Apparent Electrical Conductivity Research Articles

Variable-rate in corn sowing for maximizing grain yield

Sowing density is one of the most influential factors affecting corn yield. Here, we tested the hypothesis that, according to soil attributes, maximum corn productivity can be attained by varying the seed population. Specifically, our objectives were to identify the soil attributes that affect grain yield, in order to generate a model to define the optimum sowing rate as a function of the attributes identified, and determine which vegetative growth indices can be used to predict yield most accurately. The experiment was conducted in Chapadão do Céu-GO in 2018 and 2019 at two different locations. Corn was sown as the second crop after the soybean harvest. The hybrids used were AG 8700 PRO3 and FS 401 PW, which have similar characteristics and an average 135-day cropping cycle. Tested sowing rates were 50, 55, 60, and 65 thousand seeds ha−1. Soil attributes evaluated included pH, calcium, magnesium, phosphorus, potassium, organic matter, clay content, cation exchange capacity, and base saturation. Additionally, we measured the correlation between the different vegetative growth indices and yield. Linear correlations were obtained through Pearson’s correlation network, followed by path analysis for the selection of cause and effect variables, which formed the decision trees to estimate yield and seeding density. Magnesium and apparent electrical conductivity (ECa) were the most important soil attributes for determining sowing density. Thus, the plant population should be 56,000 plants ha−1 to attain maximum yield at ECa values > 7.44 mS m−1. In addition, the plant population should be 64,800 plants ha−1 at values < 7.44 mS m−1 when magnesium levels are greater than 0.13 g kg−1, and 57,210 plants ha−1 when magnesium content is lower. Trial validation showed that the decision tree effectively predicted optimum plant population under the local experimental conditions, where yield did not significantly differ among populations.

Electrical Signatures of Diffusion-Limited Mixing: Insights from a Milli-fluidic Tracer Experiment

We investigate how diffusion-limited mixing of a layered solute concentration distribution within a porous medium impacts bulk electrical conductivity. To do so, we perform a milli-fluidic tracer test by injecting a fluorescent and electrically conductive tracer in a quasi two-dimensional (2D) water-saturated porous medium. High resolution optical- and geoelectrical monitoring of the tracer is achieved by using a fluorimetry technique and equipping the flow cell with a resistivity meter, respectively. We find that optical and geoelectrical outputs can be related by a temporal re-scaling that accounts for the different diffusion rates of the optical and electrical tracers. Mixing-driven perturbations of the electrical equipotential field lines cause apparent electrical conductivity time-series, measured perpendicularly to the layering, to peak at times that are in agreement with the diffusion transport time-scale associated with the layer width. Numerical simulations highlight high sensitivity of such electrical data to the layers’ degree of mixing and their distance to the injection electrodes. Furthermore, the electrical data correlate well with time-series of two commonly used solute mixing descriptors: the concentration variance and the scalar dissipation rate.

Spatial and Temporal Variability of Soil Salinity in the Yangtze River Estuary Using Electromagnetic Induction

