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1.
Management zones (MZs) for southern root-knot nematode (RKN) from the integration of terrain (TR) and edaphic (ED) field features might facilitate variable rate nematicide applications. This study was conducted on 11 coastal plain fields in the USA. The relationships between RKN populations and five soil ED and TR attributes (apparent soil electrical conductivity [shallow (ECa-s) and deep (ECa-d)], elevation (EL), slope (SL), and changes in bare soil reflectance) were analyzed using canonical correlation. Using two ED and TR data sets, canonical predictors were used for zone delineation. Although the results showed that the zones with RKN population above the RKN field average were associated with the lowest values of ECa-s, ECa-d, normalized difference vegetation index (NDVI), and SL with respect to field average values, zone segregation was enough using ECa-s and ECa-d data. The results suggest the potential for using soil properties to identify RKN risk zones.  相似文献   

2.
The electrical conductivity of the water within the soil pores (ECp) measured with the WET sensor, appears to be a reliable estimate of soil salinity. A methodology combining the use of the WET sensor along with geostatistics was developed to delimit and evaluate soil salinity within an irrigated area under arid to semiarid Mediterranean climate in SE Spain. A systematic random sampling of 104 points was carried out. The association between ECp and the saturation‐extract electrical conductivity (ECse) was assessed by means of correlation analysis. The semivariograms for ECp were obtained at three different soil depths. Interpolation techniques, such as ordinary kriging and cokriging, were applied to obtain ECp levels in the unknown places. For each one of the soil depths, a model able to predict ECse from ECp was developed by means of ordinary least squares regression analysis. A good correlation (r = 0.818, p < 0.001) between ECp and ECse was found. Spherical spatial distribution was the best model to fit to experimental semivariograms of ECp at 10, 30, and 50 cm soil depths. Nevertheless, cokriging using the ECp of an adjacent soil depth as an auxiliary variable provided the best results, compared to ordinary kriging. An analytical propagation‐error methodology was found to be useful to ascertain the contribution of the spatial interpolation and ordinary least squares analysis to the uncertainty of the ECse mapping. This methodology allowed us to identify 98% of the study area as affected by salinity problems within a rooting depth of 50 cm, with the threshold of ECse value at 2 dS m–1. However, considering the crops actually grown and 10% potential reduction yield, the soil‐salinity‐affected area decreased to 83%. The use of sensors to measure soil salinity in combination with geostatistics is a cost‐effective way to draw maps of soil salinity at regional scale. This methodology is applicable to other agricultural irrigated areas under risk of salinization.  相似文献   

3.
High spatial variability of soil salinity in coastal reclamation regions makes it difficult to obtain accurate scale-dependent information. The objectives of this study were to describe the spatial patterns of saline-sodic soil properties (using soil pH, electrical conductivity (EC1:5) and sodium ion content (SIC) as indicators) and to gain knowledge of the scaling relationships between those variables. The soil pH, EC1:5 and SIC data were measured at intervals of 285 m along a 13,965-m temporal transect in a coastal region of China. The spatial variability of soil pH was weak but it was strong for soil EC1:5 and SIC at the measurement scale. There was a significant positive correlation between soil EC1:5 and SIC, while correlations between soil pH and either EC1:5 or SIC were weak and negative. For each saline-sodic soil parameter, the variability changed with the decomposition scales. The high-variance area at the larger scales (≥570 m) occupied less than 10% of the total area in the local wavelet spectrum, which meant that the spatial variations of the salinity indicators were insignificant at these scales. For local wavelet coherency, at a scale of 1500–2800 m and a sampling distance of 0–4500 m, the covariance was statistically significant between any two of the saline-sodic soil parameters.  相似文献   

