Changes In Soil Water Content Research Articles

Soil Hydrothermal Dynamics in the Hengduan Mountains of Southeast Tibet and Associated Influencing Factors

Soil water and soil temperature are important ecological factors and driving forces for ecosystem restoration and sustainable development, possessing great significance for climate modeling and prediction. The Hengduan Mountains in southeastern Tibet, China, are located in a climate-change-sensitive area, and the study of soil hydrothermal dynamics in this area is of great significance for local and global climatic change and water resource utilization. This study, based on the soil hydrothermal and meteorological data of the Hengduan Mountain area in Southeast Tibet, analyzes the dynamic change patterns of soil hydrothermal and meteorological factors and explores their influencing relationships. It was found that the dynamic change in soil water content affected by precipitation was “bimodal” type. Among the meteorological factors, soil water content has the strongest correlation with relative humidity. The intra-annual variation curve of soil temperature is similar to that of the atmospheric temperature, showing a “unimodal” type, and has the highest correlation with atmospheric temperature. Specifically, it takes 70 mm and 170 mm of precipitation to change the soil water content and soil temperature at the 150 cm depth. For every 20 °C change in atmospheric temperature, soil temperature above 150 cm changes by an average of 7.2 °C.

Multi-Model Comprehensive Inversion of Surface Soil Moisture from Landsat Images Based on Machine Learning Algorithms

Soil moisture plays an important role in maintaining ecosystem stability and sustainable development, especially for the upper reaches of the Yellow River region. Therefore, accurately and conveniently monitoring soil moisture has become the focus of scholars. This study combines three machine learning algorithms: random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN)—with the traditional monitoring of soil moisture using remote sensing indices to construct a more accurate soil moisture inversion model. To enhance the accuracy of the soil moisture inversion model, 27 environmental variables were screened and grouped, including vegetation index, salinity index, and surface temperature, to determine the optimal combination of variables. The results show that screening the optimal independent variables in the Xijitan landslide distribution area lowered the root mean square error (RMSE) of the RF model by 16.95%. Of the constructed models, the combined model shows the best applicability, with the highest R2 of 0.916 and the lowest RMSE of 0.877% with the test dataset; the further research shows that the BPNN model achieved higher overall accuracy than the other two individual models, with the test set R2 being 0.809 and the RMSE 0.875%. The results of this study can provide a theoretical reference for the effective use of Landsat satellite data to monitor the spatial and temporal distribution of and change in soil water content on the two sides of the upper Yellow River basin under vegetation cover.

Estimating soil water content from thermal images with an artificial neural network

Data of soil water content measured at regular time intervals could be used to set an irrigation schedule for farmland. Changes in soil water content could also provide an early warning of potential slope slippage. An artificial neural network is proposed for detecting soil water content from five variables: soil and water temperature changes after sunlight exposure, plant coverage rates, fine-grained soil content, and effective soil particle size. Temperature changes were detected using thermal images taken using an unmanned aerial vehicle equipped with a thermal camera in the morning and at noon. Plant coverage was estimated using visible light photograph, and the fine-grained soil content, effective particle size, and soil water content were determined from laboratory tests of soil samples. An optimized neural network was established by analyzing the performance of various neural network parameters. The mean square error of the optimized neural network reached a minimum value of 0.10061 in the validation data set at the 12th epoch, the training was stopped to avoid overfitting. The correlation coefficients for the training, validation, testing, and overall data sets were 0.880, 0.772, 0.810, and 0.847 respectively. The target values and the predicted values of the neural network are highly correlated. Thus, the proposed neural network is effective for predicting soil water content. A relative importance analysis revealed that soil and water temperature changes after sunlight exposure had the greatest influence on the neural network’s results.

