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Grasslands are one of the most common land-cover types, providing important ecosystem services globally, yet few studies have examined grassland critical-zone functioning throughout hillslopes. This study characterised a coastal grassland over a small hillslope at Point Reyes National Seashore, California, using multidisciplinary techniques, combining remotely-sensed, geophysical, plant, and soil measurements. Clustering techniques delineated the study area into four landscape zones, up-, mid-, and down-slope, and a bordering riparian ecotone, which had distinct environmental properties that varied spatially across the site, with depth, and time. Soil moisture increased with depth and down slope towards a bordering riparian zone, and co-varied with soil CO2 flux rates both spatially and temporally. This highlighted three distinct controls of soil moisture on soil respiration: CO2 fluxes were inhibited by high moisture content in the down-slope during the wet winter months, and converged across landscape positions in the dry summer months, while also displaying post-rain pulses. The normalised difference vegetation index (NDVI) ranged from 0.32 (September)–0.80 (April) and correlated positively with soil moisture and aboveground biomass, moving down slope. Yet, NDVI, aboveground biomass, and soil moisture were not correlated to soil organic carbon (SOC) content (0.4%–4.5%), which was highest in the mid-slope. The SOC content may instead be linked to shifts in dominant grassland species and their rhizosphere properties with landscape position. This multidisciplinary characterisation highlighted significant heterogeneity in grassland properties with landscape position, and demonstrated an approach that could be used to characterise other critical-zone environments on hillslopes.

Studying the characteristics of soil moisture in afforestation area and its response to different grades of rainfall is helpful to quantitatively analyze the change law of soil moisture in afforestation area and provide theoretical basis for rational and efficient use of soil moisture. In this study, four artificial plant communities were selected, including Hedysarum scoparium community, Calligonum mongolicum community, H. scoparium- C. mongolicum community and C. mongolicum- H. scoparium community. The soil moisture content, soil moisture storage and their response characteristics to different grades of rainfall from June to October were compared and analyzed. The results showed that the SMC of H. scoparium- C. mongolicum community and C. mongolicum- H. scoparium community were lower than that of H. scoparium community and C. mongolicum community. The coefficient of variation of soil moisture content in C. mongolicum community was the largest, which was 22.7%, and the coefficient of variation of soil moisture content in H. scoparium-C. mongolicum community was the smallest, which was 19.4%. The recharge of rainfall to soil moisture storage of four artificial plant communities was different. The recharge of different grades of rainfall to soil moisture storage of C. mongolicum community and C. mongolicum-H. scoparium community was higher than that of H. scoparium community and H. scoparium-C. mongolicum community. In general, the utilization of soil moisture content in different soil layers by H. scoparium-C.mongolicum community is more balanced, and the soil moisture content in each soil layer is less affected by rainfall and has higher stability. Therefore, considering the configuration mode of H. scoparium- C. mongolicum community in the future aerial seeding afforestation process is conducive to the rational and efficient utilization of soil moisture in the afforestation area. [ABSTRACT FROM AUTHOR]

Soil moisture content is a key factor influencing plant growth and agricultural productivity, directly impacting water uptake, nutrient absorption, and stress resistance. This study proposes a rapid, low-cost, non-destructive method for dynamically monitoring soil moisture at depths of 0–200 cm throughout the crop growth period under dryland conditions, with validation in soybean cultivation. During critical soybean growth stages, UAV multispectral data of the canopy were collected, and ground measurements were conducted for three GPS-referenced 50 cm × 50 cm plots to obtain canopy leaf water content, coverage, and soil volumetric moisture at 20 cm intervals. Ten vegetation indices were constructed from multispectral data to explore statistical relationships between vegetation indices, surface soil moisture, canopy leaf water content, and deeper soil moisture. Predictive models were developed and evaluated. Results showed that the NDVI-based nonlinear regression model achieved the best performance for leaf water content (R2 = 0.725), and a significant correlation was found between canopy leaf water content and 0–20 cm soil moisture (R2 = 0.705), enabling predictions of deeper soil moisture. Surface soil models accurately estimated 0–200 cm soil moisture distribution (R2 = 0.9995). Daily water dynamics simulations provided robust support for precision irrigation management. This study demonstrates that UAV multispectral remote sensing combined with ground sampling is a valuable tool for soybean water management, supporting precision agriculture and sustainable water resource utilization. [ABSTRACT FROM AUTHOR]

Volatile and semi-volatile compounds, such as petroleum hydrocarbons and equipment lubricating oils, often contaminate soil due to accidents, posing significant ecological and health risks. Traditional soil remediation methods, such as thermal desorption and bioremediation, are time-consuming and resource-intensive, prompting researchers to explore more efficient alternatives. This study investigates the effectiveness of an in situ reactor for microwave-assisted soil remediation, specifically focusing on the impact of soil type and moisture content on pollutant removal efficiency. The reactor, designed to operate within a modified household microwave oven, provides direct microwave irradiation to the soil surface, enabling precise control of heating conditions. Experiments were conducted using soil samples of varying particle sizes and moisture levels under standardized conditions (1000 W microwave power, 2.45 GHz frequency). The results show that moisture content plays a critical role in pollutant removal efficiency, with an optimal moisture content of 10 wt % enhancing microwave absorption and energy transfer, thus improving pollutant recovery. In comparison with traditional resistive heating, microwave heating achieved a faster temperature rise and higher final temperatures, significantly improving pollutant removal efficiency in a shorter time frame. This study highlights the advantages of microwave heating, including its superior energy efficiency, faster pollutant volatilization, and the potential for optimized soil remediation in real-world applications. These findings provide valuable insights for the development of more sustainable and efficient soil remediation technologies. [ABSTRACT FROM AUTHOR]

This study conducted an in-depth analysis of the landslide problem in the loess hill and gully area in northern Shaanxi Province, selecting the loess landslide site in Quchaigou, Ganquan County, Yan'an City, as the object to assess the stability of loess slopes under the conditions of different plant root densities and soil moisture contents through field investigation, physical mechanics experiments and numerical simulation of the GeoStudio model. Periploca sepium, a dominant species in the plant community, was selected to simulate the stability of loess slope soils under different root densities and soil water contents. The analysis showed that the stability coefficient of Periploca sepium natural soil root density was 1.263, which was a stable condition, but the stability of the stabilized slopes decreased with the increase in soil root density. Under the condition of 10% soil moisture content, the stability coefficient of the slope body is 1.136, which is a basic stable state, but with the increase in soil moisture content, the stability of the stable slope body decreases clearly. The results show that rainfall and human activities are the main triggering factors for loess landslides, and the vegetation root system has a dual role in landslide stability: on the one hand, it increases the soil shear strength, and on the other hand, it may promote water infiltration and reduce the shear strength. In addition, the high water-holding capacity and permeability anisotropy of loess may lead to a rapid increase in soil deadweight under rainfall conditions, increasing the risk of landslides. The results of this study are of great significance for disaster prevention and mitigation and regional planning and construction, and they also provide a reference for landslide studies in similar geological environments. [ABSTRACT FROM AUTHOR]