4.
This study attempted to characterize the spatial distributions of soil pH and electrical conductivity (ECe) of coastal fields in the Miyandoroud region, northern Iran, for three soil layer depths by assessing spatial variability and comparing different interpolation techniques such as inverse distance weighting (IDW), ordinary kriging (OK), and conditional simulations (CS). Three soil composite samples were collected from 0–50, 50–100, and 100–150 cm depths at 105 sampling sites. At all three soil depths, pH and ECe were best fitted by exponential and spherical models, respectively. Nugget effects were higher for soil ECe data sets compared with soil pH at all three soil depths showing soil ECe had a spatial variability in small distances. The prediction accuracy of the interpolation methods indicated that the minimum error for all data sets was achieved with the OK method, except for pH at 50–100 cm depth, and the CS technique revealed the largest error. The effect of different numbers of simulations (100, 500 and 1000) in the CS interpolation method resulted not in a realistic mapping for the soil ECe and pH. Considering the high importance of irrigated agriculture in the Caspian Sea coastal areas, more subsoil salinity build-up and groundwater salinity monitoring plans are needed as a prerequisite for sustainable agricultural production systems of the future.  相似文献   

5.
ABSTRACT

In the glaciated regions of the northern Great Plains, water - either too much or too little - influences soil development, carbon storage, and plant productivity. Integrating site-specific water variability information directly into management is difficult. Simulation models that employ remotely sensed data can generate hard to measure values such as evapotranspiration (ET). This information can be used to identify management zones. The objective of this study was to determine if the METRIC (Mapping Evapotranspiration at High Resolution and with Internalized Calibration) model, which uses weather station and remote sensing data can be used as a tool in site-specific management. This study was conducted on a 65 ha corn (Zea mays L.) field located in east central South Dakota. The METRIC model used Landsat 7 data collected on August 4, 2001 to calculate ET values with spatial resolution of 30 m. ET values were correlated with corn yield (r = 0.85??), apparent electrical conductivity (ECa; r = 0.71??), soil organic carbon (SOC; r = 0.32?), and pH (r = 0.28?). In the footslope positions, high ET values were associated with high corn yields, SOC, EC a , and pH values, while in the summit/shoulder areas low ET values were associated with low yields, SOC, ECa, and pH values. The strong relationship between ET and productivity was attributed to landscape processes that influenced plant available water, which in turn influenced productivity. Cluster analysis of the ET and EC data showed that these data bases complimented each other. Remote sensing-based ET data was most successful in identifying areas where water stress reduced corn yields, while ECa was most successful in identifying high yielding management zones. Findings from this study suggest that remote sensing-based ET estimates can be used to improve management zone delineation.  相似文献   

6.
In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (ECp – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R2 = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (ECe – dS/m). Our interpretation based on estimates of ECe suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.  相似文献   

7.
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 conducted to develop a universal regression model to estimate a conversion factor (CF) for predicting ECe from EC of soil-water extracts at a 1:5 ratio (EC1:5), by employing a site-specific soil texture (i.e., sand content). A regression model, CF=8.910 5e0.010 6sand/1.298 4 (r2=0.97, P < 0.001), was developed based on the results of coastal saline soil surveys (n=173) and laboratory experiments using artificial saline soils with different textures (n=6, sand content=10%-65%) and salinity levels (n=7, salinity=1-24 dS m-1). Model performance was validated using an independent dataset and demonstrated that ECe prediction using the developed model is more suitable for highly saline soils than for low saline soils. The feasibility of the regression model should be tested at other sites. Other soil factors affecting EC conversion factor also need to be explored to revise and improve the model through further studies.  相似文献   

8.
Since 1954, the electrical conductivity of the saturated paste extract (ECe) has been the preferred index for soil salinity. Based on this value, remediation strategies were developed and widely used but this approach is time consuming and not routinely offered by many soil testing facilities. However, many laboratories determine the EC1:1 value of a 1:1 soil to solution ratio extract. The objective of this study was to identify the relationship between ECe and EC1:1 and determine if EC1:1 can be used as a proxy in the northern Great Plains for ECe. Samples were collected across five studies and from AGVISE Laboratory. The samples were analyzed for EC1:1 and ECe. The relationship between the ECe and EC1:1 showed that soil parent materials need to be considered in the conversion of EC1:1 values to ECe values. A failure to consider parent materials in this conversion may have short and long-term sustainability ramifications.  相似文献   

