Gravimetric Soil Moisture Research Articles

Calibration of Low-Cost Capacitive Soil Moisture Sensors for Irrigation Management Applications.

The calibration of capacitive soil moisture sensors is an essential step towards their integration into smart solutions. This study investigates the calibration of a widely used low-cost capacitive soil moisture sensor (SKU:SEN0193, DFRobot, Shanghai, China) in a loamy silt soil typically found in the Puglia region of Italy. The calibration function was derived from a random sample of 12 sensors, with three soil sample replicas per sensor, each of which had one of five gravimetric soil moisture contents, from relatively dry (5%) to full saturation (40%). The study reports the resulting calibration function along with the accuracy achieved with the generalized calibration function. The sensors proved to be accurate, with an R2 value ranging between 0.85 and 0.87 and a root mean square value (RMSE) ranging between 4.5 and 4.9%. The variation between the sensors was also investigated. The results showed that with higher soil moisture contents (above 30%), the sensor-to-sensor variability becomes significant, with a coefficient of variation (CV) ranging between 10 and 16%; meanwhile, in lower soil moisture contents, the CV ranged between 6.5 and 10.3%, implying that it is more consistent in lower moisture content within this soil condition. The resulting calibration function enhances the integration of such low-cost sensors into smart farming solutions. With proper calibration, these affordable capacitive sensors can achieve a high degree of accuracy, making them a viable option for widespread use in cost-effective precision agricultural applications.

Quantifying the Impact of Soil Moisture Sensor Measurements in Determining Green Stormwater Infrastructure Performance

The ability to track moisture content using soil moisture sensors in green stormwater infrastructure (GSI) systems allows us to understand the system’s water management capacity and recovery. Soil moisture sensors have been used to quantify infiltration and evapotranspiration in GSI practices both preceding, during, and following storm events. Although useful, soil-specific calibration is often needed for soil moisture sensors, as small measurement variations can result in misinterpretation of the water budget and associated GSI performance. The purpose of this research is to quantify the uncertainties that cause discrepancies between default (factory general) sensor soil moisture measurements versus calibrated sensor soil moisture measurements within a subsurface layer of GSI systems. The study uses time domain reflectometry soil moisture sensors based on the ambient soil’s dielectric properties under different soil setups in the laboratory and field. The default ‘loam’ calibration was compared to soil-specific (loamy sand) calibrations developed based on laboratory and GSI field data. The soil-specific calibration equations used a correlation between dielectric properties (real dielectric: εr, and apparent dielectric: Ka) and the volumetric water content from gravimetric samples. A paired t-test was conducted to understand any statistical significance within the datasets. Between laboratory and field calibrations, it was found that field calibration was preferred, as there was less variation in the factory general soil moisture reading compared to gravimetric soil moisture tests. Real dielectric permittivity (εr) and apparent permittivity (Ka) were explored as calibration options and were found to have very similar calibrations, with the largest differences at saturation. The εr produced a 6% difference while the Ka calibration produced a 3% difference in soil moisture measurement at saturation. Ka was chosen over εr as it provided an adequate representation of the soil and is more widely used in soil sensor technology. With the implemented field calibration, the average desaturation time of the GSI was faster by an hour, and the recovery time was quicker by a day. GSI recovery typically takes place within 1–4 days, such that an extension of a day in recovery could result in the conclusion that the system is underperforming, rather than it being the result of a limitation of the soil moisture sensors’ default calibrations.

Study of soil heterotrophic respiration as a function of soil moisture under different land covers