Spatial variation in grassland vegetation and herbage mass in the Ejina oasis along the Heihe River in the southern Gobi Desert, China

AbstractOur study area, Ejina, is located in the southern Gobi Desert, Inner Mongolia, China, where the annual precipitation is <50 mm and the annual potential evaporation is >3,600 mm. The Heihe River flows from the Qilian Range through the study area, and the desert landscape along the river includes oases formed by forests and grasslands. We established a survey area of approximately 1.5 ha in grassland facing a seasonal swamp, a branch of the Heihe River. We posed the following questions in this desert grassland: (1) how do soil water content (SWC) and salinity change with distance from the riverbank? (2) How do vegetation, aboveground biomass, and species richness change with distance from the riverbank? (3) How much can be supplied to grazing animals, and how many animals can be grazed? Our results indicated that (1) the SWC is high within 20 m of the riverbank and low beyond 20 m, the standing biomass is positively correlated with SWC, but there was no evidence of a correlation between salinity and distance to water; the electrical conductivity was high both inland and adjacent to the riparian swamp. (2) Vegetation could be classified to six classes, following a gradient from the riverbank (swamp margin) to the dry inland area beyond 20 m from the riverbank. Species richness and aboveground biomass were highly correlated. Classification result led us to recommend that generalist species tolerant of the distinct moisture gradient in the site be used in restoration efforts. (3) Per‐hectare aboveground biomass was 478.93 dry weight (dw) kg, 321.2 ha of equivalent grassland would be required to graze 100 goats annually.

陕西神木弃耕1–49年的苜蓿地土壤理化特性和植物物种多样性数据集

The abandonment of alfalfa fields is on the rise in semi–arid regions. It is important to clarify the changes of soil water content, soil bulk density, soil porosity, soil saturated water conductivity, soil pH, soil NH4-N, NO3-N, soil available phosphorus, soil organic carbon, soil total nitrogen, Margalef index, Pielou index, Simpson index, and Shannon–Wiener index in abandoned alfalfa fields, which is essential for vegetation restoration efforts in semi-arid areas. This dataset is credited to the National Field Scientific Observation and Research Station of Shenmu Erosion and Environment in Shaanxi Province (also referred to as Shenmu Station). we conducted surveys on alfalfa fields in 2003, 2014, and 2022 with varying abandonment periods (1-30 years, 12-41 years, and 20-49 years, respectively) to obtain data on soil physicochemical characteristics and plant species diversity. This dataset was compiled in strict adherence to international observation technical standards, including field investigations, laboratory measurements, data calculations, data storage, and quality control procedures. The high-quality data can serve as solid foundation for the study of alfalfa succession process in semi-arid areas.

Grass cover and shallow tillage inter-row soil cultivation affecting CO2 and N2O emissions in a sloping vineyard in upland Balaton, Hungary

Greenhouse gas (GHG) emissions can accelerate the current climate change trend, resulting in higher soil temperatures and changes in soil water contents. The aim of the study was to investigate to what extent different cultivation methods and sampling positions on the slope (location along the slope) are affecting soil GHG emissions in vineyards. In the study, different inter-row soil cultivation methods were investigated including shallow soil tillage (ST) and grass cover (GC) inter-rows. Intact soil columns were collected and different soil water contents were applied under a laboratory environment to simulate different precipitation conditions. Our findings showed significant differences in the emitted CO2 and N2O between the cultivation methods, where the GC soil had 2.9 times higher average CO2 and 6.1 times higher N2O emissions compared to the bare, ST soil. The amount of initial and longer term soil water content significantly influenced soil-derived GHG emissions, with the highest averaged values observed for the continuously high water content soil samples (0.377 mgCO2 m−2 s−1 and 0.076 μgN2O m−2 s−1). For the GC inter-rows, we also observed significant differences in the GHG emissions among the different sampling positions of the investigated slopes. We found that besides the different soil water contents, the main causes for these differences could be the variances in the soil physical and chemical properties. The findings of this study can be used to better estimate soil GHG emissions at various water contents, for different inter-row soil management cultivation methods, and potentially mitigate some adverse effects of global warming by reducing the amount of GHG released into the atmosphere.