Soil salt-water dynamics in the Yangtze River Estuary (YRE) is complex and soil salinity is an obstacle to regional agricultural production and the ecological environment in the YRE. Runoff into the sea is reduced during the impoundment period as the result of the water-storing process of the Three Gorges Reservoir (TGR) in the upper reaches of the Yangtze River, which causes serious seawater intrusion. Soil salinity is a problem due to shallow and saline groundwater under serious seawater intrusion in the YRE. In this research, we focused on the temporal variation and spatial distribution characteristics of soil salinity in the YRE using geostatistics combined with proximally sensed information obtained by an electromagnetic induction (EM) survey method in typical years under the impoundment of the TGR. The EM survey with proximal sensing method was applied to perform soil salinity survey in field in the Yangtze River Estuary, allowing quick determination and quantitative assessment of spatial and temporal variation of soil salinity from 2006 to 2017. We developed regional soil salinity survey and mapping by coupling limited laboratory data with proximal sensed data obtained from EM. We interpreted the soil electrical conductivity by constructing a linear model between the apparent electrical conductivity data measured by an EM 38 device and the soil electrical conductivity (EC) of soil samples measured in laboratory. Then, soil electrical conductivity was converted to soil salt content (soil salinity g kg−1) through established linear regression model based on the laboratory data of soil salinity and soil EC. Semivariograms of regional soil salinity in the survey years were fitted and ordinary kriging interpolation was applied in interpolation and mapping of regional soil salinity. The cross-validation results showed that the prediction results were acceptable. The soil salinity distribution under different survey years was presented and the area of salt affected soil was calculated using geostatistics method. The results of spatial distribution of soil salinity showed that soil salinity near the riverbanks and coastlines was higher than that of inland. The spatial distribution of groundwater depth and salinity revealed that shallow groundwater and high groundwater salinity influenced the spatial distribution characteristics of soil salinity. Under long-term impoundment of the Three Gorges Reservoir, the variation of soil salinity in different hydrological years was analyzed. Results showed that the area affected by soil salinity gradually increased in different hydrological year types under the impoundment of the TGR.

SPATIAL VARIABILITY OF SOIL APPARENT ELECTRICAL CONDUCTIVITY - EFFECT OF THE NUMBER OF SUBSAMPLES

Soil apparent electrical conductivity (ECa) sensors have been used to detect spatial variability because they correlate with soil attributes. Studies with soil attributes have shown that the number of subsamples and sampling points influences mapping. However, there are no studies that investigated the influence of sampling or subsampling density on ECa maps. Therefore, this study verified the influence of ECa readings per sample point on the semivariance and kriging analysis. The data were collected from an area (2.5 ha) of coffee plants. One hundred sampling points were measured considering 20 readings each. 1, 5, 10, 15, and 20 sample point readings were tested. The influence of the number of readings per sampling point on the ECa mapping was determined using linear regression analysis at a significance level of 5%. The results obtained showed that ECa readings per sampling point significantly influence ECa maps. In addition, they demonstrated that reducing the number of readings per sampling point increases prediction errors by kriging. Thus, ECa maps determined with the highest readings per sampling point were mostly accurate.

Time‐lapse imaging of soil moisture using electromagnetic conductivity imaging: Wetting phase

AbstractThe Vertosols of the lower Namoi valley in New South Wales, Australia, have been extensively developed for irrigated cotton (Gossypium hirsutum L.) production. However, there are competing demands for water from mining, environmental needs, and agriculture, with climate change forecasts suggesting reduced rainfall and hotter climates. This means irrigators need to improve water efficiency, which requires methods to monitor the soil volumetric water content (θ, m3 m–3). The aim of this study is to assess if a multicoil electromagnetic instrument (DUALEM‐421), which measures soil apparent electrical conductivity (ECa, mS m–1), can be used to value add to limited measurements of θ and allow extrapolation along a uniformly heavy clay irrigation furrow. Specifically, we seek to invert ECa using EM4Soil software to generate electromagnetic conductivity images, whereby the estimates of soil true electrical conductivity (σ, mS m–1) in the topsoil, subsurface, and subsoil are correlated with θ measured by Decagon GS3 sensors installed at the same depths. We also compared the usefulness of inversion of different combinations of DUALEM ECa: DUALEM‐1, DUALEM‐2, and DUALEM‐21. We found estimated σ from inversion of DUALEM‐421 ECa had good agreement (i.e., Lin's concordance = .84) with measured soil bulk electrical conductivity (σb, mS m–1). We explored the relationship between DUALEM‐421 estimated σ and θ using an artificial neural network. The predictions showed the model had good coefficient of determination (R2 = .74) with good accuracy (RMSE = 0.04 m3 m–3) and demonstrated good agreement between measured and predicted θ (Lin's = .84).

