Search

Abstract: Quantifying evapotranspiration (ET) is critical to accurately predict vegetation health, groundwater recharge, and streamflow generation. Hillslope aspect, the direction a hillslope faces, results in variable incoming solar radiation and subsequent vegetation water use that drive ET. Previous work in watersheds with a single dominant vegetation type (e.g., trees) have shown that equator‐facing slopes (EFS) have higher ET compared to pole‐facing slopes (PFS) due to higher evaporative demand. However, it remains unclear how differences in vegetation type (i.e., grasses and trees) influence ET and water partitioning between hillslopes with opposing aspects. Here, we quantified ET and root‐zone water storage deficits between a PFS and EFS with contrasting vegetation types within central coastal California. Our results suggest that the cooler PFS with oak trees has higher ET than the warmer EFS with grasses, which is counter to previous work in landscapes with a singule dominant vegetation type. Our root‐zone water storage deficit calculations indicate that the PFS has a higher subsurface storage deficit and a larger seasonal dry down than the EFS. This aspect difference in subsurface water storage deficits may influence the subsequent replenishment of dynamic water storage, groundwater recharge and streamflow generation. In addition, larger subsurface water deficits on PFS may reduce their ability to serve as hydrologic refugia for oaks during multi‐year droughts. This research provides a novel integration of field‐based and remotely‐sensed estimates of ET required to properly quantify hillslope‐scale water balances. These findings emphasize the importance of resolving hillslope‐scale vegetation structure within Earth system models, especially in landscapes with diverse vegetation types.

The USDA Livestock Forage Disaster Program (LFP) offers financial assistance to farmers and ranchers with grazed forage losses caused by fire or drought. Payments for drought losses are based on the U.S. Drought Monitor (USDM), which is designed to integrate meteorological, agricultural, hydrological, ecological, and socioeconomic droughts. Because soil moisture deficit is a more specific measure of agricultural drought, we hypothesized that basing LFP payments on soil moisture observations could better reduce producers' risk. Therefore, our objectives were to 1) quantify relationships of forage yield with USDM-based LFP payment multipliers and with in situ soil moisture, 2) develop an alternative LFP payment multiplier structure based on in situ soil moisture, and 3) quantify risk reduction using the current and alternative payment structures. We focused on Oklahoma, United States, which has led the nation in LFP payments received and has >25 years of in situ soil moisture observations statewide. Using non-alfalfa hay yield as a surrogate for forage production, we found that LFP payment multiplier values and soil moisture anomaly were each related to yield, and soil moisture anomaly explained 54% of yield variability. However, the USDM-based LFP payment structure sometimes resulted in payments for above-average yield, and higher payments did not always correspond with greater yield losses. We developed an alternative soil-moisture-based payment structure that reduced financial risk by >20% compared with the current USDM-based structure. Our study identifies an improved LFP payment structure for Oklahoma that can be evaluated and refined in other states or nationwide using other soil moisture data sources. SIGNIFICANCE STATEMENT: The USDA Livestock Forage Disaster Program (LFP) offers financial assistance to farmers and ranchers with grazed forage losses caused by fire or drought, with payment eligibility based on drought severity estimates from the U.S. Drought Monitor (USDM). We hypothesized that the LFP could be improved by instead using soil moisture measurements to trigger payments. We found that forage production in Oklahoma was closely linked to soil moisture, and drought relief payments based on soil moisture measurements increased risk protection compared with those based on the USDM. Our work outlines a method for soil-moisture-based LFP payments in Oklahoma, a state that has led the nation in LFP payments. While challenges remain, this work also provides a framework to develop similar payment structures at the regional and national levels. KEYWORDS: Drought; Soil moisture; Agriculture; Policy; Risk assessment, 1. Introduction The USDA Livestock Forage Disaster Program (LFP) was introduced with the 2008 U.S. Farm Bill to help farmers and ranchers offset losses of grazed forages caused by fire [...]

We demonstrate an indirect, rather than direct, role of quasi-resonant amplification of planetary waves in a summer weather extreme. We find that there was an interplay between a persistent, amplified large-scale atmospheric circulation state and soil moisture feedbacks as a precursor for the June 2021 Pacific Northwest Heat Dome event. An extended resonant planetary wave configuration prior to the event created an antecedent soil moisture deficit that amplified lower atmospheric warming through strong nonlinear soil moisture feedbacks, favoring this unprecedented heat event.

BACKGROUND: Poly‐γ‐glutamic acid (γ‐PGA) is employed extensively in agriculture to enhance soil water retention; however, the underlying mechanism by which γ‐PGA improves soil structure and soybean productivity in arid regions remains poorly understood. A micro‐scale field experiment was conducted in the arid region of northwest China, employing five concentrations of γ‐PGA to investigate its impacts on soybean yield, photosynthesis, and water‐use efficiency, as well as soil aggregates and water distribution. The five levels of γ‐PGA were 0 (CK), 10 (P1), 20 (P2), 40 (P3), and 80 kg ha−1 (P4). RESULTS: The results demonstrated that the application of γ‐PGA significantly improved soybean yield, photosynthesis, and chlorophyll content. It resulted in a decrease in soil aggregate content with a maximum diameter of less than 0.053 mm and an increase in the stability of soil aggregates in the uppermost layer of the soil (0–30 cm). The application of γ‐PGA significantly increased soil water content, particularly in the uppermost layer of the soil, and effectively reduced water consumption and improving water use efficiency in soybeans. Overall, the P3 treatment exhibited the most pronounced improvement of soybean yield, photosynthesis, water‐use efficiency, as well as distribution of soil aggregates and water. The correlation matrix heatmap also revealed a strong correlation between improvement of soybean yield or photosynthesis at various γ‐PGA application levels and the enhancement of soil stability or soil water content. CONCLUSION: The multivariate regression analysis revealed that an optimal application level of 46 kg ha−1γ‐PGA could enhance effectively both yield and water use efficiency of soybean in the arid region of northwest China. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The impact of land variables on temperature forecasts in atmospheric cycling is often underestimated or overlooked. This oversight primarily occurs due to the abundance of meteorological measurements available for assimilation and partly because soil states are assumed to be quickly reset by atmospheric forcing, such as precipitation, justifying no spin‐ups or no updates of soil states during cycling. In this study, by updating soil moisture every 6 hr using different analysis data sets for May 2019, considerable discrepancies were found, highlighting large uncertainties in soil moisture analysis. Different soil moisture analyses produced systematically different temperature forecasts, with errors growing over cycles to be comparable to a typical error magnitude of 2‐m temperature observations (∼2°K). This study demonstrates that temperature forecasts are significantly influenced by whether and how soil moisture is updated, not only near the surface but also up to the low‐mid troposphere and throughout the cycles. Plain Language Summary: This study examines the impact of soil moisture on temperature forecasts in the context of atmospheric cycling. During the 6‐hr cycling for May 2019 over the CONUS domain, updating soil moisture systematically affects temperature forecast bias (>±1°K) in the low‐mid troposphere throughout the cycles. We also observe significant uncertainties across state‐of‐the‐art soil moisture analysis data sets, highlighting the challenges of land data assimilation. Key Points: Temperature forecast errors are significantly affected by whether or how soil moisture is updated in atmospheric cyclingLarge uncertainties are found across different soil moisture analysis data setsUpdating soil moisture alone can mitigate temperature forecast bias in the low‐mid troposphere, systematically improving forecast accuracy [ABSTRACT FROM AUTHOR]

