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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]

• The accuracy of GNSS-IR soil moisture inversion model can be greatly improved. • Provide an effective solution for abnormal data detection and repair. • The proposed method can be widely used in the field of GNSS-IR monitoring. Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) technology can realize continuous and dynamic monitoring of soil moisture at areas. The GNSS signal is highly susceptible to the influence of external factors, causing anomalous terms in the process of characteristic parameters extraction. In this study, an integrated outlier detection method is proposed. This method first detects outlier based on Inter-Quartile Range, Grubbs test and Hampel filter outlier detection methods for the characteristic parameters data, and derives the outlier location by complementing the detection results with each other and corrected by rainfall data. Then, four kinds of outlier repair methods, namely, moving average, locally weighted primary linear regression, locally weighted quadratic linear regression, and robust locally weighted quadratic linear regression, were used to repair and filter out the optimal data according to the location of the outlier. Finally, three machine learning methods, namely, Bagging Tree, Support Vector Machine (SVM), and Gaussian Process Regression (GPR), were combined to build a soil water content inversion model and evaluate the accuracy. The results show that the proposed comprehensive outlier detection method in this study can accurately detect the location of outlier. The correlation between the characteristic parameters and in-situ soil moisture can be effectively improved after remediation treatment. The outlier repair helped to improve the inversion accuracy of the soil moisture inversion model, and the R2 of the soil moisture inversion model increased by 13.21 % to 27.08 % (mean 18.25 %), the RMSE decreased by 12.97 % to 23.61 % (mean 18.16 %). The comprehensive outlier detection and repair method proposed in this study can provide reference for the quality control of input data before the establishment of GNSS-IR model, and effectively improve the inversion accuracy of GNSS-IR soil moisture inversion model. [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]

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]

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]

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]

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]

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]

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]

[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 CO, 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 CO. 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 CO, 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 (Co value). [ABSTRACT FROM AUTHOR]

Exploring the impact of different monitoring angles from unmanned aerial vehicle (UAV) on the monitoring accuracy of soil moisture content (SMC) is crucial for precision irrigation. To this end, experiment was conducted to monitor the SMC of winter wheat at different growth stages under different irrigation treatments in Yangling, Shaanxi, China. At a solar zenith angle of 45°, multispectral remote sensing data from a UAV were collected at thirteen different monitoring zenith angles. Simultaneously, the SMC in the wheat root zone was measured. From the UAV multispectral images, spectral reflectance was extracted for the construction of vegetation indices. Then the correlation between the vegetation indices and the measured SMC was analyzed. With the vegetation indices as input variables, SMC monitoring models were constructed using Extreme Learning Machine (ELM), Random Forest (RF), and Back Propagation Neural Network (BPNN). The study also examined the effect of specific angles (hotspot and dark spot angles) on the estimation accuracy of the SMC at the nadir angle. The results indicated that different monitoring angles significantly impact the SMC estimation accuracy. Band reflectance and vegetation indices exhibited significant peak values and angular effects at the monitoring zenith angle of 45°. The models achieved the optimal inversion accuracy at the hotspot angle (at a monitoring zenith angle of 45° in the solar principal plane), and the accuracy was ranked as follows: BPNN (R2 = 0.71; RMSE = 1.69) > ELM (R2 = 0.52; RMSE = 1.94) > RF (R2 = 0.48; RMSE = 2.10). By eliminating shadows at the nadir angle through the threshold of dark spot, inversion accuracy similar to the hotspot direction is achieved. This study provides a basis for appropriate selection of UAV flight angles for the monitoring of SMC in the root zone of winter wheat. [ABSTRACT FROM AUTHOR]

Continuous time-series spectral data are important for inversion of crop or soil information. UAV remote sensing is usually selected under clear and windless weather conditions, but it is not possible to have such weather every day, which results in the UAV not collecting continuous daily spectral information. To explore this issue, we focused on summer maize with four irrigation levels as the research subject. A UAV platform with a multispectral sensor was used to acquire measured spectra of the maize canopy. The solar zenith angle was calculated and substituted into the Ambrals kernel-driven model to obtain simulated spectral data for the maize canopy, and the time-series UAV spectral data were complemented. Then, four vegetation indices (VIs) were established using simulated and measured spectral data, respectively, and the accuracy of the simulated VIs was evaluated. Finally, the simulated and measured VIs were used to monitor and evaluate variations in soil surface moisture content, respectively, and provide irrigation warning. The results demonstrated that Ambrals kernel-driven model can be used to simulate the reflectance of maize canopy collected by UAV. The simulated reflectance can complement time-series UAV spectral data and be used to establish VIs, among which Structure Intensive Pigment Index (SIPI) was established with the highest accuracy (R = 0.729). The VIs established by simulated reflectance can be used to monitor soil surface moisture content, and the monitoring effect is similar to the measured data (R2 = 0.642, RMSE = 0.42). It can evaluate the soil moisture a few days after irrigation and ensure the continuity and timeliness of soil moisture data, so as to improve the crop irrigation system and carry out irrigation warning. These results have certain reference for the supplementation of time-series spectral data and farmland irrigation using UAV multispectral remote sensing. [ABSTRACT FROM AUTHOR]