Taking different land use types in the southern edge of Horqin Sandy Land as the research object, the soil moisture content of 0- 150 cm soil layer was measured from April to October 2022, and the soil moisture content of 0-150 cm soil layer was measured from April to October 2022, and the distribution characteristics of soil vertical profile moisture content, soil water storage change characteristics and soil evapotranspiration change characteristics under five patterns of the vertical profile distribution characteristics, soil water storage change characteristics, and soil evapotranspiration change characteristics of soil moisture under five patterns of Pinus sylvestris var. mongolica forest land, shrubbery, grassland, farmland and semi fixed sandy land were analyzed. The results showed that the distribution of soil moisture content in shrublands and grasslands was in a “W” shape;the water content distribution of Pinus sylvestris plantation and semi fixed sandy land was “U” shape;the distribution of water content in farmland showed an inverted “U” shape, and the position of the minimum water content was related to the distribution of vegetation roots in the land use type. The soil water storage capacity of different land use types had obvious seasonal variation characteristics, and the changes in rainfall were basically consistent. All 5 types of land use had surplus soil moisture, among which grassland had the best entropy retention effect. Therefore, priority should be given to herbaceous plants for ecological vegetation restoration in the region, in order to facilitate the soil water environment and its sustainable utilization. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to reveal the transpiration and water consumption pattern of typical plants (trees, shrubs, herbs) in Robinia pseudoacacia forests in the loess area of western Shanxi, and to clarify the dominant environmental factors of transpiration water consumption in Robinia pseudoacacia plantation forests in this area. [Methods] Li-6400XT portable photosynthesizer was used to measure the transpiration process of the plants, and the data of soil moisture and meteorological factors were synchronously collected to analyze the characteristics of the transpiration of typical plants in Robinia pseudoacacia forests and their environmental responses. [Results] (1) In July, the diurnal variation characteristics of transpiration rate of Robinia pseudoacacia and Festuca elata showed bimodal curves, and the diurnal variation characteristics of transpiration rate of Periploca sepium showed unimodal curves. The diurnal variation characteristics of transpiration rate of the three plants in September showed a unimodal curve, and there was an obvious change pattern. (2) The average soil volume water content of 0—20 cm soil layer during the study period was between 9.37% and 18.09%, and the average soil moisture contents were 12.16% on the upslope and 15.43% on the downslope. The response of the transpiration rate of Robinia pseudoacacia and Periploca sepium to soil water content on the upslope was greater than that on the downslope, while the transpiration rate of Festuca elata on the upslope was less responsive to soil water content than that on the downslope. (3) On the upslope, photosynthetically effective radiation was the dominant factor driving transpiration of forest stands. On the downslope, the dominant factor of each vegetation was different. The dominant factor of Robinia pseudoacacia was air relative humidity. The dominant factor of Periploca sepium was photosynthetically effective radiation. The dominant factor of Festuca elata was atmospheric CO2 concentration. (4) During the entire research period, a stepwise regression fitting was established. The stepwise regression equations established for Robinia pseudoacacia and Periploca sepium could more accurately simulate transpiration rate, while the effect of Festuca elata was not ideal. [Conclusion] In the loess area of western Shanxi, the transpiration characteristics of different plants differ significantly, and the environmental factors affecting the transpiration characteristics of each plant vary according to the slope position, so the role of slope position as well as environmental factors should be considered comprehensively in the process of vegetation construction. [ABSTRACT FROM AUTHOR]

Field studies were conducted in the North China Plain (NCP) during the 2023–2024 season to investigate the vertical microclimate, yield, and yield-related characteristics of winter wheat during the grain-filling stage under no-till direct seeding and conventional tillage. The aim was to compare the differences in microclimate between the two tillage methods in wheat fields and the impact of microclimate on yield. The results indicated that, compared to conventional tillage, no-till direct seeding reduced the air temperature and increased the relative humidity of the air at 20 cm and 100 cm above the ground during the wheat grain-filling period. The soil moisture content at 20 cm below the ground under no-till direct seeding was higher than under conventional tillage during the early grain-filling stage. Seven days before the wheat harvest, the dry weight per plant and the dry weight per spike were significantly greater under no-till direct seeding than under conventional tillage. Consequently, the thousand-grain weight of no-till direct seeding was significantly higher than that of conventional tillage, with an increase of 7.9%. The number of wheat sterile spikelets under no-till direct seeding was significantly lower than that under conventional tillage. Furthermore, the number of grains per spike was higher than that of conventional tillage. Although the number of harvested spikes under no-till direct seeding was 10.8% lower than under conventional tillage, the increase in thousand-grain weight and the number of grains per spike compensated for the reduced number of harvested spikes. As a result, the grain yield of winter wheat under no-till direct seeding was higher than that of conventional tillage, increasing by 2.7%. Therefore, adopting no-till direct seeding in the NCP is conducive to increasing winter wheat production and efficiency, as well as supporting sustainable agricultural development. [ABSTRACT FROM AUTHOR]

To reduce the amount of pesticides in the environment, it is necessary to consider the wettability properties of pesticide droplets on the leaf surface to improve the spraying effect. The wettability properties of the droplet on the leaf surface are related not only to the properties of the liquid itself but also to the properties of the leaf surface. It is typically believed that leaf surface properties are difficult to control, and thus research has generally ignored this aspect of pesticide use. However, in the field environment, the structure and properties of the leaf surface can be altered by changing the moisture content of the soil where plants are grown. In this study, the roughness, contact angle, and surface free energy of the leaf surface were measured and calculated under different soil moisture contents to study the changes in the leaf surface wettability properties, with the aim of achieving efficient pesticide spraying by adjusting the soil water content. The results showed that the surface composition and microstructure of leaves were altered by the change in the soil moisture content, and the wettability properties of leaves decreased initially and then increased with a decrease in the soil moisture content. When the amount of soil water was sufficient or seriously insufficient, the wettability properties of the leaves were increased, but a lack of soil water may lead to irreversible damage to the plants. Therefore, before spraying pesticides on the leaf surfaces, the plants should be fully watered to improve the wettability properties of the leaf surface, which is conducive to the deposition and adhesion of pesticide droplets on the leaf surface and improved application effectiveness. The results of this study can provide a useful reference for the theoretical research and practices of precision spraying. [ABSTRACT FROM AUTHOR]

