Apparent Electrical Conductivity Research Articles

How do soil and topographic drivers determine tree diameter spatial distribution in even aged cork oak stands installed in average to high productivity areas

AbstractLocal terrain or microsite conditions influence the development of trees, particularly at early ages. These conditions might be described by edaphic or topographic variables. We mapped soil and topographic variables from four even-aged and even-spaced cork oak plantations located in two climatically distinct Portuguese regions. The major goal of this research was to understand the relation between soil and topographic fine-scale conditions and tree growth expressed by diameter without cork annual growth (idu). The methodology consisted in (1) analysing the spatial variability and autocorrelation of idu; (2) modelling idu with ordinary least squares (OLS) regressions; (3) comparing with spatial modelling of idu, incorporating spatial autocorrelation. The driest stands A and B, exhibited weaker spatial autocorrelation, distributed in smaller clusters (R2 < 0.03, OLS models), while stands C (R2 = 0.18, OLS models) and D (R2 = 0.11, OLS models) showed higher predictive capacity. Spatial models increased R2 scores, keeping most variables from OLS models and accounting for spatial autocorrelation. A + B + C + D OLS model obtained an R2 = 0.34 and respective spatial model R2 = 0.58. Apparent electrical conductivity at 0.5 (ECa0.5) and 1 m of soil depth, slope, elevation and topography position index were included as predictors (OLS), but only ECa0.5, slope and elevation were selected in the spatial model. Models were fitted using average to high productivity stands and should be used cautiously outside this range. Local terrain conditions determine the growth of young cork oak trees. Mapping soil and topographic variables before establishing new plantations may identify limiting microsite conditions where using cork oak species is not suitable due to low growth rates expectations.

Time stability of soil volumetric water content and its optimal sampling design in contrasting forest catchments

Characterizing the temporal variation across a forest catchment soil volumetric water content (VWC) with different environmental conditions at different depths for different seasons requires a robust point-scale sampling strategy that balances monitoring cost with predicting accuracy. In the study, the most time-stable locations (MTSLs) using the time-stable concept and the minimum number of required samples (NRS) based on random selection method were studied to explore the optimal sampling design for catchment-mean VWC with an acceptable error (the relative bias (RBIAS) less than 10 %). VWC monitoring was conducted at approximately 25 plots throughout the year in five forest catchments with varying areas and VWC status. Results revealed that the number of MTSLs for mean-VWC estimation with RBIAS within 10 % was directly related to the spatial variability of VWC. Specifically, one MTSL was feasible for accurately estimating mean VWC at different depths in high-VWC catchments with area around or less than 100 ha; one MTSL was adequate for each depth in the catchment with moderate-level VWC; whereas more than one MTSL were necessary for each depth in low-VWC catchment with a larger are of 1800 ha. To identify the MTSL for each catchment, the location with mean VWC was typically situated at the mid-elevation or soil apparent electrical conductivity (ECa) at a certain spatial scale. The NRS for catchment-mean VWC could be determined based on the combination of VWC status, its temporal stability, and potential catchment size, and could be ranked based on the spatial variation of each catchment. If the MTSL cannot be guaranteed for a catchment, the NRS might be a better alternative for optimum sampling design. These finding could be useful in providing guidelines for optimizing VWC monitoring in forest catchments by sampling at a few selected locations representative of the catchment-scale behavior.

Predicting wetland soil properties using machine learning, geophysics, and soil measurement data