Groundcover Mulching in Mediterranean Vineyards Improves Soil Chemical, Physical and Biological Health Already in the Short Term

Vineyards are among the land uses with the highest soil degradation rate in Mediterranean Europe, mainly due to intensive tillage management. Therefore, practices able to foster soil health are critical to promote sustainable wine production. We studied the following treatments in two organic farms in Chianti Classico (Italy): conventional tillage, spontaneous vegetation, pigeon bean (Vicia faba var. minor) incorporated in spring and a mixture of barley (Hordeum vulgare) and squarrosum clover (Trifolium squarrosum), both incorporated and left as mulch. An innovative approach, based on gamma-ray and apparent electrical conductivity, was used to account for the fine-scale soil variability that was included in the statistical model. Mulched groundcovers were associated with higher soil organic matter compared to tillage, already after two years. An increased N availability was found under all groundcovers compared with tillage. The effect of soil management practices on P2O5 strongly varied across farms and years, while it was not statistically significant on K availability. Spontaneous vegetation positively influenced the soil structure index, soil penetration resistance and soil biological health. The results show that mulched groundcovers can improve soil health already in the short term, thereby potentially increasing the sustainability of the wine sector.

Apparent soil electrical conductivity in the delineation of management zones for cocoa cultivation

Apparent electrical conductivity is an important parameter for describing the spatial variability of physical and chemical attributes of the soil and for the delineation of management zones. The objective of this work is to outline management zones for the cocoa cultivation based on the spatial variability of the productivity and the apparent electrical conductivity (ECa) of the soil. Data collection was performed in a regular sample grid containing 120 points in an area cultivated with cocoa trees, located in the municipality of Ilhéus, state of Bahia, Brazil. At each sampling point (cocoa tree), soil samples were collected to determine chemical attributes. Productivity was measured for one year, counting, monthly, the number of fruits, which were classified into off-season cocoa, harvest and annual production. Measurements of the apparent electrical conductivity of the soil were performed at different times of the year using a portable conductivity meter. The data were analyzed using classical statistics and geostatistics. The management zones were delineated using the fuzzy k-means algorithm. The ideal number of class was defined using the fuzziness performance index (FPI) and the entropy of the modified partition (MPE) indexes. The Kappa coefficient was used to validate the management zones, assessing their agreement with the chemical attributes of the soil. The ECa of the soil values presented moderate temporal variation, with maximum amplitude of 19.37mSm−1 and minimum of 0.82mSm−1 between measurement periods; higher averages of the ECa coincided with the highest levels of water in the soil. The measurements of the ECa of the soil carried out in April and October showed greater correlation with the chemical attributes of the soil, with significant values for 11 and 8 of the 17 attributes evaluated, respectively. The management zones from the ECa measured in April showed: a) reduced number of classes; b) spatial continuity between classes, and; c) agreement from reasonable (kappa between 0.20 and 0.40) to good (kappa > 0.41) with most of the chemical attributes of the soil. The ECa of the soil measured in April is, individually, the variable recommended for the management of soil fertility in tropical areas cultivated with cocoa trees.

Effects of large-scale heterogeneity and temporally varying hydrologic processes on estimating immobile pore space: A mesoscale-laboratory experimental and numerical modeling investigation