This study presents a pioneering approach that combines artificial intelligence and laser-induced breakdown spectroscopy (LIBS) to predict soil moisture content (MC). The traditional laboratory-based method of MC measurement, involving soil weight comparison before and after heating, is time-consuming, labor-intensive, and prone to low accuracy. In this work, we propose a non-destructive soil MC measurement technique utilizing robust nonlinear models based on LIBS-derived elemental intensities. Support vector regression (SVR) and AdaBoost-based SVR models (SVR-ADB), employing Gaussian Kernel and input features from LIBS data, were employed for MC prediction. Model performance was assessed using standard metrics such as root mean square error, mean absolute error, Nash–Sutcliffe efficiency (NSE), and correlation coefficient (CC) between predicted and actual moisture content. The study employed 485 datapoints generated in our laboratory. An advanced feature optimization technique based on the correlation between the soil MC and the descriptors was employed to select relevant mineral elements as input features. Three feature combinations (Combo-1, Combo-2, and Combo-3) were evaluated to identify the most effective configurations for accurate soil MC predictions. SVR-ADB-3 (Combo-3) demonstrated the highest prediction efficiency in the testing phase, achieving an impressive CC of 0.9998 and NSE of 0.9997. Consistently, Combo-3 outperformed other configurations, emphasizing the importance of the selected features. Validation of the developed models on soils treated with cement and lime stabilizers, whose data were not used during model calibration and verification, confirmed the generalization capability of the models. This study provides valuable insights for policymakers and industry stakeholders, facilitating optimized soil moisture management practices. [ABSTRACT FROM AUTHOR]

To clarify the differences soil moisture content in response to rainfall at diverse slopes of forested hillslope, a typical hillslope covered by Larix gmelinii var. principis-rupprechtii plantation in the semi-humid zone of Liupan Mountain was selected for this study. Meteorological factors such as rainfall and variations in soil moisture content in the rhizosphere at 0-60 cm and different slope positions（upper, middle, and lower slopes） were observed continuously from May to October of 2022, and the response of soil moisture content to varied rainfall events and their slope position differences within the growing season were analyzed. Our results showed differences in soil moisture content among slope positions, with the mean values showing a upper＞middle＞lower slope pattern, with the fluctuations in soil moisture content being the greatest in the middle slope. Moreover, rainfall recharge of soil moisture content varied by slope, with the recharge thresholds being lowest（1.9 mm） in the middle slope and highest（2.3 mm） in the lower slope. Slope differences were also observed in the response of soil moisture content to rainfall at various rhizosphere levels. The response duration of soil moisture content in the 0-20 cm soil layer was the shortest for all slopes under light rainfall conditions. However, the duration of soil moisture content response was shorter in the 40-60 cm soil layer than in the 20-40 cm soil layer at the top and middle of the slope under moderate and heavy rainfall events. These observations indicated that there might be a preferential flow of water in the soil at the middle and upper slopes. The results of this study are important for understanding soil moisture movement and its redistribution on slopes. [ABSTRACT FROM AUTHOR]

This study addresses the impact of climate variability on latex yield. Field research was carried out in the Indonesian Rubber Research Institute Experimental Field, located in South Sumatra, Indonesia for 2020 to 2022. The study used mature IRR 118 clones of rubber (Hevea brasiliensis) planted in clay loam soil. Latex yields for dry and rainy seasons were compared to obtain the effects of climatic factors. A purposive sampling of latex clone IRR 118 was applied in the field. The results showed that declined rainfall and soil moisture content contributed to the low latex yield during dry season. A declined water availability acts as a limiting factor resulting in decreased latex yield. Latex yield consistently decreased when soil moisture content fell below 21.5%. Based on statistical analysis, the correlation between latex yield and climate factor was 0.36, 0.42, and 0.52 for rainfall, soil moisture content, and evapotranspiration, respectively. Our findings highlight the crucial influence of climatic factors, emphasizing the significance of optimal water availability for latex production. [ABSTRACT FROM AUTHOR]

Cyperus esculentus is a hard-to-control, destructive perennial weed propagating and spreading through rhizomes and tubers. Currently, a combination of mechanical, cultural, and chemical measures sustained over time is required for satisfactory control of arable crops. Hood steaming is a promising thermal technique for rapid depletion of the soil tuber bank. The effect of hood steaming on C. esculentus tuber vitality was investigated using a dose–response experiment on two localities in Belgium. Steaming was performed for five durations (2, 4, 8, 16, and 32 min). Moreover, the effects of tuber burial depth (5, 15, and 25 cm), genetic C. esculentus clone (three different clones), and pedohydrological parameters (sandy and sandy, loamy soil) on hood steaming efficacy were determined. Additionally, the heat sensitivity of genetically diverse C. esculentus tubers was investigated in a laboratory incubator experiment in which incubation temperature and duration varied. To control tubers at depths of 5, 15, and 25 cm, treatment durations of 8, 16, and 32 min were required, respectively. In general, clones producing large tubers showed lower heat sensitivity. As pedohydrological parameters may affect heat transfer into the soil, they may affect steaming efficacy as well. To obtain complete control of C. esculentus tubers, soil temperature should be 50 °C or more for at least 42 min. Hood steaming is a highly suitable alternative technique for a rapid and strong depletion of the soil tuber bank in small well-delineated C. esculentus patches. [ABSTRACT FROM AUTHOR]