Abstract Purpose Machine learning models can improve the prediction of spatial variation of wetland soil properties, such as soil moisture content (SMC) and soil organic matter (SOM). Their performance, however, relies on the quantity of data used to train the model, limiting their use with insufficient data. In this study, we assessed the use of synthetic data constrained by limited field data for training an eXtreme Gradient Boosting (XGBoost) algorithm used to predict the distribution of soil properties based on geophysical measurements constrained by soil samples. Materials and methods A spatial distribution of soil apparent electrical conductivity (ECa) and laboratory measurements of SOM and SMC from twenty-two core samples were acquired at the St. Michael restored wetland near Defiance, Ohio. The correlations between ECa, SOM, and SMC were explored for predicting the spatial distribution of SOM and SMC. We used a Beta Variational AutoEncoder (β-VAE) approach to synthetically generate over 70,000 training data from the original twenty-two data from soil cores. The training data samples were taken from the latent space. The XGBoost algorithm was then trained on the β-VAE generated data and used to predict the spatial distribution of SOM and SMC at the site. We also validated the accuracy of the XGBoost predictions using an original holdout model validation technique. Results and discussions The generated synthetic data using the β-VAE include both soil attributes and ECa, which are larger and more diverse than the original training set with an absolute mean reconstructed error for SMC and SOM ranging from 0.018 to 0.022 and 0.026 to 0.041, respectively. This indicates that the β-VAE successfully generated a realistic synthetic dataset and overcame the technical barrier of using limited datasets. In addition, using generated data to expand the original training data helps the XGBoost model make more accurate predictions compared to training on the original data. The XGBoost prediction performance yielded average Lin’s concordance correlation coefficient (LCCC) values of 0.82 and 0.85 for SOM and SMC and a ratio of performance to deviation (RPD) values of 1.92 and 2.22 respectively, indicating a good performance. Conclusions This study validated the use of β-VAE to successfully generate synthetic wetland soil datasets with attributes of the original field data that can be effectively used to train the machine learning XGBoost model. The proposed framework offers an efficient solution for mapping the spatial variability of soil property in data-scarce wetland soil environments.

Cowpea storage: Can small farmers use polyethylene terephthalate bottles and wood ash as an alternative to avoid damage caused by Callosobruchus maculatus?

The aim of this study was to investigate the effectiveness of the combination of Polyethylene terephthalate (PET) bottles and ash in controlling Csllosobruchus maculatus and maintaining the quality of grains of cowpea cultivars. The following storage structures were used: PET bottle in airtight conditions and glass jars in non-airtight condition (control). The wood ash used in this research came from the burning process of eucalyptus (Eucalyptus grandis) wood. The experiment was carried out in a 2 × 2 × 4 factorial scheme, with two cowpea cultivars (BRS Tumucumaque and BRS Guariba), two forms of storage (PET bottle – airtight and glass jars – non-airtight) and four doses of eucalyptus wood ash (0.0; 1.0; 2.0 and 4.0 kg t−1), in a completely randomized design with three replications. Each plot contained 500 g of cowpea. The effectiveness of treatments was measured by the instantaneous population growth rate (ri) of C. maculatus. In ri, 35 adult, non-sexed insects were used. Adult progenies were counted after 60 days of storage. For qualitative grain evaluation the following characteristics were analyzed: water content, germination, apparent specific mass and electrical conductivity. The results obtained in this study confirm for the first time that the combined use of PET and ash do not affect grain quality and it is efficient in the management of C. maculatus in cowpea cultivars. In the present study, a significant reduction in the emergence of C. maculatus was observed in the BRS Tumucumaque, making it possible that this cultivar has some potential for resistance to bruchid.

Mapping saltwater intrusion via Electromagnetic Induction (EMI) for planning a Managed Aquifer Recharge (MAR) facility in Maltese Island