Numerical Study of Pore Water Pressure in Frozen Soils during Moisture Migration

Frost heaving in soils is a primary cause of engineering failures in cold regions. Although extensive experimental and numerical research has focused on the deformation caused by frost heaving, there is a notable lack of numerical investigations into the critical underlying factor: pore water pressure. This study aimed to experimentally determine changes in soil water content over time at various depths during unidirectional freezing and to model this process using a coupled hydrothermal approach. The agreement between experimental water content outcomes and numerical predictions validates the numerical method’s applicability. Furthermore, by applying the Gibbs free energy equation, we derived a novel equation for calculating the pore water pressure in saturated frozen soil. Utilizing this equation, we developed a numerical model to simulate pore water pressure and water movement in frozen soil, accounting for scenarios with and without ice lens formation and quantifying unfrozen water migration from unfrozen to frozen zones over time. Our findings reveal that pore water pressure decreases as freezing depth increases, reaching near zero at the freezing front. Notably, the presence of an ice lens significantly amplifies pore water pressure—approximately tenfold—compared to scenarios without an ice lens, aligning with existing experimental data. The model also indicates that the cold-end temperature sets the maximum pore water pressure value in freezing soil, with superior performance to Konrad’s model at lower temperatures in the absence of ice lenses. Additionally, as freezing progresses, the rate of water flow from the unfrozen region to the freezing fringe exhibits a fluctuating decline. This study successfully establishes a numerical model for pore water pressure and water flow in frozen soil, confirms its validity through experimental comparison, and introduces an improved formula for pore water pressure calculation, offering a more accurate reflection of the real-world phenomena than previous formulations.

Soil Salt and Water Regulation in Saline Agriculture Based on Physical Measures with Model Analysis

Enhancing crop production in the saline regions of the Yellow River Delta (YRD), where shallow saline groundwater is prevalent, hinges on optimizing water and salt conditions in the root zone. This study explored the effects of various physical methods on soil water and salt dynamics during the cotton growing season in these saline areas. Three approaches were tested: plastic film mulching (FM), plastic film mulching with an added compacted soil layer (FM+CL), and ridge-furrow planting (RF). The HYDRUS-2D model (Version 3.02) was used to analyze changes in soil water and salt content in the root zone over time. The results showed that subsoil compaction significantly lowered salt build-up in the root zone, especially in the top 20 cm. Film mulching was crucial for reducing water loss in the Yellow River Delta. Crop transpiration increased by 7.0% under FM and 10.5% under FM+CL compared to RF planting. Additionally, FM+CL reduced soil salinity in the top 10 cm by 11.5% at cotton harvest time compared to FM alone. The study concludes that combining film mulching with a soil compaction layer is a promising strategy for local farmers, addressing soil water retention, salt management, and boosting cotton yields.

Characteristics of Soil Moisture and Heat Change during Freeze–Thaw Process in the Alpine Grassland of Duogerong Basin in the Source of the Yellow River

To deeply understand the characteristics of soil freeze–thaw water–heat change in the alpine grassland in the Duogerong Basin of the Yellow River source, the soil water–heat profile change monitoring was carried out based on the field monitoring station in the Duogerong Basin of the Yellow River source. By analyzing the comprehensive monitoring data from September 2022 to September 2023, the characteristics of the soil temperature and water content changes in the freeze–thaw cycle of the alpine grassland in the Duogerong Basin at the source of the Yellow River were explored. The results showed that the temperature and water content of each layer of the soil profile changed periodically, and the range of change was negatively correlated with the depth. The annual freeze–thaw process at the observation site is divided into five stages: 31 October to 3 November is the short initial freezing period, 4 November to 18 April is the stable freezing period, 19 April to 26 April is the early ablation period, 27 April to 30 April is the late ablation period, and 1 May to 30 October is the complete ablation period. The maximum soil freezing depth during the observation period was about 250 cm. Soil temperature and moisture content change affect each other; soil water is essential in heat transfer, and the two correlate well. The research results provide theoretical support for further understanding the characteristics of soil hydrothermal changes during the freeze–thaw process in the alpine grassland permafrost area at the source of the Yellow River.