High wind speed is one of the limiting factors for plant cultivation in coastal sandy land. To mitigate this, sweet corn, an annual plant, can be cultivated as a windbreaker. Water spinach is one of the short-lived plants cultivated by farmers in coastal sandy land. The study aimed to figure out the microweather change, growth, and yield of water spinach protected by sweet corn as windbreaker in various planting locations in Samas coastal sandy land. The research had been conducted in Samas coastal sandy land, Bantul Regency, Yogyakarta Special Region, from October to November 2022. The experiment was arranged in Randomized Complete Block Design non-factorial, containing five treatment levels and four repetitions. The treatment of the planting location of sweet corn as a windbreaker is without sweet corn, sweet corn positioned on the "east", sweet corn on the "east and west", sweet corn on the "south", and sweet corn on the "south and north". The data were analyzed using analysis of variance (ANOVA), and if the difference was marked, the data analysis was proceeded to Tukey HSD with α = 5%. The results showed that sweet corn windbreaker placed on the "east" was the most effective in decreasing wind speed, thereby modifying the microweather of water spinach plot by lowering air temperature and increasing air humidity and soil moisture content during November on the Samas coastal sandy land. Growth, dry matter accumulation, and water spinach yield increased, although the dry harvest index and fresh harvest index did not show any improvement. [ABSTRACT FROM AUTHOR]

Purpose: The fuel reduction prescribed burning and biochar application can have significant impacts on water and nitrogen (N) use efficiency of understory acacia species as well as soil carbon (C) and N pools in a suburban native forest subject to N deposition in Southeast Queensland, Australia. Methods: We evaluated the impact of biochar application rates (0, 5.0 and 10.0 t biochar per hectare) and prescribed burning on soil-plant interactions in carbon (C) and N cycling in a suburban native forest in the first two years of biochar application or three and half years of the recently prescribed burning. Results: Anthropogenic N deposition not only enhanced N losses caused by N leaching and denitrification, but also inhibited biological N fixation (BNF) by increasing N availability in forest systems. The Acacia leiocalyx with higher water use efficiency was more inclined to utilize easily available N resources (from N deposition), compared with A. disparismma. In this study, biochar application could indeed reduce N loss in forest soil and improve soil fertility by improving plant water and N use efficiency. Meanwhile, soil moisture content affected by biochar application also influenced soil N transformations by affecting soil microbial activity. Conclusion: For urban forest soils, the high N availability caused by N deposition could inhibit the BNF in a suburban native forest ecosystem. The high-porosity physical structure of biochar applied increased the soil water content and soil N retention capacity. [ABSTRACT FROM AUTHOR]

To establish the Qiannian Xiulin (Millennium Forest of Xiong'an) that closely resembles a natural forest and is stable and self-adjusting, this study utilized soil precipitation and soil moisture data from 32 weather stations and the Qiannian Xiulin Regional Observation Station in Xiong'an New Area, Hebei Province. Machine learning and mathematical analysis methods were employed to investigate the change pattern of soil moisture content in the Qiannian Xiulin. The results indicate that the soil moisture content of the Qiannian Xiulin in Xiong'an is significantly influenced by the monsoon climate and exhibits distinct seasonal fluctuations. Specifically, soil moisture content increases slightly after soil thawing from January to March, gradually decreases from March to June, and reaches the lowest point in June. After June, it shows an upward trend, reaching the maximum in August, and then decreases again after September. The relative humidity of the surface soil at a depth of 0-10 cm in Qiannian Xiulin is positively correlated with precipitation, while the change pattern of relative soil humidity and precipitation in 20-30 cm and 30-50 cm soil layers are not obvious. There are differences in the impact of precipitation intensity on relative soil moisture in different soil layers. Significant changes in soil moisture will only occur when the daily precipitation exceeds 20 mm. The soil moisture content at these weather stations is consistent with the field observation data of Qiannian Xiulin. This study provides scientific soil moisture change rules for artificial planting management of the Qiannian Xiulin. [ABSTRACT FROM AUTHOR]

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Abstract The replacement of farmland by native hygro-plants is increasingly common globally within the context of wetland ecosystem restoration. Understanding the long-term effects of this replacement on the abundance and persistence of soil organic carbon (SOC) in mountain marshes is important for soil carbon management. Here, the restored plateau mountain marshes of Duliu River Wetland Provincial Nature Reserve, China was selected. The properties, soil moisture content (SMC), pH, texture, free-form iron oxides ( $$\:{\text{F}\text{e}}_{\text{d}}$$ ), amorphous iron oxides ( $$\:{\text{F}\text{e}}_{\text{o}}$$ ), mineral-associated organic carbon (MAOC), and iron-bound organic carbon (Fe-OC) were analyzed in topsoil samples (0 ~ 20 cm) during the restoration of rice paddies to Sphagnum palustre L. wetlands for 0, 2, 10, and 20 years. Natural Sphagnum wetlands were also used as the control. We found that marsh restoration increased SMC, $$\:{\text{F}\text{e}}_{\text{o}}$$ , $$\:{\text{F}\text{e}}_{\text{d}}$$ , $$\:{\text{F}\text{e}}_{\text{o}}$$ / $$\:{\text{F}\text{e}}_{\text{d}}$$ , SOC, MAOC, Fe-OC, Fe-OC/SOC, but decreased the MAOC/SOC ratio. MAOC/SOC ratio of marshes were expectedly lower than the proportion of labile SOC in total SOC during the restoration period. SMC, SOC, and MAOC were higher in the natural Sphagnum wetlands than in other habitats. Both SOC and Fe-OC/SOC were positively correlated with SMC, $$\:{\text{F}\text{e}}_{\text{o}}$$ , $$\:{\text{F}\text{e}}_{\text{d}}$$ , $$\:{\text{F}\text{e}}_{\text{o}}$$ / $$\:{\text{F}\text{e}}_{\text{d}}$$ , and Fe-OC, but negatively correlated with soil pH. MAOC/SOC was negatively correlated with SMC, $$\:{\text{F}\text{e}}_{\text{o}}$$ and $$\:{\text{F}\text{e}}_{\text{o}}$$ / $$\:{\text{F}\text{e}}_{\text{d}}$$ . These results emphasized the significance of reconverting rice paddies to marsh wetlands for increasing the sequestration of labile SOC and Fe-OC. Further studies are required to identify and quantify the organo-mineral stabilization mechanisms of SOC at the different SOC fractionations throughout the restoration period.

