Apparent Electrical Conductivity in Correspondence to Soil Chemical

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Apparent Electrical Conductivity in Correspondence to Soil Chemical Properties and Plant Nutrients in Soil Article in Communications in Soil Science and Plant Analysis · June 2011 DOI: 10.1080/00103624.2011.577862

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

A. Gholizadeha; M. S. M. Amina; A. R. Anuarb; W. Aimrunc a Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra, Malaysia b Department of Land Management, Faculty of Agriculture, Universiti Putra, Malaysia c Smart Farming Technology Laboratory, Institute of Advanced Technology, Universiti Putra, Malaysia Online publication date: 20 June 2011

To cite this Article Gholizadeh, A. , Amin, M. S. M. , Anuar, A. R. and Aimrun, W.(2011) 'Apparent Electrical Conductivity

in Correspondence to Soil Chemical Properties and Plant Nutrients in Soil', Communications in Soil Science and Plant Analysis, 42: 12, 1447 — 1461 To link to this Article: DOI: 10.1080/00103624.2011.577862 URL: http://dx.doi.org/10.1080/00103624.2011.577862

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Communications in Soil Science and Plant Analysis, 42:1447–1461, 2011 Copyright © Taylor & Francis Group, LLC ISSN: 0010-3624 print / 1532-2416 online DOI: 10.1080/00103624.2011.577862

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

Downloaded By: [Universiti Putra Malaysia - Full Library] At: 15:25 24 June 2011

A. GHOLIZADEH,1 M. S. M. AMIN,1 A. R. ANUAR,2 AND W. AIMRUN3 1 Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra, Malaysia 2 Department of Land Management, Faculty of Agriculture, Universiti Putra, Malaysia 3 Smart Farming Technology Laboratory, Institute of Advanced Technology, Universiti Putra, Malaysia

Spatial variability and relationship between soil apparent electrical conductivity (ECa ), 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. ECa 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 ECa within a Malaysian rice cultivation area using VerisEC sensor. The ECa values were compared to some soil properties after delineation. Measured parameters were mapped using kriging technique and their correlation with soil ECa 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. Keywords

Precision farming, site-specific, spatial variability, VerisEC sensor

Introduction Precision farming is a crop management strategy which seeks to address within-field variability and to optimize inputs on a point-by-point basis within fields. By reducing over-application and under-application of inputs such as nutrients and pesticides on a sitespecific basis, this strategy has the potential to improve profitability for the producer and also to reduce the threat of ground or surface water contamination from agricultural chemicals. Precision farming is being adopted by innovative producers in many parts of the world. Agricultural equipment manufacturers, farm input suppliers, and a host of other businesses are working along with public-sector research and educated personnel to provide the necessary tools for farmers to implement this management strategy (Sudduth, Drummond, and Kitchen 2001). Received 28 October 2009; accepted 21 February 2011. Address correspondence to A. Gholizadeh, Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia. E-mail: [email protected]

