Investigating soil physical properties and yield response in a grassland field using a dual‐sensor penetrometer and EM38

Precision‐farming applications are mainly based on site‐specific information of soil properties at the field scale. For this purpose, a number of novel sensor techniques have been developed but not intensively tested under different field conditions. This study presents a combined application of a self‐developed dual‐sensor vertical penetrometer (DVP) for measuring volumetric soil water content (VSWC) and cone index (CI), and an EM38 for soil apparent electrical conductivity (EC_a) in a pasture (1.4 ha). To verify the feasibility of the DVP for interpreting the depth‐specific information in the field, not only the soil physical properties and their geographical coordinates were measured, but also geo‐referenced yield data were collected. We found that the yield pattern was quite similar to the soil water‐content pattern of each layer (layer‐1: 5–15 cm; layer‐2: 15–25 cm, layer‐3: 25–35 cm) and EC_a pattern. Using the map‐based comparisons in conjunction with the statistical analyses, the effect of each measured soil physical property (VSWC, CI, and EC_a) on the yield was investigated. The regression between the yield and VSWC at each layer fitted a quadratic equation (R² = 0.515 at 5–15 cm; R² = 0.623, at 15–25 cm; R² = 0.406 at 25–35 cm). The negative correlation between yield and CI at each layer fitted a linear model with R² ≥ 0.510.     

Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity

The site‐specific cultivation as part of the precision‐agriculture concept is more and more introduced into practical farming. However, soil information is often not available in a spatial resolution intrinsically needed for precision farming or other site‐specific soil use and management purposes. One approach to obtain spatially high‐resolution soil data is the non‐invasive measurement of the apparent electrical conductivity (EC_a). In this study, we recorded the EC_a on three fields with an EM38 (Geonics, Canada). The EC_a data were compared with (1) ground truth data obtained by conventional drilling, (2) traditional soil maps (large scale, ≤1:5,000), (3) the growth and yield of corn. The temporal variability of the EC_a due to varying soil moisture and temperature was taken into account by repeated measurements of the same fields and subsequent averaging of the EC_a values. Significant correlations (r² = 0.76) were found between the mean weighted clay content (0–1.5 m) and the EC_a. Furthermore, in soils with differently textured layers, EC_a was used to estimate the thickness of the uppermost loess layer. A comparison of EC_a and large‐scale soil maps reveals some pros and cons of EC_a measurements. The main advantages of EC_a recordings are the high spatial resolution in combination with low efforts. Yet, the EC_a signal is no direct measure for a soil type or unit. Depending on the variability of substrates and layering, the EC_a pattern can be a precise indicator for the spatial distribution of different soils. A strong conformity of the spatial variability of plant growth (derived from orthophotos and yield maps) and EC_a patterns within a field indicates that the EC_a signal per se—without conversion to traditional soil parameters—integrates the effects of various soil variables that govern soil fertility. Altogether, EC_a surveys can be a powerful tool to facilitate and improve conventional soil mapping.     

Modelling soil salinity across a gilgai landscape by inversion of EM38 and EM31 data

The soil in arid and semi‐arid areas is often markedly saline, which can severely limit agricultural productivity. Increasingly, geophysical methods are being implemented to map the levels and areal extent of soil salinity. One of the most effective methods is electromagnetic (EM) induction with instruments designed to measure apparent soil electrical conductivity (EC_a). This study describes the generation of electromagnetic conductivity images (EMCIs) by inverting EC_a data obtained with the EM38 and EM31 devices along two closely‐spaced transects by the EM inversion approach in the EM4Soil package. The EM38 EC_a data are shown to be a more effective predictor of soil EC_e. Calibration equations based on calculated true electrical conductivity (σ) and measured electrical conductivity of a saturated soil‐paste extract (EC_e) provide reliable estimates of EC_a. The patterns of σ in a test of the method in soil developed over thick alluvium on a clay plain in central New South Wales, Australia, compare favourably with existing pedological mapping; the mounds and depressions of gilgai were strongly differentiated from the more sandy alluvial sediments that characterize prior stream channels. The overall approach is potentially useful for generating a single calibration equation that can be used to predict EC_e at various depths in the soil. Improvements in EMCI modelling can also be sought by joint inversion of EM with other geophysical datasets.     