Accurate and timely monitoring of drought conditions in grape-producing regions is crucial for achieving healthy growth of grapes. Current soil moisture (SM) products are primarily available at coarse resolutions (e.g. several to tens of kilometres), constraining its applications at fine scales. Here, we trained a weighted stacking ensemble model including three tree-based models (categorical boosting, random forest, and gradient boosting decision tree), using seven forcing parameters related to spectral reflectance (SR), land surface temperature (LST), and evapotranspiration (ET), in conjunction with the digital elevation model (DEM) feature. The weighted stacking ensemble model exhibited an average R2 of 0.86 and an average RMSE of 0.021 m3/m3 in simulating SM in the vegetive stage and the mid-ripening stage of grape. Then we generated high spatiotemporal downscaled SM (HSM) data at a grape growing area at high spatiotemporal resolutions (30 m, 8-day) from 2009 to 2018. Our HSM dataset demonstrated strong spatial, seasonal and interannual dynamics that align with 500 m SM dataset derived from single MODIS data, and the HSM dataset shows more details in SM distribution. Additionally, the SM time series in the HSM is consistently correlated with drought events, offering intricate spatiotemporal information for drought monitoring. The application of downscaled SM results identified a concentration of drought events in the eastern foothills of the Helan Mountains, particularly severe drought conditions were observed in the Hongsipu production area. Drought occurrences in the Hongsipu production area ranged from 90% to 91% during May and June, decreasing to 73% and 41% in July and August, respectively. These findings significantly contribute to enhancing high spatiotemporal SM monitoring capabilities, offering valuable guidance for timely water management in grape-growing regions. [ABSTRACT FROM AUTHOR]

This study assessed the spatio-temporal variation of the degree of curing (DoC) in the Golden Gate Highlands National Park (GGHNP) from 2016 to 2020. The estimation of the degree of grass curing was carried out with the high spatial resolution fused remotely sensed data of MODIS and Sentinel-2 employing Index-then-Blend (IB) data fusion methods on Google Earth Engine (GEE) to compute Grassland Curing Index (GCI) using soil and vegetation moisture content indices-based grassland curing algorithm. Grassland curing indices were computed on Google Earth Engine and converted into Grassland Curing Maps (GCMs), which were then synthesized into fire danger maps. The study showed that all selected indices have the highest DoC in the month of September, with the minor time variation observed in MODIS and Sentinel 2-derived GCI estimated using Shortwave Infrared Water Stress (GCI_SIWSI) in the months of August and March, respectively. The analysis further revealed that GCI_SIWSI (44%) derived from Sentinel-2 data yielded the highest area of extremely high fire danger, followed by GCI estimated using Normalized Difference Moisture Index (GCI_NDMI) (25%) derived from fused data, MODIS-derived GCI_SIWSI and fused-derived GCI estimated using Global Vegetation Monitoring Index (GCI_GVMI) (both 16%). The GCI_SIWSI derived from Sentinel-2 data also showed that more than 95% of the entire landmass of the study area was within a high to extremely high fire danger zone, making the park's vegetation susceptible to fire. Among all the four selected indices correlated against fire point, only SIWSI revealed a positive relationship across MODIS, Sentinel-2 and Fused data. It is also evident from the study that GCMs derived from Fused data outperformed MODIS and Sentinel-2 with the highest R2 and F values of 0.65 and 380, respectively. These results indicate that GCI derived from fused remotely sensed data is a promising GCI for accurately assessing the DoC over mountainous grassland environments. The study paved the way for increasing the spatial resolution for estimating the DoC for fire and fuel management of parks and other fire agencies within mountainous grassland environments. [ABSTRACT FROM AUTHOR]

Blackgram, a protein-rich pulse crop (24%), is crucial for combating food insecurity, particularly in malnourished and economically weak countries. Enhancing blackgram production requires improved, input-saving management practices. Given the challenges of climate change and population growth, efficient water management is vital for increasing pulse productivity and water use efficiency with minimal investment. This study aimed to identify cost-effective irrigation methods to optimise blackgram yields. Experiments were conducted at the National Pulses Research Centre in Vamban, Pudukkottai, and the Agricultural College and Research Institute in Kumulur, Tiruchirappalli, during the kharif season of 2021 and 2022. The study compared different treatments of irrigation methods, such as check basin, raised bed, drip, sprinkler and rain hose irrigation. Results showed that the rain hose system maintained the highest soil moisture (23.93% at 10 cm depth and 19.71% at 20 cm depth). Even though drip irrigation resulted in a higher seed yield (1363 kg ha−1), the rain hose system proved to be more cost-effective, saving 27.09% in costs and achieving a 15.23% higher benefit–cost ratio. These findings suggest that the rain hose method, combined with current agronomic practices, is a viable low-cost technique for sustainable blackgram cultivation, optimising water use and maximising profits. This research provides valuable insights into water-saving irrigation methods for pulse crops. [ABSTRACT FROM AUTHOR]

Two L-band passive microwave satellite sensors, onboard the Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015, are specifically designed for surface soil moisture (SM) monitoring. The first global continuous fused L-band satellite SM product based on SMOS and SMAP observations (SMOS-SMAP-INRAE-BORDEAUX, the so-called Fused-IB) was recently released to the public. Currently, the performance of Fused-IB has only been evaluated collectively over the entire data records in the study period, without specific evaluation for individual seasons. To fill this gap, this study intercompared the Fused-IB and the enhanced SMAP-L3 version 6 (SMAP-E) SM products against in situ SM data from the International Soil Moisture Network (ISMN) from 2016 to 2020 regarding the whole period and different seasons. We aim to investigate the performance of these two products at different time scales and to explore the potential ecohydrological factors (i.e., precipitation and vegetation) driving their seasonal variations. Results show that both SM products are in good agreement with the in situ measurements, demonstrating high median correlation (R) and low ubRMSD (median R = 0.70 and ubRMSD = 0.058 m³/m³ for Fused-IB vs. R = 0.68 and ubRMSD = 0.059 m³/m³ for SMAP-E) during 2016-2020. For most land use and land cover (LULC) types, Fused-IB outperformed SMAP-E with higher accuracy and lower errors, particularly in forests, partly due to the advantage of the robust SMAP-IB (SMAP-INRAE-BORDEAUX) algorithm used to generate Fused-IB in forests, which avoids the pronounced saturation effects of vegetation optical depth caused by relying on optical information. Besides, both products had superior performances across most LULC types in summer (JJA) and autumn (SON), yet increased uncertainties were observed in forests, grasslands, and croplands during spring (MAM) and winter (DJF). These uncertainties could be mainly attributed to the effects of vegetation growth in forests, grasslands and croplands, and the interception of water from rainfall events in grasslands. The results of this study can serve as a reference for algorithm developers to enhance the accuracy of SM and thus promote hydrometeorological applications that benefit from L-band radiometer soil moisture products. [ABSTRACT FROM AUTHOR]

In root research, hydroponic plant cultivation is commonly used and soil experiments are rare. We investigated the response of 12‐day‐old barley roots, cultivated in soil‐filled rhizotrons, to different soil water potentials (SWP) comparing a modern cultivar (cv. Scarlett) with a wild accession ICB181243 from Pakistan. Water potentials were quantified in soils with different relative water contents. Root anatomy was studied using histochemistry and microscopy. Suberin and lignin amounts were quantified by analytical chemistry. Transcriptomic changes were observed by RNA‐sequencing. Compared with control with decreasing SWP, total root length decreased, the onset of endodermal suberization occurred much closer towards the root tips, amounts of suberin and lignin increased, and corresponding biosynthesis genes were upregulated in response to decreasing SWP. We conclude that decreasing water potentials enhanced root suberization and lignification, like osmotic stress experiments in hydroponic cultivation. However, in soil endodermal cell suberization was initiated very close towards the root tip, and root length as well as suberin amounts were about twofold higher compared with hydroponic cultivation. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of testing the hypothesis of what the authors call a nested block circular‐compound symmetric (NBCCS) covariance structure for the population covariance matrix. This is a covariance structure which has an outer block compound symmetric structure, where the diagonal blocks are themselves block circular matrices, while the off‐diagonal blocks are formed by all equal matrices. The NBCCS null hypothesis is decomposed into sub‐hypotheses, allowing this way for a simpler way to obtain a likelihood ratio test and its associated statistic. The exact moments of this statistic are derived, and its distribution is carefully examined. Given the complicated nature of this distribution, highly precise near‐exact distributions were developed. Numerical studies are conducted to assess the proximity between these near‐exact distributions and the exact distribution, highlighting the performance of these approximations, even in the case of very small sample sizes. Furthermore, three datasets, on bone mineral content, metabolic rates of glucose, and soil moisture are used to exemplify the practical application of the methodology derived in this study. [ABSTRACT FROM AUTHOR]

