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

Abstract

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

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

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

An application of PCA and fuzzy C-means to delineate management zones and variability analysis of soil

Within-field variability is a well-known phenomenon and its study is at the centre of precision agriculture (PA). In this paper, site-specific spatial variability (SSSV) of apparent Electrical Conductivity (EC_a) and crop yield apart from pH, moisture, temperature and di-electric constant information was analyzed to construct spatial distribution maps. Principal component analysis (PCA) and fuzzy c-means (FCM) clustering algorithm were then performed to delineate management zones (MZs). Various performance indices such as Normalized Classification Entropy (NCE) and Fuzzy Performance Index (FPI) were calculated to determine the clustering performance. The geo-referenced sensor data was analyzed for within-field classification. Results revealed that the variables could be aggregated into MZs that characterize spatial variability in soil chemical properties and crop productivity. The resulting classified MZs showed favorable agreement between EC_a and crop yield variability pattern. This enables reduction in number of soil analysis needed to create application maps for certain cultivation operations.     

SOIL SALINITY AND SODICITY APPRAISAL BY ELECTROMAGNETIC INDUCTION IN SOILS IRRIGATED TO GROW COTTON

In the Far West Texas region in the USA, long‐term irrigation of fine‐textured valley soils with saline Rio Grande River water has led to soil salinity and sodicity problems. Soil salinity [measured by saturated paste electrical conductivity (EC_e)] and sodicity [measured by sodium adsorption ratio (SAR)] in the irrigated areas have resulted in poor growing conditions, reduced crop yields, and declining farm profitability. Understanding the spatial distribution of EC_e and SAR within the affected areas is necessary for developing management practices. Conventional methods of assessing EC_e and SAR distribution at a high spatial resolution are expensive and time consuming. This study evaluated the accuracy of electromagnetic induction (EMI), which measures apparent electrical conductivity (EC_a), to delineate EC_e and SAR distribution in two cotton fields located in the Hudspeth and El Paso Counties of Texas, USA. Calibration equations for converting EC_a into EC_e and SAR were derived using the multiple linear regression (MLR) model included in the EC_e Sampling Assessment and Prediction program package developed by the US Salinity Laboratory. Correlations between EC_a and soil variables (clay content, EC_e, SAR) were highly significant (p ≤ 0·05). This was further confirmed by significant (p ≤ 0·05) MLRs used for estimating EC_e and SAR. The EC_e and SAR determined by EC_a closely matched the measured EC_e and SAR values of the study site soils, which ranged from 0·47 to 9·87 dS m⁻¹ and 2·27 to 27·4 mmol^1/2 L^−1/2, respectively. High R² values between estimated and measured soil EC_e and SAR values validated the MLR model results. Results of this study indicated that the EMI method can be used for rapid and accurate delineation of salinity and sodicity distribution within the affected area. Copyright © 2012 John Wiley & Sons, Ltd.     

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

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

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

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

Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area

Recent advances in on-the-go soil sensing, terrain modelling and yield mapping have made available large quantities of information about the within-field variability of soil and crop properties. But the selection of the key variables for an identification of management zones, required for precision agriculture, is not straightforward. To investigate a procedure for this selection, an 8 ha agricultural field in the Loess belt of Belgium was considered for this study. The available information consisted of: (i) top- and subsoil samples taken at 110 locations, on which soil properties: textural fractions, organic carbon (OC), CaCO₃ and pH were analysed, (ii) soil apparent electrical conductivity (EC_a) obtained through an electromagnetic induction based sensor, and (iii) wetness index, stream power index and steepest slope angle derived from a detailed digital elevation model (DEM). A principal component analysis, involving 12 soil and topographic properties and two EC_a variables, identified three components explaining 67.4% of the total variability. These three components were best represented by pH, EC_a that strongly associated with texture and OC. However, OC was closely related to some more readily obtainable topographic properties, and therefore elevation was preferred. A fuzzy k-means classification of these three variables produced four potential management classes. Three-year average standardized yield maps of grain and straw showed productivity differences across these classes, but mainly linked to their landscape position. In the loess area with complex soil-landscape interactions pH, EC_a and elevation can be considered as key properties to delineate potential management classes.     