Relationship between apparent electrical conductivity and soil physical properties in a Malaysian paddy field

Site-specific crop management, well-established in some developed countries, is now being considered in developing countries such as Malaysia. The apparent electrical conductivity (EC_a) of the soil can be used as an indirect indicator of a number of soil physical properties and even crop yield. Commercially available EC_a sensors can efficiently develop the spatially dense data sets desirable in describing within-field spatial soil variability for precision farming. The main purpose of this study was to generate a variability map of soil EC_a within a Malaysian paddy field using a VerisEC sensor. The EC_a values were then compared with some soil variables within classes after delineation. Measured parameters were mapped using the kriging technique and their correlation with soil EC_a was determined. The study showed that the VerisEC can determine soil spatial variability, and can acquire soil EC_a information quickly. Spatial variability of shallow and deep EC_a showed the same patterns. Estimation of soil properties based on EC_a varied from one soil parameter to another and all could be estimated better by deep EC_a. Cross-validation results showed that shallow and deep EC_a, and also bulk density, gave more accurate estimates compared with other variables.     

Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (EC_p – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R² = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (EC_e – dS/m). Our interpretation based on estimates of EC_e suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.     

Establishing soil electrical conductivity ‐ Depth relations from electromagnetic induction measurements

Abstract

Principles of electromagnetic induction (EM) and field calibration approaches are discussed as they pertain to the application of EM to soil systems for the purpose of deriving soil electrical conductivity ‐ depth relations. Evidence is provided to support the utility of EM‐derived estimates of EC_a‐depth relations. Limitations of using electromagnetic induction to determine EC_a for discrete depth intervals through the soil are discussed. Current research designed to increase the accuracy of EC_a‐depth determinations by dealing with the spatial variability problem associated with salinity in soil and by mitigating some of the inherent limitations of the calibration approaches is described.     

Mapping soil salinity and pH across an estuarine and alluvial plain using electromagnetic and digital elevation model data

Increasing pressures from agriculture and urbanization have resulted in drainage of many floodplains along the eastern Australian coastline, which are underlain by sulphidic sediments, to lower water tables and reduce soil salinity. This leads to oxidation of the sediments with a rapid decline in pH and an increase in salinity. Accurately mapping soil salinity and pH in coastal acid sulphate soil (CASS) landscapes is therefore important. One required map is the extent of highly acidic (i.e. pH < 4.5) areas, so that the application of alkaline amendments (e.g. lime) to neutralize the acid produced can be specifically targeted to the variation in pH. One approach is to use digital soil mapping (DSM) using ancillary information, such as an EM38, digital elevation models (DEM – elevation) and trend surface parameters (east and north). We used an EM38 in the horizontal (EM38h) and vertical (EM38v) modes together with elevation data to develop multiple linear regressions (MLR) for predicting EC_1:5 and pH. For pH, best results were achieved when the EM38 EC_a data were log‐transformed. By comparing MLR models using REML analysis, we found that using all ancillary data was optimal for mapping EC_1:5, whereas the best predictors for pH were north, log‐EM38v and elevation. Using residual maximum likelihood (REML), the final EC_1:5 and pH maps produced were consistent with previously defined soil landscape units, particularly CASS. The DSM approach used is amenable for mapping saline soils and identifying areas requiring the application of lime to manage acidic soil conditions in CASS landscape.     

Characterization of field‐scale dryland salinity with depth by quasi‐3d inversion of DUALEM‐1 data

To understand the limitations of saline soil and determine best management practices, simple methods need to be developed to determine the salinity distribution in a soil profile and map this variation across the landscape. Using a field study in southwestern Australia, we describe a method to map this distribution in three dimensions using a DUALEM‐1 instrument and the EM4Soil inversion software. We identified suitable parameters to invert the apparent electrical conductivity (EC_a – mS/m) data acquired with a DUALEM‐1, by comparing the estimates of true electrical conductivity (σ – mS/m) derived from electromagnetic conductivity images (EMCI) to values of soil electrical conductivity of a soil‐paste extract (EC_e) which exhibited large ranges at 0–0.25 (32.4 dS/m), 0.25–0.50 (18.6 dS/m) and 0.50–0.75 m (17.6 dS/m). We developed EMCI using EM4Soil and the quasi‐3d (q‐3d), cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor (λ) of 0.07. Using a cross‐validation approach, where we removed one in 15 of the calibration locations and predicted EC_e, the prediction was shown to have high accuracy (RMSE = 2.24 dS/m), small bias (ME = ?0.03 dS/m) and large Lin's concordance (0.94). The results were similar to those from linear regression models between EC_a and EC_e for each depth of interest but were slightly less accurate (2.26 dS/m). We conclude that the q‐3d inversion was more efficient and allowed for estimates of EC_e to be made at any depth. The method can be applied elsewhere to map soil salinity in three dimensions.     

