Scope to map soil management units at the district level from remotely sensed γ‐ray spectrometry and proximal sensed EM induction data

The traditional method of soil mapping involves classifying soil into pre‐existing classes using morphological observations and then air‐photograph interpretation to extrapolate the information. To accelerate the process, less costly ancillary data can be used to assist mapping. However, digital soil mapping (DSM) is still affected by the classifications used to identify soil types. One reason is because the morphological characteristics are not mutually exclusive, which causes misclassification. In this study, we used a DSM approach, where ancillary data were surrogate for morphological data, with soil types identified by numerical clustering of remotely and proximally sensed data collected across a farming district near Gunnedah, Australia. Remotely sensed data were obtained from an air‐borne gamma‐ray (γ‐ray) spectrometer survey, including potassium (K), thorium (Th), uranium (U) and total counts. Proximally sensed data were measured using EM38 (i.e. EM38h and EM38v). Using fuzzy k‐means and a linear mixed model with measured physical (e.g. clay) and chemical (e.g. CEC) properties from the topsoil (0–0.30 m) and subsoil (0.9–1.2 m), we found that k = 5 was also optimal given that mean‐squared prediction error (i.e. ) was minimised. The approach highlighted subtle differences in physical and chemical properties in productive areas. The DSM was unsuccessful in identifying small units; however, inclusion of elevation data might overcome this limitation. This research has implications for providing fast, accurate and meaningful DSM at a district scale, where traditional methods are too expensive.     

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.     

Effects of coal derived humic substances on water retention and structural stability of Mediterranean soils

Abstract. The potential for using coal-derived humic substances to improve the available water holding capacity (AWC) and aggregate stability of typical Mediterranean soils was evaluated in the laboratory using an agricultural surface (0–20 cm) soil from each of three regions of Italy, (Sicily, Tuscany and Venetia) and five rates of humic acids (HA), 0,0.05,0.10,0.50 and 1.00 g/kg. There were significant ( P < 0.05) differences between the field capacity (FC), permanent wilting point (PWP), and available water capacity (AWC) values of the controls and those treated with 0.05 g/kg of the HA. Beyond this rate, differences in these properties were not significant. At the 1.00 g/kg HA rate, the relative improvements in AWC over the three controls were 30%, 10% and 26%. Low rates (0.05 to 0.10 g/kg) of HA were also needed to obtain a 40 to 120% improvement in aggregate stability of these soils relative to the controls. These results indicate that the addition of highly humified organic matter such as coal-derived humic substances can improve the structural and water retention properties of degraded arable soils. However, since there is not yet any direct evidence that these humic materials can ameliorate soils under field conditions, field studies will be needed to validate these results.     

Field level digital mapping of soil mineralogy using proximal and remote‐sensed data

Primary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time‐consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory data by developing multiple linear regression (MLR) relationships between mineralogy and ancillary data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air‐photographs and soil apparent electrical conductivity (EC_a – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log‐ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various ancillary data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed data. Our approach also enables predictions to sum to 100. We conclude that the most useful ancillary data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of ancillary data to map mineralogical components combined with ALR‐MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.     

Estimating water retention curves of forest soils from soil texture and bulk density

Forest soils differ significantly from the arable land in their distribution of the soil bulk density and humus content, but the water retention parameters are primarily derived from the data of agricultural soils. Thus, there is a need to relate physical parameters of forest soils with their water retention characteristics and compare them with those of agricultural soils. Using 1850 water retention curves from forest soils, we related the following soil physical parameters to soil texture, bulk density, and C content: air capacity (AC), available water capacity (AWC), and the permanent wilting point (PWP). The ACs of forest soils were significantly higher than those of agricultural soils which were related to the low bulk densities of the forest soils, whereas differences in AWCs were small. Therefore, for a proper evaluation of the water retention curves (WRCs) and the parameters derived from them, further subdivisions of the lowest (< 1.45 g cm^‐3) of the three bulk density classes was undertaken to the wide range of low soil densities in forest soils (giving a total of 5 bulk density classes). In Germany, 31 soil texture classes are used for the estimation of soil physical parameters. Such a detailed classification is not required because of insignificant differences in WRCs for a large number of these classes. Based on cluster analysis of AC, AWC, and PWP parameters, 10 texture collectives were obtained. Using 5 classes of bulk densities, we further calculated the ACs, AWCs, and the PWPs for these 10 classes. Furthermore, “van Genuchten parameters” (θ r, θ s, α, and n) were derived which described the average WRC for each designated class. In a second approach using multiple regression analysis, regression functions for AC, AWC, and PWP and for the van Genuchten parameter were calculated.     