In coastal areas, saltwater intrusion causes a depletion of the resource by reducing potable and irrigation freshwater supplies and causing severe deterioration of groundwater quality. This trend is observed in Pwales Valley, in the North part of Malta where the management of water resources plays a crucial role for the environmental sustainability of the area, given the importance of intensive agricultural activity along this valley. In order to tackle such phenomenon, actions or adaptation measures against climate change are strongly required. For example, Managed Aquifer Recharge (MAR) is an increasingly important water management strategy to maintain, enhance and secure stressed groundwater systems and to protect and improve water quality. To accurately plan a Managed Aquifer Recharge scheme, it is crucial to define a hydrogeological model of the studied area, with the use of traditional hydrogeological measurements and innovative unconventional techniques. In recent years, Electromagnetic Induction measurements, based on induction of em fields, have been increasingly used for investigating the saltwater intrusion dynamics due to their high sensitivity to the salinity. In the study area of Pwales Valley, a Managed Aquifer Recharge scheme is being planned and, for this aim, a hydrogeological model has been developed through an Electromagnetic Induction survey. More than 20,000 apparent electrical conductivity (ECa) data were collected to generate a quasi 3D high-resolution model of electrical conductivity of the Pwales Valley. The results highlighted the spatial extension of the tongue-shape salt water intrusion from east to west along the valley, as well as some geological-hydrogeological peculiarities such as the thickness of the salt wedge and the irregular top surface of the bottom impermeable layer, otherwise undetectable with other direct techniques at the field scale resolution. The approach was confirmed to be a useful tool for an effective hydrogeological characterisation, essential for planning adaptation measures to a changing climate, such as the implementation of a Managed Aquifer Recharge scheme.

Implementation of Proximal and Remote Soil Sensing, Data Fusion and Machine Learning to Improve Phosphorus Spatial Prediction for Farms in Ontario, Canada

One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P. Seven spatially variable fields located in Ontario, Canada are clustered into two zones; four fields are located in eastern Ontario and three others are located in western Ontario. This study compares Bayesian Additive Regression Trees (BART), Support Vector Machine regressor (SVM), and Ordinary Kriging (OK), along with novel data fusion concepts, to analyze integrated high-density spatial data layers related to spatial variability in soil available P. Feature selection and interaction detection using BART variable selection and Recursive Feature Elimination (RFE) for SVM were applied to 42 predictors, including soil-vegetation indices derived from PlanetScope multispectral imagery, high-density apparent soil electrical conductivity (ECa), and high-resolution topographic attributes derived from DUALEM-21S and a Real-Time Kinematic (RTK) global navigation satellite systems (GNSS) receiver, respectively. Modeling spatial heterogeneity of soil available P with BART showed higher accuracy than SVM and OK in both zones of this study when trained and tested on ground truth data from clusters of farms. A BART variable selection approach resulted in six auxiliary predictors of soil available P in the eastern zone, while only four predictors were selected to predict P in the western zone. RFE for SVM resulted in models with 15 and 12 auxiliary predictors in the eastern and western Ontario zones. Topographic elevation was the most influential predictor of soil available P in both zones. Compared with the SVM and OK methods, BART exhibited lower average RMSE values for individual fields of 1.86 ppm and 3.58 ppm across the eastern and western Ontario zones, respectively, along with higher R2 values of 0.85 and 0.83, respectively. In contrast, SVM had RMSE values for individual fields in the eastern and western Ontario zones, respectively, averaging 5.04 ppm and 7.51 ppm and R2 values of 0.27 and 0.43. RMSE values for soil available P in individual fields across the eastern and western Ontario zones averaged 4.77 ppm and 7.81 ppm, respectively, with the OK method, while R2 values averaged 0.19 and 0.44. The selection of suitable auxiliary predictors and data fusion, combined with BART spatial machine learning algorithms, have potential to be a useful tool to accurately estimate spatial patterns in soil available P for agricultural fields in Ontario, Canada.

Monitoring and predicting corn grain quality on the transport and post-harvest operations in storage units using sensors and machine learning models

Monitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon dioxide sensors was developed to determine the equilibrium moisture content and the respiration of the grain mass. These same variables were monitored during storage. At drying process, the drying air and grain mass temperatures, as well as the relative humidity, were monitored. For the prediction of the physical and physical–chemical quality of the grains, the results obtained from the monitoring were used as input data for the multiple linear regression, artificial neural networks, decision tree, and random forest models. A Pearson correlation was applied to verify the relationship between the monitored and predicted variables. From the results obtained, we verified that the intergranular relative humidity altered the equilibrium moisture content of the grains, contributing to the increased respiration and hence dry matter losses along the transport. At this stage, the artificial neural network model was the most indicated to predict the electrical conductivity, apparent specific mass, and germination. The random forest model satisfactorily estimated the dry matter loss. During drying, the air temperature caused volumetric contraction and thermal damage to the grains, increasing the electric conductivity index. Artificial neural network and random forest models were the most suitable for predicting the quality of dry grains. During storage, the environmental conditions altered the moisture contents causing a reduction in the apparent specific mass, germination, and crude protein, crude fiber, and fat contents. Artificial neural network and random forest were the best predictors of moisture content and germination. However, the random forest model was the best predictor of apparent specific mass, electrical conductivity, and starch content of stored grains.