Abstract Spatial variability and uncertainty associated with soil volumetric moisture content (SVMC) is crucial in moisture prediction accuracy, this paper sets out to address this point of SVMC by developing data-driven model. Grid samples of SVMC covered approximately a 3-ha field during the jointing growth stage of winter wheat, and SVMC were measured by Time Domain Reflectometry (TDR), located in North China Plain, China. Bayesian inference was performed to explore spatial heterogeneity, robustness, transparency, interpretability and uncertainty related to SVMC using python-based PyMC3 combined with Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation (INLA-SPDE) model. The results showed that the prediction surface of SVMC, the lower and upper limits of 95% credible intervals quantified uncertainty associated with SVMC, cauchy prior of the flexibility and adaptability to obtain state-of-the-art predictive performance is more robust than gaussian prior for SVMC prediction, the transparency and interpretability of SVMC prediction model were revealed by MCMC (Markov-Chain Monte-Carlo) trace plots, KDE (Kernel density estimates), and rank plots. The uncertainty associated with SVMC can explicitly be described using the highest-posterior density interval, the prediction lower and upper limits.

This study aimed to explore the inversion method of soil moisture content by using numerical simulation and field detection. The researchers used the early signal amplitude envelope (AEA) method to directly invert soil moisture in the shallow part of the soil, which avoided the transmission error of the Topp formula. The Auto-Regressive Moving Average Model (ARMA) was used to calculate the power spectrum of radar signals, and the BP neural network was used to train the power spectrum of different Gaussian windows, so as to improve the inversion accuracy. According to the study, the average error of soil moisture content inverted by AEA method was 0.45% in the range of 0-0.41m, while the error of ARMA method in depth range of 0.1-1.0m was less than 1%. The results showed that the combination of the two methods can effectively invert the soil moisture content within the radar detection range.

[Objective] Soil respiration occupies an important proportion in the annual total soil respiration during freeze-thaw process. The effects of water content, freezing temperature and freeze-thaw cycles on soil carbon mineralization dynamics during soil freeze-thaw process were studied. [Methods] The study focuses on chernozem soil from the Nenjiang County Heshan Farm Jiusan Soil and Water Experimental Station in Heilongjiang Province, China was used as the research object to carry out an indoor freeze-thaw degree simulation test. Seven freeze-thaw cycles were carried out, and three soil moisture content were set 100% field water holding capacity (100% WHC), 60% field water holding capacity (60% WHC), and 30% field water holding capacity (30% WHC). Additionally, three environmental temperatures were used a constant 10 °C (control), -5 °C (mild freezing) and -15 °C (severe freezing). [Results] The number of freeze-thaw cycles, moisture content and freezing temperature had significant effects on CO2 emissions and the impact degrees were -0.63, 0.21 and 0.14, respectively. The thawing process notably increased soil carbon mineralization. The soil carbon mineralization of 60% WHC in the first three freeze-thaw cycles increased 33.0% and 35.2%, respectively, compared with 100% WHC and 30% WHC in the last 4 freeze-thaw cycles. In the case of severe freezing, the carbon mineralization of 100% WHC soil in the first 2 freeze-thaw cycles increased by 25.2% and 68.0% respectively, compared with 60% WHC and 30% WHC soil in the last 5 freeze-thaw cycles. [Conclusion] The number of freeze-thaw cycles had the greatest effect on soil CO2 emission, followed by moisture content, and the least freezing temperature. The freeze-thaw effect increased the cumulative CO2 emissions of soil with low water content. The cumulative CO2 emission of soil with high water content was reduced. For soils with medium moisture content, mild freezing increased CO2 cumulative emissions, while severe freezing decreased them. The first-order kinetic equation fitted the CO2 emissions from freeze-thaw soil well (R2 > 0.997), both moisture content and freezing temperature significantly influenced the potential for organic carbon mineralization (C0 value).

Soil steam disinfection technology has significant advantages in preventing and controlling soil borne diseases, and is one of the important means to solve the problem of soil continuous cropping obstacles. The heat transfer characteristics of steam in soil are closely related to the specific physical parameters of the soil. The main physical parameters affecting the heat and mass transfer characteristics of Yunnan unique red soil were determined by drying method, pycnometer method, thermal probe method and Metastability plate method. The soil bulk density was 0.736 g/cm³, the soil moisture content was 26.2%, the soil particle density was 2.398 g/cm², the soil porosity was 69.3%, and the soil thermal conductivity was 0. 296 2 W/(m°C), the specific heat capacity of soil mass was 57.840 3 J/(kg. C). The research results provide preliminary experimental data support for subsequent soil steam disinfection experiments and the construction of heat and mass transfer simulation models. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to evaluate the impact of subsurface drip fertigation (SDF) on soil moisture content, potato growth, and tuber yield in loam soils, and compare these results with conventional surface drip fertigation (CF). The focus was on determining whether SDF could improve water use efficiency and yield quality, particularly in water-scarce regions. Methods: The experiment was conducted during the 2022 spring growing season in Xunyang, Ankang, Shaanxi Province, China. A randomized complete block design (RCBD) was used with three treatments: subsurface drip fertigation (SDF), conventional surface drip fertigation (CF), and a no-fertilization control (NF), with four replications per treatment. Soil moisture content at a 20 cm depth was monitored, and plant growth parameters such as plant height, stem diameter, leaf color index, and chlorophyll fluorescence index were measured during the flowering and harvest stages. Tuber yield characteristics, including tuber diameter, number of tubers per plant, total yield, and marketable yield, were also assessed. Important findings: The results indicated that subsurface drip fertigation significantly improved soil moisture content, with up to 45.5% higher moisture retention compared to conventional fertigation, particularly in the early stages of fertilization. This improved moisture availability led to enhanced plant growth and tuber development. Tuber diameter increased by 6.9 mm, and the number of tubers per plant increased by 18.1% under SDF. Marketable tuber yield was approximately 10% higher in the SDF treatment compared to CF. However, the study found that soil texture plays a critical role in the effectiveness of SDF, and further research is needed to explore its application in other soil types. [ABSTRACT FROM AUTHOR]

Spatial variability and uncertainty associated with soil volumetric moisture content (SVMC) is crucial in moisture prediction accuracy, this paper sets out to address this point of SVMC by developing data-driven model. Grid samples of SVMC covered approximately a 3-ha field during the jointing growth stage of winter wheat, and SVMC were measured by Time Domain Reflectometry (TDR), located in North China Plain, China. Bayesian inference was performed to explore spatial heterogeneity, robustness, transparency, interpretability and uncertainty related to SVMC using python-based PyMC3 combined with Integrated Nested Laplace Approximation with the Stochastic Partial Differential Equation (INLA-SPDE) model. The results showed that the prediction surface of SVMC, the lower and upper limits of 95% credible intervals quantified uncertainty associated with SVMC, cauchy prior of the flexibility and adaptability to obtain state-of-the-art predictive performance is more robust than gaussian prior for SVMC prediction, the transparency and interpretability of SVMC prediction model were revealed by MCMC (Markov-Chain Monte-Carlo) trace plots, KDE (Kernel density estimates), and rank plots. The uncertainty associated with SVMC can explicitly be described using the highest-posterior density interval, the prediction lower and upper limits. [ABSTRACT FROM AUTHOR]