Comparison of Geonics EM38 and Dualem 1S electromagnetic induction sensors for the measurement of salinity and other soil properties

The electromagnetic induction (EMI) Geonics EM38 (G‐EM38) and Dualem 1S (D‐1S) sensors are used frequently for assessment of soil salinity and other soil characteristics in irrigated agriculture. We compared these two sensors to determine whether they could be used interchangeably for the measurement of apparent soil electrical conductivity (ECa) in horizontal (ECa‐h) and vertical (ECa‐v) coil receiver modes. Readings were taken at 201 locations identified in three irrigation districts in both modes, and statistical comparisons were made on the raw data and from maps of a 2‐ha irrigated field made using 1680 horizontal mode readings. Both sensors gave the same ECa‐v readings (mean G‐EM38 and D‐1S difference = 0), whereas the ECa‐h readings were slightly greater with the Geonics EM38 than with the Dualem D‐1S (mean difference = 0.075 and 0.05 dS/m for the 201 and 1680 observations, respectively). The degree of coincidence between both sensors for soil profile ECa classification was acceptable: 82% for normal profiles (i.e. ECa‐h/ECa‐v < 0.9) and 90% for inverted profiles (i.e. ECa‐h/ECa‐v > 1.1). In practical terms, Geonics EM38 and Dualem 1S sensors could be used interchangeably with similar or very close results.     

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.     

Spatial distribution of soil physical properties of alluvial soils: a geostatistical approach

Knowledge of spatial variation of soil is important in site-specific farming and environmental modeling. Soil particles size and water distribution are most important soil physical properties that governing nearly all of the other attributes of soils. The objectives of this study were to determine the degree of spatial variability of sand, silt and clay contents, and water content at field capacity (FC), permanent wilting point (PWP), and available water content (AWC) of alluvial floodplain soils. Data were analyzed both statistically and geostatistically to describe the spatial distribution of soil physical properties. Soil physical properties showed large variability with greatest variation was observed in sand content (68%). Exponential and spherical models were fit well for the soil physical properties. The nugget/sill ratio indicates except clay all other soil physical properties were moderate spatially dependent (37–70%). Cross-validation of the kriged map shows that prediction of the soil physical properties using semivariogram parameters is better than assuming mean of observed value for any unsampled location. The spatial distribution of water retention properties closely followed the distribution pattern of sand and clay contents. These maps will help to planner to develop the variable rate of irrigation (VRI) for the study area.     

Effects of Sugar Foam Liming on the Water-Retention Properties of Soil

The influence of sugar foam amendment on the moisture-retention properties of three profiles of an acidic vineyard soil in Retuerta del Bullaque (Ciudad Real, Spain) has been studied. The values obtained for the surface horizons of modified soils and the original soil (under natural vegetation) were compared, as were those for the surface and subsurface horizons of the liming profiles. The water-retention curves (drying curve) were determined in triplicate on the sieved soil with Richards plates and the field capacity (FC), permanent wilting point (PWP), and available water-retention capacity (AWRC) were calculated. In the original soil FC, PWP, and AWRC values were greater than the average values for the amended soils (36.5 percent, 15.1 percent, and 21.5 percent versus 23.5 percent, 10.35 percent, and 13.1 percent, respectively). Comparison of the surface horizons and the subsurface horizons of the three profiles showed that the values for the AWRC were greater in the former (13.1 percent, 12.5 percent, and 14 percent for P1, P2, and P3, respectively) than in the latter (11.9 percent, 9 percent, and 8.6 percent for P1, P2, and P3, respectively), although FC and PWP were lower in A horizons than in B horizons.     

terraGIS – a web GIS for delivery of digital soil maps in cotton‐growing areas of Australia

Effective management of soil requires the spatial distribution of its various physical, chemical and hydrological properties. This is because properties, for example clay content, determine the ability of soil to hold cations and retain water. However, data acquisition is labour intensive and time‐consuming. To add value to the limited soil data, remote sensing (e.g. airborne gamma‐ray spectrometry) and proximal sensing, such as electromagnetic (EM) induction, are being used as ancillary data. Here, we provide examples of developing Digital Soil Maps (DSM) of soil physical, chemical and hydrological properties, for seven cotton‐growing areas of southeastern Australia, by coupling soil data with remote and proximal sensed ancillary data. A greater challenge is how to get these DSM to a stakeholder in a way that is useful for practical soil use and management. This study describes how we facilitate access to the DSMs, using a simple‐to‐use web GIS platform, called terraGIS. The platform is underpinned by Google Maps API, which is an open‐source development environment for building spatially enabled Internet applications. In conclusion, we consider that terraGIS and the supporting information, available on the sister web page ( http://www.terragis.bees.unsw.edu.au/ ), allow easy access to explanation of DSM of soil properties, which are relevant to cotton growers, farm managers, consultants, extension staff, researchers, state and federal government agency personnel and policy analysts. Future work should be aimed at developing error budget maps to identify where additional soil and/or ancillary data is required to improve the accuracy of the DSMs.     

