Apparent Soil Electrical Conductivity Research Articles

A Holistic Approach to the Evaluation of the Montado Ecosystem Using Proximal Sensors.

The Montado is a silvo-pastoral system characterized by open canopy woodlands with natural or cultivated grassland in the undercover and grazing animals. The aims of this study were to present several proximal sensors with potential to monitor relevant variables in the complex montado ecosystem and demonstrate their application in a case study designed to evaluate the effect of trees on the pasture. This work uses data collected between March and June 2016, at peak of dryland pasture production under typical Mediterranean conditions, in twenty four sampling points, half under tree canopy (UTC) and half outside tree canopy (OTC). Correlations were established between pasture biomass and capacitance measured by a commercial probe and between pasture quality and normalized difference vegetation index (NDVI) measured by a commercial active optical sensor. The interest of altimetric and apparent soil electrical conductivity maps as the first step in the implementation of precision agriculture projects was demonstrated. The use of proximal sensors to monitor soil moisture content, pasture photosynthetically active radiation and temperature helped to explain the influence of trees on pasture productivity and quality. The significant and strong correlations obtained between capacitance and pasture biomass and between NDVI and pasture nutritive value (in terms of crude protein, CP and neutral detergent fibre, NDF) can make an important contribution to determination of key components of pasture productivity and quality and implementation of site-specific pasture management. Animal tracking demonstrated its potential to be an important tool for understanding the interaction between various factors and components that interrelate in the montado ecosystem and to support grazing management decisions.

Three-dimensional imaging of active acid sulfate soil using a DUALEM-21S and EM inversion software

One of the major environmental issues in Finland is the presence of large tracts of acid sulfate soil (ASS) landscapes along the coast. Accurately identifying the distribution of ASS sediments, and in particular soil pH, is essential for developing targeted management strategies. One approach is the use of digital soil mapping (DSM) with various ancillary information. Although electromagnetic (EM) induction data has shown potential in mapping ASS, few studies have been conducted to map the spatial distribution of pH at different depths. In this study, a DUALEM-21S was used to collect apparent soil electrical conductivity (ECa) data across a 23-ha field near Vaasa, which lies along the western coast of Finland. A quasi-3D inversion algorithm was used to calculate the estimated true electrical conductivity (σ – mS m−1). A calibration relationship was developed between σ and incubation-pH measured at various depths from topsoil (0–0.2 m), subsurface (0.2–0.4 m) and subsoil (e.g. 0.4–0.6 and 1.8–2 m) using an artificial neural network (ANN) model. The performance of the ANN model was good given the large R2 values for calibration (0.72) and validation (0.65). It was concluded that the combination of ECa data and quasi-3D inversion algorithm (in EM4Soil) was able to map the spatial distribution of incubation-pH associated within an ASS landscape. The approach has the potential to be applied across the coastal areas of Finland and elsewhere to map incubation-pH and identify active-ASS areas and thereby improve the management of these areas.

Apparent soil electrical conductivity in two different soil types

ABSTRACT Mapping the apparent soil electrical conductivity (ECa) has become important for the characterization of the soil variability in precision agriculture systems. Could the ECa be used to locate the soil sampling points for mapping the chemical and physical soil attributes? The objective of this work was to examine the relations between ECa and soil attributes in two fields presenting different soil textures. In each field, 50 sampling points were chosen using a path that presented a high variability of ECa obtained from a preliminary ECa map. At each sampling point, the ECa was measured in soil depths of 0-20, 0-40 and 0-60 cm. In addition, at each point, soil samples were collected for the determination of physical and chemical attributes in the laboratory. The ECa data obtained for different soil depths was very similar. A large number of significant correlations between ECa and the soil attributes were found. In the sandy clay loam texture field there was no correlation between ECa and organic matter or between ECa and soil clay and sand content. However, a significant positive correlation was shown for the remaining phosphorus. In the sandy loam texture field the ECa had a significant positive correlation with clay content and a significant negative correlation with sand content. The results suggest that the mapping of apparent soil electrical conductivity does not replace traditional soil sampling, however, it can be used as information to delimit regions in a field that have similar soil attributes.