Evaluation of AquaCrop’s Ability to Simulate Water Stress Based on 2-Year Case Study of Maize Crop

To apply AquaCrop to the study of agricultural soil moisture in the North China Plain, a water-treatment experiment on summer maize was carried out at the Fengqiu experimental station of the Chinese Academy of Sciences from 2017 to 2018. A water treatment was used to achieve field water capacities of 20 (W1), 40 (W2), and 60 (W3) cm soil layers under irrigation, and a rain-fed treatment (W0) was added. The model parameters were calibrated using the measured data in 2017. Then, they were applied to 2018 to verify the ability of the model to simulate water stress. The results showed that the variation trends for crop yield, canopy coverage, total soil water content, and the curve in the growth cycle simulated via AquaCrop were consistent with the actual observations; the results of a discrete analysis showed that the values of b (regression coefficient), R2 (determination coefficient), and EF (efficiency) were close to 1, and the values of the RMSE (root mean square error) were close to 0, which proved that the model could simulate dynamic changes in summer maize yield, canopy coverage, and total soil water content well. AquaCrop had good applicability in the North China Plain and could be applied to the study of agricultural water consumption and water-use efficiency in this area. The simulated values obtained can serve as an easily obtainable source of long-term experimental data in areas with frequent non-extreme weather events.

Determination of Evapotranspiration and Crop Coefficients of Irrigated Legumes on Different Soil Textures Using the FAO56 Approach

Climates and locations specific crop coefficients (Kc) plays pivotal roles in effective water management and crop yield optimization. Research on Kc for many staple crops remains limited in many regions across Africa. This study examined the crop coefficients of two varieties of cowpea (Sampea 14 and Sampea 17) and a variety of soybean (TGX 1465-1D) across different soil textures for optimized water management and crop yield. This study ascertained the Kc of Sampea14 (V1), Sampea17 (V2), and TGX 1465-1D (V3) cultivated on sandy, sandy clay loam, and sandy loam soils, within Port Harcourt, Southern Nigeria. This formed a 3 by 3 factorial experiment in three replicates. Changes in soil water content and crops’ agronomic parameters were monitored regularly. Meteorological data were obtained from the National Aeronautic and Space Administration website and reference evapotranspiration were estimated from the meteorological data using the FAO Penman Monteith’s method. Kc were estimated from the actual crop water use and reference crop evapotranspiration using the FAO56 method. During the initial-growth stage, the average Kc for V1, V2, and V3 were computed as 0.55, 0.72, and 0.81, respectively. At the mid-growth stage, these coefficients increased to 1.48, 1.30, and 1.60 and at the late season, the average Kc were 0.80, 0.69, and 0.86, for the respective varieties. The findings from this study hold the potential to significantly contribute to proper water management practices in the studied area. The understanding of Kc for these specific legume varieties across different soil textures offers valuable insights for optimizing irrigation scheduling and ultimately enhancing crop yield and water productivity.

Predicting organic carbon in European soils: Only in Southern Ukraine can we expect an increase in humus content