The advection-dispersion equation (ADE) often fails to predict solute transport, in part due to incomplete mixing in the subsurface, which the development of non-local models has attempted to deal with. One such model is dual-domain mass transfer (DDMT); one parameter that exists within this model type is called immobile porosity. Here, we explore the complexity of estimating immobile porosity under varying flow rates and density dependencies in a large-scale heterogeneous system. Immobile porosity is estimated experimentally and using numerical models in 3-D flow systems, and is defined by domains of comparatively low advective velocity instead of truly immobile regions at the pore scale. Tracer experiments were conducted in a mesoscale 3-D tank system with embedded large impermeable zones and the generated data were analyzed using a numerical model. The impermeable zones were used to explore how large-scale structure and heterogeneity affect parameter estimation of immobile porosity, assuming a dual-porosity model, and resultant characterization of the aquifer system. Spatially and temporally co-located fluid electrical conductivity (σf) and bulk apparent electrical conductivity (σb)—using geophysical methods—were measured to estimate immobile porosity, and numerical modeling (i.e., SEAWAT and R3t) was conducted to explore controls of the immobile zones on the experimentally observed flow and transport. Results showed that density-dependent flow increased the hysteresis between measured fluid and bulk electrical conductivity, resulting in larger interpreted immobile pore-space estimates. Increasing the dispersivity in the model simulations decreased the estimated immobile porosity; flow rate had no impact. Overall, the results of this study highlight the difficulty faced in determining immobile porosity values in field settings, where hydrogeologic processes may vary temporally. Our results also highlight that immobile porosity is an effective parameter in an upscaled model whose physical meaning is not necessarily clear and that may not align with intuitive interpretations of a porosity.

Micro-Water Harvesting and Soil Amendment Increase Grain Yields of Barley on a Heavy-Textured Alkaline Sodic Soil in a Rainfed Mediterranean Environment

This paper focuses on the adverse effects of soil sodicity and alkalinity on the growth of barley (Hordeum vulgare L.) in a rainfed environment in south-western Australia. These conditions cause the accumulation of salt (called ‘transient salinity’) in the root zone, which decreases the solute potential of the soil solution, particularly at the end of the growing season as the soil dries. We hypothesized that two approaches could help overcome this stress: (a) improved micro-water harvesting at the soil surface, which would help maintain soil hydration, decreasing the salinity of the soil solution, and (b) soil amelioration using small amounts of gypsum, elemental sulfur or gypsum plus elemental sulfur, which would ensure greater salt leaching. In our experiments, improved micro-water harvesting was achieved using a tillage technique consisting of exaggerated mounds between furrows and the covering of these mounds with plastic sheeting. The combination of the mounds and the application of a low rate of gypsum in the furrow (50 kg ha−1) increased yields of barley grain by 70% in 2019 and by 57% in 2020, relative to a control treatment with conventional tillage, no plastic sheeting and no amendment. These increases in yield were related to changes in ion concentrations in the soil and to changes in apparent electrical conductivity measured with the EM38.

Evaluation of a multi‐sensor for measuring solution electrical conductivity in coir

AbstractThe simultaneous measurements of both volumetric water content (θ) and electrical conductivity of pore solutions (ECw) are essential for nutrient solution management within soilless culture systems. The present study was carried out in order to evaluate a capacitance sensor (METER 5TE) for the measurement θ and ECw within coir. Following the evaluation of the spatial sensitivity of the 5TE sensor, a calibration experiment was executed with six levels of θ (0.30, 0.35, 0.40, 0.45, 0.50, and 0.55 m3 m−3) and four levels of KCl solutions (1.0, 3.0, 5.0, and 7.0 dS m−1). The measured relationship between θ and dielectric permittivity (εa) for coir significantly deviated from Topp's equation for organic soil, but was similar to the calibration equation for growing media from the sensor manufacturer and for peat soils. The Hilhorst (2000) model recommended by the manufacturer overestimated ECw within coir. On the other hand, accounting for EC dependency on the real part of the complex dielectric permittivity of the solution within the Hilhorst model will greatly improve ECw prediction. In contrast, the Rhoades (1976) model was more capable of describing the dependence of the apparent soil electrical conductivity (ECa) on θ and ECw for the coir samples for ECa &lt; 2.9 dS m−1. Hence, when ECw−ECa−θ relations are known, the capacitance sensor is a powerful tool for horticultural practices because a single measurement can yield both θ and ECw.