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

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

Modeling within field variation of the compaction layer in a paddy rice field using a proximal soil sensing system

A key characteristic of flooded paddy fields is the plough pan. This is a sub‐soil layer of greater compaction and bulk density, which restricts water losses through percolation. However, the thickness of this compacted layer can be inconsistent, with consequences such as variable percolation and leaching losses of nutrients, which therefore requires precision management of soil water. Our objective was to evaluate a methodology to model the thickness of the compacted soil layer using a non‐invasive electromagnetic induction sensor (EM38‐MK2). A 2.7 ha alluvial non‐saline paddy rice field was measured with a proximal soil sensing system using the EM38‐MK2 and the apparent electrical conductivity (EC_a) of the wet paddy soil was recorded at a high‐resolution (1.0 × 0.5 m). Soil bulk density (n = 10) was measured using undisturbed soil cores, which covered locations with large and small EC_a values. At the same locations (within 1 m²) the depth of the different soil layers was determined by penetrometer. Then a fitting procedure was used to model the EC_a – depth response functions of the EM38‐MK2, which involved solving a system of non‐linear equations and a R² value of 0.89 was found. These predictions were evaluated using independent observations (n = 18) where a Pearson correlation coefficient of 0.87 with an RMSEE value of 0.03 m was found. The EC_a measurements allowed the detail estimation of the compacted layer thickness. The link between water percolation losses and thickness of the compacted layer was confirmed by independent observations with an inverse relationship having a Pearson correlation coefficient of 0.89. This rapid, non‐invasive and cost‐effective technique offers new opportunities to measure differences in the thickness of compacted layers in water‐saturated soils. This has potential for site‐specific soil management in paddy rice fields.     

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

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

Evapotranspiration and Mineral Content of Sedum kamtschaticum Fischer Under Saline Irrigation

An alternative water resource such as graywater could be used for irrigation on green roofs during hot, dry summers, although it contains salt. In this study, the response to high-salt stress of a C3–CAM (Crassulacean acid metabolism) intermediate species, Sedum kamtschaticum Fischer, was evaluated over a 2-month experiment in terms of evapotranspiration (ET) and chemical compounds in plant tissue in triplicate for both experiments. High ET (10–15 mm day^?1) was observed under non-stressed conditions. On the day following the first saline irrigation, the peak ET at noon decreased as much as one-third of the maximum. After 9 days, ET remained below 3 mm day^?1, corresponding mostly to evaporation from the wet soil surface. The balance of chemical component contents in leaves changed depending on the electrical conductivity of irrigation water electrical conductivity (EC_i). The potassium to sodium (K⁺/Na⁺) ratio, which indicates levels of sodium toxic for plant growth, decreased with higher EC_i, while it excluded sodium from roots. However, based on enhanced water use efficiency under higher EC_i regardless of reduced carbon dioxide (CO₂) assimilation under salinity stress, the plant’s method of photosynthesis shifted from C3 to CAM metabolism. These findings show that S. kamtschaticum could survive for more than 2 months under low or moderate salinity of irrigation water in hot conditions.     

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.     

Soil texture affects the conversion factor of electrical conductivity from 1:5 soil-water to saturated paste extracts

Electrical conductivity(EC) of soil-water extracts is commonly used to assess soil salinity. However, its conversion to the EC of saturated soil paste extracts(ECe), the standard measure of soil salinity, is currently required for practical applications. Although many regression models can be used to obtain ECe from the EC of soil-water extracts, the application of a site-specific model to different sites is not straightforward due to confounding soil factors such as soil texture. This study was...     

Key soil nutrient requirements for different yield levels in North China

ABSTRACT

Soil chemical properties are closely related to crop production levels. Understanding the relationships between soil nutrients and different yield levels is important for improving the efficiency of fertilization management programs. The objectives of this study were to understand the key soil nutrient requirements for different crop yield levels using 10 experimental wheat-maize rotation sites and to optimize fertilization applications in North China. The results found significant differences between the soil chemical properties among the study sites, with average contents in the range of 10.07–14.72 g/kg for soil organic carbon (SOC), 0.38–1.29 g/kg for total nitrogen (TN), 56.43–89.77 mg/kg for available nitrogen (AN), 17.36–48.54 mg/kg for available phosphorus (AP), 79.4–184.5 mg/kg for available potassium (AK), 0.78–5.97 mg/kg for soil Cu, and 0.75–2.20 mg/kg for soil Zn. The soil pH values were 6.46–8.19. Significant correlations (p < 0.05) were found between high-level yields and higher contents of SOC, TN, AN, and AP when a suitable soil pH were present. The higher levels of soil SOC and TN were important for maintaining high-level yields in these regions. Soil AN and pH are two key limitations that could significantly (p < 0.05) improve medium-level yields. Although some soil indicators, including SOC, TN, AN, AP, soil pH, soil Zn, and Cu could significantly influence low-level yields, soil amendments with C, N, and available P and having a suitable soil pH were especially important for improving low-level yields. These results could be used to improve conventional methods of fertilization management and increase the efficiency of fertilizer use in North China.     

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

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

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.     