The relationship between soil heterotrophic respiration (Rh) and soil moisture has been often studied using disturbed soil samples and simple gravimetric and volumetric soil moisture indicators. The objective of this study was to investigate the relationship between Rh and soil moisture in terms of water-filled porosity (WFP), matric potential (Ψm), and relative soil gas diffusivity (Dp/Do) using undisturbed soil cores obtained under different land covers. Soil CO2 efflux, WFP, and Ψm were measured in undisturbed soil samples (250 cm3) collected in the 0–5 cm soil layer (without any vegetation or living roots) under laboratory conditions by combining a CO2 gas analyzer, a scale, and precision mini-tensiometers. For each site and land cover, we also measured soil chemical properties, soil physical properties, and soil microbial composition using phospholipid fatty acid analysis. Grassland soils had the largest total microbial biomass (6275 ng g−1), followed by soils from riparian (5327 ng g−1), and cropland (2745 ng g−1) sites. Bacteria were the dominant group representing 46% (SD = 5%) of the total microbial biomass across all sites and land covers. Maximum Rh was 1.88 (SD = 0.40) μmol CO2 m−2 s−1 in grassland, 1.64 (SD = 0.82) μmol CO2 m−2 s−1 in riparian, and 0.94 (SD = 0.56) μmol CO2 m−2 s−1 in cropland soils. Considering all land cover and soil types, our observations revealed that peak Rh occurred at mean WFP = 0.81 ,Ψm = −6 kPa, and Dp/Do = 0.003. Thus, we recommend avoiding the traditional field capacity definition of −33 kPa for representing peak microbial activity. Water-filled porosity was a more consistent predictor of Rh than Ψm or Dp/Do across soils with contrasting organic matter content, total microbial biomass, soil texture, and soil structure.

Spatial variability of soil penetration resistance in cotton growing areas

Resistance to soil penetration is a problem in cotton growing areas; this harms plant development and crop productivity. An analysis of spatial variability allows studying the variability of soil physical properties, especially those related to compaction and resistance to penetration. The hypothesis of this study was based on the possibility of evaluating soil compaction in areas cultivated with cotton crops through an analysis of spatial variability. Thus, the aim of the study was to evaluate the spatial variability of soil resistance to penetration and its effects on cotton productivity. The research was carried out in Paraíso das Águas, Mato Grosso do Sul, Brazil, in an agricultural area belonging to Fazenda Indaiá II. The experimental area consisted of a plot of 60 ha, where cotton seeds of the FM 978 6LTP cultivar were planted. The x and y directions of the Cartesian coordinate system were defined and the experimental mesh was staked, spaced 9.9 m apart. Cotton productivity was evaluated (t ha-1) as well as gravimetric soil moisture at a depth of 0 to 0.20 m as well as mechanical resistance to penetration in the following layers: 0 to 0.10 m (RP1), 0.10 to 0.20 m (RP2), 0.20 to 0.30 m (RP3), 0.30 to 0.40 m (RP4), 0.40 to 0.50 m (RP5), 0.50 to 0.60 m (RP6), including average penetration resistance (RPM). For each evaluated variable, classical descriptive analysis and analysis of spatial variability were carried out, with the construction of semi-variogram and later kriging and cokriging maps. To analyze the dependence and spatial interdependence between the variables, the soil resistance to penetration showed spatial variability in the cotton crops; and there was an inversely proportional relationship between cotton productivity and soil resistance to penetration, where the lower the penetration resistance, the higher the cotton productivity.

Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance

Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions. Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability.This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r2 = 0.76, RMSE = 3.13 %, validation r2 = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters.This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r2 = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r2 = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r2 = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved significantly. Notably, using raw low-cost sensor data to calibrate AquaCrop led to poorer performances than using the literature. Hence using literature values could save sensor costs without compromising model performance if sensor calibration was not possible. The results suggest the essentiality of calibrating low-cost soil moisture sensors for crop modeling calibration to improve crop water productivity.

Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation

Abstract. Cosmic-ray neutron sensing (CRNS) has emerged as a reliable method for soil moisture and snow estimation. However, the applicability of this method beyond research has been limited due to, among others, the use of relatively large and expensive sensors. This paper presents the tests conducted on a new scintillator-based sensor especially designed to jointly measure neutron counts, muons and total gamma rays. The neutron signal is first compared against two conventional gas-tube-based CRNS sensors at two locations. The estimated soil moisture is further assessed at four agricultural sites, based on gravimetric soil moisture collected within the sensor footprint. Muon fluxes are compared to the incoming neutron variability measured at a neutron monitoring station and total gammas counts are compared to the signal detected by a gamma ray spectrometer. The results show that the neutron dynamic detected by the new scintillator-based CRNS sensor is well in agreement with conventional CRNS sensors. The derived soil moisture also agreed well with the gravimetric soil moisture measurements. The muons and the total gamma rays simultaneously detected by the sensor show promising features to account for the incoming variability and for discriminating irrigation and precipitation events, respectively. Further experiments and analyses should be conducted, however, to better understand the accuracy and the added value of these additional data for soil moisture estimation. Overall, the new scintillator design shows to be a valid and compact alternative to conventional CRNS sensors for non-invasive soil moisture monitoring and to open the path to a wide range of applications.