Influence of soil heterogeneity on soybean plant development and crop yield evaluated using time-series of UAV and ground-based geophysical imagery

Understanding the interactions among agricultural processes, soil, and plants is necessary for optimizing crop yield and productivity. This study focuses on developing effective monitoring and analysis methodologies that estimate key soil and plant properties. These methodologies include data acquisition and processing approaches that use unmanned aerial vehicles (UAVs) and surface geophysical techniques. In particular, we applied these approaches to a soybean farm in Arkansas to characterize the soil–plant coupled spatial and temporal heterogeneity, as well as to identify key environmental factors that influence plant growth and yield. UAV-based multitemporal acquisition of high-resolution RGB (red–green–blue) imagery and direct measurements were used to monitor plant height and photosynthetic activity. We present an algorithm that efficiently exploits the high-resolution UAV images to estimate plant spatial abundance and plant vigor throughout the growing season. Such plant characterization is extremely important for the identification of anomalous areas, providing easily interpretable information that can be used to guide near-real-time farming decisions. Additionally, high-resolution multitemporal surface geophysical measurements of apparent soil electrical conductivity were used to estimate the spatial heterogeneity of soil texture. By integrating the multiscale multitype soil and plant datasets, we identified the spatiotemporal co-variance between soil properties and plant development and yield. Our novel approach for early season monitoring of plant spatial abundance identified areas of low productivity controlled by soil clay content, while temporal analysis of geophysical data showed the impact of soil moisture and irrigation practice (controlled by topography) on plant dynamics. Our study demonstrates the effective coupling of UAV data products with geophysical data to extract critical information for farm management.

Assessing the dynamics of soil salinity with time-lapse inversion of electromagnetic data guided by hydrological modelling

Abstract. Irrigated agriculture is threatened by soil salinity in numerous arid and semi-arid areas of the world, chiefly caused by the use of highly salinity irrigation water, compounded by excessive evapotranspiration. Given this threat, efficient field assessment methods are needed to monitor the dynamics of soil salinity in salt-affected irrigated lands and evaluate the performance of management strategies. In this study, we report on the results of an irrigation experiment with the main objective of evaluating time-lapse inversion of electromagnetic induction (EMI) data and hydrological modelling in field assessment of soil salinity dynamics. Four experimental plots were established and irrigated 12 times during a 2-month period, with water at four different salinity levels (1, 4, 8 and 12 dS m−1) using a drip irrigation system. Time-lapse apparent electrical conductivity (σa) data were collected four times during the experiment period using the CMD Mini-Explorer. Prior to inversion of time-lapse σa data, a numerical experiment was performed by 2D simulations of the water and solute infiltration and redistribution process in synthetic transects, generated by using the statistical distribution of the hydraulic properties in the study area. These simulations gave known spatio-temporal distribution of water contents and solute concentrations and thus of bulk electrical conductivity (σb), which in turn were used to obtain known structures of apparent electrical conductivity, σa. These synthetic distributions were used for a preliminary understanding of how the physical context may influence the EMI-based σa readings carried out in the monitored transects as well as being used to optimize the smoothing parameter to be used in the inversion of σa readings. With this prior information at hand, we inverted the time-lapse field σa data and interpreted the results in terms of concentration distributions over time. The proposed approach, using preliminary hydrological simulations to understand the potential role of the variability of the physical system to be monitored by EMI, may actually allow for a better choice of the inversion parameters and interpretation of EMI readings, thus increasing the potentiality of using the electromagnetic induction technique for rapid and non-invasive investigation of spatio-temporal variability in soil salinity over large areas.