Magnetic depletion and enhancement in the evolution of paddy and non‐paddy soil chronosequences

The objective of this study was to understand the rates and controlling factors of magnetic depletion and enhancement during anthropogenic soil evolution. To this end, the study compared the dynamic changes in magnetic properties as well as iron oxide species of paddy and non‐paddy soil chronosequences with the same parent materials. A two‐way analysis of variance (anova ) showed that paddy management resulted in significant (P < 0.01) decreases in magnetic susceptibility (χ) and other magnetic properties. Paddy management‐induced χ losses increased gradually from 24 to 55% as the cultivation history increased from 50 to 700 years. The rates of χ decrease were most rapid within the first 50 years of paddy cultivation, after which the rate slowed. The rapid decline in χ is probably caused by accelerated depletion of fine‐grained maghemite and ultrafine magnetite by iron‐reducing bacteria during soil waterlogging and consequent reducing conditions. By contrast, a significant decrease in hard isothermal remanent magnetization (HIRM) occurred only after 700 years of paddy cultivation, which matches the time taken to leach CaCO₃ from the profile. In contrast, although magnetic enhancement was observed in the non‐paddy surface horizon, there was no increasing trend at the millennium time‐scale, probably because of the large CaCO₃ content of the soil. We show that magnetic properties of paddy and non‐paddy soil derived from calcareous sediments are mainly controlled by the changing soil moisture regime and soil carbonate status along different paths of soil development. Our study suggests that differences in soil moisture regime caused by land use are substantially more important than the period of cultivation.     

Identifying potential management zones in a layered soil using several sources of ancillary information

Variation in soil texture has a profound effect on soil management, especially in texturally complex soils such as the polder soils of Belgium. The conventional point sampling approach requires high sampling intensity to take into account such spatial variation. In this study we investigated the use of two ancillary variables for the detailed mapping of soil texture and subsequent delineation of potential management zones for site‐specific management. In an 11.5 ha arable field in the polder area, the apparent electrical conductivity (EC_a) was measured with an EM38DD electromagnetic induction instrument. The geometric mean values of the EC_a measured in both vertical and horizontal orientations strongly correlated with the more heterogeneous subsoil clay content (r = 0.83), but the correlation was weaker with the homogenous topsoil clay content (r = 0.40). The gravimetric water content at wilting point (θg_{(?1.5 MPa)}) correlated very well (r = 0.96) with the topsoil clay content. Thus maps of topsoil and subsoil clay contents were obtained from 63 clay analyses supplemented with 117θg_{(?1.5 MPa)} and 4048EC_a measurements, respectively, using standardized ordinary cokriging. Three potential management zones were identified based on the spatial variation of both top and subsoil clay contents. The influence of subsoil textural variation on crop behaviour was illustrated by an aerial image, confirming the reliability of the results from the small number of primary samples.     

Soil salinity and its distribution determined by soil sampling and electromagnetic techniques

Abstract. Diagnosis of soil salinity and its spatial variability is required to establish control measures in irrigated agriculture. This article shows the usefulness of electromagnetic (EM) and soil sampling techniques to map salinity. We analysed the salinity of a 1‐ha plot of surface‐irrigated olive plantation in Aragon, NE Spain, by measuring the electrical conductivity of the saturation extract (EC_e) of soil samples taken at 22 points, and by reading the Geonics EM38 sensor at 141 points in the horizontal (EM_H) and vertical (EM_V) dipole positions. EM_H and EM_V values had asymmetrical bimodal distributions, with most readings in the non‐saline range and a sharp transition to relatively high readings. Most salinity profiles were uniform (i.e. EM_H=EM_V), except in areas with high salinity and concurrent shallow water tables, where the profiles were inverted as shown by EM_H > EM_V, and by EC_e being greater in shallow than in deeper layers. The regressions of EC_e on EM readings predicted EC_e with R² > 84% for the 0–100 to 0–150 cm soil depths. We then produced salinity contour maps from the 141 EC_e values estimated from the electromagnetic readings and the 22 measured values of EC_e. Owing to the high soil sampling density, the maps were similar (i.e. mean surface‐weighted EC_e values between 3.9 dS m^?1 and 4.2 dS m^?1), although the electromagnetically estimated EC_e improved the mapping of details. Whereas soil sampling is preferred for analysing the vertical distribution of soil salinity, the electromagnetic sensor is ideal for mapping the lateral variability of soil salinity.     