Topsoil and Vegetation Dynamics 14 Years after Eucalyptus grandis Removal in Eastern Cape Province of South Africa.

A great deal of effort has been made to clear invasive alien plants in South Africa, yet it remains unclear if the clearing efforts are yielding positive soil and vegetation recovery trajectories. A few short-term studies have been conducted to monitor soil and vegetation recovery after alien plant removal in South Africa, but convincing, long-term monitoring studies are scarce yet needed. We investigated topsoil and vegetation recovery following Eucalyptus grandis removal 14 years ago by Working for Water in Makhanda, Eastern Cape province of South Africa. The detailed topsoil and vegetation surveys were conducted on forty 10 m × 10 m plots that were in paired cleared and natural sites. The results show no significant differences for the measured soil pH, total N, total C, K, Ca, and Na between the cleared and natural sites, an indication that the two sites are becoming similar. Similarly, the gravimetric soil moisture content shows no significant differences between the two sites, although monthly variations are observed. The topsoils in the cleared sites are hydrophobic as compared to those in the natural sites, which are wettable. We observed no significant vegetation diversity differences between the two sites, with native woody species, such as Crassula pellucida and Helichrysum petiolare, frequently occurring in the cleared sites. We recorded low reinvasion by E. grandis and other secondary invaders like Acacia mearnsii and Rubus cuneifolius in the cleared sites. Based on these results, we conclude that 14 years after E. grandis clearing, both topsoil and vegetation recovery are following a positive trajectory towards the natural sites. However, both reinvasion and secondary invasion have the potential to slow down soil and native vegetation recovery. Recommendations such as timeous follow-up clearing and incorporating restoration monitoring in the WfW clearing programme are discussed.

Spatial Variability of Soil Resistance to Penetration in Fruit Cultivation in Eastern Amazonia

The application of precision agriculture in cocoa and papaya cultivation in Brazil is still incipient. This study aimed to evaluate the spatial variability of the physical attributes of soil cultivated with a consortium of papaya and cocoa. The study was conducted in two sampling grids of 50 points, in two areas cultivated with papaya and cocoa with different planting times (three and eleven months). The soil attributes soil resistance to penetration (RP) and soil gravimetric moisture (UG) were determined at soil depths of 0–20, 20–40 and 40–60 cm. The data were submitted to an exploratory and descriptive analysis. Subsequently, a geostatistical analysis was performed to quantify spatial dependence and then interpolation of the data through kriging. The maps showed weak spatial variability for the UG and RP. In the two areas, it was observed that the depth of 0–20 cm had a lower RP (1.7 Mpa) and a higher UG (40 g g−1), and as the depth was higher, had a higher RP (4.4 Mpa) and a lower UG (38 g g−1). Area 1 presented higher RP values in depth, showing greater susceptibility to compaction. The area characterized by the consortium of papaya and cocoa presented more susceptible to compaction. The mechanical resistance of the soil to penetration was more critical in the 40–60 cm layer for the two consortia evaluated, evidencing areas with possible restriction to plant growth.

Estimation of Surface Soil Moisture from Agricultural Lands Using Multi-Spectral Optical Satellite Data: A Study of Bhagwanpur-I CD Block, East Medinipur, West Bengal, India