Determining sensor-based field capacity for irrigation scheduling

Irrigated agriculture is a major consumer of freshwater and acreage of irrigated land has increased as producers have become increasingly reliant on irrigation to ensure adequate yields and reduce production risks. Information about the soil’s field capacity, or the water content after a saturated soil has drained for at least 24 h, is needed to determine the soil’s readily available water, or the amount of water that can be safely removed by plants. To address the challenges associated with using soil water sensors in highly variable soils, soil water measurements were included in ongoing cotton irrigation studies during the 2017 through 2019 growing seasons at the University of Missouri Fisher Delta Research Center near Portageville. Sensor-based field capacity values were compared among multiple commercially available sensors. Time-domain reflectometry sensors (Acclima TDR 315) were installed at four depths and five locations each of the three years. In addition, resistance-type sensors (Irrometer Watermark 200SS) were installed at five depths and five locations for two years. The goal was to provide guidance on how best to use soil moisture sensor data for irrigation management. Although water continued to move in the profile for more than 24 h after saturation, the average change between 24 and 48 h at each observed depth was < 0.015 m3 m−3. Observed values for coarser textured soils were much wetter after 24 h than expected based on published field capacity values and in one case, even a small difference in sensor location had a large effect on the observed field capacity. Furthermore, soil apparent electrical conductivity was highly correlated to measured field capacity, which could result in a much faster and less expensive way to estimate field capacity compared to collecting and analyzing soil cores. These findings demonstrate that the application of soil moisture sensors for irrigation management is site specific, and differences can be observed over short distances within a field. The research is continuing to better meet the needs of agricultural producers, consultants, research and extension personnel, and others for information to improve irrigation management.

A soft-computing approach to estimate soil electrical conductivity

Soil apparent electrical conductivity (EC a ) is an indirect and rapid measurement for soil salinity, but because of its dependency on some physical and chemical properties of soil in addition to salinity, consideration of the soil extract EC is preferred for monitoring soil salinity, especially in semi-arid areas, though its measurement needs laboratory processes. This study, therefore, sought to develop a multivariable model to estimate the soil EC e from soil EC a , temperature, moisture content, bulk density, and clay percentage, using radial basis function (RBF) artificial neural network (ANN). In the first step, a set of tests was performed in laboratory in Box-Behnken design (BBD) to train the RBF-ANN. The developed RBF estimated the soil EC e with R 2 = 0.99 and RMSE = 0.005 dS.m −1 . Moreover, a quadratic response surface model (RSM) was also developed to compare with the RBF model. The sensitivity analysis revealed that EC a , moisture, bulk density, and temperature had the maximum to minimum effect on the estimation of soil EC e , respectively. In the second step, the RBF and RSM models were validated by another dataset obtained from three sites located in a semi-arid area. They were applied in-field with a multi-sensor portable device. The R 2 and RMSE of the estimation of EC e by the RBF were equal to 0.801 and 0.350 dS.m −1 , respectively. While, R 2 and RMSE of the RSM model were 0.735 and 0.439 dS.m −1 , respectively. The results of the study indicated excellent ability of the RBF-ANN in the rapid and precise estimation of soil EC e . • Modelling soil ECe based on some soil physical and electrical properties. • Developing a portable multi-sensor soil surveying device to estimate soil ECe. • Investigating the effects of soil physical properties on the models. • Validating the model and the device in-field and comparing with the lab results.

A Novel Method for Thickness Measurement in Conducting Materials by Apparent Conductivity Calculation Through Transient Eddy Current NDE