Gestão diferenciada das pastagens baseada na monitorização da condutividade eléctrica aparente do solo

Fertilization is the main cost of maintenance of permanent pastures. Its incorrect use results in important direct economic losses and in environmental pollution. Usually, soils utilised for pasture and livestock production in Southern region of Portugal have high spatial variability. Consequently, it is necessary to determine the spatial patterns of the main soil properties as the first stage in implementing site-specific management strategies. However, this has to be performed using an inexpensive technique because the profitability in this type of farms are very low, so owners need an economical, effective, and reliable method to know which zones have similar potential management. The use of soil apparent electrical conductivity (ECa), as it integrates many soil properties affecting crop productivity (for instance, soil texture, organic matter and cation exchange capacity), constitutes one of the most appropriate soil variables to characterize the management zones of a field. In the present study ECameasurements obtained with a Veris 2000 XA contact-type sensor and soil samples taken at 0-0.30m depth in two permanent pastures were used. The aim was to assess the spatial pattern of some soil properties, based on ECameasurements as ancillary information and a guided soil sampling. Relationships between ECa and some important soil properties were analyzed and, later, some maps were produced to visualize the spatial variability of the soil properties. Finally, homogeneous zones were delimited for attaining a site-specific management of the fields. Results showed that ECa can be considered as support information with the aim of obtaining data to be used to implement site-specific management (intelligent soil sampling or differential and optimal fertilization), in soils with permanent pastures and to contribute to the increased sustainability of these systems.

Spatiotemporal monitoring of soil water content profiles in an irrigated field using probabilistic inversion of time-lapse EMI data

Abstract Monitoring spatiotemporal variations of soil water content (θ) is important across a range of research fields, including agricultural engineering, hydrology, meteorology and climatology. Low frequency electromagnetic induction (EMI) systems have proven to be useful tools in mapping soil apparent electrical conductivity (σa) and soil moisture. However, obtaining depth profile water content is an area that has not been fully explored using EMI. To examine this, we performed time-lapse EMI measurements using a CMD mini-Explorer sensor along a 10 m transect of a maize field over a 6 day period. Reference data were measured at the end of the profile via an excavated pit using 5TE capacitance sensors. In order to derive a time-lapse, depth-specific subsurface image of electrical conductivity (σ), we applied a probabilistic sampling approach, DREAM ( Z S ) , on the measured EMI data. The inversely estimated σ values were subsequently converted to θ using the Rhoades et al. (1976) petrophysical relationship. The uncertainties in measured σa, as well as inaccuracies in the inverted data, introduced some discrepancies between estimated σ and reference values in time and space. Moreover, the disparity between the measurement footprints of the 5TE and CMD Mini-Explorer sensors also led to differences. The obtained θ permitted an accurate monitoring of the spatiotemporal distribution and variation of soil water content due to root water uptake and evaporation. The proposed EMI measurement and modeling technique also allowed for detecting temporal root zone soil moisture variations. The time-lapse θ monitoring approach developed using DREAM ( Z S ) thus appears to be a useful technique to understand spatiotemporal patterns of soil water content and provide insights into linked soil moisture vegetation processes and the dynamics of soil moisture/infiltration processes.

Potential of apparent soil electrical conductivity to describe the soil pH and improve lime application in a clayey soil

One of the main limitations in applying fertilisers at a variable rate according to soil requirements is the number of samples required to spatially represent soil attributes. Currently, to map soil attributes with high accuracy, a high sample density is needed, which is often unfeasible. One technique that minimizes the number of samples required is the use of maps that have prior information of the soil spatial variability, making it possible to identify representative sampling locations in the field. Soil apparent electrical conductivity (ECa) is a powerful indicator of the characteristics of the soil matrix, and it can be easily measured. The objectives of this study were to determine the efficacy of ECa for mapping the spatial variability of soil properties using a targeted and reduced number of samples (which is economically feasible for growers). A high sampling density was applied in an experimental sugarcane field to assess the spatial variability of soil attributes, and ECa was read by a direct contact sensor. Thematic maps of important agronomic attributes were generated using the high sample density and compare with a targeted and reduced sampling helped by ECa measurements. The methodology used to obtain the spatial variability maps of soil chemical and physical properties indicated that it was possible to obtain maps of acceptable accuracy (r=0.82 and 0.59 for clay and pH, respectively) that could be used to formulate lime recommendations for variable-rate applications. The total lime spent (28 tons) based on the application maps generated by a targeted sampling mesh (20 sampling points) was very similar to that (30 tons) obtained based on high-density sampling (204 sampling points), representing a large improvement in soil sampling methodology. Here we demonstrated that ECa can improve prediction maps and enables efficient sample orientation. This approach provides an interesting way to map soil properties over large areas using a feasible sampling method and can help farmers in crop management while respecting environmental and soil requirements.

