Search Results (9)

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 (EC_a), 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. Full article

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. Full article

Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (EC_a), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil attributes and evaluate the potential of site-specific management (SSM) of nutrients. Elevation and EC_a at multiple depths were collected from two experimental fields (referred as Field_Und, Field_Flat) in Normandin, Quebec, Canada. Soil samples were collected at two depths (0–0.05 m and 0.05–0.15 m) and analyzed for a range of soil properties. Statistical analyses of fruit yield, soil, and sensor data were used to characterize within-field variability. Fruit yield showed large variability in both fields (CV_Und = 54.4%, CV_Flat = 56.5%), but no spatial dependence. However, several soil attributes showed considerable variability and moderate to strong spatial dependence. Elevation and the shallowest depths of both the Veris (0.3 m) and DUALEM (0.54 m) EC_a sensors showed moderate to strong spatial dependence and correlated significantly to most soil properties in both study sites, indicating the feasibility of SSM. In place of management zone delineation, a quadrant analysis of the shallowest EC_a depth vs. elevation provided four sensor combinations (scenarios) for theoretical field conditions. ANOVA and Tukey–Kramer’s post hoc test showed that the greatest differentiation of soil properties in both fields occurred between the combinations of high EC_a/low elevation versus low EC_a/high elevation. Vegetation indices (VIs) obtained from satellite data showed promise as a biomass indicator, and bare spots classified with satellite imagery in Field_Und revealed significantly distinct soil properties. Combining proximal and multispectral data predicted within-field variations of yield-determining soil properties and offered three theoretical scenarios (high EC_a/low elevation; low EC_a/high elevation; bare spots) on which to base SSM. Future studies should investigate crop response to fertilization between the identified scenarios. Full article

The delineation of management zones (MZs) has been suggested as a solution to mitigate adverse impacts of soil variability on potato tuber yield. This study quantified the spatial patterns of variability in soil and crop properties to delineate MZs for site-specific soil fertility characterization of potato fields through proximal sensing of fields. Grid sampling strategy was adopted to collect soil and crop data from two potato fields in Prince Edward Island (PEI). DUALEM-2 sensor, Time Domain Reflectometry (TDR-300), GreenSeeker were used to collect soil ground conductivity parameter horizontal coplanar geometry (HCP), soil moisture content (θ), and normalized difference vegetative index (NDVI), respectively. Soil organic matter (SOM), soil pH, phosphorous (P), potash (K), iron (Fe), lime index (LI), and cation exchange capacity (CEC) were determined from soil samples collected from each grid. Stepwise regression shortlisted the major properties of soil and crop that explained 71 to 86% of within-field variability. The cluster analysis grouped the soil and crop data into three zones, termed as excellent, medium, and poor at a 40% similarity level. The coefficient of variation and the interpolated maps characterized least to moderate variability of soil fertility parameters, except for HCP and K that were highly variable. The results of multiple means comparison indicated that the tuber yield and HCP were significantly different in all MZs. The significant relationship between HCP and yield suggested that the ground conductivity data could be used to develop MZs for site-specific fertilization in potato fields similar to those used in this study. Full article

The continuous growth of cities in combination with future climate changes present urban planners with significant challenges, as traditional urban sewer systems are typically designed for the present climate. An easy and economically feasible way to mitigate this is to introduce a Sustainable Urban Drainage System (SUDS) in the urban area. However, the lack of knowledge about the geological and hydrogeological setting hampers the use of SUDS. In this study, 1315 ha of high-density electromagnetic (DUALEM-421S) data, detailed lithological soil descriptions of 614 boreholes, 153 infiltration tests and 250 in situ vane tests from 32 different sites in the Central Denmark Region were utilised to find quantitative and qualitative regional relationships between the resistivity and the lithology, the percolation rates and the undrained shear strength of cohesive soils at a depth of 1 meter below ground surface (m bgs). The qualitative tests enable a translation from resistivity to lithology as well as a translation from lithology to percolation rates with moderate to high certainty. The regional cut-off value separating sand-dominated deposits from clay-dominated deposits is found to be between 80 to 100 Ωm. The regional median percolation rates for sand and clay till is found to be 9.9 × 10⁻⁵ m/s and 2.6 × 10⁻⁵ m/s, respectively. The quantitative results derived from a simple linear regression analysis of resistivity and percolation rates and resistivity and undrained shear strength of cohesive soils are found to have a very weak relationship on a regional scale implying that in reality no meaningful relationships can be established. The regional qualitative results have been tested on a case study area. The case study illustrates that site-specific investigations are necessary when using geophysical mapping to directly estimate lithology, percolation rates and undrained shear strength of cohesive soils due to the differences in soil properties and the surrounding environment from site to site. This study further illustrates that geophysical mapping in combination with lithological descriptions, infiltration tests and groundwater levels yield the basis for the construction of detailed planning maps showing the most suitable locations for infiltration. These maps provide valuable information for city planners about which areas may preclude the establishment of infiltration-based SUDS. Full article