This paper describes a novel method for thickness measurement in conducting material by combining the measurement of the decaying induced eddy current and its associated apparent conductivity calculation. In conventional frequency domain NDE (Non-destructive Evaluation), the skin depth is the limiting factor due to the single frequency excitation. In contrast, with a pulsed excitation, the induced eddy currents decay with time and the decay of the eddy current consists of continuum of frequencies and hence the entire thickness of the conducting material could be investigated in a single shot. The time at which the decay of the eddy current is maximum is called diffusion time “tm” and the corresponding calculated value of apparent electrical conductivity “σmapp” both of which can be directly correlated to the thickness of the material. Since the induction of the eddy current and its associated secondary magnetic field measured by using suitable magnetic sensors [hz α (σ/t)3/2 for B-field and Vz α (σ3/2/t5/2) for an induction coil] is directly related to the conductivity of the materials, one can calculate the apparent conductivity σapp(t) as a function of decay time. Apparent conductivity calculations have been made for aluminum plates based on the thin conducting sheet model in which the applied magnetic field is uniform throughout the thickness. Subsequently, transient measurements have been performed with stacks of aluminum plates by using pickup coils in the form of an absolute as well as differential configuration. From this, it has been shown that the square root of the ratio of the maximum diffusion time, tm and the corresponding apparent conductivity, σmapp called as maximum diffusion depth, δm which is proportional to the plate thickness. The diffusion depth is a parameter derived from standard electromagnetic equations which is analogous to the skin depth in the frequency domain. In this work, induction coil sensors of both types have been used to measure the transients, and the other instruments such as transmitter, transmitter controller and data acquisition system used for this work are the same ones used for TDEM (time domain electro-magnetic) based geophysical applications.

Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada

Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties, and therefore similar crop fertility requirements. Consequently, such MZs can often be used for site-specific management of crop production inputs. This study evaluated the effectiveness of four classification methods for delineating MZs in an 8-ha commercial potato field located in Prince Edward Island, Canada. The apparent electrical conductivity (ECa) at two depths from a commercial Veris sensor were used to delineate MZs using three classification methods without spatial constraints (i.e., fuzzy k-means, ISODATA and hierarchical) and one with spatial constraints (i.e., spatial segmentation method). Soil samples (0.0–0.15 m depth) from 104 sampling points was used to measure soil physical and chemical properties and their spatial variation in the field were used as reference data to evaluate four delineation methods. Significant Pearson correlations between ECa and soil properties were obtained (0.22 &lt; r &lt; 0.85). The variance reduction indicated that two to three MZs were optimal for representing the field’s spatial variability of soil properties. For two MZs, most soil physical and chemical properties differed significantly between MZs for all four delineation methods. For three MZs, there was greater discrimination among MZs for several soil properties for the spatial segmentation-based method compared with other delineation methods. Moreover, consideration of the spatial coordinates of the data improved the delineation of MZs and thereby increased the number of significant differences among MZs for individual soil properties. Therefore, the spatial segmentation method had the greatest efficiency in delineation of MZs from statistical and agronomic perspectives.

Modeling and simulation of a multi-parametric fuzzy expert system for variable rate nitrogen application

Nitrogen (N) excess due to mineral fertilization in conventional crop farming has a significant negative impact on the environment. Variable rate N application (VRNA) is a promising tool to increase N recovery rates in spatially heterogeneous fields. Real-time sensor systems for VRNA usually consider only the crop’s N status and their fertilization algorithms are abundantly deterministic. Due to their education and professional experience, farmers have a considerable knowledge base that should be used to describe the dynamic and non-deterministic interactions of multiple parameters for a locally adapted N fertilization. Fuzzy systems present an effective way to integrate expert knowledge into an automated multi-parametric control. This paper describes, how fuzzy logic can be used to fuse the plant-related information from a real-time sensor system with further parameters to create a multi-parametric system for VRNA. Using sets of input–output data acquired with a Yara N-Sensor ALS2 system, an adaptive, fuzzy logic-based model of its agronomic algorithms was identified, optimized and validated. The results indicated high accordance with the N-Sensor algorithms and good automated adaptability to different calibrations with values of the Pearson correlation coefficient higher than 0.99 and a maximum percentage root mean square error of 0.14%. In a case study, the model was combined with the apparent soil electrical conductivity (ECa) as an indicator for spatially varying soil productivity, as well as a case distinction for different weather conditions. Simulations with historic ECa data and N-Sensor recordings have shown the high flexibility of the multi-parametric fuzzy expert system. With the presented method, specific deficiencies of one-parametric approaches can be moderated and the application can be adapted to the prevailing conditions in a straightforward manner. Also, the target orientation could be influenced based on the specific preferences of the expert.