黄土高原土壤水分性质及其分区

黄土高原土壤具有很好的调节作物供水的功能,这和土壤的水分性质有关。本文研究了土壤的持水性能、有效性能和移动性能等。影响黄土高原土壤水分性质的主要因素是土壤质地。文内绘制了包括五个质地带的土壤质地分区图。田间持水量在轻壤土、中壤土和重壤土范围内均为20%±2,未表现出明显相关。萎蔫湿度则几乎完全决定于土壤质地。文内绘制了田间持水量和萎蔫湿度的等值线图。土壤水物理蒸发影响深度可达2-3米。两米土层内物理蒸发失水量轻壤土和中壤土达田间持水量的25-35%;具有下伏粘化层的重壤土,60厘米以下矢水量不大于10%。后者为作物准备了较多的储水。文内列出了不同质地土壤的水分特征曲线的经验方程,所有资料汇总于土壤水分性质表中。本研究结果为合理利用土地提供了基础数据。     

Estimation of soil texture from permanent wilting point measured with a chilled‐mirror dewpoint technique

Soil texture is an important factor governing a range of physical properties and processes in soil. The clay and fine fractions of soil are particularly important in controlling soil water retention, hydraulic properties, water flow and transport. Modern soil texture analysis techniques (x‐ray attenuation, laser diffraction and particle counting) are very laborious with expensive instrumentation. Chilled‐mirror dewpoint potentiameters allows for the rapid measurement of the permanent wilting point (PWP) of soil. As the PWP is strongly dictated by soil texture, we tested the applicability of PWP measured by a dewpoint potentiameter in predicting the clay, silt and sand content of humid tropical soils. The clay, silt, and sand content, organic matter and PWP were determined for 21 soils. Three regression models were developed to estimate the fine fractions and validated using independent soil data. While the first model showed reasonable accuracy (RMSE 16.4%; MAE 13.5%) in estimating the clay, incorporating the organic matter into the equation improved the predictions of the second model (RMSE 17.3%; MAE 10.9%). When used on all soil data, the accuracy of the third model in predicting the fine fraction was poor (RMSE 31.9%; MAE 24.5%). However, for soils with silt content greater than 30%, the model prediction was quite accurate (RMSE 7–12%; MAE 7–9%). The models were used to estimate the sand content and soil textures of soils, which proved relatively accurate. The dewpoint potentiometer can serve a dual purpose of rapidly estimating the PWP and the clay, fine fraction, and soil texture of soils in a cost efficient way.     

Mapping soil salinity in 3‐dimensions using an EM38 and EM4Soil inversion modelling at the reconnaissance scale in central Morocco

Large areas of Morocco require irrigation and although good quality water is available in dams, farmers augment river water with poorer quality ground water, resulting in salt build‐up without a sufficient leaching fraction. Implementation of management plans requires baseline reconnaissance maps of salinity. We developed a method to map the distribution of salinity profiles by establishing a linear regression (LR) between calculated true electrical conductivity (σ, mS/m) and electrical conductivity of the saturated soil‐paste extract (ECe, dS/m). Estimates of σ were obtained by inverting the apparent electrical conductivity (ECa, mS/m) collected from a 500‐m grid survey using an EM38. Spherical variograms were developed to interpolate ECa data onto a 100 m grid using residual maximum likelihood. Inversion was carried out on kriged ECa data using a quasi‐3d model (EM4Soil software), selecting the cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor of 3.0. Using a ‘leave‐one‐out cross‐validation' (LOOCV), of one in 12 of the calibration sites, the use of the q‐3d model yielded a high accuracy (RMSE = 0.42 dS/m), small bias (ME = ?0.02 dS/m) and Lin's concordance (0.91). Slightly worse results were obtained using individual LR established at each depth increment overall (i.e. RMSE = 0.45 dS/m; ME = 0.00 dS/m; Lin's = 0.89) with the raw EM38 ECa. Inversion required a single LR (ECe = 0.679 + 0.041 × σ), enabling efficiencies in estimating ECe at any depth across the irrigation district. Final maps of ECe, along with information on water used for irrigation (ECw) and the characterization of properties of the two main soil types, enabled better understanding of causes of secondary soil salinity. The approach can be applied to problematic saline areas with saline water tables.     