Filtering processes across environmental gradients can structure patterns of trait variation within communities. The community‐weighted mean (CWM) is a metric that is commonly used to indicate the directionality of such filtering processes and the optimal adaptive strategy of taxa within community. Proximity to the CWM indicates higher fitness, and deviations from this optimal value result in changes in the relative abundances of coexisting species. We investigated patterns of intraspecific trait variation in four coexisting carrion beetle species (Coleoptera: Silphidae) across elevational gradients. The study was conducted in temperate forest ecosystems with distinct natural vegetation zones ranging from 950 m to 1700 m above sea level. Of the 12 traits measured, we found that intraspecific variation ranged from 34% (body length) to 100% (ratio of elytra length, head length and head width to body length) and accounted for a larger proportion of variation than interspecific variation in 7 traits. For most traits, trait range, which indicates the niche breadth of species at a given elevation, was positively correlated with relative abundance. The CWMs of traits associated with long‐distance dispersal decreased with elevation, whereas those associated with microhabitat use showed the opposite trend. Soil temperature influenced tibia length after controlling for the effects of species identity, but soil water content had no effect on trait variation. Patterns of variation in body width and thorax width of two beetle species supported the CWM‐optimality hypothesis; however, patterns of trait variation in body width and thorax length of another species showed an opposite trend. Our study identifies several traits that can be highly variable within carrion beetle species. Such high levels of intraspecific trait variation may enable populations to adapt across a wide range of elevations and vegetation types. [ABSTRACT FROM AUTHOR]

For many drylands, both long‐ and short‐term drought conditions can accentuate landscape heterogeneity at both temporal (e.g., role of seasonal patterns) and spatial (e.g., patchy plant cover) scales. Furthermore, short‐term drought conditions occurring over one season can exacerbate long‐term, multidecadal droughts or aridification, by limiting soil water recharge, decreasing plant growth, and altering biogeochemical cycles. Here, we examine how experimentally altered seasonal precipitation regimes in a mixed shrub grassland on the Colorado Plateau impact soil moisture, vegetation, and carbon and nitrogen cycling. The experiment was conducted from 2015 to 2019, during a regional multidecadal drought event, and consisted of three precipitation treatments, which were implemented with removable drought shelters intercepting ~66% of incoming precipitation including: control (ambient precipitation conditions, no shelter), warm season drought (sheltered April–October), and cool season drought (sheltered November–March). To track changes in vegetation, we measured biomass of the dominant shrub, Ephedra viridis, and estimated perennial plant and ground cover in the spring and the fall. Soil moisture dynamics suggested that warm season experimental drought had longer and more consistent drought legacy effects (occurring two out of the four drought cycles) than either cool season drought or ambient conditions, even during the driest years. We also found that E. viridis biomass remained consistent across treatments, while bunchgrass cover declined by 25% by 2019 across all treatments, with the earliest declines noticeable in the warm season drought plots. Extractable dissolved inorganic nitrogen and microbial biomass nitrogen concentrations appeared sensitive to seasonal drought conditions, with dissolved inorganic nitrogen increasing and microbial biomass nitrogen decreasing with reduced soil volumetric water content. Carbon stocks were not sensitive to drought but were greater under E. viridis patches. Additionally, we found that under E. viridis, there was a negative relationship between dissolved inorganic nitrogen and microbial biomass nitrogen, suggesting that drought‐induced increases in dissolved inorganic nitrogen may be due to declines in nitrogen uptake from microbes and plants alike. This work suggests that perennial grass plant–soil feedbacks are more vulnerable to both short‐term (seasonal) and long‐term (multiyear) drought events than shrubs, which can impact the future trajectory of dryland mixed shrub grassland ecosystems as drought frequency and intensity will likely continue to increase with ongoing climate change. [ABSTRACT FROM AUTHOR]

Soil moisture is one of the most influencing factors on the crop growth in agricultural production, particularly for the water management, yield estimation, drought monitoring and precision irrigation. Therefore, it is very necessary to rapidly and accurately detect the soil moisture. Fortunately, deep learning techniques have been widely applied into the soil moisture inversion in recent years. However, much efforts have been focused mainly on the optimization of model structures. It is still lacking to explore the hyperparameter settings of the models. In this study, an optimization strategy was proposed for a convolutional neural network (CNN) model using rime optimization (RIME), in order to improve the performance of soil moisture inversion in winter wheat farmlands. The polarization decomposition was also combined to correct the impact of vegetation on the accuracy of soil moisture inversion in the vegetation-covered areas The reason was that the vegetation was negatively correlated with the soil moisture inversion. The RIME was then employed to optimize the hyperparameters of CNN, in order to form the RIME-CNN model. Subsequently, the RIME-CNN model was utilized to adaptively extract the feature parameters. Soil moisture was then estimated to regularize and feed into the feature parameters using support vector regression (SVR). Additionally, the wealth polarization characteristics were contained in fully polarized data. Various techniques were employed to perform the polarization decomposition on the RADARSAT-2 data. As such, the wide ranges of characteristic parameters were acquired after polarization. The original feature space of SAR data was further enriched to eliminate the data redundancy for the convergence of the network. Mutual information (MI) was also used to optimize the characteristic parameters. Synthetic aperture radar (SAR) and optical remote sensing data were used to invalidate the efficacy of the RIMECNN-SVR model in the soil moisture inversion of the winter wheat farmlands. The results showed that: 1) The high accuracy of soil moisture inversion was achieved to improve the correlation between surface backscatter coefficient and soil moisture. Therefore, the polarization decomposition was effectively weakened the interference of vegetation. Among them, there was the highest correlation between the surface backscatter coefficient under HH polarization and soil moisture. 2) The MI was employed to optimize the feature. The unnecessary feature parameters were effectively reduced the data redundancy. The performance of network training was enhanced for the high accuracy of soil moisture inversion. 3) The RIME-CNN-SVR model was obtained in the higher inversion accuracy, compared with the CNN, RIME-CNN and CNN-SVR models, in which the determination coefficient was 0.72, the root mean square error (RMSE) was 2.78%, and the mean absolute error was 2.2%. At the same time, the RIME-CNN-SVR model was also feasible and suitable for the inversion of soil moisture in the winter wheat fields. The finding can also provide the accurate and reliable means of soil moisture monitoring for agricultural production. [ABSTRACT FROM AUTHOR]

Background and aims: Altitude, season, and slope aspect have significant impacts on bacterial variation in forest soils. However, it is currently unclear which factor has the greatest influence. Methods: Quantitative PCR (qPCR) and high-throughput sequencing were employed to investigate the changes in the bacterial communities with altitude, season and slope aspect in the subtropical forests of Mount Ailao. Results: The effects of altitude, season and slope on bacterial abudance were in the order altitude (η2 = 0.964) > season (η2 = 0.831) > slope (η2 = 0.590). In addition, the bacterial abundance increased with altitude, and the rainy season but not the dry season, the eastern slope rather than the western slope had a higher bacterial abundance. Furthermore, altitude (η2: 0.877–0.942) and season (η2: 0.229–0.761), resulted in higher bacterial α-diversity at low altitudes and in the dry season, respectively. Synergistetes/Verrucomicrobia (low altitude), Acidobacterium (high altitude), Actinobacteria/Planctomycetes (dry season) and Armatipnoadetes (rainy season) were the richest in the soils. Correlation networks showed that bacteria were very stable at higher altitudes and in the dry season. In addition, soil water content, soil organic matter, total nitrogen and ammonium nitrogen were the key edaphic factors significantly affecting the bacterial abundance, α-diversity, and β-diversity. Conclusions: Altitudes rather than seasons and slope aspects had the greatest effect on the bacterial communities in subtropical forests. Therefore, we suggest that altitude, season, and slope aspect be considered necessary factors for comprehensive analysis when studying soil microbial changes in different forests. [ABSTRACT FROM AUTHOR]