Temporal Stability of Management Zone Patterns: Case Study with Contact and Non-Contact Soil Electrical Conductivity Sensors in Dryland Pastures.

Precision agriculture (PA) intends to validate technological tools that capture soil and crop spatial variability, which constitute the basis for the establishment of differentiated management zones (MZs). Soil apparent electrical conductivity (ECa) sensors are commonly used to survey soil spatial variability. It is essential for surveys to have temporal stability to ensure correct medium- and long-term decisions. The aim of this study was to assess the temporal stability of MZ patterns using different types of ECa sensors, namely an ECa contact-type sensor (Veris 2000 XA, Veris Technologies, Salina, KS, USA) and an electromagnetic induction sensor (EM-38, Geonics Ltd., Mississauga, ON, Canada). These sensors were used in four fields of dryland pastures in the Alentejo region of Portugal. The first survey was carried out in October 2018, and the second was carried out in September 2020. Data processing involved synchronizing the geographic coordinates obtained using the two types of sensors in each location and establishing MZs based on a geostatistical analysis of elevation and ECa data. Although the basic technologies have different principles (contact versus non-contact sensors), the surveys were carried out at different soil moisture conditions and were temporarily separated (about 2 years); the ECa measurements showed statistically significant correlations in all experimental fields (correlation coefficients between 0.449 and 0.618), which were reflected in the spatially stable patterns of the MZ maps (averaging 52% of the total area across the four experimental fields). These results provide perspectives for future developments, which will need to occur in the creation of algorithms that allow the spatial variability and temporal stability of ECa to be validated through smart soil sampling and analysis to generate recommendations for sustained soil amendment or fertilization.

Potential of ground-penetrating radar to calibrate electromagnetic induction for shallow soil water content estimation

Ground-penetrating radar (GPR) and electromagnetic induction (EMI) are used to determine and map soil water content (SWC) in the agricultural landscape. While GPR provides a straightforward estimation of SWC, EMI requires site-specific calibrations mainly based on point-scale measurements such as time domain reflectometry (TDR). However, there is a significant difference in the measurement volumes between EMI and point-scale TDR measurements. This study aimed to enhance the calibration of EMI for estimating SWC in the agricultural landscape by leveraging the larger sampling volumes provided by GPR. Apparent electrical conductivity (ECa) from a multi-coil EMI sensor and dielectric constant from GPR with two center frequencies (500 MHz and 250 MHz) were collected as soil proxies along with TDR measurements. Calibration models were developed under irrigated conditions and the model evaluation was conducted under natural moisture conditions. Correlations were assessed between the proxies from GPR and EMI with TDR-derived SWCs. Simple linear regression (SLR) models were developed between EMI and GPR data to predict SWC. Strong positive correlations (r ≥ 0.80) were observed between the proxies (GPR and the shorter inter-coil spacing (0.32 m) of EMI) and TDR-measured SWC. ECa data from vertical and horizontal coil orientations of the shorter inter-coil spacings were selected to develop SLR models with GPR. The SLR models with the GPR 500 MHz frequency showed a higher coefficient of determination (R2 > 0.70) for both coil orientations of EMI. Results showed the potential of using GPR to calibrate EMI for shallow SWC estimation (below 0.5 m). Nevertheless, the EMI estimated SWCs were not effectively verified with GPR-estimated SWCs, which could be attributed to specific site conditions in the study area. Further research must focus on improving the calibration and model evaluation by incorporating the variability of other soil parameters (soil porosity, pore-water conductivity, and soil salinity) that may affect the SWC–ECa relationship.

Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering

IntroductionThe mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. MethodsMulti-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.Results and discussionThe method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.

Time-Lapse Electromagnetic Conductivity Imaging for Soil Salinity Monitoring in Salt-Affected Agricultural Regions

In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using electromagnetic induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated soil apparent electrical conductivity (ECa) measurements were taken using an electromagnetic conductivity meter (CMD2) and inverted (using a time-lapse inversion algorithm) to generate electromagnetic conductivity images (EMCIs), representing soil electrical conductivity (σ) distribution. This process involved converting EMCI data into salinity cross-sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated accurate prediction ability of soil salinity with an R2 value of 0.81. The soil salinity cross-sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmers’ income.

Delineation of Soil Management Zones and Validation through the Vigour of a Fodder Crop

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties.

Autocorrelação espacial de populações de plantas de soja e atributos físicos de um Latossolo

ABSTRACT Among the necessary phytotechnical adjustments, the correct recommendation of the plant population of each soybean cultivar is of paramount importance for success in crop production. Thus, the objective of this study was to analyze the distribution and spatial autocorrelation of the soybean plant population according to the spatial variability of the physical and chemical attributes of Oxisol in the Brazilian Cerrado region. The attributes studied were plant population, soybean grain yield, apparent soil electrical conductivity, soil organic matter content, cation exchange capacity, hydrogenionic potential, and clay content in the soil. The plant population varied inversely as a function of apparent soil electrical conductivity. The areas with the highest values of apparent soil electrical conductivity, soil organic matter content, cation exchange capacity, and clay content had the lowest plant populations. Soybean yield was not influenced by increase or decrease in the plant population. Thus, it is economically viable to plant with lower plant density to improve the farmer’s profit margin.

Improving a regional peat thickness map using soil apparent electrical conductivity measurements at the field-scale

IntroductionThe increased adoption of proximal sensors has helped to generate peat mapping products: they gather data quickly and can detect the peat-mineral later boundary. A third layer, made of sedimentary peat (limnic layers, gyttja), can sometimes be found in between them. This material is highly variable spatially and is associated with degraded soil properties when located near the surface.MethodsThis study aimed to assess the potential of direct current resistivity measurements to predict the maximum peat thickness (MPT), defined as the non-limnic peat thickness, to facilitate soil conservation and management practices at the field-scale. The results were also compared to a regional map of the MPT from a previous study used and also tested as a covariate. This study was conducted in a shallow (MPT = 8-138 cm) cultivated organic soil from Québec, Canada. The MPT was mapped using the apparent electrical conductivity (ECa) from a Veris Q2800, and a digital elevation model, with and without a regional MPT map (RM) as a covariate to downscale it. Three machine-learning algorithms (Cubist, Random Forest, and Support Vector Regression) were compared to ordinary kriging (OK), multiple linear regression, and multiple linear regression kriging (MLRK) models.Results and discussionThe best predictive performance was achieved with OK (Lin’s CCC = 0.89, RMSE = 13.75 cm), followed by MLRK-RM (CCC = 0.85, RMSE = 15.7 cm). All models were more accurate than the RM (CCC = 0.65, RMSE = 29.85 cm), although they underpredicted MPT > 100 cm. Moreover, the addition of the RM as a covariate led to a lower prediction error and higher accuracy for all models. Overall, a field-scale approach could better support precision soil conservation interventions by generating more accurate management zones. Future studies should test multi-sensor fusion and other geophysical sensors to further improve the model performance and detect deeper boundaries.