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.     

苗期土壤含水率变化对冬小麦根、冠生物量累积动态的影响

为合理进行冬小麦生长过程的适时水分调控，该文对不同生育期土壤含水率对冬小麦根冠影响的试验进行分析。采用的试验包括5种水分处理，即苗期充分供水，其它生育期进行中度胁迫(FB)、重度胁迫(FC)处理和从苗期开始的中度水分胁迫(SB)、重度水分胁迫(SC)处理以及全生育期充分供水的对照处理。试验结果表明：苗期土壤含水率对冬小麦根、冠的生物量，生物量的累积速率产生不同影响，使全生育期内根、冠占植株总量的比例和根冠比发生改变。当苗期水分改变时，生育初期，根、冠均没有明显响应，但到播后16 d，播后20 d，根、冠生物量分别随胁迫程度的增加而减小(FB>SB，FC>SC)；在播后28 d，SB和SC的根系质量累积速率超过对应FB和FC处理，且苗期受胁迫处理的冬小麦在生殖生长阶段所维持的根系大于苗期不受胁迫处理的根系；冠的累积速率则在播后28 d和35 d也出现SB>FB，SC>FC的结果，到播后42 d，SB、SC的冠质量分别超过对应的FB、FC的冠质量。在此过程中，根、冠生物量占总质量的比例发生改变，根表现为SB>FB，SC>FC；冠在营养生长阶段FB>SB，FC>SC，在生殖生长期SB达到最大；相应根冠比改变。     

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.     

Comparative Analysis of MLR,ANN, and ANFIS Models for Prediction of Field Capacity and Permanent Wilting Point for Bafra Plain Soils

ABSTRACT

Soil hydraulic parameters like moisture content at field capacity and permanent wilting point constitute significant input parameters of various biophysical models and agricultural practices (irrigation timing and amount of irrigation to be applied). In this study, the performance of three different methods (Multiple linear regression – MLR, Artificial Neural Network – ANN and Adaptive Neuro-Fuzzy Inference System – ANFIS) with different input parameters in prediction of field capacity and permanent wilting point from easily obtained soil characteristics were compared. Correlation analysis indicated that clay content, sand content, cation exchange capacity, CaCO₃, and organic matter had significant correlations with FC and PWP (p < .01). Validation results revealed that the ANN model with the greatest R² and the lowest MAE and RMSE value exhibited better performance for prediction of FC and PWP than the MLR and ANFIS models. ANN model had R² = 0.83, MAE = 2.36% and RMSE = 3.30% for FC and R² = 0.81, MAE = 2.15%, RMSE = 2.89% for PWP in training dataset; R² = 0.80, MAE = 2.27%, RMSE = 3.12% for FC and R² = 0.83, MAE = 1.84%, RMSE = 2.40% for PWP in testing dataset. Also, Bayesian Regularization (BR) algorithm exhibited better performance for both FC and PWP than the other training algorithms.     

Analyzing the efficiency of soil amendments and irrigation for plant production on heterogeneous sandy soils under greenhouse conditions

Variability in soil properties is a complication for fertilization, irrigation, and amendment application. However, only limited progress has been made in managing soil variability for uniform productivity and increased water‐use efficiency. This study was designed to ameliorate the poor‐productivity areas of the variable sandy soils in Florida citrus groves by using frequent small irrigations and applying organic and inorganic soil amendments. Two greenhouse experiments were set up with sorghum and radish as bioassay crops in a randomized complete block design (RCBD). The factors studied were two soil‐productivity classes (very poor and very good), two water contents (50% and 100% of field capacity), two amendments (phosphatic clay and Fe humate), and two amendment rates (10 and 25 g kg^–1 for sorghum and 50 and 100 g kg^–1 for radish). Amendments applied at 50 and 100 g kg^–1 increased the water‐holding capacity (WHC) of poor soil by 2‐ to 6‐fold, respectively. The lower rates (10 and 25 g kg^–1) of amendments were not effective in enhancing sorghum growth. The higher rates (50 and 100 g kg^–1) doubled the radish growth as compared to the control. The results indicate that rates greater than 50 g kg^–1 of both amendments were effective in improving water retention and increasing productivity. Irrigation treatment of 100% of field capacity (FC) increased the sorghum and radish growth by about 2‐fold as compared with the 50%–water content treatment. The results suggest that the root‐zone water content should be maintained near FC by frequent small irrigations to enhance water availability in excessively drained sandy soils. In addition, application of soil amendments in the root zone can enhance the water retention of these soils. Furthermore, managing variable sandy soils with WHC‐based irrigation can increase water uptake and crop production in the poor areas of the grove.     

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.