Restoring biological crust (biocrust) in disturbed drylands is challenging due to the difficult environmental conditions, such as limited soil moisture, low soil nutrients, and extreme temperatures, that impede growth. Understanding how the key components of biocrust—mosses, lichens, and cyanobacteria—react to different environmental factors informs the optimal timing, locations, and species composition for biocrust reintroduction, thereby increasing the likelihood of establishment. Here, we inoculated soils with a diverse range of biocrust organisms, analogous to seeding an area with diverse vascular plant seeds, and varied environmental conditions to observe how these changes influenced the development and functions of reintroduced biocrust. We found that by manipulating soil texture and time spent wet, we can change the proportional cover of biocrust within a restoration‐like setting. Specifically, we found that 4 months after inoculation, finer textured soils that received more water become dominated by moss cover, while coarser textured soils with less water remained dominated by cyanobacteria cover, and the interactions between texture and time spent wet strongly influenced cover. We found biocrust morphological group cover had a small, but detectable, effect on ecosystem functions (soil stability and nitrogenase activity, a proxy for nitrogen fixation), but that environmental conditions had a stronger impact on the functions we measured. Manipulative experiments in controlled environments, like this one, can help elucidate the mechanisms underlying the establishment rate and patterns of biocrusts post‐inoculation, and inform implementation of inoculations in the field. [ABSTRACT FROM AUTHOR]

Various factors influence biocrust effects on infiltration and soil moisture. On China's Loess Plateau, well‐developed moss‐dominated biocrusts reduce infiltration and suppress shrub growth, however, the interrelationships between rainfall conditions and disturbance management to alleviate these impacts are unknown. In a semiarid watershed on the Loess Plateau, we applied two forms of disturbance, scarification (controlled harrowing) and compaction (simulated grazing), at four intensity levels (control‐intact, light, moderate, and heavy) to well‐developed moss‐dominated biocrusts and a bare soil (BS) for comparisons to form eight treatments with three replications. Rainfall intensity and depth, soil moisture, run‐off, and sediment yield were measured during the rainy season (July–October). In total, well‐developed biocrusts did not significantly affect infiltration but reduced soil moisture at depths of 120–180 cm (p < 0.05), and disturbance increased infiltration and deep soil moisture beneath biocrusts (p < 0.05). Rainfall conditions changed the effects of biocrusts and disturbance. Under long, low‐intensity rainfall, moss‐dominated biocrust infiltration was slightly greater than BS, but scarification increased infiltration and compaction reduced it (p < 0.001). Under short, high‐intensity rainfall, biocrusts reduced infiltration, and disturbance intensity increased infiltration and sediment yields (p < 0.001). Light grazing or using light or moderate scarification improves well‐developed moss‐dominated biocrust infiltration in the pastures during May–June before the rainy season during July–October on the Loess Plateau, while avoiding increased soil erosion. [ABSTRACT FROM AUTHOR]

Nutrient cycling in desert ecosystems is heavily reliant on the fine root turnover and litter decomposition processes of dominant plant species. Based on field investigations and laboratory experiments, we explored fine root production (FRP) and turnover of Salsola passerina in Alxa steppe desert, and assessed fine root and leaf litter input and its correlation with soil C and N stocks, and then compared the variability of soil physicochemical properties at the site below and the interspace of S. passerina canopy. Results showed that fine root biomass dynamics of S. passerina were consistent with soil water content. Annual FRP of S. passerina was 243.3 g m−2 yr−1 and the turnover rate was 2.0 per year. S. passerina contributed 172.2 g C m−2 and 6.8 g N m−2 to soil from its fine root senescence and leaf litter input annually. Compared to the canopy interspace, the soil texture and fertility beneath the shrubs significantly improved, while the soil salinity decreased. The results indicated that the activities of desert shrub growth provided the degraded soil with abundant nutrients through fine root turnover and litter decomposition processes, which promoted soil texture and nutrition heterogeneity at the stand scale, and played an important role in degraded dryland restoration. [ABSTRACT FROM AUTHOR]

Spring‐fed wetlands within arid and semiarid systems are hotspots for endemism and distribution of rare plants. Interactions among groundwater and the geomorphic and climatic features of the setting control the abiotic conditions, particularly soil salinity and moisture, that support these plants. However, water uncertainty and land use change challenge the persistence of conditions necessary to support rare plant communities. Wetland management can be implemented to sustain abiotic processes that support rare plant communities, but key information is needed to guide management practices. In this study, we evaluate the relationships of rare plants to abiotic conditions in a managed spring‐fed arid wetland. Soil salinity and moisture conditions were monitored and related to the presence and abundance of rare plants within management units. Soil salinity and moisture variability were related to groundwater dynamics near springs, but wetland management influenced variability in seasonally flooded areas. Permanently saturated conditions and low soil salinities during the spring season supported higher plant diversity and the presence and greater abundance of rare plants. Rare plant presence and abundance were negatively related to low soil moisture, particularly in the summer. Results indicate that increases in soil salinity during the early establishment of plants may affect their distribution and abundance, an important management consideration in arid landscapes and hydrologically altered systems. Our findings inform the restoration and management of rare plant communities and contribute to the management of spring‐fed arid wetlands. [ABSTRACT FROM AUTHOR]

Northwest China has undergone notable alterations in climate and vegetation growth in recent decades. Nevertheless, uncertainties persist concerning the response of different vegetation types to climate change and the underlying mechanisms. This study utilized the Normalized Difference Vegetation Index (NDVI) and three sets of meteorological data to investigate the interannual variations in the association between vegetation and climate (specifically precipitation and temperature) from 1982 to 2015. Several conclusions were drawn. (1) RNDVI-GP (relationship between Growing Season NDVI and precipitation) decreased significantly across all vegetation, while RNDVI-GT (relationship between Growing Season NDVI and temperature) showed an insignificant increase. (2) Trends of RNDVI-GP and RNDVI-GT exhibited great variations across various types of vegetation, with forests displaying notable downward trends in both indices. The grassland exhibited a declining trend in RNDVI-GP but an insignificant increase in RNDVI-GT, while no significant temporal changes in RNDVI-GP or RNDVI-GT were observed in the barren land. (3) The fluctuations in RNDVI-GP and RNDVI-GT closely aligned with variations in drought conditions. Specifically, in regions characterized by VPD (vapor pressure deficit) trends less than 0.02 hpa/yr, which are predominantly grasslands, a rise in SWV (soil water volume) tended to cause a reduction in RNDVI-GP but an increase in RNDVI-GT. However, a more negative trend in SWV was associated with a more negative trend in both RNDVI-GP and RNDVI-GT when the VPD trend exceeded 0.02 hPa/yr, primarily in forests. Our results underscore the variability in the relationship between climate change and vegetation across different vegetation types, as well as the role of drought in modulating these associations. [ABSTRACT FROM AUTHOR]

The effect of machine learning and other enhancements on statistical–dynamical forecasts of soil moisture (0–10 and 0–100 cm) and a reference evapotranspiration fraction [evaporative stress index (ESI)] on subseasonal time scales (15–28 days) are explored. The predictors include the current and past land surface conditions and dynamical model hindcasts from the Subseasonal to Seasonal Prediction project (S2S). When the methods are enhanced with machine learning and other improvements, the increases in skill are almost exclusively coming from predictors drawn from observations of current and past land surface states. This suggests that operational S2S flash drought forecasts should focus on optimizing use of information on current conditions rather than on integrating dynamically based forecasts, given the current state of knowledge. Nonlinear machine learning methods lead to improved skill over linear methods for soil moisture but not for ESI. Improvements for both soil moisture and ESI are realized by increasing the sample size by including surrounding grid points in training and increasing the number of predictors. In addition, all the improvements in the soil moisture forecasts predominantly impact soil moistening rather than soil drying—i.e., prediction of conditions moving away from drought rather than into drought—especially when the initial soil state is drier than normal. The physical reasons for the nonlinear machine learning improvements are also explored. Significance Statement: Rapidly intensifying droughts pose extra challenges for predictability. Here, dynamical forecast model output is combined with nonlinear machine learning methods to improve forecasts of rapid changes in soil moisture and the evaporative stress index (ESI). [ABSTRACT FROM AUTHOR]