In order to provide a theoretical basis and reference for the large-scale promotion of the technology of reinforced loess slope protection, the soil moisture content and slope morphology of the polypropylene fiber-reinforced loess protective slope were tested in situ for 6 months. The results show that the PP fiber-reinforced loess has a good slope protection effect, and the soil moisture content of the reinforced loess protective slope is generally stable. The average variability of soil moisture content in different soil layers is 10.89% lower than that of the untreated slope. This is because the morphology of the reinforced loess protective layer is generally complete, and it can resist the continuous impact of adverse external environments such as rainfall, dry-wet cycle, and freeze-thaw cycle for a long time. However, the vegetation growth on the PP fiber-reinforced loess protective slope is slow, and the protective layer is easy to differentiate and detach from the undisturbed soil. The improvement strategies of the reinforced loess protective slope are put forward, such as water retention improvement of the reinforced loess, the design of the multi-functional layer with rapid vegetation, and the physical anti-detachable technology of the material with strong operability. [ABSTRACT FROM AUTHOR]

Increasing wildfire frequency in the Mediterranean Basin could affect future plant-soil–water-dynamics. The capacity of soils to retain water is a key parameter affecting plant post-fire regeneration. Yet, few research has looked at how different soil properties related to water retention is affected by increasing wildfire frequency. This study aimed at understanding the relationship between wildfire frequency, soil–water-related properties and the dynamics of surface water in soils. To this, after a 2012-summer wildfire in Portugal, three sets of three replicate maritime pine stands with contrasted wildfire frequency were selected (0 vs. 1 vs. 4 fires since 1975). At each of the nine study sites, three re-sprouter shrubs and neighbouring bare soil were chosen (54 microsites). There, soil cover, soil–water retention curves and surface (0–5 cm) soil–water-related properties (texture, bulk density, organic matter content, soil moisture, soil surface water repellency) were monitored for one year. Furthermore, records of post-fire soil moisture dynamics were analysed continuously using 72 probes installed at 2.5 and 7.5 cm depth. The hillslopes affected by 1 fire showed higher plant recovery than the 4 fires hillslopes. During the dry season, the threshold for water stress was reached 17 days sooner in the 4 fires hillslopes, and also 10 days sooner on bare microsites. Periods of plant water stress were longer and bare soil patches size bigger. The increase in wildfire frequency promoted high soil organic matter contents but less available water content, stressing the importance of soil organic matter quality characterization in water-related properties. [ABSTRACT FROM AUTHOR]

Soil moisture content plays a vital role in agricultural production, significantly influencing crop growth, development, and yield. Thoroughly understanding the specific soil moisture content in cotton fields is crucial for enhancing agricultural efficiency and driving sustainable agricultural development. This study utilized the gradient-boosting regression–random forest (GBR-RF) algorithm and the GBR and RF algorithms separately, in conjunction with Sentinel-2 satellite images, to estimate cotton soil moisture content, focusing on the B1–B8 bands and in particular the sensitive B6, B7, and B8 bands. The soil data in the jujube orchard of the study area were collected using soil augers at a depth of 30 cm, with soil data collected from a depth of 20 to 30 cm. The findings revealed that the integrated learning algorithm GBR-RF demonstrated high accuracy, with R2, MAE, and MSE results of 0.8838, 1.0121, and 1.6168, respectively. In comparison, the results using just the GBR algorithm yielded R2, MAE, and MSE values of 0.8158, 1.1327, and 1.9645, respectively, while those obtained from the RF algorithm were 0.8415, 1.0680, and 1.8331, respectively. These results indicate that the algorithms exhibited strong generalization, robustness, and accuracy, with GBR-RF outperforming GBR and RF by 8.34% and 5.03%, respectively, in combination with using the B1–B8 bands for inversion. Furthermore, utilizing the full-band data resulted in R2 values that were up to 24.27% higher than those of the individual bands, affirming the efficacy of band combinations for improved accuracy. This study's demonstration of the positive impact of integrated learning algorithms on estimating cotton soil moisture content underscores the advantages of multi-band data combinations over single-band data, highlighting their ability to enhance accuracy without significantly impacting errors. Importantly, this study's findings, while not limited to a single experimental field, have broad applicability in cotton precision agriculture, offering valuable insights for research on yield enhancement and agricultural efficiency. [ABSTRACT FROM AUTHOR]