The replacement of farmland by native hygro-plants is increasingly common globally within the context of wetland ecosystem restoration. Understanding the long-term effects of this replacement on the abundance and persistence of soil organic carbon (SOC) in mountain marshes is important for soil carbon management. Here, the restored plateau mountain marshes of Duliu River Wetland Provincial Nature Reserve, China was selected. The properties, soil moisture content (SMC), pH, texture, free-form iron oxides (), amorphous iron oxides (), mineral-associated organic carbon (MAOC), and iron-bound organic carbon (Fe-OC) were analyzed in topsoil samples (0 ~ 20 cm) during the restoration of rice paddies to Sphagnum palustre L. wetlands for 0, 2, 10, and 20 years. Natural Sphagnum wetlands were also used as the control. We found that marsh restoration increased SMC, , , / , SOC, MAOC, Fe-OC, Fe-OC/SOC, but decreased the MAOC/SOC ratio. MAOC/SOC ratio of marshes were expectedly lower than the proportion of labile SOC in total SOC during the restoration period. SMC, SOC, and MAOC were higher in the natural Sphagnum wetlands than in other habitats. Both SOC and Fe-OC/SOC were positively correlated with SMC, , , / , and Fe-OC, but negatively correlated with soil pH. MAOC/SOC was negatively correlated with SMC, and / . These results emphasized the significance of reconverting rice paddies to marsh wetlands for increasing the sequestration of labile SOC and Fe-OC. Further studies are required to identify and quantify the organo-mineral stabilization mechanisms of SOC at the different SOC fractionations throughout the restoration period. [ABSTRACT FROM AUTHOR]

The apple orchards in Liaoning, one of the four major apple-producing areas in Bohai Bay, Northeast China, play a crucial role in regulating the carbon sink effect. However, there is limited information on the variation in carbon flux and its influential factors in apple orchards in this region. To address this, CO2 flux data were monitored throughout the entire apple growth seasons from April to November in 2017 and 2018 in the apple (Malus pumila Mill. cv Hanfu) orchard in Shenyang, China. The energy closure of the apple orchard was calculated, and variations in net ecosystem exchange (NEE) at different time scales and its response to environmental factors were analyzed. Our results showed that the energy balance ratio of the apple was 0.74 in 2017 and 1.38 in 2018. NEE was generally positive in April and November and negative from May to October, indicating a strong carbon sink throughout the growth season. The daily average NEE ranged from − 0.103 to 0.094 mg m−2 s−1 in 2017 and from − 0.134 to 0.059 mg m−2 s−1 in 2018, with the lowest values observed in June and July. NEE was negatively correlated with net radiation, atmospheric temperature, saturated vapor pressure deficit, and soil temperature. These findings provide valuable insights for predicting carbon flux in orchard ecosystems within the context of global climate change. [ABSTRACT FROM AUTHOR]

The use of environmental-stress resilient plant growth promoting rhizobacteria (PGPR) offers an organic solution to sustainable agriculture, under rapid climate change. Our laboratory has previously reported the drought-ameliorating property of an exopolysaccharide-secreting PGPR strain, Bacillus endophyticus J13 on Arabidopsis thaliana. In this study, A. thaliana roots were inoculated with J13 under well-watered and water-stressed conditions (under controlled plant growth conditions). To understand the mechanism of drought amelioration, impact of J13 on plant ABA biosynthesis and signaling was analyzed. It was found that the ABA levels in water-stress, inoculated plants were lower than the water-stressed plants, without inoculation. Also, the expression of ABA biosynthesis genes: NCED3, AAO3 and ABA2, was downregulated in inoculated treatments under water-stress as compared to non-inoculated, water-stress treatments. J13 did not cause any modulation in the expression of ABA-dependent signaling gene SnRK2 and the ABA-independent signaling gene, DREB2A, under water-stress in Arabidopsis shoots. On treating the bacteria with EDTA (which negatively impacts biofilm levels), we observed that J13 fails to impart stress tolerance to plants under water deficit conditions. The soil moisture content in soil adhered to roots and exopolysaccharide (EPS) content was significantly higher in the plants under water-stressed, inoculated treatments than the non-inoculated plants. This study unravels the mechanism of drought amelioration by J-13 on Arabidopsis by modulating soil moisture content through EPS secretion, thereby eliminating the need for enhanced ABA biosynthesis and signaling. [ABSTRACT FROM AUTHOR]

Desert grasslands are a crucial component of terrestrial ecosystems that play vital roles in regional and global hydrological cycling, climate change, and ecosystem balance through variations in their soil moisture content (SMC). Despite this, current research on the SMC of desert grasslands remains insufficient, with many areas remaining underexplored. In this study, we focused on a typical desert grassland located in the northern foothills of the Yinshan Mountains. Ground-penetrating radar (GPR) exploration and soil sampling were used to test existing mixed-media models, and a new mixed-media model was calibrated using cross-validation methods. Among the three general mixed-media models, the Topp and Roth models yielded more accurate SMC estimates for the study area, with root mean square errors of 0.0091 g/cm3 and 0.0054 g/cm3, respectively, and mean absolute percentage errors of 25.86% and 19.01%, respectively, demonstrating their high precision. A comparison of the calibrated and original mixed-media models revealed that the estimation accuracy was significantly improved after parameter calibration. After parameter calibration, the Ferre model achieved an accuracy comparable to that of the Topp model. Parameter-calibrated models can be used to estimate the SMC using GPR data, offering a higher precision than general models and possessing greater suitability for the study area. The soil in the study area is primarily composed of sand particles and is therefore more compatible with the parameters of the Topp model, whereas the Ferre model requires further parameter calibration to achieve effective application. [ABSTRACT FROM AUTHOR]

With the increasing focus on irrigation management, it is crucial to consider cost-effective alternatives for soil water monitoring, such as multi-point monitoring with low-cost soil moisture sensors. This study assesses the accuracy and functionality of low-cost sensors in a sandy loam (SL) soil amended with biochar at rates of 15.6 and 31.2 tons/ha by calibrating the sensors in the presence of two nitrogen (N) and potassium (K) commercial fertilizers at three salinity levels (non/slightly/moderately) and six soil water contents. Sensors were calibrated across nine SL-soil combinations with biochar and N and K fertilizers, counting for 21 treatments. The best fit for soil water content calibration was obtained using polynomial equations, demonstrating reliability with R 2 values greater than 0.98 for each case. After a second calibration, low-cost soil moisture sensors provide acceptable results concerning previous calibration, especially for non- and slightly saline treatments and at soil moisture levels lower than 0.17 cm3cm−3. The results showed that at low frequencies, biochar and salinity increase the capacitance detected by the sensors, with calibration curves deviating up to 30% from the control sandy loam soil. Due to changes in the physical and chemical properties of soil resulting from biochar amendments and the conductive properties influenced by fertilization practices, it is required to conduct specific and continuous calibrations of soil water content sensor, leading to better agricultural management decisions. [ABSTRACT FROM AUTHOR]