Soil moisture distribution mapping in topsoil and its effect on maize yield

AbstractSoil moisture content directly influences yield. Mapping within field soil moisture content differences provides information for agricultural management practices.In this study we aimed to find a cost-effective method for mapping within field soil moisture content differences. Spatial coverage of the field sampling or TDR method is still not dense enough for site-specific soil management. Soil moisture content can be calculated by measuring the apparent soil electrical conductivity (Soil moisture map was also compared to yield map showing correlation (

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

AbstractPrimary (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 (ECa – 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.

Improved evaluation of field experiments by accounting for inherent soil variability

Abstract Well-controlled field experiments are used to test agronomic management practices and evaluate the performance of cultivars in highly managed plots at experimental stations, in breeding nurseries or on-farm. However, the performance of crops and therefore the interpretation of experiments is affected by the inherent soil variability. To avoid large residual errors, replicate measurements or optimized designs are usually helpful but seldom adequately consider the unknown soil variability. The use of spatial covariates, such as proximally sensed data, in the statistical modelling of the target variable may provide a better estimate of such experimental residual variations (errors). Therefore, the purpose of this study was to determine whether the apparent soil electrical conductivity, topographical parameters and location information (expressed as Gaus-Kruger coordinates) could be used for an enhanced spatial and temporal characterization of the long-term and annual wheat yields within a static, long-term nitrogen fertilizer experiment that included six different forms of nitrogen and three levels of nitrogen fertilizer. Furthermore, this investigation aimed to propose statistical strategies for analysing this background variation by testing ANOVA (Analysis of variance) and ANCOVA (Analysis of covariance). ANCOVA with soil ECa, location information and topographic parameters as covariates improved the accuracy of the yield estimates of the multi-annual means for all treatments. Without these independent variables in ANOVA, the coefficient of determination (R2) was smaller and the root mean square difference (RMSD) was larger than those of ANCOVA (fertilized plots ANOVA: R2 = 0.19, RMSD = 3.26 dt ha−1; ANCOVA: R2 = 0.87, RMSD = 1.29 dt ha−1). In addition to the factor level of fertilization and form of nitrogen fertilizer, ECa was the dominant covariate for the averaged long-term and annual yields. The ECa was measured with different sensors and configurations and represented a significant independent variable. Of the topographic relief parameters, the predictor plancurvature was the dominant independent variable. The inclusion of plot-wise, time-invariant soil and relief parameters significantly improved the discrimination of testing the treatment performance within the long-term field trial. A further application of this approach to other experimental sites and breeding nurseries would likely be highly rewarding.

Sensor data fusion for soil health assessment

Assessment of soil health involves determining how well a soil is performing its biological, chemical, and physical functions relative to its inherent potential. Due to high cost, labor requirements, and soil disturbance, traditional laboratory analyses cannot provide high resolution soil health data. Therefore, sensor-based approaches are important to facilitate cost-effective, site-specific management for soil health. In the Central Claypan Region of Missouri, USA, visible and near-infrared (VNIR) diffuse reflectance spectroscopy has successfully been used to estimate biological components of soil health as well as Soil Management Assessment Framework (SMAF) scores. In contrast, estimation models for important chemical and physical aspects of soil health have been less successful with VNIR spectroscopy. The primary objective of this study was to apply a sensor fusion approach to estimate soil health indicators and SMAF scores using VNIR spectroscopy in conjunction with soil apparent electrical conductivity (ECa), and penetration resistance measured by cone penetrometer (i.e., cone index, CI). Soil samples were collected from two depths (0–5 and 5–15cm) at 108 locations within a 10-ha research site encompassing different cropping systems and landscape positions. Soil health measurements and VNIR spectral data were obtained in the laboratory, while CI and ECa data were obtained in situ. Calibration models were developed with partial least squares (PLS) regression and model performance was evaluated using coefficient of determination (R2) and root mean square error (RMSE). Models integrating ECa and CI with VNIR reflectance data improved estimates of the overall SMAF score (R2=0.78, RMSE=7.21%) relative to VNIR alone (R2=0.69, RMSE=8.41%), reducing RMSE by 14%. Improved models were also achieved for estimates of the individual chemical, biological, and physical soil health scores, demonstrating reductions in RMSE of 2.8, 5.4, and 10.0%, respectively. The results of this study illustrate the potential for rapid quantification of soil health by fusing VNIR sensor data with auxiliary data obtained from complementary sensors.