Revealing subsurface structure at the former municipal solid waste disposal site Leuwi Gajah of Cimahi Town by using geophysical data: a preliminary result

Municipal solid waste disposal site Leuwi Gajah, situated in Cimahi, is near Bandung City. It was a vast open dumping site closed in 2006 due to a harmful geological hazard in 2005, causing hundreds of victims’ deaths. After 16 years of the tragedy, Bandung local government planned for the former municipal solid waste to become a food field with high technology that benefits the community around the area. We applied the Electromagnetic Induction (EMI) technique to image the study area’s subsurface structure as a non-invasive geophysical method. We collected apparent electrical conductivity (ECa) and apparent susceptibility data from the study site in a gridded pattern to map their distribution using EM-38 MK-2 technology. We chose three sites to map those parameter values: site 1, site 2, and site 3. Site 1 is cassava cultivation land by the community. Site 2 is the boundary between former waste and non-waste land, and site 3 is the same land as site 1 but has a steeper slope (20°). The selection of sites 1 and 3 compares land at higher and lower elevations affected by the former landfill landslide. Site 2 shows the difference in characteristics between waste and non-waste land.Our research in the study area delivers good results. First, we can identify the boundaries of waste and non-waste material, as shown in the low susceptibility value at site 2. This identification is probably related to the thermo-remanent magnetization that results from burning waste. On the other hand, water content influences the distribution of ECa values. Moreover, topography has effects that cause water accumulation in certain areas at sites 1 and 3. These findings recommend using other geophysical methods to detect the boundary between the waste deposit body and underlying bedrock and to characterize the waste body.

Comparing quasi-3D soil moisture derived from electromagnetic induction with 1D moisture sensors and correlation to barley yield in variable duplex soil

Farms in Western Australia (WA) are highly variable in soil texture and water retention capacity; therefore, information of soil moisture spatial variability in the field is important for effective crop management. In practice, farmers often rely on point sensors to determine soil moisture in their fields for crop planning. The limitation of point measurements to account for spatial variability highlights the need to develop methods that can assess soil moisture across variable broadacre fields. This information can enable more effective site-specific crop management practices. In this study, we used a mobile non-intrusive electromagnetic induction (EMI) sensor to map soil apparent electrical conductivity (ECa) to predict soil moisture levels at three different depths (0–0.5, 0.5–0.8 and 0.8–1.6 m) across the field and compared it with barley yield production. To convert the depth specific electrical conductivity (EC) from EMI surveys into soil moisture estimates, calibrations between electrical resistivity tomography (ERT) to volumetric moisture were used. These calibrations were developed for the different soil textural classes of the field, with R2 of 0.97–0.99. There was no significant difference between the estimated soil moisture from EMI surveys and the point moisture measurements obtained from soil moisture sensors and soil sample extracts, with Pearson R values of 0.64 for 0–0.5 m depth and 0.73 for 0.5–0.8 m depth. Additionally, correcting the temperature drift relative to the measured soil temperature on EMI surveys by 10% in dry season and 31% in wet season, improved the moisture estimation results. This study successfully demonstrated the effectiveness of spatial soil moisture estimation using EMI sensor in a field characterized by horizontal and vertical soil texture variability. However, no significant correlation was found between the mapped soil moisture or soil texture with barley yield. This may be due to relatively high initial moisture levels resulting from two years of fallow rotation.

Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model.

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest in incorporating technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, south of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture; pH; organic matter-OM; phosphorous-P2O5; potassium-K2O; the sum of the exchange bases-SEB; and cation exchange capacity-CEC), which were determined from 20 soil samples taken in the different MZs. Validation was also performed using Rasch measures, which were defined based on the formulation of the objective and probabilistic Rasch model, integrating the information from the aforementioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. Finally, an additional validation was conducted using a vegetation index to consider the plant response, which was different in each MZ. The use of a non-contact sensor to measure ECa constitutes an efficient technological tool for implementing site-specific management in viticulture, which allows for the improvement of decision-making processes by considering the inherent spatial variability of the soil.

Pedology in Precision Agriculture from a Brazilian context

Precision agriculture (PA) is advancing in Brazil concerning several crops, mainly for medium to large-sized farms, occasionally evolving towards automation and digital agriculture. Soil knowledge is fundamental in this process, requiring studies on a detailed scale greater than 1:5,000, and demanding overcoming soil taxonomy concepts. This review article presents and discusses the most effective methods of PA from a soil management perspective, inducing advances for farming and producers in Brazil. Topography, electrical conductivity, proximal and remote sensing, and productivity work outlined soil mapping and their relationship to soil. The modeling of topographic variables through artificial intelligence offers new perspectives. The soil's apparent electrical conductivity can work at various depths, providing information about several pedological parameters. Finally, proximal, and remote sensing techniques could simulate different soil attributes, potentially integrating productivity data on studying plant attributes. Despite some differences, the four themes are complementary, and integrating data through geographic information systems results in a consistent option for defining management zones.