Water management is critical for wheat production under extreme drought conditions, and the mechanisms by which root dynamics and soil water utilization affect wheat yield are uncertain. This study was conducted in 2023–2024 under a mesophilic semi-arid climate with a two-factor partitioned experimental design, aiming to assess the response of different irrigation amounts in winter wheat crops on root growth and development, soil water utilization, and yields in different soil horizons. The results showed that variety and irrigation volume had significant effects on the spatial and temporal distribution of root and yield components, with irrigation volume having the greatest effect on yield. Compared with CK, deficit-regulated irrigation significantly promoted root penetration to deeper layers and delayed root senescence. DRWD, RLD, RSA, and RV decreased gradually with increasing soil depth, and the peaks of RLD, RSA, and RV appeared at the tassel to flowering stage, respectively; and under deficit-regulated irrigation, the contribution of the A2W4 treatment to stable yield was greater. Therefore, A2W4 is an effective water-saving irrigation method to improve grain yield and water-use efficiency under deficit-regulated irrigation. [ABSTRACT FROM AUTHOR]

Long-term intensive tillage has led to soil environment degradation, reduced fertility, and difficulty in increasing crop yield in the Mollisol region of northeast China. In order to improve the soil'shydrothermal environment and nutrient content, we conducted field experiments to investigate the effects of different tillage practices and the amount of straw mulching on soil hydrothermal environment and nutrient content in agricultural soils in seasonal permafrost areas. Four treatments were established: no-tillage without straw (NT0), no-tillage with half straw mulching (NT1), no-tillage with full straw mulching (NT2), and rotary tillage without straw (CK) as the control treatment. The results indicate that the no-tillage with straw mulching treatments increased the soil ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3−-N) content, accompanied by improvements in the soil's water content and regulation of soil temperature changes, as compared to the CK treatment. Specifically, the soil's NH4+-N and NO3−-N content in the NT2 treatment were significantly increased by 25.65% and 38.81%, respectively. Our study indicates that NT2 treatment is the most suitable tillage practice and straw-returning method in the Mollisol region of northeast China. This study can provide a theoretical basis and reference for the efficient utilization of farmland soil in seasonal permafrost areas. [ABSTRACT FROM AUTHOR]

The accurate prediction of soil moisture content helps to evaluate the quality of farmland. Taking the black soil in the Nanguan District of Changchun City as the research object, this paper proposes a stacking ensemble learning model integrating hybrid neural networks to address the issue that it is difficult to improve the accuracy of inversion soil moisture content by a single model. First, raw hyperspectral data are processed by removing edge noise and standardization. Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). To estimate soil water content, the hybrid neural network model is integrated into the stacking model along with Bagging and Boosting algorithms and the feedforward neural network. Experimental results demonstrate that the GWO–CNN–GRU–Attention model proposed in this paper can better predict soil water content; the stacking method of integrating hybrid neural networks overcomes the limitations of a single model's instability and inferior accuracy. The relative prediction deviation (RPD), root mean square error (RMSE), and coefficient of determination (R2) on the test set are 4.577, 0.227, and 0.952, respectively. The average R2 and RPD increased by 0.056 and 1.418 in comparison to the base learner algorithm. The study results lay a foundation for the fast detection of soil moisture content in black soil areas and provide a data source for intelligent irrigation in agriculture. [ABSTRACT FROM AUTHOR]

Accurate estimation of soil moisture content (SMC) is vital for effective farmland water management and informed irrigation decision-making. The utilization of unmanned aerial vehicle (UAV)-based remote sensing technology to monitor SMC offers advantages such as mobility, high timeliness, and high spatial resolution, thereby compensating for the limitations of in-situ observations and satellite remote sensing. However, previous research has primarily focused on SMC diagnostics for the entire crop growth period, often neglecting the development of targeted soil moisture modeling paradigms that account for the specific characteristics of the canopy and root zone at different growth stages. Furthermore, the variations in soil moisture status between fields, resulting from the hysteresis of water flow in irrigation channels at different levels, may influence the development of soil moisture modeling schemes, an area that has been seldom explored. In this study, SMC models based on UAV spectral information were constructed using Random Forest (RF) and Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithms. The soil moisture modeling paradigms (i.e., input–output mapping) under different growth stages and soil moisture conditions of summer maize were systematically compared and discussed, along with the corresponding physical interpretability. Our results showed that (1) the SMC modeling schemes differ significantly across the various growth stages, with distinct input–output mappings recommended for the early (i.e., jointing, tasselling, and silking stages), middle (i.e., blister and milk stages), and late (i.e., maturing stage) periods. (2) these machine learning-based models performed best at the jointing stage, while subsequently, their accuracy generally exhibited a downward trend as the maize grew. (3) the RF model demonstrates superior robustness in estimating soil moisture status across different fields (moisture conditions), achieving optimal estimation accuracy in fields with overall higher SMC in line with the PSO-SVM model. (4) unlike the RF model's robustness in spatial SMC diagnostics, the PSO-SVM model more reliably captured the temporal dynamics of SMC across different growth stages of summer maize. This study offers technical references for future modelers in UAV-based SMC modeling across various spatial and temporal conditions, addressing both the types of models as well as their input features. [ABSTRACT FROM AUTHOR]

Accurate estimation of soil moisture content (SMC) in the field is a critical aspect of precise irrigation management. The development of unmanned aerial vehicle (UAV) platforms has provided an economically efficient means for field-scale SMC measurements. However, previous studies have mostly focused on single-sensor estimates of SMC. Additionally, the lack of differentiation between various crops and their growth stages has resulted in an unclear understanding of how crop types and growth stages affect the accuracy of SMC estimation at different soil depths. Therefore, the purpose of this paper was to use UAV multimodal remote sensing and a machine learning algorithm to estimate the SMC in agricultural fields and investigate estimation's effectiveness under different scenarios. The results indicated the following: (1) The multispectral remote sensing method provided higher accuracy in SMC estimation compared to thermal infrared remote sensing. Moreover, the integration of multimodal data improved the accuracy of SMC estimation, enhancing the coefficient of determination (R2) by approximately 14% over that achieved through the use of multispectral data alone and 39% over that of thermal infrared data alone. (2) Across the entire growth period, the optimal soil depths of SMC estimation for soybean were 10 cm and 20 cm (average R2 were 0.81 and 0.82, respectively), while for corn, they were 10 cm, 20 cm, and 40 cm (average R2 were 0.59, 0.60, and 0.55, respectively). (3) The SMC estimation model performed better for both crops during the first three growth stages, with accuracy declining in the maturity stage. These results demonstrate that this approach can provide relatively accurate root zone SMC estimates for different crops throughout their main growth periods. Thus, it can be employed for SMC monitoring and precision irrigation system design. [ABSTRACT FROM AUTHOR]