MAPEAMENTO DO SOLO E OCORRÊNCIA DE PLANTAS DANINHAS E ESTIMATIVAS DO CUSTO DE PRODUÇÃO DE CANA-DE-AÇÚCAR

The use of Precision Agriculture (PA) tools for variable rate herbicide application may contribute to the use of herbicides only in the areas with most weed occurrence, reducing environmental impacts and production costs. The aim of this research was to evaluate the soil apparent electrical conductivity and vegetation indexes, as well as to estimate the economic return of variable rate herbicide application for weed control in the sugarcane culture. The study was conducted in a field of 11 ha planted with sugarcane variety RB855453, which had a large infestation of bermudagrass. Measurements of soil electrical conductivity and NDVI were performed. Based on the maps generated, the area for application of pre- and post-planting herbicides was estimated. The results observed in the ECa maps indicated that the study area could be divided into two sections with different soil textures. NDVI showed the bermudagrass occurrence areas, which represented 18% of the total. The simulation demonstrated that the use of PA could assists in reducing sugarcane production costs by 0.9 % in the stage of soil preparation and planting, 7.2% in the stage of cane-plant and 11.4% in the stage of sugarcane ratoon.

Simplifying field-scale assessment of spatiotemporal changes of soil salinity

Monitoring soil salinity (ECe) is important for planning and implementing agronomic and irrigation practices. Salinity can be measured through soil sampling directed by geospatial measurements of apparent soil electrical conductivity (ECa). Using data from a long-term (1999–2012) monitoring study at a 32.4-ha saline field located in California, USA, two established field-scale approaches to map and monitor soil salinity using ECa are reviewed: one that relies on a single ECa survey to identify locations that can be repeatedly sampled to infer the frequency distribution of ECe; and another based on repeated ECa surveys that are calibrated, each time, to ECe estimation using ground-truth data from soil samples. The reviewed approaches are very accurate and reliable, but require extensive soil sampling. Subsequently, we propose a novel approach – temporal analysis of covariance (t-ANOCOVA) modeling – that results in accurate spatiotemporal salinity estimations using ECa surveys with a significant reduction in the number of soil samples needed for calibration of ECa to ECe. In this modeling framework, the ECe-ECa relationship is described with a log-transformed linear function. The regression slope indicates the magnitude of the contribution of ECe to ECa and is assumed to remain constant over time, while the intercept represents the secondary factors influencing ECa that are not related to ECe (e.g., soil tillage). Once the t-ANOCOVA slope is established for a field, in subsequent surveys as few as three soil samples are used to estimate a time-specific t-ANOCOVA intercept so that ECa measurements can be converted to ECe estimations. Our results suggest that this approach is reliable at low salinity values (i.e., where common crops can grow). The t-ANOCOVA approach requires further validation before real-world implementations, but represents a significant step towards the use of ECa mobile sensor technology for inexpensive soil salinity monitoring at high temporal resolution.

Retrospective 70 y-spatial analysis of repeated vine mortality patterns using ancient aerial time series, Pléiades images and multi-source spatial and field data

For any wine estate, there is a need to demarcate homogeneous within-vineyard zones (‘terroirs’) so as to manage grape production, which depends on vine biological condition. Until now, the studies performing digital zoning of terroirs have relied on recent spatial data and scant attention has been paid to ancient geoinformation likely to retrace past biological condition of vines and especially occurrence of vine mortality. Is vine mortality characterized by recurrent and specific patterns and if so, are these patterns related to terroir units and/or past landuse? This study aimed at performing a historical and spatial tracing of vine mortality patterns using a long time-series of aerial survey images (1947–2010), in combination with recent data: soil apparent electrical conductivity EM38 measurements, very high resolution Pléiades satellite images, and a detailed field survey. Within a 6 ha-estate in the Southern Rhone Valley, landuse and planting history were retraced and the map of missing vines frequency was constructed from the whole time series including a 2015-Pléiades panchromatic band. Within-field terroir units were obtained from a support vector machine classifier computed on the spectral bands and NDVI of Pléiades images, EM38 data and morphometric data.Repeated spatial patterns of missing vines were highlighted throughout several plantings, uprootings, and vine replacements, and appeared to match some within-field terroir units, being explained by their specific soil characteristics, vine/soil management choices and the past landuse of the 1940s. Missing vines frequency was spatially correlated with topsoil CaCO3 content, and negatively correlated with topsoil iron, clay, total N, organic C contents and NDVI. A retrospective spatio-temporal assessment of terroir therefore brings a renewed focus on some key parameters for maintaining a sustainable grape production.