Organic carbon quantity and forms as influenced by tillage and cropping sequence

Abstract

Soil organic matter and its chemical fractions have a profound impact on soil chemical and physical properties. In turn, the effect of management (cropping and tillage) on the quantity and chemical properties of soil organic matter can be substantial. The objective of this study was to compare the effects of specific tillage regimes and crop sequences commonly used in the central Great Plains of the United States on the quantity, quality, and distribution with depth of soil organic carbon (SOC). Soils were sampled in 1 cm or 2 cm increments to a depth of 10 cm from experimental field plots on a Sharpsburg silty clay loam (fine, montmorillonitic, mesic Typic Argiudoll). The plots had been under 6 continuous tillage regimes since 1978 and cropped to continuous corn, continuous soybean, or corn‐soybean in rotation since 1985. Soils were analyzed for total SOC, fulvic acid (FA) carbon, and humic acid (HA) carbon. No‐till and continuous corn (Zea mays L.) management generally had the highest SOC, with a sharp reduction in SOC below 2 cm. Only no‐till increased FA, which also decreased with depth, especially between 2 and 4 cm. Humic acid concentration was highest under continuous corn but was unaffected by tillage. Humic acid also was highest in the 1‐ to 2‐cm increment of continuous corn. Two ratios which are used as indices of degree of humification, HA/FA and (HA+FA)/SOC, gave different estimates of the effect of management. Only continuous com increased HA/FA, suggesting increased humification. No treatment affected (HA+FA)/SOC. Overall, continuous corn and no‐till contributed the greatest amounts of residue and maintained a soil environment conducive to preserving the resulting organic matter. These management options increase not only total SOC, but also alter the quality of that SOC as measured by HA and FA. These changes in SOC characteristics may have implications for long‐term soil quality and soil productivity.     

Measurement of soil water and nutrients by its electrical properties

Abstract

An instrument for measurement of soil dielectric constant ε _r , electrical conductivity EC _a, and soil temperature was tested on soils under potato crop to investigate contents of soil volumetric water θ and nutrients for eventual use in a field crop model.

To approximate the dependence of θ on ε _r , a logarithmic equation was chosen. Satisfactory results were obtained on stone-free areas, with the mean relative variance between θ-values determined by dielectric constant and converted from a gravimetric method remaining within the limits of measuring error. However, variances were higher for stony soils, with ε _r -values at the same θ being considerably higher. To reconcile data from stony and stone-free soils, a formula was composed.

Salinity, calculated by a semi-empirical model based on Hilhorst's theory using measured values of EC _a, ε _r , and soil temperature, correlated well with contents of K and Mg in the soil. A lower correlation resulted for P, and was practically absent for Ca. Inequality of these regression equations at different measuring sites demonstrates the necessity of considering soil pH when assessing plant nutrients in the soil.     

Statistical Modeling of the Partitioning of Nonylphenol in Soil

Partition coefficients K _P of nonylphenol (NP) in soil were determined for 193 soil samples which differed widely in content of soil organic carbon (SOC), hydrogen activity, clay content, and in the content of dissolved organic carbon (DOC). By means of multiple linear regression analysis (MLR), pedotransfer functions were derived to predict partition coefficients from soil data. SOC and pH affected the sorption, though the latter was in a range significantly below the pK_a of NP. Quality of soil organic matter presumably plays an important but yet not quantified role in sorption of NP. For soil samples with SOC values less than 3 g kg^?1, model prediction became uncertain with this linear approach. We suggest that using only SOC and pH data results in good prediction of NP sorption in soils with SOC higher than 3 g kg^?1. Considering the varying validity of the linear model for different ranges of the most sensitive parameter SOC, a more flexible, nonlinear approach was tested. The application of an artificial neuronal network (ANN) to predict sorption of NP in soils showed a sigmoidal relation between K _P and SOC. The nonlinear ANN approach provided good results compared to the MLR approach and represents an alternative tool for prediction of NP partitioning coefficients.     

Phytotoxicity of some chloroanilines and chlorophenols, in relation to bioavailability in soil

Soil adsorption and the effect of four chlorophenols and three chloroanilines on the growth of lettuce (Lactuca sativa) were determined in two soil types differing in organic matter content and pH. Adsorption increased with increasing organic matter content of the soils. Phytotoxicity, based on dosed amounts, was significantly higher in the soil with the low level of organic matter. This difference could be reduced by recalculating the EC₅₀ values for the effect of the test substances on plant growth in mg kg-1 dry soil towards concentrations in mg L-1 pore water using data from soil adsorption experiments. For pentachlorophenol only this recalculation increased rather than decreased the difference between the two soils, however, when the EC50 values for pentachlorophenol were corrected for the difference in soil pH, almost the same values resulted for both soils. Calculated EC₅₀ values on the basis of pore water concentrations appeared to be in good agreement with values determined in nutrient solution tests. These results indicate that, for plants, the toxicity and therefore the bioavailability of organic chemicals in soil mainly depend on the concentration in the soil solution, and can be predicted on the basis of sorption data. Attempts to develop QSARs relating log EC₅₀ values in μmol L^?1 pore water with lipophilicity (expressed as the octanol/water partition coefficient: log K_ow,) of the test substances resulted in a statistically significant relationship. This relationship was further improved by correcting the chlorophenol data for dissociation effects.