9.
Two approaches have emerged as the preferred means for assessing salinity at regional scale: (i) vegetative indices from satellite imagery (e.g., MODIS enhanced vegetative index, NDVI) and (ii) analysis of covariance (ANOCOVA) calibration of apparent soil electrical conductivity (ECa) to salinity. The later approach is most recent and least extensively validated. It is the objective of this study to provide extensive validation of the ANOCOVA approach. The validation comprised 77 fields in California's Coachella Valley, ranging from 1.25 to 30.0 ha in size with an average size of 12.8 ha. Mobile electromagnetic induction (EMI) equipment surveyed the fields obtaining geospatial measurements of ECa. Soil sample sites selected following ECa‐directed soil sampling protocols characterized the range and spatial variation in ECa across the field. From the data, a regional ANOCOVA model was developed. The regional ANOCOVA model successfully reduced cross‐validated, average log salinity prediction error (variance) estimate by more than 30% across the 77 fields and improved the depth‐averaged prediction accuracy in 58 of the 77 fields. The results show that the ANOCOVA modelling approach improves soil salinity predictions from EMI signal data in most of the surveys conducted, particularly fields where only a limited number of calibration sampling locations were available. The establishment of ANOCOVA models at each depth increment for a representative set of fields within a regional‐scale study area provides slope coefficients applicable to all future fields within the region, significantly reducing ground‐truth soil samples at future fields.  相似文献   

10.
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 (ECa) was measured with an EM38DD electromagnetic induction instrument. The geometric mean values of the ECa 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 4048ECa 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.  相似文献   

11.
基于土壤电导率时空变异性的管理分区技术研究   总被引：2，自引：0，他引：2  
LI Yan  SHI Zhou  LI Feng 《土壤圈》2007,17(2):156-164
A coastal saline field of 10.5 ha was selected as the study site and 122 bulk electrical conductivity （ECb） measurements were performed thrice in situ in the topsoil （0-20 cm） across the field using a hand held device to assess the spatial variability and temporal stability of the distribution of soil electrical conductivity （EC）, to identify the management zones using cluster analysis based on the spatiotemporal variability of soil EC, and to evaluate the probable potential for sitespecific management in coastal regions with conventional statistics and geostatistical techniques. The results indicated high coefficients of variation for topsoil salinity over all the three samplings. The spatial structure of the salinity variability remained relatively stable with time. Kriged contour maps, drawn on the basis of spatial variance structure of the data, showed the spatial trend of the salinity distribution and revealed areas of consistently high or consistently low salinity, while a temporal stability map indicated stable and unstable regions. On the basis of the spatiotemporal characteristics, cluster analysis divided the site into three potential management zones, each with different characteristics that could have an impact on the way the field was managed. On the basis of the clearly defined management zones it was concluded that coastal saline land could be managed in a site-specific way.  相似文献   

12.
Microbial biomass and its activities in salt-affected coastal soils   总被引：2，自引：0，他引：2  
Seasonal fluctuations in salinity are typical in coastal soils due to the intrusion of seawater in the groundwater. We studied the effect of salinity on the microbial and biochemical parameters of the salt-affected soils of the coastal region of Bay of Bengal, Sundarbans, India. The average pH values and average organic C (OC) contents of soils from nine different sites cultivated with rice (Oryza sativa) ranged from 4.8 to 7.8 and from 5.2 to 14.1 g kg−1, respectively. The average electrical conductivity of the saturation extract (ECe) during the summer season was about five times higher than that during the monsoon season. Within the nine sites, three soils (S3, S4, and S5) were the most saline. The average microbial biomass C (MBC), average basal soil respiration (BSR), and average fluorescein diacetate hydrolyzing activity (FDHA) were lowest during the summer season, indicating a negative influence of soil salinity. About 59%, 50%, and 20% variation in MBC/OC, FDHA/OC, and BSR/MBC (metabolic quotient, qCO2), respectively, which are indicators of environmental stress, could be explained by the variation in ECe. The decrease in MBC and microbial activities with a rise in salinity is probably one of the reasons for the poor crop growth in salt-affected coastal soils.  相似文献   

13.
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 (ECa – 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 (ECe) 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 ECe, 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 ECa and ECe 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 ECe to be made at any depth. The method can be applied elsewhere to map soil salinity in three dimensions.  相似文献   

