effect depends on good tillage macropore characteristics and distribution created by reasonable fertilization ratio, which provided a theoretical basis for high-yield, efficient, green, and sustainable mechanized direct-sowing production of rapeseed.

Effects of Habitat Loss on Tenebrionidae in Gravel–Sand Mulching Areas of Desert Steppe in Ningxia, China

The desert steppe in Ningxia is the largest natural steppe type in the region, characterized by a fragile ecological environment and low carrying capacity. Gravel–sand mulching, a local agricultural practice, involves using a sand and gravel covering layer to maintain soil warmth and moisture. However, this method has led to ecological problems, such as habitat loss and biodiversity loss. Tenebrionidae, one of the most widely distributed beetle families, is commonly used to indicate habitat degradation and is a significant steppe pest in desert steppe areas. This study, conducted in the gravel–sand mulching areas of Shapotou District, Zhongwei City, Ningxia, classified the habitat loss from low to high in these areas into five grades (I-V) and examined the effects of habitat loss on Tenebrionidae. We collected 6565 Tenebrionidae, with Blaps femoralis, Anatolica nureti, and Pterocoma vittata being the dominant species. The findings reveal that habitat loss grade I had the highest abundance, diversity index, and evenness index of Tenebrionidae, significantly higher than those of grades II–IV. Habitat loss had a significant negative effect on Tenebrionidae abundance, a significant positive effect on the richness index, no significant effect on the vegetation diversity index, a significant positive effect on soil available potassium (APP), and a significant negative effect on soil total phosphorus (TP). Redundancy analysis indicated a positive correlation between Tenebrionidae abundance and the vegetation diversity index; a negative correlation between Tenebrionidae richness, the diversity index, and vegetation indices; a positive correlation between Tenebrionidae abundance and soil TP; and a negative correlation between the Tenebrionidae diversity index and soil TP and soil APP. These findings will contribute to biodiversity conservation and ecological restoration and provide a theoretical basis for steppe management, sustainable agricultural development, and pest monitoring in desert steppe environments.

Stabilization strategy of normal and chrome tanning effluent mixed subsoil: a novelty to enhance the soil characteristics

Soil stabilisation is a unique method to effectively and accurately find a solution to the issues caused by loose subsoil. This research investigates the problems of uncontrolled industrial effluent disposal, mainly from tannery enterprises, which presents severe environmental problems in emerging nations due to soil properties changing and large regions being unfit for cultivation and human habitation. The study uses Atterberg's limits, sieve analysis, and direct shear testing to examine the effects of untreated tannery effluents on soil parameters. The findings show that the soil's shear strength, moisture content, and flexibility have all significantly decreased. Lime and waste stone powder were added to lessen these impacts, and this improved soil stability was shown by an increase in the values of all the variables. For efficient soil remediation, the study suggests using waste stone powder and lime in the following proportions: 0%, 3%, 6%, and 9%. Combining lime stabilisation methods with industrial by-products like slag and fly ash opens up new possibilities for enhancing the geotechnical characteristics of polluted soils. This study emphasises how important it is to use customised geotechnical design techniques to handle soil contamination issues in infrastructure and foundation projects, especially in areas where the leather industry predominates.

Surface soil moisture remote sensing in the visible–short-wave infrared range: from the laboratory to the field

Surface soil moisture remote sensing in the visible–short-wave infrared range: from the laboratory to the field

Effect of Some Ecological Factors on Earthworms (Lumbercus terrestris) in Wasit Governorate

Monthly earthworm samples were collected from two locations in Suwairah City‎ of Wasit Governorate. where the first location was northwest of the district and is characterized by an abundance of citrus trees such as orange, lemon and Punica granatum as well as many palm trees of different types, the second area is characterized by an abundance of vegetable plants like Cauliflower, Allium cepa, and Brassica oleracea. Soil temperature, moisture, pH, texture and organic matter levels were measured at all sample sites. Soil temperature ranged between 11C° to 50C° as recorded during February and August respectively. The values of PH have ranged between 6.8 to 8.0. Soil moisture and Organic matter content were highest in Region (B) samples, 40% and 6.6% respectively. However, lowest organic matter and soil moisture percentage were 17% and 3.13% recorded at in Region (A) samples respectively. All sites were characterized by loamy acidic reactive soils.

Impact of Natural and Human Factors on Dryland Vegetation in Eurasia from 2003 to 2022.

Eurasian dryland ecosystems consist mainly of cropland and grassland, and their changes are driven by both natural factors and human activities. This study utilized the normalized difference vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) to analyze the changing characteristics of vegetation activity in Eurasia over the past two decades. Additionally, we integrated the mean annual temperature (MAT), the mean annual precipitation (MAP), the soil moisture (SM), the vapor pressure deficit (VPD) and the terrestrial water storage (TWS) to analyze natural factors' influence on the vegetation activity from 2003 to 2022. Through partial correlation and residual analysis, we quantitatively described the contributions of both natural and human factors to changes in vegetation activity. The results indicated an overall increasing trend in vegetation activity in Eurasia; the growth rates of vegetation greenness, productivity and photosynthetic capacity were 1.00 × 10-3 yr-1 (p < 0.01), 1.30 g C m-2 yr-2 (p < 0.01) and 1.00 × 10-3 Wm-2μm-1sr-1yr-1 (p < 0.01), respectively. Furthermore, we found that soil moisture was the most important natural factor influencing vegetation activity. Human activities were identified as the main driving factors of vegetation activity in the Eurasian drylands. The relative contributions of human-induced changes to NDVI, GPP and SIF were 52.45%, 55.81% and 74.18%, respectively. These findings can deepen our understanding of the impacts of current natural change and intensified human activities on dryland vegetation coverage change in Eurasia.