Soil organic carbon comprises the majority of the terrestrial soil carbon pool and plays an important role in the global carbon cycle and balance. Even minor changes in soil organic carbon (SOC) can have a significant impact, not only on climate but also on ecosystem stability, due to its key role in soil-atmosphere carbon exchange, plant growth, and food production. In order to assess the feedbacks between the terrestrial carbon cycle and climate change, and to maintain ecosystem functions, it is crucial to understand the spatial and temporal changes in SOC and the drivers of these changes. The role of soil as a source or sink of atmospheric CO2 is primarily influenced by changes in climate and soil water content. Climate change, particularly global warming, can have a direct or indirect impact on the decomposition of organic matter by regulating soil microbes and fauna, enzyme activity, and soil respiration. A warming climate may increase the loss of soil carbon to the atmosphere because warming has a stronger effect on respiration than on photosynthesis, resulting in a positive soil carbon-climate feedback. Climate warming can significantly affect soil organic matter stocks, with the magnitude of the effect largely dependent on the initial organic matter stock size. Soil carbon content is a crucial aspect of terrestrial ecosystems that affects their functional properties and the climate. Conversely, climate also has an impact on soil organic carbon content. The spatial variability of soil organic carbon content and the predictions made for the west-central European region are also important considerations. The study identified the spatial variation of soil organic carbon throughout Europe and forecast its dynamics in the next 50–70 years, considering global climate change. Digital soil mapping enables a more precise representation of soil properties in space, including the spatial quantification of prediction errors. The accuracy of these predictions increases as more local observations, such as soil profiles, are available to construct the prediction model. Digital soil mapping allows flexible spatial development of soil property maps. Soil properties, such as nutrient concentration and stocks, carbon, heavy metals, pH, cation exchange capacity, and physical properties like particle size and bulk density, can be modelled at different depths and spatial resolutions depending on the project's objectives and available input data. The r GSOCmap project used a 1 km grid to model soil organic matter content. In Europe, the range of soil organic carbon content is from 0 to 750 t/ha, with a mean of 78.1 t/ha and a standard deviation of 50.1 t/ha. Climatic factors were found to account for 29% of the variation in soil organic carbon based on regression analysis. The study revealed that an increase in air temperature leads to a decrease in soil organic carbon content, while an increase in precipitation results in an increase in this indicator. Additionally, the content of soil organic matter is negatively impacted by an increase in the seasonality of precipitation. According to the forecast, global climate change will cause an increase of 1.0–1.1 t/ha in the organic carbon content of 3.6% of the continent's area over the next 70 years. On 7.6% of the area, the changes will be insignificant. The soil organic carbon content is expected to decrease on 88.9% of the area. Of this, 35.1% will experience a slight decrease in carbon content by 0–1 t/ha, 28.4% will experience a moderate decrease in soil organic carbon content by 1.0–1.1 t/ha, and 25.3% will experience a significant decrease by 1.1–1.3 t/ha. The Baltic countries, Belarus, and the Black Earth zone of Russia are at the highest risk. The risk of Russia becoming highly dependent on food imports is increased by this fact. The prospects for Ukraine are quite optimistic. Even in the northern Azov region, we can expect an intensification of humus accumulation processes in the near future, mainly due to increased precipitation. Precipitation in southern Ukraine is a limiting factor that significantly affects agricultural productivity. Increased precipitation and organic matter growth in the soil present positive prospects for agriculture in southern Ukraine, including the northern Azov, Black Sea, and Crimea. It is possible that the occupation of these territories, which are promising for agricultural production, is one of the goals of Russian armed aggression against Ukraine.

Reversible strengthening behaviour of subsurface layers in sandy soils – understanding variable response to strategic deep tillage

Deep sandy soils cover more than 10 M ha of Australia's southern dryland cropping lands. Farming these soils is inherently challenging, and yield gaps are often wide due to multiple cropping constraints. Physical constraints that limit rooting depth are commonplace and often attributed to traffic-induced soil compaction. However, recent observations draw attention to a reversible hardsetting behaviour in response to soil water content. This hardsetting phenomenon is poorly understood and it may explain inconsistent crop responses to deep ripping in sandy soils. We examined the dynamic behaviour of soil strength under different soil water contents in deep ripped and unripped sands from four South Australian sites. We analyzed the elemental, mineralogical, chemical and physical compositions to differentiate the nature of hardened subsurface layers. Soil compaction and cementation were ruled out due to the soil's bulk density (<1.6 g cm−3) encountered and the rapid slaking that occurred when a hardened pan was submerged in water. The hardsetting behaviour was confirmed by the abrupt change in soil strength in response to the soil water content, measured using intact samples in a laboratory-based penetrometer. This significant increase in soil strength was also observed for soils collected from the ripped plots and for repacked soil samples, with penetration resistance ranging from 0.4 to 5.7 MPa depending on soil content. In contrast to our hypotheses, the hardsetting layer could not be distinguished from the layers above or below it by any analytical method. Given soil strength is typically measured on field moist soil, and standard chemical analysis did not differentiate hardsetting behaviour, alternative methods of diagnosis are needed to understand where deep tillage is likely to have the desired long-term effect. Identifying the nature of potential binding agents responsible for increased strength upon drying would be valuable. Our results confirmed reversible hardsetting in deep sandy soils is driven by small changes in soil water content, from 0.08 g g−1 to 0.05 g g−1 upon drying. Given that rapid re-consolidation of the soil matrix after expensive deep ripping interventions are reported, there is a strong imperative to improve diagnosis, and to identify if soil ameliorants could improve longevity by disrupting hardsetting behaviour.