This study aimed to explore the inversion method of soil moisture content by using numerical simulation and field detection. The researchers used the early signal amplitude envelope (AEA) method to directly invert soil moisture in the shallow part of the soil, which avoided the transmission error of the Topp formula. The Auto-Regressive Moving Average Model (ARMA) was used to calculate the power spectrum of radar signals, and the BP neural network was used to train the power spectrum of different Gaussian windows, so as to improve the inversion accuracy. According to the study, the average error of soil moisture content inverted by AEA method was 0.45% in the range of 0-0.41 m, while the error of ARMA method in depth range of 0.1-1.0 m was less than 1%. The results showed that the combination of the two methods can effectively invert the soil moisture content within the radar detection range. [ABSTRACT FROM AUTHOR]

In order to study the influence of fissure morphology on soil moisture-content changes under different fissure types, this study established HYDRUS 2.0 numerical models of stepped fissures and planar fissures with different fissure widths and depths based on the experimental condition parameters obtained from physical simulation tests. Then, we simulated the spatial and temporal variation rules of soil moisture around the fissures. The results showed a high level of agreement between the HYDRUS numerical simulations and actual measurements, indicating that the model accurately reflects the movement of soil moisture near fissures. The study found that ground fissures affected the spatial distribution of soil moisture, leading to an increased rate of moisture loss in the deep soil near the fissure walls. Moreover, larger fissures had greater horizontal and vertical effects on soil moisture. The soil moisture content is lower closer to the fissure walls. As the soil depth increased, the influence of the fissures gradually diminished. For planar fissure with a depth of 50 cm, the soil moisture content was 30.6%, 17.8%, and 8.4% lower at depths of 10, 30, and 50 cm, respectively, compared to a fissure with a depth of 10 cm. For a stepped fissure with a depth of 50 cm, the soil moisture content was 29.2%, 20.9%, and 13.9% lower at depths of 10, 30, and 50 cm, respectively, compared to a fissure with a depth of 10 cm. Under the same conditions of fissure width and depth, stepped fissures exhibit faster moisture loss, and the larger the fissure, the more significant the additional moisture loss compared to planar fissures. [ABSTRACT FROM AUTHOR]

By integrating the thermal characteristics from thermal-infrared remote sensing with the physiological and structural information of vegetation revealed by multispectral remote sensing, a more comprehensive assessment of the crop soil-moisture-status response can be achieved. In this study, multispectral and thermal-infrared remote-sensing data, along with soil-moisture-content (SMC) samples (0~20 cm, 20~40 cm, and 40~60 cm soil layers), were collected during the flowering stage of soybean. Data sources included vegetation indices, texture features, texture indices, and thermal-infrared vegetation indices. Spectral parameters with a significant correlation level (p < 0.01) were selected and input into the model as single- and fuse-input variables. Three machine learning methods, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP), were utilized to construct prediction models for soybean SMC based on the fusion of UAV multispectral and thermal-infrared remote-sensing information. The results indicated that among the single-input variables, the vegetation indices (VIs) derived from multispectral sensors had the optimal accuracy for monitoring SMC in different soil layers under soybean cultivation. The prediction accuracy was the lowest when using single-texture information, while the combination of texture feature values into new texture indices significantly improved the performance of estimating SMC. The fusion of vegetation indices (VIs), texture indices (TIs), and thermal-infrared vegetation indices (TVIs) provided a better prediction of soybean SMC. The optimal prediction model for SMC in different soil layers under soybean cultivation was constructed based on the input combination of VIs + TIs + TVIs, and XGBoost was identified as the preferred method for soybean SMC monitoring and modeling, with its R2 = 0.780, RMSE = 0.437%, and MRE = 1.667% in predicting 0~20 cm SMC. In summary, the fusion of UAV multispectral and thermal-infrared remote-sensing information has good application value in predicting SMC in different soil layers under soybean cultivation. This study can provide technical support for precise management of soybean soil moisture status using the UAV platform. [ABSTRACT FROM AUTHOR]

The distribution of vegetation in coastal wetlands is significantly influenced by soil properties. However, the mechanisms of how soil characteristics impact the physiological processes of Tamarix chinensis forests remain underexplored. This study examined changes in the soil physicochemical properties and structural attributes of natural T. chinensis forests in the Yellow River Delta with increasing distance from the shoreline. T. chinensis trees were classified into healthy, intermediate, and dying categories based on growth potential, and dynamic changes in salt ions and non-structural carbohydrates (NSCs) were investigated. Results indicated that increasing distance from the shoreline corresponded to decreased soil salinity and pH, and increased soil moisture. T. chinensis mortality rate decreased, while tree height and ground diameter increased with distance. Soil salt content was positively correlated with T. chinensis mortality, but negatively correlated with tree height and ground diameter. Trees with lower growth potential had higher Na+ but lower K+ and K+/Na+ ratio. Soil salt content was positively correlated with root and stem Na+, while soil moisture was positively correlated with leaf NSCs. These findings suggest that soil salt content and moisture significantly influence T. chinensis ion absorption and NSC accumulation, with sodium toxicity being a key factor in the spatial distribution of T. chinensis forests. [ABSTRACT FROM AUTHOR]