A tremendous number of landmines has been buried during the last decade. In recent years, various autonomous platforms equipped with ground-penetrating radars (GPRs) have been proposed for the detection of landmines. These systems have already demonstrated their performance in controlled environments with known ground truth. However, it has been observed that the influence of surface conditions in the form of vegetation and roughness as well as soil moisture content significantly reduce the detection probability. The influence of these individual factors on a ground-offset GPR is presented and discussed in this work. Each of these factors significantly degrades the backscattered signal. With increasing soil moisture, the signal gets attenuated more strongly; however, the signature is maintained in the phase of the C-Scans. An increase in surface roughness deteriorates the target pattern making it difficult to detect buried objects unambiguously. Vegetation, especially with irregular leaf structures, can appear as a ghost target and scatter the electromagnetic waves. In most cases, the target is easier to detect in the phase of the B- or C-Scan. [ABSTRACT FROM AUTHOR]

The accurate measurement of soil moisture content emerges as a critical parameter within the ambit of agricultural irrigation management, wherein the precise prediction of this variable plays an instrumental role in enhancing the efficiency and conservation of agricultural water resources. This study introduces an innovative, cutting-edge hybrid model that ingeniously integrates Gated Recirculation Unit (GRU) and Transformer technologies, meticulously crafted to amplify the precision and reliability of soil moisture content forecasts. Leveraging meteorological and soil moisture datasets amassed from eight monitoring stations in Hebei Province, China, over the period from 2011 to 2018, this investigation thoroughly assesses the model's efficacy against a diverse array of input variables and forecast durations. This assessment is concurrently contrasted with a range of conventional machine learning and deep learning frameworks. The results demonstrate that (1) the GRU–Transformer model exhibits remarkable superiority across various aspects, particularly in short-term projections (1- to 2-day latency). The model's mean square error (MSE) for a 1-day forecast is notably low at 5.22%, reducing further to a significant 2.71%, while the mean coefficient of determination (R2) reaches a high of 89.92%. Despite a gradual increase in predictive error over extended forecast periods, the model consistently maintains robust performance. Moreover, the model shows exceptional versatility in managing different soil depths, notably excelling in predicting moisture levels at greater depths, thereby surpassing its performance in shallower soils. (2) The model's predictive error inversely correlates with the reduction in parameters. Remarkably, with a streamlined set of just six soil moisture content parameters, the model predicts an average MSE of 0.59% and an R2 of 98.86% for a three-day forecast, highlighting its resilience to varied parameter configurations. (3) In juxtaposition with prevalent models such as Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), XGBoost, Random Forest, and deep learning models like Deep Neural Network (DNN), Convolutional Neural Network (CNN), and standalone GRU-branch and Transformer-branch models, the GRU–Transformer framework demonstrates a significant advantage in predicting soil moisture content with enhanced precision for a five-day forecast. This underscores its exceptional capacity to navigate the intricacies of soil moisture data. This research not only provides a potent decision-support tool for agricultural irrigation planning but also makes a substantial contribution to the field of water resource conservation and optimization in agriculture, while concurrently imparting novel insights into the application of deep learning techniques in the spheres of agricultural and environmental sciences. [ABSTRACT FROM AUTHOR]

A field experiment was conducted to investigate the optimum lateral spacing and irrigation frequency of a subsurface drip irrigation system for broccoli crop grown in the semi-arid region of Haryana (India). The effects on soil water dynamics, growth parameters (i.e. plant height, leaf length, no. of leaves/plant, curd weight and curd diameter), irrigation water use efficiency (IWUE), fertilize use efficiency (FUE) and yield response of broccoli were assessed under two lateral spacings (45 cm and 60 cm) and four irrigation frequencies (daily, and after one, two & three days) in the years of 2019–20 and 2020–21. The results show that soil moisture content was higher near the dripper, and it decreased with increasing radial distance from the drippers. Further, the moisture content was high at 45 cm lateral spacing as compared to 60 cm lateral spacing. The effects of selected treatments on growth and yield parameters were found significant and maximum broccoli yield (147. 41 q ha−1) was found at 45 cm lateral spacing with alternate days of irrigation. The highest IWUE and FUE of 9.93 kg/m3 and 30.68 kg/kg, respectively, were also found at alternate days irrigation with 45 cm lateral spacing. It may be concluded from the present study that alternate days irrigation with 45 cm lateral spacing is the best choice to obtain the maximum yield of broccoli grown in winter season in the semi-arid region of India. [ABSTRACT FROM AUTHOR]

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy's original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model's accuracy follows the order: 1.5 order > 2.0 order > 1.0 order > 0.5 order > original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF > SVM > BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models. [ABSTRACT FROM AUTHOR]