Effect of seasonal distribution in precipitation on soil nitrogen mineralization in a subtropical forest

Soil N mineralization is a key process of nutrient cycling in ecosystems. The mechanism of the seasonal distribution of precipitation on soil N mineralization remains unclear. We conducted a precipitation manipulation experiment in a subtropical forest in the middle and lower reaches of the Yangtze River in China from 2020 to 2022, with three treatments, including control (CK), decreased precipitation in the dry season with extremely increased precipitation in the wet season (T1), and decreased precipitation in the dry season with proportionally increased precipitation in the wet season (T2). With in situ resin core method, we explored the effect of seasonal distribution of precipitation on soil N mineralization. The results showed that T1 and T2 significantly decreased dry season net nitrification rate by 57.9% and 72.5% and the net N mineralization rate by 82.5% and 89.6%, respectively, and significantly increased wet season net nitrification rate by 64.3% and 79.5% and net N mineralization rate by 64.2% and 81.1%, respectively. Proportionally increased precipitation in the wet season was more conducive to soil N mine-ralization process than extremely increased precipitation in the wet season. Results of the structural equation model showed that change in seasonal distribution of precipitation could significantly affect soil N mineralization processes in the subtropical forest by changing soil water content, ammonium nitrogen, microbial biomass nitrogen, and soil C:N. Our results had important reference for understanding soil nitrogen cycling and other ecological processes, and were conducive to more accurate assessment on the impacts of future changes in seasonal precipitation pattern on subtropical forest ecosystems.

Linking Climate Change Information with Crop Growing Seasons in the Northwest Ethiopian Highlands

In Ethiopia, the impacts of climate change are expected to have significant consequences for agriculture and food security. This study investigates both past (1981–2010) and future (2041–2070) climate trends and their influence on the length of the growing season (LGS) in the North-Western Ethiopian highlands. Climate observations were obtained from the National Meteorological Agency of Ethiopia, while the best performing and highest resolution models from the CMIP5 experiment and RCP6 (Coupled Models Intercomparison Project and representative concentration pathway 6) were used for the analysis. Standard statistical methods were applied to compute soil water content, evaluate climate variability and trends, and assess their impact on the length of the growing season. Maximum temperature (tasmax) and minimum temperature (tasmin) inter-annual variability anomalies show that the region has experienced cooler years than hotter years in the past. However, in the future, the coolest years are expected to decrease by −1.2 °C, while the hottest years will increase by +1.3 °C. During the major rainfall season (JJAS), the area has received an adequate amount of rainfall in the past and is very likely to receive similar rainfall in the future. On the other hand, the rainfall amount in the season February to May (FMAM) is expected to assist only with early planting. For the season October to January (ONDJ), the rainfall amount may help lengthen the growing season of JJAS if properly utilized; otherwise, the season has the potential to destroy crops before and during the harvesting time. The soil water content changes in the future remain close to those of the past period. The length of growing seasons has less variable onset and cessation dates, while in the future, the length of the growing period (LGP) of 174 to 177 days will be suitable for short- and long-cycle crops, as well as double cropping, benefiting crop production yield in the North-Western Ethiopian highlands in the future.