Field Study of Variable Rate Irrigation Management in Humid Climates

AbstractComparison of variable rate irrigation (VRI) management with uniform rate irrigation (URI) management in soybean and corn crops was studied for 2 years in Stoneville, Mississippi, USA. The experiments were conducted on two 6.7 ha fields. Each of them was equally split into two sectors. VRI management was performed in one sector and URI management in the other. A centre pivot VRI system was used for delivering irrigation water. Soil apparent electrical conductivity (EC) of the fields was used to delineate VRI management zones and create a VRI prescription map. The VRI treatment used 25% less irrigation water and produced 2.8% more yield in soybean and 0.8% more yield in corn than the URI treatment. Irrigation water productivity (WP) of soybean under VRI management was 0.84 kg m−3 which is 31.2% higher than URI. The WP of corn under VRI management was 1.69 kg m−3, 27.1% higher than URI. Yield of the rainfed treatment was significantly lower than the VRI and URI treatments for both soybean and corn in the 2015 season (p < 0.05). Results in this study demonstrated that VRI management was superior to URI in terms of water use efficiency. Copyright © 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Copyright © 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Repeated electromagnetic induction measurements for mapping soil moisture at the field scale: validation with data from a wireless soil moisture monitoring network

Abstract. Electromagnetic induction (EMI) measurements are widely used for soil mapping, as they allow fast and relatively low-cost surveys of soil apparent electrical conductivity (ECa). Although the use of non-invasive EMI for imaging spatial soil properties is very attractive, the dependence of ECa on several factors challenges any interpretation with respect to individual soil properties or states such as soil moisture (θ). The major aim of this study was to further investigate the potential of repeated EMI measurements to map θ, with particular focus on the temporal variability of the spatial patterns of ECa and θ. To this end, we compared repeated EMI measurements with high-resolution θ data from a wireless soil moisture and soil temperature monitoring network for an extensively managed hillslope area for which soil properties and θ dynamics are known. For the investigated site, (i) ECa showed small temporal variations whereas θ varied from very dry to almost saturation, (ii) temporal changes of the spatial pattern of ECa differed from those of the spatial pattern of θ, and (iii) the ECa–θ relationship varied with time. Results suggest that (i) depending upon site characteristics, stable soil properties can be the major control of ECa measured with EMI, and (ii) for soils with low clay content, the influence of θ on ECa may be confounded by changes of the electrical conductivity of the soil solution. Further, this study discusses the complex interplay between factors controlling ECa and θ, and the use of EMI-based ECa data with respect to hydrological applications.

Potential to map depth-specific soil organic matter content across an olive grove using quasi-2d and quasi-3d inversion of DUALEM-21 data

Owing to conducive economic conditions and a suitable climate Andalucía has become the largest olive oil producing area in the world. However, extensive rill and severe gully erosion are threatening sustainability. To conserve the soil, land management strategies have to be implemented that minimize erosion and enhance at the same time rain water conservation to safeguard crop yields. An indicator of soil quality and the success of soil management will be organic matter content (OM—%). There is therefore a need to measure, map, manage and monitor its content. Proximal sensors such as electromagnetic induction instruments may be useful in mapping this because the apparent soil electrical conductivity (ECa—mS/m) is related to clay, salinity and mineralogy, which influence organic matter content. In this research we collect data from a single frequency and multiple-coil DUALEM-21 along a transect and across an olive grove in the “La Manga” catchment in Setenil de las Bodegas in the southwest of Spain. We inverted the data using EM4Soil software and developed calibrations between estimates of true electrical conductivity (σ—mS/m) with depth against measured OM % using the quasi-2d algorithm along a single transect. We did this by using a fitted linear regression model and by varying the forward modelling algorithm (cumulative function and full solution), inversion algorithm (S1 and S2) and damping factor (λ) to determine a suitable electromagnetic conductivity image for 2-d and 3-d mapping. Our results along a detailed transect showed promise and suggest differences in OM content could be mapped down a topographic sequence. We applied this calibration to a quasi-3d model across the entire olive grove and to predict OM at various depths. The results across the olive grove were compromised in some locations and within geomorphological complex locations in the landscape, such as near the erosion gully where frequent erosion and deposition occurs. We conclude that better results may have been achieved if more detailed ECa data collection was undertaken in and around the gully and also across a larger extent.