Soil apparent electrical conductivity and must carbon isotope ratio provide indication of plant water status in wine grape vineyards

Proximal sensing is being integrated into vineyard management as it provides rapid assessments of spatial variability of soils’ and plants’ features. The electromagnetic induction (EMI) technology is used to measure soil apparent electrical conductivity (ECa) with proximal sensing and enables to appraise soil characteristics and their possible effects on plant physiological responses. This study was conducted in a micro irrigated Cabernet Sauvignon (Vitis vinifera L.) vineyard to investigate the technical feasibility of appraising plant water status and its spatial variability using soil ECa and must carbon isotope ratio analysis (δ13C). Soil temperature and soil water content were monitored in-situ using time domain reflectometry (TDR) sensors. Soil ECa was measured with EMI at two depths [0–1.5 m (deep ECa) and 0–0.75 m (shallow ECa)] over the course of the crop season to capture the temporal dynamics and changes. At the study site, the main physical and chemical soil characteristics, i.e. soil texture, gravel, pore water electrical conductivity (ECe), organic carbon, and soil water content at field capacity, were determined from samples collected auguring the soil at equidistant points that were identified using a regular grid. Midday stem water potential (Ψstem) and leaf gas exchange, including stomatal conductance (gs), net carbon assimilation (An), and intrinsic water use efficiency (WUEi) were measured periodically in the vineyard. The δ13C of produced musts was measured at harvest. The results indicated that soil water content (relative importance = 24%) and texture (silt: relative importance = 22.4% and clay: relative importance = 18.2%) were contributing the most towards soil ECa. Deep soil ECa was directly related to Ψstem (r2 = 0.7214) and gs (r2 = 0.5007). Likewise, δ13C of must was directly related to Ψstem (r2 = 0.9127), gs (r2 = 0.6985), and An (r2 = 0.5693). Results from this work provided relevant information on the possibility of using spatial soil ECa sensing and δ13C analysis to infer plant water status and leaf gas exchange in micro irrigated vineyards.

The Relationship between Soil Electrical Parameters and Compaction of Sandy Clay Loam Soil

Soil spatial variability mapping allows the delimitation of the number of soil samples investigated to describe agricultural areas; it is crucial in precision agriculture. Electrical soil parameters are promising factors for the delimitation of management zones. One of the soil parameters that affects yield is soil compaction. The objective of this work was to indicate electrical parameters useful for the delimitation of management zones connected with soil compaction. For this purpose, the measurement of apparent soil electrical conductivity and magnetic susceptibility was conducted at two depths: 0.5 and 1 m. Soil compaction was measured for a soil layer at 0–0.5 m. Relationships between electrical soil parameters and soil compaction were modelled with the use of two types of neural networks—multilayer perceptron (MLP) and radial basis function (RBF). Better prediction quality was observed for RBF models. It can be stated that in the mathematical model, the apparent soil electrical conductivity affects soil compaction significantly more than magnetic susceptibility. However, magnetic susceptibility gives additional information about soil properties, and therefore, both electrical parameters should be used simultaneously for the delimitation of management zones.

Comparative analysis of soil-sampling methods used in precision agriculture

The aim of this study was to compare three different soil-sampling methods used in Precision Agriculture and their environmental impact in the agricultural production environment. The sampling methods used were: management zones by elevation (MZA), grid sampling (GS), and sampling oriented by apparent soil electrical conductivity (OS). It was tested in three different fields. When the recommendations were compared, a significant difference among the suggested dosages was observed, indicating the need to improve the soil-sampling techniques, since there were doubts about input deficits or overdoses, regardless of the technology studied. The GS method was the most environmentally viable alternative for P compared to other methods and the OS presented as the better option for K and N. However, the use of soil sensors has been shown to be a viable technology that needs further improvement in order to improve productivity and, hence, economic and environmental gains.