Imaging of the electrical activity in the root zone under limited-water-availability stress: a laboratory study for Vitis vinifera

Abstract. Understanding root signals and their consequences for the whole plant physiology is one of the keys to tackling the water-saving challenge in agriculture. The implementation of water-saving irrigation strategies, such as the partial root zone drying (PRD) method, is part of a comprehensive approach to enhance water use efficiency. To reach this goal tools are needed for the evaluation of the root's and soil water dynamics in time and space. In controlled laboratory conditions, using a rhizotron built for geoelectrical tomography imaging, we monitored the spatio-temporal changes in soil electrical resistivity (ER) for more than a month corresponding to eight alternating water inputs cycles. Electrical resistivity tomography (ERT) was complemented with electrical current imaging (ECI) using plant-stem-induced electrical stimulation. To estimate soil water content in the rhizotron during the experiment, we incorporated Archie's law as a constitutive model. We demonstrated that under mild water stress conditions, it is practically impossible to spatially distinguish the limited-water-availability effects using ECI. We evidenced that the current source density spatial distribution varied during the course of the experiment with the transpiration demand but without any significant relationship to the soil water content changes. On the other hand, ERT showed spatial patterns associated with irrigation and, to a lesser degree, to RWU (root water uptake) and hydraulic redistribution. The interpretation of the geoelectrical imaging with respect to root activity was strengthened and correlated with indirect observations of the plant transpiration using a weight monitoring lysimeter and direct observation of the plant leaf gas exchanges.

Fall/winter cover crop roots change soil hydrology to control the drought status of subsequent season summer maize in Ultisol

Climate change has increased drought frequency, necessitating strategies to reduce water stress, increase water use efficiency, and improve agricultural productivity. The impacts of different fall/winter cover crops on alleviating maize drought in the subsequent season by changing soil water content remain unclear in Ultisol. Our aim was to investigate whether different fall/winter cover crops (taproot and fibrous root) can improve soil water content to attenuate subsequent season maize drought and its underlying mechanism. This research compared four fall/winter cover crops (2 rapeseed cultivars, lucerne and vetiver) and fallow control (no cover crop in winter) to investigate their root traits, root effect on subsequent season soil water content (SWC), soil drought degree (D), maize leaf and root water potential (Ψl, Ψr), maize crop water stress index (CWSI), and maize growth components (aboveground plant and root parameters) in 2019 (a dry year), 2021 (a normal wet year), and 2022 (a severe dry year). The fibrous-rooted vetiver displayed higher average root diameter and higher average root length density, leading to increased SWC (17%, 15%, 15%), and lower D values in all three years. Additionally, it exhibited a more substantial gradient between Ψl, and Ψr and lower CWSI than taproot (rapeseed cultivars and lucerne) and fallow treatment. Furthermore, the cover crop treatments enhanced the gradient between maize Ψl and Ψr to varying degrees in different drought years. Ultimately, the improvement in SWC resulting from cover crop treatments increased maize aboveground growth (plant height, stem diameter, and leaf area) and maize root development, ultimately improving maize yield. This study introduces a new perspective on investigating the role of cover crops in alleviating seasonal drought in subsequent crops, especially by changing soil water properties in the subtropical climate.

Effects of forest fire smoke deposition on soil physico-chemical properties and bacterial community