14.
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 (ECe)] 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 ECe and SAR within the affected areas is necessary for developing management practices. Conventional methods of assessing ECe 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 (ECa), to delineate ECe and SAR distribution in two cotton fields located in the Hudspeth and El Paso Counties of Texas, USA. Calibration equations for converting ECa into ECe and SAR were derived using the multiple linear regression (MLR) model included in the ECe Sampling Assessment and Prediction program package developed by the US Salinity Laboratory. Correlations between ECa and soil variables (clay content, ECe, SAR) were highly significant (p ≤ 0·05). This was further confirmed by significant (p ≤ 0·05) MLRs used for estimating ECe and SAR. The ECe and SAR determined by ECa closely matched the measured ECe 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 mmol1/2 L−1/2, respectively. High R2 values between estimated and measured soil ECe 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.  相似文献   

15.
Soil is usually presented as a complex dynamical system. Nevertheless, evidences based on the theoretical background of complex system physics are still scarce. The main objective of this work was to search for chaotic parameters using some basic concepts of nonlinear dynamical system theory with spatial series of soil properties. Three spatial series consisting of 1000 data point transects were used. We selected horizontal and vertical electrical conductivity (ECh and ECv, respectively) and gravimetric water content from a Vertisol (Typic Hapludert) under rice cropping. Each spatial transect was oriented from South to North with 1-m spacing. It was used the TISEAN Software Package (a public domain software available at http://www.mpipks-dresdren.mpg.de/~tisean) for deriving nonlinear parameters from spatial series. We found interesting evidences of chaotic behaviour as maximal Lyapunov exponents were all positive. They ranged from λm = 0.129 for water content to λm = 0.219 for ECv (filtered series in each case). Original (unfiltered), filtered, and surrogate spatial series confirmed these findings as they also showed positive Lyapunov exponents. All the spatial series showed a higher deterministic component (|κ|> 0.9 in most cases). The Lyapunov range of correlation (ρ) was within the limits 4.56 m (ECv) to 7.75 m (gravimetric water content) as usually reported from geostatistical investigations. Future works based on dynamical system theory will advance our knowledge on spatial variability of important soil properties and the emergence of deterministic and/or stochastic components.  相似文献   

16.
We hypothesised that digital mapping of various forms of salt‐affected soils using high resolution satellite imagery, supported by field studies, would be an efficient method to classify and map salinity, sodicity or both at paddock level, particularly in areas where salt‐affected patches are small and the effort to map these by field‐based soil survey methods alone would be inordinately time consuming. To test this hypothesis, QuickBird satellite data (pan‐sharpened four band multispectral imagery) was used to map various forms of surface‐expressed salinity in an agricultural area of South Australia. Ground‐truthing was performed by collecting 160 soil samples over the study area of 159 km2. Unsupervised classification of the imagery covering the study area allowed differentiation of severity levels of salt‐affected soils, but these levels did not match those based on measured electrical conductivity (EC) and sodium adsorption ratio (SAR) of the soil samples, primarily because the expression of salinity was strongly influenced by paddock‐level variations in crop type, growth and prior land management. Segmentation of the whole image into 450 paddocks and unsupervised classification using a paddock‐by‐paddock approach resulted in a more accurate discrimination of salinity and sodicity levels that was correlated with EC and SAR. Image‐based classes discriminating severity levels of salt‐affected soils were significantly related with EC but not with SAR. Of the spectral bands, bands 2 (green, 520–600 nm) and 4 (near‐infrared, 760–900 nm) explained the majority of the variation (99 per cent) in the spectral values. Thus, paddock‐by‐paddock classification of QuickBird imagery has the potential to accurately delineate salinity at farm level, which will allow more informed decisions about sustainable agricultural management of soils. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

17.
Texture and salt type influence the relationships between saturated paste electrical conductivity (ECe) and EC of other soil/water ratios. The objectives of this study were to develop and validate relationships between ECe and EC1:5 suspension for soils in Yazd Province and evaluate the effects of soil texture and gypsum on those relationships. Three hundred twenty-two soil samples were collected, of which 272 (all data) were used to develop the models and 50 were used to validate them. The soils were divided into two general textural categories of coarse and fine and two categories of with (G) and without gypsum (NG). Gypsum content had a stronger impact on the accuracy of the suspension method in predicting ECe than texture. The ECe = 5.60 EC1:5 ? 4.37 and ECe = 5.37 EC1:5 + 0.57 models are recommended for soils with and without gypsum, respectively. The methodology can be implemented in other regions, particularly if gypsum is present.  相似文献   