Peatlands constitute extremely valuable areas because of their ability to store large amounts of soil organic carbon (SOC). Investigating different key peat soil properties, such as the extent, thickness (or depth to mineral soil) and bulk density, is highly relevant for the precise calculation of the amount of stored SOC at the field scale. However, conventional peat coring surveys are both labor-intensive and time-consuming, and indirect mapping methods based on proximal sensors appear as a powerful supplement to traditional surveys. The aim of the present study was to assess the use of a non-invasive electromagnetic induction (EMI) technique as an augmentation to a traditional peat coring survey that provides localized and discrete measurements. In particular, a DUALEM-421S instrument was used to measure the apparent electrical conductivity (EC_a) over a 10-ha field located in Jutland, Denmark. In the study area, the peat thickness varied notably from north to south, with a range from 3 to 730 cm. Simple and multiple linear regressions with soil observations from 110 sites were used to predict peat thickness from (a) raw EC_a measurements (i.e., single and multiple-coil predictions), (b) true electrical conductivity ($\sigma$) estimates calculated using a quasi-three-dimensional inversion algorithm and (c) different combinations of EC_a data with environmental covariates (i.e., light detection and ranging (LiDAR)-based elevation and derived terrain attributes). The results indicated that raw EC_a data can already constitute relevant predictors for peat thickness in the study area, with single-coil predictions yielding substantial accuracies with coefficients of determination (R²) ranging from 0.63 to 0.86 and root mean square error (RMSE) values between 74 and 122 cm, depending on the measuring DUALEM-421S coil configuration. While the combinations of EC_a data (both single and multiple-coil) with elevation generally provided slightly higher accuracies, the uncertainty estimates for single-coil predictions were smaller (i.e., smaller 95% confidence intervals). The present study demonstrates a high potential for EMI data to be used for peat thickness mapping. Full article

The clay alluvial plains of Namoi Valley have been intensively developed for irrigation. A condition of a license is water needs to be stored on the farm. However, the clay plain was developed from prior stream channels characterised by sandy clay loam textures that are permeable. Cheap methods of soil physical and chemical characterisations are required to map the supply channels used to move water on farms. Herein, we collect apparent electrical conductivity (EC_a) from a DUALEM-421 along a 4-km section of a supply channel. We invert EC_a to generate electromagnetic conductivity images (EMCI) using EM4Soil software and evaluate two-dimensional models of estimates of true electrical conductivity (σ—mS m⁻¹) against physical (i.e., clay and sand—%) and chemical properties (i.e., electrical conductivity of saturated soil paste extract (EC_e—dS m⁻¹) and the cation exchange capacity (CEC, cmol(+) kg⁻¹). Using a support vector machine (SVM), we predict these properties from the σ and depth. Leave-one-site-out cross-validation shows strong 1:1 agreement (Lin’s) between the σ and clay (0.85), sand (0.81), EC_e (0.86) and CEC (0.83). Our interpretation of predicted properties suggests the approach can identify leakage areas (i.e., prior stream channels). We suggest that, with this calibration, the approach can be used to predict soil physical and chemical properties beneath supply channels across the rest of the valley. Future research should also explore whether similar calibrations can be developed to enable characterisations in other cotton-growing areas of Australia. Full article

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected by these sensors may provide essential information for precision or site-specific management in a production field. Data clustering techniques are crucial for data mining, and high-density data analysis is important for field management. A new clustering technique was introduced and compared with existing clustering tools to determine the relatively homogeneous parts of agricultural fields. A DUALEM-21S sensor, along with high-accuracy topography data, was used to characterize soil variability in three agricultural fields situated in Ontario, Canada. Sentinel-2 data assisted in quantifying bare soil and vegetation indices (VIs). The custom Neighborhood Search Analyst (NSA) data clustering tool was implemented using Python scripts. In this algorithm, part of the variance of each data layer is accounted for by subdividing the field into smaller, relatively homogeneous, areas. The algorithm’s attributes were illustrated using field elevation, shallow and deep apparent electrical conductivity (EC_a), and several VIs. The unique feature of this proposed protocol was the successful development of user-friendly and open source options for defining the spatial continuity of each group and for use in the zone delineation process. Full article

The main objective of this study was to compare two apparent soil electrical conductivity (EC_a) sensors (Veris 2000 XA and DUALEM 1S) for mapping variability of soil properties in a Mediterranean shallow soil. This study also aims at studying the effect of soil cover vegetation on the EC_a measurement by the two types of sensors. The study was based on two surveys carried out under two very different situations: in February of 2012, with low soil moisture content (SMC) and with high and differentiated vegetation development (non grazed pasture), and in February of 2013, with high SMC and with short and relatively homogeneous vegetation development (grazed pasture). The greater temporal stability of Veris sensor, despite the wide variation in the SMC and vegetation ground cover indicates the suitability of using this sensor for monitoring soil properties in permanent pastures. The survey carried out with the DUALEM sensor in 2012 might have been affected by the presence of a 0.20 m vegetation layer at the soil surface, masking the soil properties. These differences should be considered in the selection of EC_a sensing systems for a particular application. Full article