Time‐lapse monitoring of soil water content using electromagnetic conductivity imaging

The volumetric soil water content (θ) is fundamental to agriculture because its spatiotemporal variation in soil affects the growth of plants. Unfortunately, the universally accepted thermogravimetric method for estimating volumetric soil water content is very labour intensive and time‐consuming for use in field‐scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatiotemporal variation of θ. However, depth‐specific variation in θ, which is important for irrigation management, has been little explored. The objective of this study was to develop a relationship between θ and estimates of true electrical conductivity (σ) and to use this relationship to develop time‐lapse images of soil θ beneath a centre‐pivot irrigated alfalfa (Medicago sativa L.) crop in San Jacinto, California, USA. We first measured the bulk apparent electrical conductivity (EC_a – mS/m) using a DUALEM‐421 over a period of 12 days after an irrigation event (i.e. days 1, 2, 3, 4, 6, 8 and 12). We used EM4Soil to generate EM conductivity images (EMCIs). We used a physical model to estimate θ from σ, accounting for soil tortuosity and pore water salinity, with a cross‐validation RMSE of 0.04 cm³/cm³. Testing the scenario where no soil information is available, we used a three‐parameter exponential model to relate θ to σ and then to map θ along the transect on different days. The results allowed us to monitor the spatiotemporal variations of θ across the surveyed area, over the 12‐day period. In this regard, we were able to map the soil close to field capacity (0.27 cm³/cm³) and approaching permanent wilting point (0.03 cm³/cm³). The time‐lapse θ monitoring approach, developed using EMCI, has implications for soil and water use and management and will potentially allow farmers and consultants to identify inefficiencies in water application rates and use. It can also be used as a research tool to potentially assist precision irrigation practices and to test the efficacy of different methods of irrigation in terms of water delivery and efficiency in water use in near real time.     

World's Oldest Cotton Experiment: Relationships between Soil Chemical and Physical Properties and Apparent Electrical Conductivity

Abstract

Measuring and mapping apparent soil electrical conductivity (EC_a) is a potentially useful tool for delineating soil variability. The “Old Rotation,” the world's oldest continuous cotton (Gossypium hirsutum L.) experiment (ca. 1896), provides a valuable resource for evaluating soil spatial variability. The objectives of this study were to determine the relationship between soil chemical and physical properties and EC_a in the Old Rotation, to determine spatial differences in these properties, and to relate differences in these properties to long‐term management effects. Soils at the site classified as fine, kaolinitic, thermic Typic Kanhapludults. Soil EC_a was measured at 0–30‐ and 0–90‐cm depths (EC_a‐30 and EC_a‐90) using a Veris^® 3100 direct contact sensor with georeferencing. Soils were grid sampled (288 points) at close intervals (1.5×3.0 m) for chemical properties and grid sampled (65 cells, 7.5×6.9 m) for soil texture. Soil organic carbon (SOC) and total nitrogen (N), extractable phosphorus (P), potassium (K), calcium (Ca), pH, buffer pH, and estimated cation exchange capacity (CEC_est) were measured at two depths (0–5‐ and 5–15‐cm). Soil EC_a was highly spatially correlated. The EC_a‐30 was more highly correlated with clay content (r=0.58, P≤0.01) and P(r=0.43, P≤0.01) than other soil properties. Total nitrogen and SOC had little or no relationship with EC_a‐30. Cropping systems affected chemical properties in the Old Rotation, indicating crop rotation and cover crops are beneficial for soil productivity. The relatively poor relationship between soil chemical parameters and EC_a suggest that mapping plant nutrients and SOC using EC_a is problematic because of strong dependence on clay content.     

Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China

In coastal China, there is an urgent need to increase land for agriculture. One solution is land reclamation from coastal tidelands, but soil salinization poses a problem. Thus, there is need to map saline areas and identify appropriate management strategies. One approach is the use of digital soil mapping. At the first stage, auxiliary data such as remotely sensed multispectral imagery can be used to identify areas of low agricultural productivity due to salinity. Similarly, proximal sensing instruments can provide data on the distribution of soil salinity. In this study, we first used multispectral QuickBird imagery (Bands 1–4) to provide information about crop growth and then EM38 data to indicate relative salt content using measurements of apparent soil electrical conductivity (EC_a) in the horizontal (EC_h) and vertical (EC_v) modes of operation. Second, we used a fuzzy k‐means (FKM) algorithm to identify three salinity management zones using the normalized difference vegetation index (NDVI), EC_h and EC_v/EC_h. The three identified classes were statistically different in terms of auxiliary and topsoil properties (e.g. soil organic matter) and more importantly in terms of the distribution of soil salinity (EC_e) with depth. The resultant three classes were mapped to demonstrate that remote and proximally sensed auxiliary data can be used as surrogates for identifying soil salinity management zones.     

Effects of compaction on soil hydraulic properties,penetration resistance and water flow patterns at the soil profile scale

The aim of this study was to quantify the effects of compaction on water flow patterns at the soil profile scale. Control and trafficked plots were established in field trials at two sites. The trafficked treatment was created by four passes track‐by‐track with a three‐axle dumper with a maximum wheel load of 5.8 Mg. One year later, dye‐tracing experiments were performed and several soil mechanical, physical and hydraulic properties were measured to help explain the dye patterns. Penetration resistance was measured to 50 cm depth, with saturated hydraulic conductivity (K_s), bulk density, and macroporosity and mesoporosity being measured on undisturbed soil cores sampled from three depths (10, 30 and 50 cm). Significant effects of the traffic treatment on the structural pore space were found at 30 cm depth for large mesopores (0.3–0.06 mm diameter), but not small mesopores (0.06–0.03 mm) or macroporosity (pores > 0.3 mm). At one of the sites, ponding was observed during the dye‐tracing experiments, especially in the trafficked plots, because of the presence of a compacted layer at plough depth characterized by a larger bulk density and smaller structural porosity and K_s values. Ponding did not induce any preferential transport of the dye solution into the subsoil at this site. In contrast, despite the presence of a compacted layer at 25–30 cm depth, a better developed structural porosity in the subsoil was noted at the other site which allowed preferential flow to reach to at least 1 m depth in both treatments.     

Energy and water balance measurements for water productivity analysis in irrigated mango trees, Northeast Brazil

Crop water parameters, including actual evapotranspiration, transpiration, soil evaporation, crop coefficients, evaporative fractions, aerodynamic resistances, surface resistances and percolation fluxes were estimated in a commercial mango orchard during two growing seasons in Northeast Brazil. The actual evapotranspiration (E_a) was obtained by the eddy covariance (EC) technique, while for the reference evapotranspiration (E₀); the FAO Penman–Monteith equation was applied. The energy balance closure showed a gap of 12%. For water productivity analysis the E_a was then computed with the Bowen ratio determined from the eddy covariance fluxes. The mean accumulated E_a for the two seasons was 1419 mm year⁻¹, which corresponded to a daily average rate of 3.7 mm day⁻¹. The mean values of the crop coefficients based on evapotranspiration (K_c) and based on transpiration (K_cb) were 0.91 and 0.73, respectively. The single layer K_c was fitted with a degree days function. Twenty percent of evapotranspiration originated from direct soil evaporation. The evaporative fraction was 0.83 on average. The average relative water supply was 1.1, revealing that, in general, irrigation water supply was in good harmony with the crop water requirements. The resulting evapotranspiration deficit was 73–95 mm per season only. The mean aerodynamic resistance (r_a) was 37 s m⁻¹ and the bulk surface resistance (r_s) was 135 s m⁻¹. The mean unit yield was 45 tonne ha⁻¹ being equivalent to a crop water productivity of 3.2 kg m⁻³ when based on E_a with an economic counterpart of US$ 3.27 m⁻³. The drawback of this highly productive use of water resources is an unavoidable percolation flux of approximately 300 mm per growing season that is detrimental to the downstream environment and water users.     