18.
Increasing pressures from agriculture and urbanization have resulted in drainage of many floodplains along the eastern Australian coastline, which are underlain by sulphidic sediments, to lower water tables and reduce soil salinity. This leads to oxidation of the sediments with a rapid decline in pH and an increase in salinity. Accurately mapping soil salinity and pH in coastal acid sulphate soil (CASS) landscapes is therefore important. One required map is the extent of highly acidic (i.e. pH < 4.5) areas, so that the application of alkaline amendments (e.g. lime) to neutralize the acid produced can be specifically targeted to the variation in pH. One approach is to use digital soil mapping (DSM) using ancillary information, such as an EM38, digital elevation models (DEM – elevation) and trend surface parameters (east and north). We used an EM38 in the horizontal (EM38h) and vertical (EM38v) modes together with elevation data to develop multiple linear regressions (MLR) for predicting EC1:5 and pH. For pH, best results were achieved when the EM38 ECa data were log‐transformed. By comparing MLR models using REML analysis, we found that using all ancillary data was optimal for mapping EC1:5, whereas the best predictors for pH were north, log‐EM38v and elevation. Using residual maximum likelihood (REML), the final EC1:5 and pH maps produced were consistent with previously defined soil landscape units, particularly CASS. The DSM approach used is amenable for mapping saline soils and identifying areas requiring the application of lime to manage acidic soil conditions in CASS landscape.  相似文献   

19.
Abstract. Diagnosis of soil salinity and its spatial variability is required to establish control measures in irrigated agriculture. This article shows the usefulness of electromagnetic (EM) and soil sampling techniques to map salinity. We analysed the salinity of a 1‐ha plot of surface‐irrigated olive plantation in Aragon, NE Spain, by measuring the electrical conductivity of the saturation extract (ECe) of soil samples taken at 22 points, and by reading the Geonics EM38 sensor at 141 points in the horizontal (EMH) and vertical (EMV) dipole positions. EMH and EMV values had asymmetrical bimodal distributions, with most readings in the non‐saline range and a sharp transition to relatively high readings. Most salinity profiles were uniform (i.e. EMH=EMV), except in areas with high salinity and concurrent shallow water tables, where the profiles were inverted as shown by EMH > EMV, and by ECe being greater in shallow than in deeper layers. The regressions of ECe on EM readings predicted ECe with R2 > 84% for the 0–100 to 0–150 cm soil depths. We then produced salinity contour maps from the 141 ECe values estimated from the electromagnetic readings and the 22 measured values of ECe. Owing to the high soil sampling density, the maps were similar (i.e. mean surface‐weighted ECe values between 3.9 dS m?1 and 4.2 dS m?1), although the electromagnetically estimated ECe improved the mapping of details. Whereas soil sampling is preferred for analysing the vertical distribution of soil salinity, the electromagnetic sensor is ideal for mapping the lateral variability of soil salinity.  相似文献   

20.
Fertilization management is an important technique to alleviate the adverse effects of salinity stress on plants. A pot experiment was conducted to evaluate the ameliorative role of inorganic phosphorus (P) and organic P sources on wheat grown under salt stress in three soil types deficient in available P. Wheat (Triticum asetivum L. cv. Shakha 93) was grown on alluvial, sandy, and calcareous soils under salinity levels of 4, 8, and 12 dS m?1 of saturated paste extract (ECe) and supplied with constant rate of 30 mg P2O5 kg soil?1 as superphosphate (SP), cattle manure (CM), and 1:1 mixture of SP and CM. The results revealed that plants grown on the sandy soil were more susceptible to the adverse effects of salinity compared with those planted on the alluvial one, especially at zero P. Plants grown on the calcareous soil were moderately affected. Varying soil type caused significant differences in the aboveground biomass and uptake of nitrogen (N), potassium (K), P, and zinc (Zn). It was obvious that P ameliorated wheat growth under salt stress, and this role was greater under moderate and high salinity. The increases in N, P, K, and Zn uptake appeared driven by P application were more conspicuous in the sandy and calcareous soils. Results also indicated that combined application of inorganic and organic P sources surpassed both when applied solely under all soil types and salinity levels.  相似文献   

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