The spatiotemporal fluctuation of Surface Soil Moisture (SSM) is important for prediction of weather, modeling of hydrological cycle, water management, agricultural managing and making strategy. Optical remote sensing has demonstrated significant promise for precise surface soil moisture estimate. The study aimed to estimate the moisture content in the upper layer of agricultural fields using Landsat 8 OLI, Sentinel-2A multi-spectral satellite data as well as TVDI [Temperature Vegetation Dryness Index] data. The spatial resolution of the Landsat OLI and Sentinel-2A images was 30m and 10m, respectively. The spectral Thermal Infrared (TIR 10.9µm ), TIR (10.9µm) and along with the Short-Wave-Infrared (SWIR 2.2µm) band of the Landsat 8 and the Shore-Wave-Infrared (SWIR 2.2 µm) band of sentinel 2B satellite imagery was utilized to estimate how moist was topsoil of the agricultural lands. The soil moisture estimates using remote sensing based model were acquired and compared with in-situ soil moisture. In a depth of 10cm below the surface, the field-based soil moisture was measured. Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and TVDI were measured to estimate moisture content. The reflectance values of the TVDI show over the study area generally low, with the values ranging from -0.07 to 1.37. The statistical tests of TVDI values and gravimetric soil moisture values presented a positive correlation with RMSE= 0.17. The results of the study can give insight of better hydrological modeling, management of agriculture and policy making. However, further research is required to validate the methodology over a larger geographical area and to evaluate the preciseness of the assessed SSM at various depths of the soil.

Spatial variability of dendrometric parameters in a native tree Mabea fistulifera Mart. and its relationship with soil physical properties

Mabea fistulifera Mart. (common name: canudo-de-pito) belongs to the Euphorbiaceae family and is a native tree species in Brazil showing a high potential to recover degraded lands. This study aimed to evaluate the distribution and spatial correlation between the dendrometric parameters of the M. fistulifera plants and the physical attributes of the soil through geostatistics. The study was carried out at the Paulista State University (UNESP), in the city of Selvíria, MS, Brazil, in a typical dystrophic Red Oxisol with a clayey texture. The following properties were analyzed: for soil, penetration resistance, gravimetric moisture, particle density, and, for plants, circumference and diameter at breast height (measured at 130 cm above the ground), tree height, and total volume of the tree. An experiment grid was introduced with 35 sample points spaced 13 m x 13 m. Two soil samples were taken at each point, at 0.00 - 0.10 m and 0.10 - 0.20 m depth. Descriptive data analysis and spatial dependence analysis were carried out through semivariogram adjustments and kriging maps. The dendrometric properties of the species M. fistulifera and the soil gravimetric moisture content showed spatial dependence. The spherical semivariogram model best explained the spatial structure of circumference at breast height, diameter at breast height, tree volume, and soil gravimetric moisture. There was an emphasis on the correlation between the total volume of the tree as a function of the diameter at breast height, showing a moderate spatial dependence. Furthermore, the tree diameter at breast height proved to be a good indicator for determining the total height of the M. fistulifera tree.

Evaluation of pedotransfer functions to estimate some of soil hydraulic characteristics in North Africa: A case study from Morocco

Soil hydraulic properties are an important factor to optimize and adapt water management for a given crop. Pedotransfer functions (PTFs) present a solution to predict soil variables such as hydraulic properties, using fundamental soil properties. In this research, we compared two sources of soil information: iSDAsoil data and field data, in four regions in Morocco. We then used this data to evaluate published data and developed new PTFs using soil information to estimate soil gravimetric moisture content at saturation (w0), field capacity (w330) and permanent wilting point (w15000). A total of 331 samples were collected from four regions: Doukkala, Gharb-Loukous, Moulouya and Tadla. The data was divided into calibration and validation datasets. For development of different PTFs, we used simple linear regression, multiple linear regression, regression tree, Cubist algorithm, and random forest approaches. PTFs developed by Dijkerman (Geoderma, 1988, 42, 29–49) presented the best performance, showing lower RMSE, Bias and MAE compared to other PTFs. Using multiple linear regression to develop PTFs, models based on clay, silt and soil organic matter as input variables showed the best performance after calibration (R2 of 0.590, 0.785, 0.786 for w0, w330, and w15000, respectively). Regarding the techniques based on machine learning, random forest showed the best performance after calibration compared with other algorithms (R2 of 0.930, 0.955, 0.954 for w0, w330, and w15000, respectively). PTFs represent a low cost and easy technique to estimate soil hydraulic properties, to improve water management efficiency for the farmers.