Purpose: Accurately assessing soil moisture content (SMC) is essential for applications in agriculture and ecological sustainability. However, the dynamic monitoring and assessment of SMC presents considerable challenges due to the intricate traditional methods and the ever-evolving environmental variables. Relevant research has indicated that visible and near-infrared (vis–NIR) spectra are a practical and cost-effective alternative for accurate and convenient estimation of SMC. Advances in technology and computer hardware have enabled spectral characteristics and computer vision algorithms to show enormous potential for rapid and non-destructive characterization of soil properties. The objective of this study was to evaluate the predicted ability of SMC using vis–NIR spectral data. Materials and methods: A total of 60 topsoil samples (0–5 cm) from the maize test field at the Shanxi Central Irrigation Test station were used as the study object. A set of four spectral parameters was derived and filtered from spectral data, and C-W and W-W models were developed using Genetic Algorithm algorithm-optimized backpropagation (GA-BP) neural networks to predict SMC based on outdoor measurements. Results and discussion: The results showed that: (1) SMC can be successfully predicted using the spectral data through the C-W and W-W models; (2) the C-W model outperformed the W-W model, particularly in the context of deep soil, with R2 ranging from 0.919 to 0.991 and corresponding RMSE values from 0.619% to 0.982%. Conclusions: This study introduces two effective methodologies for accurate estimation of SMC at different depths using multispectral remote sensing, which showed a high degree of prediction accuracy. It further proves that GA-BP algorithm is still effective for predicting SMC in outdoor. The research result might be helpful for the further study of SMC measurement. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) plays a significant role in land surface water and energy cycle and is essential in performing various field-related studies. It is a crucial parameter provided by passive L-band sensors on soil moisture active passive/soil moisture ocean salinity satellite missions at a resolution of ~36–40 km. To obtain inference from the SMC data and apply it to different applications, its study and analysis are required that is achievable using any geographic information systems software. Quantum Geographic Information System (QGIS) is an open-source software with a user-friendly graphical user interface (GUI) and a repository of application-specific plugins. However, no plugin provides SMC or downscales the SMC product for a required location. Q-Daily Arial Image (Q-DAI), the QGIS plugin proposed here, implements a downscaling algorithm to obtain the low-resolution SMC product from SMAP/SMOS at fine resolution using inputs from high-resolution satellite imagery. The plugin is developed by designing a GUI using Qt Creator and defining its functionality using Python. Q-DAI is tested on QGIS 3.16.16 on Windows 10, 8 GB RAM PC and QGIS 3.22 on a macOS Ventura laptop. Q-DAI can be used to obtain high-resolution SMC for any location, and in this article, sample results of Q-DAI implemented for Delhi region data have been shown. [ABSTRACT FROM AUTHOR]

The research results of soil penetration resistance (SPR) tests carried out on sandy clay using four cone probes with different dimensions of the measuring tip are presented in this study. It was indicated that the values of SPR can be used to diagnose the cultivation layer and, on this basis, determine whether it is necessary to cultivate it and select tools for the required treatment. Tests were carried out on three levels of soil density, 1.37, 1.43 and 1.51 g∙cm−3, and two moisture contents, 7.64% and 10.4%. The results show that the probe with the smallest cone with apex angles of 30° and 60° on the least dense soil indicated higher SPR by over 50% more than other probes with the highest cone and the same opening angles. The change in cone opening angle from 30° to 60° led to an increase in probe indications in the range of 10–25%, depending on the diameter of the cone tip. The statistical analysis shows that values of probe indications were statistically significant and were influenced by soil density, probe cone tip dimensions, the surface of the base and the apex angle. The values of SPR are fundamental in diagnosing the quality of the soil's top layer, determining the necessity of breaking it up, and selecting the optimal tools for this procedure. To improve the efficiency of agricultural crop cultivation technologies. This is particularly important when carrying out cultivation procedures in an environmentally friendly manner. The measurements will help support the introduction of sustainable farming practices, including direct seeding, no-till cultivation, or precision agriculture, reducing soil degradation and increasing environmental benefits. [ABSTRACT FROM AUTHOR]

To explore the yield-increasing mechanism of mechanized potato planting with corn straw mulching, a two-year (2021 and 2022) field experiment was conducted to study the effects of mechanized no-tillage with straw mulching on potato yield and water use efficiency. This experiment included mechanized no-tillage potato planting with corn straw mulch covering (JG), plastic film mulching (HM), and open flat planting (CK). The results showed that mechanical no-tillage with straw mulching significantly affected soil water content in the 0–100 cm soil layer, yield, and water use efficiency (p < 0.05). There was no significant difference in yield between JG and HM, but it was significantly higher than that of CK. The yield of JG was 3.09~12.27% higher than that of CK. The yield increase was mainly achieved by increasing the potato weight per plant (0.697~0.862 **) and the average single potato weight (0.048~0.631). The tuber weight per plant was positively correlated with the plant height at the seedling stage (0.03~0.92 **) and positively correlated with the dry weight of stems and leaves at the tuber expansion stage and starch accumulation stage (0.74 **~0.95 **). It was negatively correlated with the number of branches at the tuber formation stage (−0.33~−0.88 **). Compared with CK, JG could significantly improve the water use efficiency of potatoes and reduce water consumption during the whole growth period of crops. In 2021, JG was 6.5% higher than CK, and HM was 6.88% lower than CK. In 2022, JG and HM increased water use efficiency by 26.17% and 14.50% compared with CK. When HM is applied in heavy soil areas, soil compaction can easily occur, which affects seedling emergence and reduces yield. At the same time, JG has strong adaptability to soil types and good yield stability. It can be seen that JG is a green and efficient mechanized potato cultivation technology suitable for dry farming areas. [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]

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[Objective] Coconut chaff matrix, rice straw, wheat straw, reed straw and corn straw were used to make a new type of ecological blanket after crushing treatment, which provided new methods and ideas for the treatment of ecological environment. [Method] 12 kinds of ecological blanket configuration were designed and made, and the effect of laying ecological blanket on surface soil moisture content was analyzed. [Result] In the data processing, it was found that the ecological blanket had a very good effect on maintaining soil moisture content and inhibiting soil water evaporation. Compared with the litter under the plantation, the maximum average water holding rate of the ecological blanket was 2.07 times that of the maximum average water holding rate of the plantation. [Conclusion] The practical application scenarios and methods of ecological blanket are summarized. In the treatment of desertification and desert, T4, T5 and T12 are selected to cooperate with grass grids to prevent wind movement and cover soil on the surface. When there is no special ecological management requirements in general areas, T12 and T4 can be used to maintain soil water content. It is suggested that T1, T4 and T8 should be selected for the restoration of bare land in mountain forests. If the average rainfall is large and prone to flood areas recommended configuration T1, T4, T8. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office 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.)

The presence of excessive algae in water is always considered as a negative factor in irrigation. However, the optimal balance between algal removal and retention in irrigation water when the algal biomass is controllable remains unknown. Therefore, this study explores the impact of low-level algal presence (Scytonema javanicum) on soil and microbial activity through controlled soil column experiments. Soil moisture was measured, and 16S rRNA gene amplicons sequencing was applied to characterize the microbial community. Slight community changes indicated no negative impact on the local microbial community of S. javanicum. Enzyme assays and quantitative polymerase chain reaction (qPCR) revealed that algae improved soil moisture retention, and enhanced the nutrient content of the topsoil. The decrease in moisture in the treatment group (from 27.53% to 26.42%) was significantly reduced (p < 0.05) compared to the control (from 27.55% to 25.17%), while the contents of ammonium (NH3-N) and total nitrogen (TN) in the treatment (0.70 mg/kg and 0.54 g/kg) were also higher (p < 0.05) than that of the control (0.43 mg/kg and 0.49 g/kg). The results of the abundance of functional gene suggested algae facilitated nitrogen fixation and nitrification. This research offers innovative insights for diversifying the sources of irrigation water. [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.)