The number of forest fires has increased globally, together with considerable smoke emission that significantly impacts the atmospheric environment and associated ecosystems. Most current studies have focused on the in situ effects of fire on the forest ecosystem. However, the mechanisms by which smoke particles affect adjacent ecosystems are largely unexplored. In this study, a simulated forest fire combustion system was developed to evaluate the effect of different smoke concentrations (control, low and high) on soil physico-chemical properties of adjacent farmland at two soil depths. The abundance and diversity of bacterial community were also determined. The results showed that smoke deposition increased the contents of total carbon (TC), total nitrogen (TN), and total phosphorus (TP) in the 0–10 cm soil layer; however, no significant changes in soil water content (SWC) and pH values was observed. The ACE(Abundance Coverage-based Fastimator) and Chao1 diversity indices of bacterial community generally showed a downward trend whereas the PD_whole_ tree diversity index increased after 180 d of smoke deposition. The relative abundance of Proteobacteria remained stable, while abundance of Firmicutes in soil decreased after 180 d of smoke deposition. Smoke deposition slightly affected the physical and chemical properties of the 10–20 cm soil, but the range of variation of the relative abundance and diversity dominant bacteria exceeded that of the 0–10 cm soil. A significant positive correlation was found between the soil properties and the alpha diversity indices during the first 30 d after smoke deposition; the correlation then decreased gradually. Redundancy analysis revealed that Proteobacteria, Firmicutes, and Actinobacteria were generally positively correlated with TC, TN, and SWC. As a whole, the study reveals that the effects of smoke deposition on soil physico-chemical properties and bacterial community depends on smoke concentration where relatively low concentration appears to be beneficial to soil bacterial community.

Searching for a deficit irrigation strategy to save water and improve fruit quality without compromising pomegranate production

Deficit irrigation (DI) strategies have frequently studied in drought resistant crops to optimise water-use-efficiency and save water. Four different DI strategies were studied in fifteen-year-old pomegranate trees (Punica granatum (L.) cv. Mollar de Elche) during the season of 2021: irrigation at 120 % of ETc during the whole season (control, C); irrigation at 50 % of ETc during the whole season (SDI); irrigation at 50 % of ETc during the linear fruit growth phase (RDIfg) and irrigation at 25 % of ETc during the ripening phase (RDIr). The effects of DI treatments on water relations, gas exchange, canopy temperature, yield and fruit quality were analysed. Water status of pomegranate responded closely to changes in soil water content. The reduction of leaf osmotic potential and leaf osmotic potential at full turgor to SDI, RDIfg and RDIr treatments indicated an osmotic adjustment. The stem water potential and the crop water stress index were more sensitive to water stress than gas exchange. SDI treatment increased non-marketable fruit weight and number. Nevertheless, RDIfg treatment could be considered a non-critical period because of the low sensitivity to water stress of trees, yield maintenance and the increase of nutritional organic compounds in fruits, saving 27 % of irrigation water. The application of RDIr produced the highest content of bioactive compounds, saving 8 % of water saving with a slight increase of non-marketable fruit number and weight. This suggests that the intensity and duration in which the deficit irrigation was applied could set the threshold irrigation conditions above which pomegranate production could be negatively affected.

Contrasting water-use strategies revealed by species-specific transpiration dynamics in the Caatinga dry forest.

In forest ecosystems, transpiration (T) patterns are important for quantifying water and carbon fluxes and are major factors in predicting ecosystem change. Seasonal changes in rainfall and soil water content can alter the sensitivity of sap flux density to daily variations in vapor pressure deficit (VPD). This sensitivity is species-specific and is thought to be related to hydraulic strategies. The aim of this work is to better understand how the sap flux density of species with low versus high wood density differ in their sensitivity to VPD and soil water content and how potentially opposing water-use strategies influence T dynamics, and ultimately, correlations to evapotranspiration (ET). We use hysteresis area analysis to quantify the sensitivity of species-specific sap flux density to changes in the VPD, breakpoint-based models to determine the soil water content threshold instigating a T response and multiscalar wavelet coherency to correlate T to ET. We found that low wood density Commiphora leptophloeos (Mart.) Gillett had a more dynamic T pattern, a greater sensitivity to VPD at high soil water content, required a higher soil water content threshold for this sensitivity to be apparent, and had a significant coherency correlation with ET at daily to monthly timescales. This behavior is consistent with a drought avoidance strategy. High wood density Cenostigma pyramidale (Tul.) E. Gagnon & G. P. Lewis, conversely, had a more stable T pattern, responded to VPD across a range of soil water content, tolerated a lower soil water content threshold to T, and had a significant coherency correlation with ET at weekly timescales. This behavior is consistent with a drought-tolerant strategy. We build on previous research to show that these species have contrasting water-use strategies that should be considered in large-scale modeling efforts.