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All over the world, much early work in precision farming focused on grid-sampling fields to determine variations in soil chemical properties and plant nutrients in soil. Measurement of these properties is expensive and time consuming since it generally involves few samples per hectare, followed by laboratory analysis. Because of this, quantifying soil chemical properties and plant nutrients in soil required for accurately mapping within-field variations has been impractical. The ideal way to measure spatially-variable soil properties would be through the application of mobile sensor systems, however, as reported by Sudduth et al. (2003), each type of commercial apparent electrical conductivity (ECa ) sensor has its own operational advantages and disadvantages. Using ECa sensor to show the contrast of soil properties in the field can however be useful to determine ECa rapidly with detail features of the soil and operated by a few workers. Data can be collected for every second and numerous data points can be presented on an ECa map. Moreover, a sensor may provide either direct or indirect measurements of the soil property of interest. Veris 3100 is a useful tool in mapping ECa in order to identify areas of contrasting soil properties. ECa is one sensor-based measurement that can provide an indirect indicator of important soil properties. The ECa of the soil is influenced by the interactions among several soil properties like soil salinity, soil texture, moisture content (MC), cation exchange capacity (CEC), organic carbon (OC), soil temperature, soil chemical properties, and plant nutrients in soil (McNeill, 1992; Jaynes, Colvin, and Ambuel 1995; Sudduth et al. 1998; Rhoades, Chanduvi, and Lesch 1999). Based on Sudduth, Drummond, and Kitchen (2001), since ECa is a function of a number of soil properties, its measurements have the potential for providing estimated indirect measures and within-field variations of these properties if the contributions of the other affecting soil properties to the ECa measurement are known or can be estimated. Also, a theoretical basis for the relationship between ECa and soil properties was developed by Rhoades et al. (1989). In this model, ECa was a function of soil water content, the electrical conductivity of the soil water, soil bulk density, and the electrical conductivity (EC) of the soil particles. Techniques also have been developed to use this model for predicting the expected correlation structure between ECa data and multiple soil properties of interest (Lesch and Corwin 2003). Mapped ECa measurements have been found to be related to a number of soil properties of interest in precision farming. The benefits of an ECa map which can be derived by Veris 3100 are able to determine the layout of the site, able to be used in the interpolation of soil chemical properties and plant nutrients in soil maps, able to guide soil sampling, able to design on-farm trials, and able to help input recipes for blending seed, nutrients, and crop protection chemicals. Where appropriate methods and suggestions will be so valuable in minimizing reluctant effects and maximizing the accuracy and reliability of the ECa data to predict soil critical characteristics, the overall objective of this study was to investigate the use of Veris 3100 (Veris Technologies, Salina, KS) and soil ECa map for indirect predicting plant nutrients in soil and selected chemical properties.

Materials and Methods Study Area This study was conducted at the Tanjong Karang Rice Irrigation Scheme. The scheme area is located on a flat coastal plain in the Integrated Agricultural Development Area (IADA). It is in the district of Kuala Selangor and Sabak Bernam on latitude 3◦ 35N and longitude


ECa Relationship with Paddy Field Soil Properties

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Figure 1. Sawah Sempadan compartment and block C (a) Satellite image for Tanjong Karang rice irrigation scheme, (b) Satellite image for Sawah Sempadan rice irrigation compartment, and (c) Block C at Sawah Sempadan rice irrigation compartment (color figure available online).

101◦05 E (Figure 1). The scheme is composed of eight compartments which are divided into 24 blocks; namely blocks A to X. Block C was selected for this study. It contains 118 lots and each lot size is 1.2 ha with the total area of about 142 ha. Soil Sampling and Lab Analysis Soil sampling was done during the dry season in June 2009 coinciding with after harvest of the off-season for 2009. The samples were taken at the center point of each lot within the root zone depth (0 to 30 cm). At each sampling point, two types of soil samples were collected. One was undisturbed sample, collected by core sampling (size of about 70 mm × 40 mm) and another was disturbed sample collected by using an auger. The core samples were weighed quickly in order to avoid the evaporation of its moisture, whereas the disturbed samples were air-dried. Disturbed samples after air drying were ground in mortar and then sieved through a 2 mm sieve. These samples were then brought to the soil laboratory for nutrient and chemical analyses. Soil total nitrogen (N) was determined using the Kjeldahl digestion procedure described by Bremner and Mulvaney (1982). While available phosphorus (P) was determined by the Bray II method (Bray and Kurtz 1945) and exchangeable potassium (K) and cation exchange capacity (CEC) were determined by neutral ammonium acetate extraction method (Schollenberger and Simon 1945). The titrimetric dicromate redox method (wet oxidation method) adopted from Schollenberger (1927) was used in this study to evaluate organic carbon (OC). Soil pH and EC also were adopted using pH meter and EC meter. ECa Data Acquisition The Veris 3100 Sensor Cart was pulled across each field behind a tractor in a series of parallel transects spaced about 15 m apart. The lot width was 60 m and the length was 200 m. Output from the Veris data logger was the conversion of resistance conductivity (1/resistance = conductivity). A Differential Global Positioning System (DGPS) with submeter accuracy was used to geo-reference ECa measurements. The Veris data logger recorded latitude, longitude, and shallow and deep ECa data (mS m−1 ) at 1-s interval in an ASCII text format. The ECa unit (mS m−1 ) was then converted to dS m−1 in order to compare with Lab EC. The location of latitude and longitude (WGS84) were then converted to