Reduced tillage is one of the options to combat land degradation problems. The study aims to examine the impacts of tillage practices on selected soil physicochemical properties, soil loss, and maize yield and yield components. Four treatments—zero, twice, and strip tillage—as well as farmers' practices, were set up in a randomized complete block design with five replications. Then, soil moisture, soil loss, and yield and yield components of maize data were collected. The findings show that, when compared to conventional tillage, conservation tillage improves soil moisture and decreases soil loss during and after crop development. The study found that implementing reduced, or zero tillage practices resulted in higher maize yields and long-term economic benefits for farmers compared to conventional and strip tillage practices. In addition, the current research demonstrated that, in comparison to strip tillage and conventional tillage, adopting reduced tillage and zero tillage increased maize yield and yield components with greater long-term economic benefits to farmers. The current findings also showed that adopting reduced tillage and zero tillage boosted maize yield and yield components, with higher economic benefits to farmers in the long run as compared to strip tillage and conventional tillage. Although zero tillage attributed to less grain yield, it resulted in higher economic benefits to farmers and saved 44.84% of soil loss relative to conventional tillage. The outcome of the economic evaluation unequivocally demonstrated that reduced tillage is the best approach, but zero tillage, strip tillage, and reduced tillage are all superior and advised for farmers. Hence, farmers can use either zero tillage or reduced tillage, depending on their investment capital. But to get considerable changes in soil and water balances, other soil physicochemical properties, and crop yield. Similar studies must be carried out on permanent field plots for longer than 2 years in the future. [ABSTRACT FROM AUTHOR]

To improve the accuracy of soil moisture estimation in Vertisols, this study took the Vertisol in Xiping County, Henan Province, China, as its research object and conducted hyperspectral measurement in the laboratory by configuring soil samples with different moisture contents after implementing smoothing (SR), logarithm of the inverse[LOG (1/R) ], first-order differentiation (FD), multiple scattering correction (MSC), and continuum removal (CR) spectral transformation processes on the soil sample hyperspectral data. The best feature bands were identified by combining the successive projection algorithm (SPA) with the machine learning methods of partial least squares regression (PLSR) and support vector machine regression (SVR) and stacking (Stacking) integrated learning methods were used to construct the soil water content inversion model. The results showed that the information related to soil water content was most enhanced in the MSC-transformed spectra. The SPA algorithm was able to downscale and extract feature information from the water content spectral data of the Vertisol. The Stacking integrated model, which integrated PLSR and SVR after MSC transformation based on the reflection spectra, had the highest coefficient of determination (R²=0.963) and the lowest root mean square error (RMSE=1.7) . This study indicates that the Stacking integrated learning model is the best inversion model for Vertisol moisture content. It effectively improves the accuracy and generalization ability of the model. [ABSTRACT FROM AUTHOR]

Rainfall and soil response are necessary to be monitored to have slope characteristics in detecting landslide occurrence. Even though much research has been carried out worldwide for rainfall monitoring, less research has been conducted in Indonesia for slope monitoring. Therefore, this research was conducted to observe the suction, soil moisture content, and rainfall in a silty sand slope. An automatic rain gauge was set on the ground to measure precipitation. Tensiometer and soil moisture content sensors were installed at depths of 0.5 m; 1 m; and 1.5 m from the slope surface. The monitoring was conducted during the peak rainy season from December 2022 to January 2023. The rainfall amount is about 436.6 mm, and the 6 hours of rainfall events contribute a relatively sizeable rainfall amount (about 31%) to the total. The safety factor of the slope is estimated to decrease by 39%-40% due to the rainfall. [ABSTRACT FROM AUTHOR]

Accurate identification of deep soil moisture content in farmland is a key basic technology to optimize the application of remote sensing interpretation results of soil surface moisture and promote moisture measurement irrigation. Based on the automatic monitoring data of soil moisture in typical Yellow River irrigated areas in Henan and Shandong, the change law of farmland soil moisture was analyzed, and the relationship between farmland soil surface and deep soil moisture was established. The studies have shown that there is a significant correlation between the soil moisture content at different observation depths, and the surface soil moisture content that can be identified by remote sensing can better reflect the deep soil moisture status. [ABSTRACT FROM AUTHOR]

The spatial distribution of soil water content is a key factor affecting agricultural irrigation decisions. In this study, 38 technology was adopted to explore the spatial distribution of soil water content in the irrigation area. With soil water content as the main forecast object, combined with the weather forecast and the growth of planted crops, and based on the field water balance equation as the theoretical basis, the agricultural irrigation water requirement in the irrigation area was estimated by using soil water content in the irrigation area, and its spatial distribution was analyzed. The results could provide scientific basis for rational water use and agricultural water-saving irrigation in irrigated areas, and also provide technical support for scientific and rational water-saving potential assessment. The results show that the predicted water demand of Xiaokaihe irrigation area in late May is 6 217 700 m³, which is consistent with the statistics of the irrigation area. The prediction technology of agricultural irrigation water demand based on 35 can realize the precision irrigation of irrigation area. [ABSTRACT FROM AUTHOR]