Integrating multisource information to delineate oasis farmland salinity management zones in southern Xinjiang, China

Soil salinization and the shortage of water resources have become important factors affecting the sustainable development of agriculture in the southern Xinjiang. Agricultural flooding in winter or spring is the main means to reduce the damage of secondary soil salinization in southern Xinjiang. However, the spatial heterogeneity of soil salinization is not considered in previous irrigation strategy. Excessive irrigation always leads to a serious waste of agricultural water resources. Irrigation zones delineation based on the degree of soil salinization is beneficial for the management of agricultural salinized land and the improvement of water use efficiency in arid areas. This study used 1599 sets of apparent electrical conductivity (ECa) data obtained before winter irrigation and after crop harvest to characterize soil salinity directly. And the spatial heterogeneity of soil salinity in the study area was analyzed directly using ECa values and statistical as well as geostatistical methods. Three main variables obtained from apparent electrical conductivity measurements (EC0.375, EC0.75, EC1.5), vegetation information (NDVI, GNDVI, SAVI), which indirectly reflected the degree of salinity, and environmental factors (Clay, Sand, Silt, DEM), which affecting the distribution of salinity, were used as auxiliary variables. Four zoning models including soil information, soil + vegetation information, soil information + environmental factors and soil + vegetation information + environmental factors were constructed. A multi-scale segmentation algorithm was used to delineate for four zone patterns and evaluate the results of management zones based on the different information. The results showed that there was a strong spatial heterogeneity of soil salinity in the study area, with the coefficients of variation of ECa all higher than 74 %. But the mean of coefficient of variation intra-zone in each zone after delineation was less than 24 %, which was significantly lower than the value before delineation. The zoning result based on only soil information was more fragmented than other zoning results, and the homogeneity intra-zone and heterogeneity among zones were both lower. This result was not desirable to the development and effective implementation of water resource allocation. Management zones delineation with additional vegetation information or environmental factors showed a better zoning result. Delineation using multisource data of soil, vegetation information and environmental factors not only had the highest homogeneity intra-zone and heterogeneity among zones, but also facilitated the implementation of variable-rate irrigation. Consequently, salinity management zoning, which integrates soil, vegetation information and environmental factors, will facilitate the implementation of efficient irrigation with variable irrigating rate.

Preparation and structural characterization of pullulan and whey protein isolate-based electrospun nanofiber

The impact of blended ratios was analyzed on the development and characterization of electrospun fibers using various combinations of pullulan (PUL) and whey protein isolate (WPI). The physical parameters like apparent viscosity, electrical conductivity and surface tension of blended solutions were improved after the addition of PUL in the blends, resulting in a uniform electrospun nanofiber. Scanning electron microscope (SEM) of nanofibers revealed bead-free, smooth and uniform structural conformation. Whereas, the uniformity of nanofiber was compromised by using higher concentrations of WPI in the blend. Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), X-ray diffraction (XRD) and Thermogravimetry analysis further confirmed the strong molecular interaction between polysaccharide and protein. The incorporation of WPI in PUL based electrospun nanofibers enhanced hydrophobicity, potentially facilitating the delivery of bioactive compounds. The WPI and PUL:WPI-based nanofibers demonstrated robust antioxidant potential, due to the presence of total phenolic contents (TPC). The stability of PUL interaction with WPI (β-lactoglobulin and α-lactalbumin) was confirmed through hydro-carbon (C–H) and hydrogen bond interactions by molecular docking. Finally, we can conclude that polysaccharide and protein-based nanofiber can be used to deliver bioactive compounds in food and pharmaceutical industries.