Effect of Different Mulches and Planting Beds On Growth and Yield of Bitter Gourd in Coastal Saline Soils

A field experiment was carried out in coastal saline soils of Bangladesh in 2018, 2019 and 2020 to evaluate the effect of different kinds of mulches and planting beds to reduce salt accumulation in the soil and to increase the yield of bitter gourd. The experiment was laid out in a factional randomized complete block design having two kinds of mulches: viz. rice straw mulch and polythene mulch along with no mulch control and three kinds of planting beds: viz. convex bed, flat bed and concave bed. The polythene mulch treatment had highest fruit yield of 21.86, 27.20 and 20.49 t ha-1, respectively followed by rice straw mulch and no mulch control treatment in 2018, 2019 and 2020, respectively. Polythene mulch reduced electrical conductivity of soil by 31% and soil sodium content by 42% compared to no mulch treatment. Polythene mulch also increased soil temperature and gravimetric soil moisture content which promoted plant growth. The convex bed method produced highest mean fruit yield of 18.37, 20.06 and 15.18 t ha-1 followed by the flat bed and concave bed method in the year 2018, 2019 and 2020, respectively. Combined use of polythene mulch and convex bed planting could provide greatest benefit to the farmers. The polythene mulch along with convex bed planting is therefore, recommended to get higher yield of bitter gourd and to reduce detrimental effect of salt on crop in coastal saline soils. Bangladesh J. Agril. Res. 46(1): 71-87, March 2021

Higher density swards have a higher load bearing capacity

AbstractIncreasing the water table is an important pathway to reduce greenhouse gas emissions from peaty soils. In order to continue utilizing grasslands on these soils, methods to increase load bearing capacity at a higher water table are sought. The objective of this study was to assess the effect of sward density on load bearing capacity, measured as both penetration resistance and the newly developed method measuring load bearing capacity through impact depth. Simultaneously, a new method to measure load bearing capacity through impact depth was developed. The study was conducted between 2018 and 2020 on 14 different farms in the western peat meadow district in the Netherlands, where the sward density measured with the point quadrat method ranged from 24% to 92%. Swards with a higher sward density showed a higher load bearing capacity than swards with a lower density. In a modelling approach an increase from 30% sward density to 90% sward density could result in a lengthening of the grazing season between three (11%) and six weeks (22%), depending on the soil moisture conditions. Load bearing capacity was also highly correlated with gravimetric soil moisture content as wetter conditions lowered load bearing capacity. In order to capture load bearing capacity more accurately a new measurement device was constructed which represents treading cattle. Both output (impact depth) and method (resembles cow hoof) are close to practice, which makes it a very suitable method allowing for easy interpretation by farmers.

Comparison of Recent Remote Sensing Data Using an Artificial Neural Network to Predict Soil Moisture by Focusing on Radiometric Indices

Remote sensing data is widely used as a common variable for digital soil mapping estimating models. The aim of this study, quite recently made available to researchers Operational Land Imager 2 (OLI–2) have structure Landsat 9 and Landsat 8 (OLI) and Sentinel 2A (MSI) to compare the performance of soil moisture estimation in multi-layer perceptron network (MLP) artificial intelligence algorithm of image data. The working area is 886.78 km2 and soil sampling was performed at 66 points for gravimetric soil moisture determination. In addition, after the satellite images were pre-processed, Soil Adjusted Vegetation Index (SAVI) and Normalized Difference Moisture Index (NDMI) were calculated. Landsat 9 (OLI-2) based SAVI and NDMI showed a moderately significant positive correlation relationship with gravimetric soil moisture (rSAVI-SM=0.62, rNMDI-SM=0.44). The relationship between Landsat 8 (OLI) (rSAVI-SM=0.57, rNDMI-SM=0.11) and Sentinel 2A (MSI) (rSAVI-SM=0.42, rNDMI-SM=0.27) based radiometric indices and soil moisture was lower than Landsat 9 (OLI-2). RMSE values of MLP models were found to be respectively 0.79, 1.16 and 1.17 for Landsat 9 (OLI-2), Landsat 8 (OLI) and Sentinel 2A (MSI). Our results showed that with an Operational Land Imager (OLI-2) and near and short-wave infrared wavelengths improvements to multispectral imaging have improved soil moisture estimation success.