A geostatistical approach for modelling and combining spatial data with different support

The paper proposes a geostatistical framework to solve the issues of heterogeneous support for spatial estimation. Apparent soil electrical conductivity (ECa) was measured in a field cropped with San Marzano tomato using a multiple frequency electromagnetic profiler with 6 operating frequencies. Mixed support kriging was used to estimate ECa taking into account the change of support. The method includes punctual kriging with the error being the dispersion variance associated with each frequency. The individual ECa maps were weighted by the dispersion variance to obtain a map which was used for field partition in management zones.

Potential of freely available remote sensing visible images to support growers in delineating within field zones

Remote sensing images offer a unique monitoring capacity for agriculture fields. Despite significant drawbacks, free historical remote sensing images are likely to provide valuable information to delineate temporally stable zones, especially for a perennial crop like vines. In order to test the potential of these free images for delineating within field soil zones, an experiment was performed on three vine fields located in southern France. On each vine field, the grower was asked to delineate within field zones that present differences in soil characteristics. The first expert zoning (ZP zoning) was only based on the grower’s knowledge. Two weeks after, 12 visible free remote sensing were provided to the grower to refine the first zoning (ZAI zoning). In order to assess the relevancy of both zonings, a soil apparent electrical conductivity (ECa) survey was performed on each field. The Rv zoning index was then computed on ECa data to assess the potential improvement between ZP and ZAI zonings. Results show that for two fields, remote sensing images improved the zoning while for one field both ZP and ZAI expert zonings were similar. This result highlights a potential interest in historical remote sensing images to improve the knowledge that growers have of their fields. It should be noted, however, that this improvement is still limited in the case of viticulture where the small size of the fields with a large number of manual operations is likely to lead to a good knowledge of within field variability.

Delineation of management zones based on the Rasch model in an olive orchard

The probabilistic Rasch model is used to get objective measures of production potential at some locations of an olive orchard located in Badajoz, southwestern Spain. Nine soil properties (soil apparent electrical conductivity, clay, sand, and silt content, organic matter, total nitrogen, available phosphorous and potassium, and cation exchange capacity), taken at 40 locations in the field, were considered and, after being integrated in the model, a ranking of all locations according to the soil production potential and the influence on the production potential of each individual soil property were determined. Moreover, those soil samples or properties which had any anomaly where highlighted; this information can be necessary to conduct site-specific treatments, leading to a more cost-effective and sustainable field management. Additionally, estimates using geostatistical algorithms were utilised to map soil production potential and to delineate with a rational basis the management zones in the field.

A strategy to instigate SSCM in Australian potato production

To explore the potential for site-specific crop management in Australian potato production, soil apparent electrical conductivity (ECa) and high resolution elevation data were used to first define the variation in soil and landscape resources in two regions in Tasmania. Variation in crop production was estimated using in-season aerial multispectral VIS-NIR reflectance measurements and then measured using a first generation on-harvester yield monitoring system. During the season, soil and crop physical, chemical and pathogen properties were measured to groundtruth the sensor-derived data. Substantial within-field and between field variation was found in soil physical, chemical and pathogen properties, elevation and crop yield. The average potato yield for the study fields was 64 t/ha, with over three-fold within-field variation recorded. The in-season aerial crop reflectance significantly correlated with soil physical variability and pathogen load when gathered early in the season and to variation in plant physical and chemical properties, as well as important soil nutrient properties and crop yield when gathered from week 14 onwards. A set of general rules for instigating Site-specific crop management (SSCM) in potato production has been devised based initially on nutrient and pathogen management with irrigation management as an option.