Dust aerosols are produced by wind erosion, and it is widely accepted that dust aerosols can be produced only if the wind speed exceeds a certain threshold velocity, which is largely controlled by soil moisture content. The relative humidity (RH) in the air could affect soil moisture content, thereby impacting dust production indirectly. However, it is not clear if the RH can directly change dust aerosol production. Here we simulated dust production and show that the RH does play a direct role in affecting the production of dust aerosol in a quite complicated way, which can be explained by a hypothesis that the RH affects both the electrostatic forces and wet‐bonding forces between soil particles in opposite directions. The current formula for dust aerosol production flux does not include the direct RH effect, and this study strongly suggests that it could lead to significant errors in estimating dust production. Plain Language Summary: Dust is a critical source of atmospheric particulate matter, which can exert significant influences on climate and human health. Strong wind can drive the soil particles to collide with each other and shake off small particles, which could be lifted up into the air and form dust aerosols. It is now generally accepted that humidity in the air could indirectly impact dust aerosol production through affecting soil moisture. However, in this study, it is found that relative humidity can directly influence dust aerosol production, possibly through affecting the electrostatic forces and wet‐bonding forces between soil particles. Our study suggests that the modeling of dust generation should include air humidity to better predict dust aerosol production. Key Points: Humidity in the air can directly affect the production of dust aerosol in a complicated wayHumidity may change the electrostatic forces and wet‐bonding forces between soil particlesHumidity should be included in the source functions of dust aerosol production [ABSTRACT FROM AUTHOR]

The study evaluates the performance of a new design of blades (T-shape) rotary plow. The field experiment was carried out on the farm of Al-Quba village, Nineveh, Iraq. In the present research, draft force (Df), energy requirement (RE), fuel consumption (FC), soil disturbed volume (SDV), and percentage of the soil clods which have a diameter of less than 5 cm (PSCD) were tested during three soil moisture content levels (10-12, 13- 15, and 16-18) %, and two tillage depth levels (5-10, and 10-20) cm. and three rotation speeds of the blade’s axis (140, 180, and 220) rpm. The results showed the superiority of soil moisture content (16-18) % with a rotation speed of (140) rpm and tillage depth (5-10) cm were recorded less Df (5.11) kN. The soil moisture content (16-18) % with a rotation speed of (140) rpm and tillage depth of (5-10) cm has recorded less ER (10.20) kw. hr. /ha. The soil moisture content (16-18) % with a rotation speed of (220) rpm and tillage depth of (5-10) cm has recorded less FC (4.10) L/ha. The soil moisture content (16-18) % with a rotation speed of (220) rpm and tillage depth of (10-20) cm has recorded the highest SDV (1237.77) m3/hr. The soil moisture content (10-12) % with a rotation speed of (220) rpm and tillage depth of (5-10) cm has recorded less PSCD (67.30) %. [ABSTRACT FROM AUTHOR]

Optimal winter irrigation may be an alternative method for controlling soil salinization under seasonal freezing and thawing conditions in the Yellow River Delta. However, few studies have focused on optimal winter irrigation based on the dynamics of soil water, heat, and salt during the freezing–thawing period in this region. Taking the seedling stage of winter wheat in the Yellow River Delta as the research object and using observation data of hydrothermal salt from the Shandong agricultural high-tech demonstration base from 17 October 2019 to 15 June 2021, a numerical simulation of the hydrothermal coupling process of freeze–thaw soil in the experimental area was carried out through the Simultaneous Heat and Water Model (SHAW). The simulation results of the model were evaluated according to the mean error (ME), root mean square error (RMSE), and Nash efficiency coefficient (NSE). The results showed that the SHAW can well simulate the soil moisture (SM), soil temperature, and soil salt during the growth of winter wheat in this region: the SM at the depth of 0–80 cm with an ME < 0.038 cm3 cm−3, RMSE < 0.064 cm3 cm−3, and NSE > 0.669; the soil temperature with an ME < 1.311 °C, RMSE < 1.493 °C, and NSE > 0.738; and the soil salinity with an ME < 0.005 g kg−1, RMSE < 0.014 g kg−1, and NSE > 0.607. Moreover, the model was used to simulate the distribution of soil water and salt in the winter wheat seedling stage under different winter irrigation methods during wet, normal, and dry years. It was suggested that the appropriate winter irrigation amount was 80 mm in wet years and normal years and 100 mm in dry years, which could be beneficial to winter wheat growth during the seedling stage. These results provide a reference for irrigation optimization in the Yellow River Delta and other similar areas. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) is one of the most important conditions to affect crop growth and development in the irrigation of agricultural production. Monitoring SMC of crop roots is much more conducive to guiding precise crop irrigation. This study aims to investigate the effect of fractional-order differentiation (FOD) combined with the spectral index on the hyperspectral inversion of SMC in the winter wheat root domain. The winter wheat was selected as the research object in the Water-saving Irrigation Experimental Station. The data was also measured for the hyperspectral reflectance and soil water content. The hyperspectral reflectance was smoothed by Savitzky-Golay (SG). Then the typical spectral index was calculated to construct the partial least squares regression (PLSR), random forest (RF), and back propagation neural network (BPNN) inversion models of soil water content. The hyperspectral reflectance was processed by fractional differentiation of 0-2.0 order (step of 0.2), in order to calculate the ratio index (RI) and normalized difference index (NDI). A systematic analysis was made to determine the two-dimensional correlation between the RI of different orders, the normalized index and SMC. The sensitive spectral index was screened and grouped to construct three inversion models (PLSR, RF, and BPNN). The results show that: There was a very different correlation between different typical spectral indices and soil moisture content. The correlation coefficient fluctuated between 0.2 and 0.6, the highest of which with the SMC was the near-infrared spectral RI (about −0.6). The best effect was achieved in the SMC inversion model using the typical spectral index, followed by RF and BPNN models. The coefficient of determination of the improved model was 0.55, the root mean square error (RMSE) was 0.027 g/g, and the relative analysis error was 1.64. There was a high correlation coefficient between the sensitive spectral index and SMC after the fractional-order differential processing. The correlation coefficient between the sensitive spectral index and SMC showed a step change at different fractional orders. The correlation coefficient between the sensitive spectral index and SMC was higher at the order 0.2-1.0, and then fluctuated around 0.76, finally gradually decreasing from the order 1.2. The correlation between the sensitivity spectral index and SMC fluctuated around 0.65 after the order of 1.6. The optimal SMC inversion model was the RF model using the normalized sensitivity index after FOD treatment. The determination coefficient of the improved model was 0.75, the RMSE was 0.024 g/g, and the relative analysis error was 2.08. SMC inversion model with the improved fractional differential shared a significantly improved effect (the coefficient of determination from 0.55 to 0.75), compared with the typical spectral index one. The spectral details were mined as much as possible, while the noise was effectively removed more thoroughly without the information variables. As such, an SMC monitoring model was established using the optimal variable set. The findings can provide a reliable way for the hyperspectral monitoring of soil water content in the winter wheat root domain. [ABSTRACT FROM AUTHOR]