Impact of wildfire on soil carbon and nitrogen storage and vegetation succession in the Nanweng'he National Natural Wetlands Reserve, Northeast China

In boreal regions, wildfires have a major impact on vegetation and permafrost. The ecosystem-protected Xing’an permafrost is sensitive to warming climate and wildfires, particularly on the southern margin of boreal coniferous forest and patchy permafrost zone. However, it remains unclear how fire disturbances are linked with changes in ecosystem composition and soil nutrients in the permafrost zones of Northeast China. Here, 13 years after the fire in the Yile’huli mountain knots, we investigated the parameters like vegetation cover, ground temperatures, active layer thickness, and soil carbon and nitrogen storage at burned and unburned sites of shrub wetlands. The fire resulted in ground warming of 0.1–5.0 °C at depths of 1.0–20.0 m and active layer deepening of 0.5 m, and gravimetric soil moisture content increasing of 26%-266%. Fire also increased the number of herbs and tall shrubs. After the fire, graminoids and tall shrubs increased significantly, and the species of herbs increased by five species. However, dwarf shrubs like Ledum palustre and Vaccinium uliginosum were missing from the burned site. A massive loss of total organic carbon (TOC) (248.40 t C/hm2) and nitrogen (TN) (11.87 t N/hm2) was observed by comparing their storage at burned and unburned sites. These results highlighted that the post-fire responses of vegetation cover and TOC and TN storage were dependent on the thermal regimes of near-surface permafrost and active layer, recovery of vegetation and organic layer, and soil moisture content. This study can provide an important reference for carbon storage and emission in boreal shrub wetlands under a warming climate and increasing fire disturbances.

Development of Electronic Wetting Front Detector for irrigation scheduling

Water is an important for plants growth, development and generating good crop yield. The rate of consumption of water by the crops depends on the type of crop, stage, soil characteristics and local climatic conditions. Irrigation scheduling takes care of when and how much water is to be applied to a crop during the entire the growing period. This calls for optimal water management, equipment, and labor in developing countries. An Electronic Wetting Front Detector was developed for irrigation scheduling by estimating the optimum soil moisture content present at the root-zone depth of the crops. The device measures the volumetric water content using electrical resistance of the soil and alarms the growers for the need of irrigation through its LED indicator installed in the device. The device was further calibrated and validated in the laboratory as well as in the farmer's field near Central University of Jharkhand, Ranchi, India. The device was tested and calibrated in the laboratory with the soil present in a container along with the Tensiometer fixed in the same container of soil and the data of the gravimetric soil moisture content was compared for accuracy by using regression analysis. Similarly, validation was also performed in the farmer's field and the result showed that it has a higher accuracy for measuring the soil water content. The developed device interprets readings from the sensors and alarms the farmer of overirrigation or underirrigation based on the sensors data. The EWFD developed is new intervention since the device could be easily used by farmer to understand the soil water content and schedule irrigation with respect to crop needs by alarming the farming. Therefore, the device may prove a valuable contribution to the list of other scientific instruments used for measuring the soil water content. The device can be used perfectly for scheduling irrigation.

Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region).

The implementation of the sustainable management of the interaction between agriculture and the environment requires an increasingly deep understanding and numerical description of the soil genesis and properties of soils. One of the areas of application of relevant knowledge is digital irrigated agriculture. During the development of such technologies, the traditional methods of soil research can be quite expensive and time consuming. Proximal soil sensing in combination with predictive soil mapping can significantly reduce the complexity of the work. In this study, we used topographic variables and data from the Electromagnetic Induction Meter (EM38-mk) in combination with soil surface hydrological variables to produce cartographic models of the gravimetric soil moisture for a number of depth intervals. For this purpose, in dry steppe zone conditions, a test site was organized. It was located at the border of the parcel containing the irrigated soybean crop, where 50 soil samples were taken at different points alongside electrical conductivity data (ECa) measured in situ in the field. The modeling of the gravimetric soil moisture was carried out with the stepwise inclusion of independent variables, using methods of ensemble machine learning and spatial cross-validation. The obtained cartographic models showed satisfactory results with the best performance R2cv 0.59–0.64. The best combination of predictors that provided the best results of the model characteristics for predicting gravimetric soil moisture were geographical variables (buffer zone distances) in combination with the initial variables converted into the principal components. The cartographic models of the gravimetric soil moisture variability obtained this way can be used to solve the problems of managed irrigated agriculture, applying fertilizers at variable rates, thereby optimizing the use of resources by crop producers, which can ultimately contribute to the sustainable management of natural resources.