Field capacity is a dubious soil physical property, but its use continues because of its perceived value for representing a soil's capacity to store water. Appropriate field capacity estimates can be useful for interpreting data from soil moisture sensors, including those in large‐scale monitoring networks, but suitable methods for defining field capacity in this context are unclear. Motivated by the desire to determine optimal field capacity values for the Oklahoma Mesonet, our objectives were (1) to develop and apply an automated time series analysis algorithm to estimate volumetric soil water content at field capacity and corresponding matric potential and (2) to compare the resulting water contents to those calculated from traditional matric potential thresholds (−33 and −10 kPa). Across 118 Oklahoma Mesonet sites and three soil depths (5, 25, and 60 cm), a matric potential threshold of −10 kPa underestimated field capacity water content by 0.010–0.014 cm cm−3 (3–4%) on average, and a threshold of −33 kPa underestimated it for every site and depth by 0.055–0.078 cm cm−3 (16%−22%) on average. Median matric potentials corresponding to field capacity were −7.6 kPa at the 5‐cm depth, −7.2 kPa at the 25‐cm depth, and −7.3 kPa at the 60‐cm depth. The algorithm developed here can be used to estimate field capacity wherever adequate data are available, and for sites where soil water retention properties are known, matric potentials at field capacity can also be estimated. Using a matric potential of −33 kPa as a standard threshold to represent field capacity is not scientifically justified and should be discontinued. [ABSTRACT FROM AUTHOR]

The rapid advancements in Agriculture 4.0 have led to the development of the continuous monitoring of the soil parameters and recommend crops based on soil fertility to improve crop yield. Accordingly, the soil parameters, such as pH, nitrogen, phosphorous, potassium, and soil moisture are exploited for irrigation control, followed by the crop recommendation of the agricultural field. The smart irrigation control is performed utilizing the Interactive guide optimizer-Deep Convolutional Neural Network (Interactive guide optimizer-DCNN), which supports the decision-making regarding the soil nutrients. Specifically, the Interactive guide optimizer-DCNN classifier is designed to replace the standard ADAM algorithm through the modeled interactive guide optimizer, which exhibits alertness and guiding characters from the nature-inspired dog and cat population. In addition, the data is down-sampled to reduce redundancy and preserve important information to improve computing performance. The designed model attains an accuracy of 93.11 % in predicting the minerals, pH value, and soil moisture thereby, exhibiting a higher recommendation accuracy of 97.12% when the model training is fixed at 90%. Further, the developed model attained the F-score, specificity, sensitivity, and accuracy values of 90.30%, 92.12%, 89.56%, and 86.36% with k-fold 10 in predicting the minerals that revealed the efficacy of the model. [ABSTRACT FROM AUTHOR]

Chickpeas are well adapted to rainfed conditions, but a lack of moisture during the reproductive phase can result in lower pod setting and ultimately reduced grain yield. The exact reasons for this reduction are not fully understood, partly because of the lack of information on soil moisture content (SMC) and water use during podding. This study aimed to address this knowledge gap by quantifying the impact of gradual drought on various yield components of desi‐type genotypes using the fraction of transpirable soil water (FTSW) method. Two water treatments were applied at the start of anthesis: well‐watered (WW) and drought‐stressed (DS). The WW was maintained at 24% SMC, whereas the DS was dried to 3% SMC. The results showed that DS caused early podding (3.3 days less), with reductions in plant height (11.4 cm), leaf area (1249.5 cm2), flowers (18.6), pods (27.4), seeds (37.0), grain yield (0.2 g), aboveground dry biomass (11.4 g) and harvest index (0.2 g), while increasing flower abortion (14.6) and seed biomass (1.5 g). However, diverse genotypic responses were observed to treatments, as well as water usage, FTSW and SMC that triggered pods which ranged from 1.12 to 1.89 L, 0.05 to 0.43 and 4.0% to 12.2%, respectively. A positive association between days taken and the amount of water used to terminate pods indicated that quicker‐triggering genotypes, such as Rupali and Genesis 836, consumed less water to develop pods than Flipper, ICCV_06109 and PBA Slasher did. Conversely, the negative association between the amount of water used and the number of pods triggered, seeds and grain yield explained the superior performance of Rupali and Genesis 836. These genotypes extracted less water (FTSW = 0.36 and 0.43; SMC = 10.3% and 12.2%, respectively) to develop pods and maintained favourable photosynthesis at lower transpiration rates for longer periods, allowing them to use residual moisture more efficiently. Our research offers crucial insights that can be beneficial to breeders and physiologists, paving the way for future studies aimed at developing drought‐tolerant genotypes. [ABSTRACT FROM AUTHOR]

The sustainability of global agriculture at higher productivity level is a concern owing to climate change, serious environmental footprints, dipping factor productivity and shrinking availability of natural resources, especially. The situation is worsening in the 'Food Bowl of India'—Indo‐Gangetic plains (IGP) by several amalgamated factors, such as declining groundwater, unpredictable precipitation owing to climate change and cultivation of heavy water duty crops. To neutralise these issues, a field experiment was executed for the period 2019–2021 to assess the efficacy of indigenous hydrogels (P‐hydrogel and Superabsorbent polymer hydrogel‐1118) and their application methods viz., seed treatment, slurry application and soil application on crop yield and water productivity, soil moisture dynamics and profitability in a soybean–wheat cropping system under irrigation and rainfed conditions. In both study years (2019–2020 and 2020–2021), due to higher seed germination percentage, irrigation application together with seed treatment and slurry application of superabsorbent polymer hydrogel‐1118 improved system productivity by 8.1%–26.7% and system water productivity by 17.6%–33.8% over control. Wheat grain yield was enhanced by 8.0% (2019–2020) to 32.2% (2020–2021) due to superabsorbent polymer hydrogel‐1118 hydrogel with 10 cm lesser use of irrigation water compared with control (no‐hydrogel). Soil moisture content in 0–15 cm soil layer was also found higher by 1.8%–2.4% in superabsorbent polymer hydrogel‐1118 and P‐hydrogel slurry‐applied plots. Therefore, higher gross profitability (31.8%), net profitability (89.8%) and B:C (26.9%) in wheat could be attributed to increased crop yields when seeds were treated with superabsorbent polymer hydrogel‐1118. Therefore, the utilisation of modified hydrogel application, in the form of seed treatment (seed coating) and slurry application has demonstrated improvement in seed germination, crop yield and water productivity and made soybean–wheat cultivation more economical. This approach presents a feasible solution to achieving a viable production system of soybean and wheat crops by reducing irrigation amounts in the IGP of India, as well as other comparable ecological places worldwide. [ABSTRACT FROM AUTHOR]

The rubber tree is an economically important tree planted in many tropical countries. While rubber planting has improved the livelihoods of growers, it has also resulted in soil degradation. However, systematic studies on assessment of soil health and its management in rubber plantations are limited. The present study explored whether intercropping of Michelia macclurei (an ornamental evergreen tree) in rubber plantation could help conserve soil health compared to traditional monoculture practice with randomised complete block design. The rubber tree density was the same in monoculture and intercropping, with the addition of Michelia macclurei in intercropping. The effects of soil properties and microbial communities was used to analyse two different planting patterns of rubber monoculture and rubber intercropping using soil sampling from field plots. The study investigated variation in soil physical structure, soil moisture, soil nutrients, soil microbes, and enzyme activities in two types of rubber plantations viz. a monoclonal plantation without intercrop (RM) and a plantation with Michelia macclurei as an intercrop (RAS). The results from above study showed an increase in soil organic carbon (SOC), total nitrogen (TN), ammonium nitrogen (AN), available phosphorus (AP), nitrate nitrogen (NN) and total potassium (TK) in plantation with intercrop as compared to monoclonal plantation without intercrop. When comparing the corresponding values of RM to RAS, bacteria and total microbial abundance of RAS increased on average by 75.51% and 48.42%, respectively. In addition, after the transition from rubber monoculture to rubber agroforestry, the annual average of SOC, TN, AP, TK nutrients and pH in soil increased by 10.94%, 4.25%, 14.53%, 1.34% and 9.74%, respectively. Soil water content, soil bulk density and soil porosity were not significantly different between RM and RAS treatments. Sucrase activity and cellulase activity also increased in the RAS intercropping system. These results show that intercropping promotes soil organic matter and microbial communities and enhances soil enzyme activities. These findings indicate that rubber intercropping creates healthy soil environments conducive to tree growth and improves the ecosystem sustainability of rubber plantations. Healthier and stronger rubber trees may increase rubber production and provide sustainable management of plantations in the future. [ABSTRACT FROM AUTHOR]