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Malaysian Rectified Skew Orthomorphic (RSO) using Global Positioning System (GPS) Pathfinder Office 2.90.


Conventional Statistical and Geostatistical Analysis The important information about variables is provided by descriptive statistics using statistical software. Measures of tendency of variables were determined by mean, median, and mode as well as computing the dispersion of a variable in variance, standard deviation, coefficient of variation (CV), and range (Park, 2008). It should be noted that smaller values of CV represent lower variability and higher values of CV represent higher variability of data (Romanuk and Kolasa, 2002). CV values of 36% indicate low, moderate, and high variability of data distribution, respectively. The matrix correlation of ECa and soil properties was determined by Pearson’s 2-tailed technique in order to check out the correlation of each particular soil property on average soil ECa . Geostatistical analyses of soil ECa , soil chemical properties and plant nutrients in soil were calculated for their semivariogram. A semivariogram indicates autocorrelation as a function of distance (semivariance versus distance separation) to plot spatial variability (Cohen, Spies, Bradshaw 1990). The components which include fitted model type, nugget variance (C0 ), structural variance sill (C0 + C), range (Ao), residual sum of square (RSS), coefficient of determination (R2 ), and proportion (C/[C0 + C]) were calculated by geostatistical software. If the ratio of C0 to (C0 + C) is less than or equal to 25%, the variable is considered strongly spatially dependent, if the ratio is between 25% and 75%, the variable is considered moderately spatially dependent, and if the ratio is greater than 75%, the variable is considered weakly spatially dependent (Cambardella et al. 1994). Mapping of Soil Properties Variability has been identified as spatial, temporal, and predictive (Blackmore, 2003). Spatial variability maps were generated using kriging. The adjusted smart quantiles method was used to classify the zone and also to visualize the variability. Spatial variability of soil variables were decided to divide the area into five zones for each of the variables based on a manageable zone which could also be easy to compare for pH 3. Because of a small range of numbers, just 3 classes were chosen.

Results and Discussion Statistical Description Bulk Soil Electrical Conductivity. The summary statistics for bulk soil electrical conductivity of Block C is presented in Table 1. The total data points were 93,884 for shallow, with the minimum and maximum values of 0.18 and 0.49 dS m−1 , respectively. The deep ECa values collected were 85,811 data points with minimum and maximum values of 0.38 and 1.17 dS m−1 . Information from these data points collected by the ECa sensor indicated that within a large field scale of about 145 ha, the soil information could be collected intensively. The CV values were 27% and 24% for shallow and deep ECa , respectively. This means shallow ECa varies more than deep ECa . Furthermore, it can be explained that low deep ECa variation indicated that it was almost homogeneous for the entire study area while higher variation as can be seen in shallow ECa indicated more heterogeneous. High


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Table 1 Descriptive statistics for deep ECa and shallow ECa (dS m−1 ) Deep ECa +

Shallow ECa ++

0.82 0.83 0.93 0.20 0.04 24 0.79 0.38 1.17

0.30 0.28 0.18 0.08 0.007 27 0.31 0.18 0.49

Mean Median Mode Standard deviation Variance Coefficient of variation (%) Range Minimum Maximum

Notes. + Deep ECa measured the average ECa from the soil surface to the depth of 90 cm. ++ Shallow ECa measured the average ECa from the soil surface to the depth of 30 cm.