A First Study of Urginea maritima Rings: A Case Study from Southern Jordan

Vegetation rings are a common pattern in water-limited environments and mostly occur in clonal plants. This study presents, for the first time, rings of the geophyte species Urginea maritima. The rings, typically 40–90 cm in diameter, are abundant in the sandy environment of Little Petra and Wadi Rum, in the southern Jordanian drylands. Soil properties were studied in the rings’ center, periphery, and matrix. Soil-water volumetric content was significantly higher in the rings’ periphery than in the center and matrix. The soil organic carbon was highest in the periphery, intermediate in the center, and lowest in the matrix. At the same time, the soil texture, hydraulic conductivity, and gravimetric moisture content at the hygroscopic level were similar in the three microenvironments. According to the results, a possible ring formation mechanism is the soil-water uptake mechanism, which results in competition between the plants at the periphery and those in the center and is generally attributed to plants with large lateral root zones. Numerical simulations of a mathematical model implemented in this study support the soil-water uptake mechanism. A second possible mechanism is negative plant-soil feedback due to the accumulation of dead biomass and its consequent decomposition, with the resultant release of autotoxic compounds. It is possible that several mechanisms occur simultaneously and synergistically affect the formation of U. maritima rings.

Assessing Carbon Storage Potential of Forested Wetland Soils in Two Physiographic Provinces of Northern Virginia, USA

This study assessed the soil carbon storage potential in terms of the total carbon (TC) and total carbon stocks (TC stocks) and associated soil physicochemical properties (i.e., soil pH, bulk density (Db), and gravimetric soil moisture (GSM)) for four forested wetlands in the urbanized region of Northern Virginia (NOVA). The study sites were balanced between the two physiographic provinces of the region (Piedmont vs. Coastal Plain); at each site, soils were sampled and analyzed (n = 180) at three depth intervals (0–10 cm; 10–20 cm; 20–30 cm). There was no significant difference in TC stocks between physiographic provinces (p > 0.05); however, wetland soils had higher TC contents at the Coastal Plain (4.32 ± 0.41%) than in Piedmont (2.57 ± 0.22%; p < 0.05). Both Db and GSM significantly differed by physiographic province and were highly correlated to TC, indicating that the TC variability is strongly explained by Db (R2 = 0.38) or GSM (R2 = 0.39), respectively (p < 0.01 for all). These outcomes highlight the capacity of urban forested wetlands to store carbon, especially in their topsoil (top 10 cm). Elucidating the carbon storage potentials of forested wetlands in an urbanized landscape may assist with future efforts to combat urban carbon emissions.

Comparing the reliability of two soil moisture probes for high clay content NSW soils

The accurate measurement of soil moisture can be a time-consuming task. Soil moisture is highly variable, and it can be difficult to capture spatially and temporally. While remote sensing has become a popular and ever-improving tool, on-ground measurement is required for both calibration, validation, and evaluation of any emerging technology. Many applications commonly require or use a relative value of soil moisture to assess the temporal persistency of moisture regimes across catchments or agricultural landscapes. This paper compares on-ground indirect soil moisture measurements from two commonly available manufacturer-calibrated in-situ measurement devices against gravimetric soil moisture data. The devices used are a Delta T Theta Probe and a Campbell Scientific CS659 while the gravimetric soil moisture data are from soil cores collected in 2014, 2015 and 2018 (308 samples) with a range of soil moisture states from wilting point to field capacity. The gravimetric and probe measurements returned R values of approximately 0.8 for 2014 and 2015. A decrease in correlation (to approximately 0.3 and 0.5) was observed for the 2018 data. The manufacturer-calibrated probe measurements did not provide a 1:1 relationship with the gravimetric soil moisture data and require calibration for bias correction. However, results show that either the Theta Probe or CS659 are reliable and comparable to the gravimetric results in most conditions. Best results will be obtained by using appropriate techniques and knowing the limitations of devices. The calibration equations developed in this study are useful for others for both understanding and measurement of soil moisture.