Soil dust generated by explosions can lead to the absorption and scattering of lasers, resulting in low detection and recognition accuracy for laser-based devices. Field tests to assess laser transmission characteristics in soil explosion dust are dangerous and involve uncontrollable environmental conditions. Instead, we propose using high-speed cameras and an indoor explosion chamber to assess the backscattering echo intensity characteristics of lasers in dust generated by small-scale explosive blasts in soil. We analyzed the influence of the mass of the explosive, depth of burial, and soil moisture content on crater features and temporal and spatial distributions of soil explosion dust. We also measured the backscattering echo intensity of a 905 nm laser at different heights. The results showed that the concentration of soil explosion dust was highest in the first 500 ms. The minimum normalized peak echo voltage ranged from 0.318 to 0.658. The backscattering echo intensity of the laser was found to be strongly correlated with the mean gray value of the monochrome image of soil explosion dust. This study provides experimental data and a theoretical basis for the accurate detection and recognition of lasers in soil explosion dust environments. [ABSTRACT FROM AUTHOR]

Quantification of soil moisture content is important for understanding physical processes that occur on and within the earth. The methods that are currently used for the determination of soil moisture content are point-based (gravimetry, time-domain reflectometry, neutron scattering, or gamma ray scanning). Remote sensing methods exist but have several disadvantages (poor resolution and dependence on local meteorological conditions). The objective of the study described herein was to determine soil moisture content by means of remote sensing from hyperspectral imagery. Reflectance values obtained from optical remote sensing (wavelengths from 350 nm to 2,500 nm) were acquired from materials with different moisture content levels. Reflectance values, obtained from a spectroradiometer, were obtained for three soils and two commercial sands and then correlated with the initial soil moisture content obtained from gravimetric methods. The correlations were based on the collected reflectance values at prescribed wavelengths and a continuum analysis across all wavelengths. For all investigated materials, the reflectance values decreased with increasing moisture content. The best results were obtained when 1,900 nm was considered (coefficients of determination greater than 0.94, indices of agreement greater than 0.97, coefficients of efficiency greater than 0.41, mean absolute error less than 1.2 percent, and root mean square error less than 1.6 percent). With the continuum analysis, 1,450 nm was the best wavelength. Based on these results, a correlation was observed to exist between soil moisture content and the spectral reflectance values. Using this technique, the determination of soil moisture content can be rapidly obtained (within seconds). [ABSTRACT FROM AUTHOR]