Using surface and balloon-sounding measurements, satellite retrievals, and ERA5 reanalysis during 2011–20, this study compares the precipitation and related wind dynamics, moisture and heat features in different areas of the South China Sea (SCS) before and after SCS summer monsoon onset (SCSSMO). The rainy sea around Dongsha (hereafter simply referred to as Dongsha) near the north coast, and the rainless sea around Xisha (hereafter simply referred to as Xisha) in the western SCS, are selected as two typical research subregions. It is found that Dongsha, rather than Xisha, has an earlier and greater increase in precipitation after SCSSMO under the combined effect of strong low-level southwesterly winds, coastal terrain blocking and lifting, and northern cold air. When the 950-hPa southwesterly winds enhance and advance northward, accompanied by strengthened moisture flux, there is a strong convergence of wind and moisture in Dongsha due to a sudden deceleration and rear-end collision of wind by coastal terrain blocking. Moist and warm advection over Dongsha enhances early and deepens up to 200 hPa in association with the strengthened upward motion after SCSSMO, thereby providing ample moisture and heat to form strong precipitation. However, when the 950-hPa southwesterly winds weaken and retreat southward, Xisha is located in a wind-break area where strong convergence and upward motion centers move in. The vertical moistening and heating by advection in Xisha enhance later and appear far weaker compared to that in Dongsha, consistent with later and weaker precipitation. [ABSTRACT FROM AUTHOR]

The spring atmospheric heat source (AHS) over the Tibetan Plateau (TP) has been suggested to affect the Asian summer monsoon and summer precipitation over South China. However, its influence on the summer precipitation in Northeast China (NEC) remains unknown. The connection between spring TP AHS and subsequent summer precipitation over NEC from 1961 to 2020 is analyzed in this study. Results illustrate that stronger spring TP AHS can enhance subsequent summer NEC precipitation, and higher soil moisture in the Yellow River Valley–North China region (YRVNC) acts as a bridge. During spring, the strong TP AHS could strengthen the transportation of water vapor to East China and lead to excessive rainfall in the YRVNC. Thus, soil moisture increases, which regulates local thermal conditions by decreasing local surface skin temperature and sensible heat. Owing to the memory of soil moisture, the lower spring sensible heat over the YRVNC can last until mid-summer, decrease the land–sea thermal contrast, and weaken the southerly winds over the East Asia–western Pacific region and convective activities over the South China Sea and tropical western Pacific. This modulates the East Asia–Pacific teleconnection pattern, which leads to a cyclonic anomaly and excessive summer precipitation over NEC. [ABSTRACT FROM AUTHOR]

Advancements in global satellite missions have revolutionized the assessment of Surface Soil Moisture (SSM) at global to local scales. However, spatio-temporal data discontinuities in specific regions remain a challenge. This study proposes a Machine Learning (ML)-based framework to extend the Soil Moisture Ocean Salinity (SMOS) SSM product both in the spatial and temporal domains, over Canada. In the first phase of the proposed framework, ML models based on Random Forest (RF) and Convolutional Neural Networks (CNN) are trained and validated with SMOS SSM as target and SSM-relevant climatic variables and geophysical variables, obtained from fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA5), for the 2011–2020 period, as predictors. Developed models, when tested on unseen data for the years 2021–2022, suggest slightly better performance of the RF model compared to CNN, with root mean square error (RMSE) of 0.033 and 0.056 respectively; prediction biases mostly noted for regions with large inter-annual variability. The spatial filling of SSM for grid cells that were excluded during the training process, with similar land types as those in the SMOS training data, yields reasonable performance, with RF (RMSE = 0.013) performing better than CNN (RMSE = 0.064). In the second phase, the RF model is selected to extend the SMOS dataset for the 2008–2010 period. The temporal correlation between extended SMOS and ASCAT (Advanced Scatterometer) SSM demonstrates a reasonable association, with correlation coefficient exceeding 0.6. Additionally, spatial correlation analysis reveals similar patterns between the two datasets, with smaller values for the summer season owing to the importance of local processes on SSM during this period. However, extending SMOS SSM spatially for surface types that were not included in the training process such as peatlands, remains a challenge warranting additional studies. The developed framework is robust and can address spatio-temporal discontinuities in other SSM products. [ABSTRACT FROM AUTHOR]

Overgeneralization and a lack of baseline data for microorganisms in high‐latitude environments have restricted the understanding of the microbial response to climate change, which is needed to establish Antarctic conservation frameworks. To bridge this gap, we examined over 17,000 sequence variants of bacteria and microeukarya across the hyperarid Vestfold Hills and Windmill Islands regions of eastern Antarctica. Using an extended gradient forest model, we quantified multispecies response to variations along 79 edaphic gradients to explore the effects of change and wind‐driven dispersal on community dynamics under projected warming trends. We also analyzed a second set of soil community data from the Windmill Islands to test our predictions of major environmental tipping points. Soil moisture was the most robust predictor for shaping the regional soil microbiome; the highest rates of compositional turnover occurred at 10–12% soil moisture threshold for photoautotrophs, such as Cyanobacteria, Chlorophyta, and Ochrophyta. Dust profiles revealed a high dispersal propensity for Chlamydomonas, a microalga, and higher biomass was detected at trafficked research stations. This could signal the potential for algal blooms and increased nonendemic species dispersal as human activities increase in the region. Predicted increases in moisture availability on the Windmill Islands were accompanied by high photoautotroph abundances. Abundances of rare oligotrophic taxa, such as Eremiobacterota and Candidatus Dormibacterota, which play a crucial role in atmospheric chemosynthesis, declined over time. That photosynthetic taxa increased as soil moisture increased under a warming scenario suggests the potential for competition between primary production strategies and thus a more biotically driven ecosystem should the climate become milder. Better understanding of environmental triggers will aid conservation efforts, and it is crucial that long‐term monitoring of our study sites be established for the protection of Antarctic desert ecosystems. Furthermore, the successful implementation of an improved gradient forest model presents an exciting opportunity to broaden its use on microbial systems globally. [ABSTRACT FROM AUTHOR]

Agricultural activities contribute to numerous waste problems and have emerged as a significant environmental concern. Nondegradable plastic residues decompose, releasing microplastics and affecting ecosystems and the environment. Consequently, biodegradable bio-composite films consisting of polylactic acid (PLA), natural rubber (NR), and rice straw (RS) have been developed with the aim of using them in agricultural applications. In this study, the PLA/NR blend, at a fixed ratio of 60/40 wt%, was filled with 3 and 5 wt% RS powder and extruded through a slit die into films. The biodegradability of all films was examined after being buried for 90 days in soil with a moisture content of 30% by weight. The neat PLA film showed the lowest weight loss percentage, 3.33%, suggesting a comparatively slower degradation rate in comparison to the PLA/NR(60:40) blend and all bio-composite films. The presence of 40 wt% NR in the film helped accelerate the biodegradation process during soil burial. The film produced from PLA/NR 60:40 wt% matrix filled with RS at 5 wt% led to rapid degradation, leading to a weight loss of 8.30%. From SEM micrographs, the morphology of all polymers after burial in soil showed fractures, the formation of pores, and obvious surface indications of fungi growing. The content of carbon decreased after soil burial, while oxygen content increased, and nitrogen was detected. The XRD analysis revealed low crystallinity in the neat PLA, consistent with the DSC analysis. The addition of NR and RS to the composites led to an increase in the crystallinity of PLA phase. All investigated materials exhibited an increase in crystallinity after being buried in soil. This research demonstrates that bio-composite films manufactured from the PLA/NR(60:40) blend filled with RS degrade more easily than unmodified PLA film. [ABSTRACT FROM AUTHOR]