Slurry field management and ammonia emissions under Mediterranean conditions

In Spain, farmers are interested in applying pig (Sus scrofa domesticus) slurry (PS) to their fields throughout the year. During the spring and summer months, ammonia (NH₃) volatilization may be high. We studied the potential range of NH₃ losses under a warm and a hot period of the year, using available field practices, and two strategies: PS directly incorporated into the soil, in spring (I‐spring); and PS applied by splash‐plate, in summer time (SP‐summer), both to bare soil. Measurements were conducted, after PS application, using the micrometeorological mass balance integrated horizontal flux method. The cumulative NH₃‐N volatilization was 35% (I‐spring) and 60% (SP‐summer) of total ammonium nitrogen applied, and half of the total NH₃‐N losses happened by 17 h and 8 h, respectively, after application. Incorporation strategy was less effective in avoiding NH₃ losses than is described in the literature. This fact has important consequences for the implementation of NH₃ mitigation measures in Mediterranean agricultural systems.     

Apparent Electrical Conductivity in Correspondence to Soil Chemical Properties and Plant Nutrients in Soil

Spatial variability and relationship between soil apparent electrical conductivity (EC_a), soil chemical properties, and plant nutrients in soil have not been well documented in Malaysian paddy fields. For this reason precision farming has been used for assessing field conditions. EC_a technique for describing soil spatial variability is used for soil data acquisition. Soil sampling provides the data used to make maps of the spatial patterns in soil properties. Maps are then used to make recommendations on the variation of application rates. The main purpose of the authors in this study was to generate variability map of soil EC_a within a Malaysian rice cultivation area using VerisEC sensor. The EC_a values were compared to some soil properties after delineation. Measured parameters were mapped using kriging technique and their correlation with soil EC_a was determined. Through this study the authors showed that the EC sensor can determine soil spatial variability, where it can acquire the soil information quickly.     

Modelling nitrogen transport and transformation in a transplanted rice field experiment with reduced irrigation

Application of water-saving irrigation technologies in transplanted rice (TPR) cultivation resulted in different soil water regimes compared to traditional flood irrigation and consequent diverse nitrogen transport and losses in paddy fields. In this study, nitrogen transport and transformations in a TPR field under multiple shallow irrigation (MSI) conditions in the Taihu Lake Basin of Eastern China were observed and simulated (Hydrus-1D model) during the 2008 and 2009 seasons based on a previous study. MSI controlled well the depth of floodwater and reduced nitrogen losses substantially through percolation and surface runoff, in particular during the 2008 season with relative less rainfall. Nitrogen balance analysis showed that both denitrification and NH₃ volatilisation were the two major paths of nitrogen loss during the two seasons. Most nitrogen transformations occurred in top soil (0–40?cm) during early-middle seasons. The overall nitrification and denitrification differed slightly between the two seasons but largely between soil depths, averagely 92.7% and 73.0% of respective total in a 120?cm soil profile occurred in 0–40?cm soil, respectively. MSI method coupled with deep applied fertiliser is recommended to substantially reduce nitrogen losses through surface runoff, percolation, and NH₃ volatilisation in TPR fields.     

Upgrading a 1/20,000 soil map with an apparent electrical conductivity survey

Despite their shortcomings, choropleth soil maps remain the most widespread source of information on soil resources. Since most nationwide soil surveys were conducted in the second half of the previous century, a need for upgrading emerges. We evaluated the potential of detailed observations made by a mobile, non-invasive proximal soil sensor to upgrade a part of the 1/20,000 choropleth soil map of Belgium. This study was conducted on a 14 ha area which had been mapped twice in the 1950s: first, during the national soil survey yielding a 1/20,000 soil map, and second, during a detailed investigation resulting in a 1/5000 map. The first map failed to identify the presence of a Tertiary clay substratum at variable depths, while the second map indicated this substratum to be present within 1.2 m below the soil surface for about a third of the area. A recent survey with the EM38DD soil sensor provided 9192 measurements of the apparent electrical conductivity (EC_a) within the study area. The depth of the substratum (D_ts) was noted at 60 calibration locations by augering and the relationship between EC_a and D_ts was modelled by an exponential curve with an R² of 0.80. This allowed the detailed mapping of D_ts by regression kriging. The predictions were validated using 46 independent observations of D_ts indicating a reasonable average error of 0.24 m and a very good correlation coefficient between observed and predicted values of 0.94. A map accuracy assessment indicated that even after classification, the D_ts classes were better predicted by the sensor data than the 1/5000 map which was based on many more auger observations. Finally an upgraded 1/20,000 soil map was presented, illustrating the potential of combining existing soil maps with proximal soil sensing technology.