• Soil shear strength measurement methods used in terramechanics studies. • Modelling of the terrain behaviour "Evaluation of the terrain" when subjected to an excitation load. • Influence of moisture content on the soil strength. • Loam sand soil shear strength results at different moisture contents using direct shear test. • Designs prepared by researchers for measuring the shear strength of the soil at higher shearing speed, thus suitable to terramechanics study case. This article aims to give a background about the soil shear strength and its measurement methods based on scientific articles and the work of researchers. A brief introduction is given about terramechanics science and the loads acting at the interaction zone between the tractive element (wheel/track) and the terrain. The most important loads exciting the terrain from the machine's tractive element are the normal and the tangential loads. The tangential load will shear the terrain/soil and might lead to slippage, thus it is important to study the shear strength of the soil. In the review the soil terrain behaviour as an elastic and a plastic region is discussed. The conventional methods for measuring the soil strength used by scientists in terramechanics studies are reviewed. The influence of moisture content on soil strength is also taken into consideration. New ideas created by terramechanics scientists that emulate a real wheel/track - terrain interaction case for measuring the soil shear strength and are not civil or geotechnical engineering methods are mentioned. Finally, the shear strength results of loam sand soil obtained using the direct shear test conducted at the Hungarian University of Agriculture and Life Sciences (MATE) are presented. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is a branch of artificial intelligence (AI) that has been successfully applied in a variety of remote sensing applications, including geophysical information retrieval such as soil moisture content (SMC). Deep learning (DL) is a subfield of ML that uses models with complex structures to solve prediction problems with higher performance than traditional ML. In this study, a framework based on DL was developed for SMC retrieval. For this purpose, a sample dataset was built, which included synthetic aperture radar (SAR) backscattering, radar incidence angle, and ground truth data. Herein, the performance of five optimized ML prediction models was evaluated in terms of soil moisture prediction. However, to boost the prediction performance of these models, a DL-based data augmentation technique was implemented to create a reconstructed version of the available dataset. This includes building a sparse autoencoder DL network for data reconstruction. The Bayesian optimization strategy was employed for fine-tuning the hyperparameters of the ML models in order to improve their prediction performance. The results of our study highlighted the improved performance of the five ML prediction models with augmented data. The Gaussian process regression (GPR) showed the best prediction performance with 4.05% RMSE and 0.81 R2 on a 10% independent test subset. [ABSTRACT FROM AUTHOR]

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi's highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were >0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models. [ABSTRACT FROM AUTHOR]

【Objective】 To study the anatomical leaf structure of seabuckthorn under different soil moisture contents and to provide basis for further research on drought and waterlogging tolerance mechanism of seabuckthorn.【Methods】 Drought and waterlogging treatments were set up using one-year Chinese seabuckthorn cuttings as experimental materials, with conventional irrigation as control (CK). Under different soil moisture contents, the changes in the anatomical structure of young and old leaves were observed through paraffin sectioning. 【Results】 Under drought conditions, the leaf blade, epidermis and palisade tissue of seabuckthorn young leaves significantly thickened (P<0.05). The thickness of leaf blade, lower epidermis and palisade tissue of old leaves were not significantly different from that of CK (P>0.05), while the upper epidermis and spongy tissue significantly thickened (P<0.05). In comparison with CK, the young leaf palisade to spongy ratio and leaf cell tense ratio (CTR) increased, while the leaf spony ratio (SR) decreased, with old leaves showing the reverse trend. The young leaf vein, vascular bundle and xylem maximal vessel diameter were not significantly different from those of CK (P>0.05), while those of the old leaves significantly thickened (P<0.05). Under waterlogging conditions, seabuckthorn young leaves became thinner. There was no significant difference between the thickness of old leaves and CK (P>0.05). In comparison to the CK, both the young and old leaves had thinning palisade tissue, thickening spongy tissue, decreased palisade to spongy ratio and CTR, and increased SR. There was no significant difference in the main vein and xylem maximal vessel diameter between young leaves and CK (P>0.05), the vascular bundle diameter decreased significantly (P<0.05), while all of those in the old leaves increased significantly (P<0.05).【Conclusion】 Seabuckthorn young leaves near the top growth point adapted to different water conditions by adjusting mesophyll structure, while old leaves near the stem base adapted to different water conditions by adjusting leaf vein vascular tissue. [ABSTRACT FROM AUTHOR]

Inoculating microbial inoculants to speed up the decomposition of returning straw is currently a hot topic. Meanwhile, the soil moisture content (SMC) could change the diversity, abundance, and metabolism of the soil microbial community structure, which affects the straw degradation rate under the straw returning condition. In this research, rumen microorganisms with strong decomposing abilities in natural systems were used as inoculants to promote straw decomposing and returning to the field. The effects of the SMC on straw decomposition under rumen fluid (RF)-induced returning were investigated. Experiments were conducted for 30 days with typical paddy soil in the south of China under conditions of 30%, 70%, and 100% SMC. With an increase in the SMC within a certain range (30~100%), the decomposition rate of straw showed a trend of first rising and then falling. Treatments of 70% SMC with RF addition generally achieved the maximum rate of straw degradation. The peak value was 49.96%, which was 2.67-fold higher than the treatments of 30% SMC with RF addition (18.74%) and 24.1% higher than those of the control with 70% SMC (40.3%) (p < 0.05). Moreover, a straw structural analysis proved that at 70% SMC, microorganisms from RF favored the destruction of functional groups on the straw surface and the degradation of cellulose. Meanwhile, it was shown that RF could promote the decay of straw, leading to increments in enzyme activities and soil nutrients. The higher the soil moisture content, the higher the key soil enzyme activities. This indicates that the diversity and abundance of cellulose-degrading bacteria and fungi in soil microorganisms and rumen microorganisms were changed with different soil moisture contents. The experimental findings suggest an innovative way to further utilize rumen microorganisms. [ABSTRACT FROM AUTHOR]