Peruvian watergrass (Luziola peruviana J.F. Gmel.) is a perennial weed often found in paddy fields in the southern region of Brazil. Its chemical control is performed mainly by herbicides such as glyphosate in burndown, whose efficacy depends on soil moisture. Under excess‐water soil conditions, glyphosate efficacy is reduced, probably due to reduced translocation and the involvement of photoassimilates partition. Furthermore, little is known about the stress tolerance of these plants subjected to water deficit. Based on that, we performed experiments to verify the effect of soil moisture on the biochemical parameters of Peruvian watergrass and glyphosate translocation to the stolon and roots. Total soluble sugars, sucrose, starch, and glyphosate concentrations in leaves, culms, roots, and glyphosate partitioning were quantified. The malondialdehyde content and the lipid peroxidation increased with the drainage. Under drainage conditions, superoxide production was higher in roots than leaves, while peroxide production was lower in roots than leaves. The electrolyte leakage increased with the drainage and glyphosate application. The total soluble sugar content varied in each organ with a significant increase in the culm 14 days after application in the draining treatment. Sucrose varied considerably and reached its maximum concentration at 21 days after herbicide application. The starch accumulation in the culm was 7 days after applying the highest glyphosate dose. The presence of glyphosate in the roots subjected to soil drainage indicates the movement of this herbicide. In conclusion, soil drainage profoundly impacts Peruvian watergrass physiology, increasing the photoassimilates and glyphosate translocation to the stolon and root. This explains why glyphosate efficacy is higher under drainage conditions. [ABSTRACT FROM AUTHOR]

Soil moisture variation plays a key role in maintaining the engineering properties of soils. One of the main factors that impact the soil moisture content is the temperature, which has a strong impact to change the initial or design moisture and results in significant soil volume changes. This study aims to investigate the effects of temperature on the performance of enzymatically stabilised soils. The enzyme used in this study is Eko-soil and the soil used is clayey silt of low plasticity (ML). The mechanical behaviour was first assessed through the unconfined compression strength and weight measurements based on samples exposed to a series of operational temperatures. X-ray μCT tomography images were then used to assess the microstructure. Porosity, size and geometrical shape of the pores were analysed at elevated temperatures. Results revealed that the enzyme is effective in maintaining the initial pore structure under the tested temperature range. [ABSTRACT FROM AUTHOR]

Drained peat soils (Histosols) were studied. In the mode of weak and moderate drainage, the trend of seasonal fluctuations (quadratic parabola) shows a weekly average increase in dehydrogenase activity with a weekly average deceleration, and in the intensive drainage mode, a weekly average deceleration with a weekly acceleration for June–October. A significant nonlinear relationship of enzyme activity was revealed: positive with redox potential and pH, and multidirectional with soil bulk moisture and soil temperature. According to canonical analysis, the discussed set cumulatively determines the seasonal dehydrogenase activity by 50–81%. The hydrological regime is statistically proved as a dominant factor. A negative conjugate interaction of dehydrogenase and peroxidase activity has been established, confirming their participation in the biochemical transformation of organic matter as a whole. [ABSTRACT FROM AUTHOR]

Recognition of the importance of soil moisture information to the optimisation of water-limited dryland cereal production has led to Australian growers being encouraged to make use of soil moisture sensors. However, irrespective of the merits of different sensing technologies, only a small soil volume is sensed, raising questions as to the utility of such sensors in broadacre cropping, especially given spatial variability in soil water holding capacity. Here, using data collected from contrasting sites in South Australia and Western Australia over two seasons, during which either wheat or barley were grown, we describe a method for extrapolating soil moisture information away from the location of a probe using freely-available NDVI time series and weather data as covariates. Relationships between soil moisture probe data, cumulative NDVI (ΣNDVI), cumulative net precipitation (ΣNP) and seasonal growing degree days (GDD) were significant (P < 0.0001). In turn, these could be used to predict soil moisture status for any location within a field on any date following crop emergence. However, differences in ΣNDVI between different within-field zones did not fully explain differences in the soil moisture from multiple sensors located in these zones, resulting in different calibrations being required for each sensor or zone and a relatively low accuracy of prediction of measured soil moisture (R2adj ~ 0.4–0.7) which may not be sufficient to support targeted agronomic decision-making. The results also suggest that at any location within a field, the range of variation in soil moisture status down the soil profile on any given date will present as greater than the spatial variation in soil moisture across the field on that date. Accordingly, we conclude that, in dryland cereal cropping, the major value in soil moisture sensors arises from an enhanced ability to compare seasons and to relate similarities and differences between seasons as a guide to decision-making. [ABSTRACT FROM AUTHOR]

There is growing interest in using canopy temperature (Tc), including crop water Stress index (CWSI), for irrigation management. However, Tc varies greatly in one day, while soil water content (SWC) varies little, which may lead to different conclusions on whether irrigation is needed based on CWSI at different times. For this end, Tc of winter wheat was continuously monitored, and the data of such environmental factors as atmospheric temperature and soil water content (SWC) were simultaneously collected. CWSI was calculated based on empirical formulation and Tc and CWSI were generalized based on the normalization formulation. The correlation SWC between Tc and CWSI before and after generalization was compared and error analysis was based on SWC theoretical formula. The results showed: (1) the accuracy of SWC retrieval by Tc and CWSI increased firstly and then decreased with time during the day. The optimal time for Tc monitoring SWC was between 10:00 ~ 16:00 (R2 > 0.72) and the optimal time for CWSI monitoring SWC was between 9:00 ~ 18:00 (R2 > 0.69). (2) CWSI and Tc were mapped based on the relationship between crop water stress and soil water deficit and normalized canopy temperature expressions characterized the relationship between crop water stress and soil water deficit. (3) The accuracy of inversion of SWC by mapping Tc from 18:00 ~ 8:00 is increased from 0.5 ~ 0.6 to 0.7 ~ 0.8; the accuracy of soil water content inversion by mapping CWSI from 18:00 ~ 8:00 was improved from 0.2 ~ 0.4 to 0.4 ~ 0.6. (4) The theoretical expression of SWC deduced based on CWSI also proves that considering the relationship between crop water stress and soil water deficit change can effectively reduce the relative error from 30 to 5% in the morning and evening. This study contributes to the understanding of the reason why the correlation between Tc and SWC varies greatly during the day and solves the time-limited problem of thermal infrared remote sensing monitoring of crop water stress. [ABSTRACT FROM AUTHOR]

Previous studies have demonstrated that saturated normally consolidated and lightly over‐consolidated clays undergo contraction when heated due to a reduction in preconsolidation pressure. A linear constitutive model is proposed to describe the thermal contraction, with this model, governing equations are developed for the coupled thermo‐hydro‐mechanical (THM) consolidation induced by a prefabricated vertical thermo‐drain (PVTD). Corresponding semi‐analytical solutions are derived employing the Laplace transform and validated via comparison with experimental results, existing numerical model, and custom finite element method (FEM) model. Subsequently, comprehensive parametric analyses are carried out to investigate the THM coupling consolidation behaviors of the clays. Outcomes show that aside from surcharge load, generation of excess pore water pressure in soils can also be induced by thermal contraction, difference in thermal expansibility between pore water and soil grains, and thermo‐osmosis, where the influence of thermal contraction on the excess pore water pressure is the most prominent among the three factors. [ABSTRACT FROM AUTHOR]

Karal soils mainly composed of swelling clays are considered as one of the main causes of degradation in civil engineering structures in Cameroon. Thus, a better knowledge on Karal-type soil behavior is required to adapt soil treatment for road construction and foundation dimensioning. The physicochemical, mineralogical and geotechnical characterization of ten soils collected in North and Far North Cameroon allowed to classify Karal among plastic soils. As expected, the optimum water contents and maximum dry densities of dynamically compacted Karal reflected the particle size distribution and the mineralogical soil composition with various amounts of swelling clay (montmorillonite) and quartz associated to sand and silt fractions. Then the swelling/shrinkage properties of compacted specimens evaluated by free swelling test and volume shrinkage test were correlated to soil characteristics to propose a suitable classification for Karal-type soils. Not only swelling but also shrinkage properties were assessed and combined to define a new parameter called the total amplitude of swelling and shrinkage deformation, allowing the exclusion of the soil initial water content when evaluating soil deformation. After comparison of usual indirect methods to classify swelling soils, a predictive method of clay-rich soils deformation under moisture variation for Karal was proposed and discussed. [ABSTRACT FROM AUTHOR]

In Highway Embankments (HE) constructed on Expansive Yazoo Clay Soil (EYCS), in situ moisture and matric suction variations are environmentally driven variables that can significantly impact the safety of the constructed slope. The collected moisture content and suction data are localized and have low spatial resolution on embankments with point borehole monitoring systems with sensor instrumentation. On the other hand, Electrical Resistivity Imaging (ERI) allows high spatial resolution monitoring and is a proven method of mapping the subsurface geologic setting. Multiple studies have proven a correlation between resistivity and soil moisture. The comparison between resistivity and matric suction, on the other hand, has been rarely attempted, much less for EYCS through field-based investigation. The objective of the current study is to compare the field-sensor-based matric suction and soil moisture data with resistivity data. This localized comparison informed the capability of the ERI data to extrapolate the moisture content and suction parameters globally or to other areas of the slope where the instrumentation isn't available. Comparing ERI, matric suction, and volumetric soil moisture content is critical to embankments reliable predictive models. This study's findings can improve performance monitoring requirements for susceptible HE. [ABSTRACT FROM AUTHOR]

Bioretention is a type of stormwater best management practice that can reduce stormwater runoff volume, and with appropriate design, simultaneously reduce nitrogen loads. To meet the water quality potential of bioretention systems, improved simulation models of the transport and transformations of nitrogen are needed. In this work, two versions of a three-nitrogen-pool (3P) model (3P-1 and 3P-m) are applied to simulate observed nitrogen transport through a field bioretention system. The 3P models provide predictions of net export of dissolved organic carbon, dissolved organic nitrogen, and inorganic nitrogen species. The 3P-m model has promising results: (1) relative to the Storm Water Management Model (SWMM), it reduced the prediction error of percent removal of total dissolved nitrogen by up to 10.6% and reduces the scaled root-mean square error of total dissolved nitrogen loads by up to 53.0%; (2) it successfully captured the correlation between impactful environmental factors and event mean concentrations of total dissolved nitrogen and nitrate nitrogen in underdrain effluent; (3) its predictions on denitrification showed good alignment with observed isotope results; and (4) it demonstrated higher stability in simulated time series among calibration results than 3P-1. The 3P-m is expected to assist the design of bioretention system and valve control strategies by providing more accurate predictions on nitrogen removal under the impacts of soil moisture content and temperature. Future research and collaboration opportunities are also proposed in this article. [ABSTRACT FROM AUTHOR]

Inland oil spill accidents pose a negative impact on the health of the soil ecological system and human beings. The oil infiltration process is the main behavior of spilled oil, and its infiltration is influenced by many environmental factors like root netting and rainfall. This study was conducted to investigate the impact of these two important factors on the infiltration process and reduce the pollution of inland oil spill accidents. For root nettings, they can change the soil permeability and pore volume distribution, which are important in liquid infiltration, while rainwater can change the soil water content, thereby affecting the pressure, capillary force, and buoyancy force of spilled oil in the infiltration process. In the present study, these two unique factors of oil infiltration were investigated by detecting the infiltration front head and concentration distribution of spilled oil in soil layers with various root netting structures as well as the rainfall modes. It is found that root netting and surface rainfall critically affect oil infiltration in soil. The nettings with a finer mesh size and denser plant roots show a more significant effect on the infiltration process, particularly when their pore size is smaller than that of the soil. The netting's position and soil particle size also play crucial roles, influencing where oil accumulates within soil layers. Rainfall timing and intensity further modify oil movement, with prior rainfall reducing infiltration, while subsequent rain can increase it. The findings can help better understand the transport of spilled oil transport and implement emergency response measures for inland oil spills. [ABSTRACT FROM AUTHOR]

Soil moisture plays a key role in hydrology, water soil balance modeling, and drought assessment. In this study, the effects of snow properties in winter on the soil moisture in spring were evaluated in a catchment in the northwest of Iran from 2000 to 2020. For this purpose, the snow depth, water equivalent to snow, and snow-covered area were calculated based on satellite images in four regions with different altitudes. Furthermore, the soil moisture was obtained at two depths of 0–10 cm and 10–40 cm using satellite images. The results showed that the Pearson's correlation coefficient was between 0.4 and 0.5 in all cases, which indicates an average correlation between the snow and soil moisture in the catchment under study. Moreover, the correlation of the snow depth and snow water equivalent with the soil moisture at the depth of 10–40 cm was more than the one at the depth of 0–10 cm. Also, the snow-covered area demonstrated the highest correlation with the soil moisture at a depth of 0–10 cm. Finally, a linear relationship was obtained between the snow properties and soil moisture at two depths using the available data. Such relationships can be used to predict soil moisture for various applications. [ABSTRACT FROM AUTHOR]

The study explores the potential of next-generation satellite hyperspectral imaging systems for screening and predicting surface-soil contamination and degradation by exploiting various spectral indices and signature-matching techniques at a heavily industrialized area in India. The soil moisture content, desertification and salinity status, clay or fine material content, heavy metal content, vegetation health status, and stress levels were assessed from continuum-removed spectral reflectance values. Results indicated the presence of water in two tailings ponds, high salinity, and desertification values in most of the tailings ponds and dump sites, clay boundary liner along four ponds, high heavy metal indices along three ponds and all dump sites, highly stressed vegetation near all tailings ponds and coal dump sites, and pollutants in nearby water channels. The results suggest a strategy for the initial identification of priority areas for ground-based investigations and an alternative rapid methodology to monitor large industrial hubs in India. [ABSTRACT FROM AUTHOR]

Climate change severely impacts sub-Saharan Africa, which relies heavily on rainfed agriculture for food production. Variable and insufficient rainfall exacerbates food insecurity across the region. Traditional in situ water harvesting (IS_WH) practices enhance soil water-holding capacity, improve infiltration, and promote soil conservation. This meta-analysis of the peer-reviewed literature examines IS_WH practices' effects on crop yield, soil moisture, runoff, and soil loss reduction across various rainfall conditions in sub-Saharan Africa. The analysis reveals that IS_WH practices significantly boost agricultural productivity, with a combined effect size showing a 71% increase in total crop yield. IS_WH practices also improve soil moisture retention by 59% and effectively reduce runoff by 53% and soil loss by 58.66%, demonstrating their robust water and soil conservation benefits. Despite their proven benefits, the adoption of IS_WH practices in sub-Saharan Africa is hindered by socioeconomic and institutional barriers, including limited technical knowledge, resource constraints, and inadequate extension services. By addressing these barriers, there is significant potential to scale up IS_WH practices, enhancing agricultural productivity and sustainability across the region. Such efforts are crucial for mitigating the impacts of climate change, ensuring food security, and promoting sustainable development in sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

Purpose: Land use changes influence soil porosity, soil water, and heat transport, which may alter freeze–thaw characteristics within the soil profile. However, the response to freeze–thaw process after long-term land use change in Northeast China is still unclear. Thus, this study explored the characteristics and dynamics of soil hydrothermal during the freeze–thaw process in Northeast China. Materials and methods: The investigation focused on grassland and bare land that have undergone a long-term transformation from cropland. The soil temperature (ST) and soil water content (SWC) data during the freeze–thaw period were collected from 2016 to 2021. Characteristics of ST and SWC at 0–180 cm soil depth were carried out in two sites during the freeze–thaw period. Results: It was found that soil in the bare land started to freeze and thaw earlier than that in the grassland. The bare land exhibited a 10.3–186.2% higher amplitude in ST at different depths and greater thermal efficiency between air and soil. In both study sites, the SWC showed a downward–stable–upward trend at different soil layers during the monitoring period. The migrated SWC in most soil layers decreased in two sites. The maximum amount of migrated water reached 2.11 and 5.14 mm in grassland and bare land, respectively. The SWC correlated exponentially with absolute temperature in two sites but decreased faster at 0–30 cm depths in the same temperature interval in bare land. Conclusions: The soil in the grassland had more stable water and heat regulation ability than that in the bare land. Our results contribute to improving the comprehension of the relationship between water and heat in different land uses in seasonal frozen regions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Soil & Water Conservation (1009-2242) is the property of Institute of Soil & Water Conservation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)