mean deep ECa was probably due to effects of soil parent materials at the deeper depth where the low depth contains marine clay (Aimrun et al. 2007). Soil Chemical Properties and Plant Nutrients in Soil. Descriptive statistics of soil chemical properties and plant nutrients in soil for 60 samples are presented in Table 2. The CV (Table 2) shows that data of available P (83%) varied the most as compared to other measured parameters. However, this classical statistic could not show the location of where it varied (location of high and low). Soil pH had the lowest CV. Sun, Zhou, and Zhaoa (2003) also reported that red soil of subtropical China’s available P shows the highest CV, while soil pH has the lowest. Other researchers also documented that a lower variance of soil pH as compared to other soil chemical properties in Bertie silt loam of Maryland and the same study area respectively (Tsegaye, Hill, and Robert 1998; Aimrun et al. 2007). Moreover, Eltaib (2003) showed that in a large scale soil sampling within the compartment, the CV values for total N was about 21% and available P was about 44%. The present results showed that this study area had higher variation in terms of total N and available P. Table 2 Descriptive statistics for soil chemical properties and plant nutrients in soil

Mean Median Mode Standard deviation Variance Coefficient of variation (%) Range Minimum Maximum

N

P

K

OC

CEC

pH

EC

0.26 0.27 0.05 0.11 0.01 42 0.40 0.05 0.52

34.48 28.91 25.06 28.66 821.53 83 160.58 0.42 161

0.38 0.35 0.33 0.15 0.02 39 0.64 0.15 0.79

5.24 4.69 3.5 2.31 5.32 44 11 2 13

23.94 23.71 32.28 7.13 50.85 30 24.71 12.64 37.35

5.28 5.30 5.3 0.26 0.07 4 1.1 4.9 6

0.17 0.17 0.17 0.05 0.003 29 0.25 0.08 0.33

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Geostatistical Description

Bulk Soil Electrical Conductivity. The variograms of the shallow and deep ECa indicate that they were best fitted to spherical function (Figure 2). The C0 which is the error in estimation process, caused by sampling intensity, positioning, chemical analysis, and soil properties (Table 3). Myers (1997) reported that it can be reduced further if the number of Soil Deep EC Variogram

Soil Shallow EC Variogram

0.0479

77.009E–04

0.0359

Semivariance

Semivariance


Geostatistical analyses of soil ECa , chemical properties, and plant nutrients in soil are presented according to their semivariograms. A semivariogram shows autocorrelation as a function of distance (semivariance versus separation distance) and when plotted it represents spatial variability (Cohen, Spies, and Bradshaw 1990). Semivariogram has components of C0 , (C0 + C), structural variance or partial sill (C), C/(C0 + C), Ao, fitted model type and its R2 . In order to compare the spatial correlation of different semivariograms, one can use the ratio of the C0 to the (C0 + C) after having fit a model to each semivariogram. Low ratios indicate strong spatial dependence and vice versa (Henebry, 1993). Therefore, the recommended model of higher R2 , low RSS and high C/(C0 + C) was then chosen to be used for spatial autocorrelation process.

0.0240 0.0120 0.0000 0.00

300.00 600.00 900.00 Separation Distance (h) (a)

57.756E–04 38.504E–04 19.252E–04

00.000E + 00 0.00

300.00 600.00 900.00 Separation Distance (h) (b)

Figure 2. Isotropic semivariogram of deep ECa (a) and shallow ECa (b).

Table 3 Summary of isotropic semivariogram parameters for deep ECa and shallow ECa

Model type Nugget variance (Co ) Structural variance sill (C0 + C) Partial sill (C) Range (Ao ) Residual sum of square (RSS) Coefficient (R2 ) Proportion (C/[C0 + C])

Deep ECa

Shallow ECa

Spherical 0.02 0.04 0.02 603 1.369E × 10−4 0.75 0.65

Spherical 0.003 0.007 0.004 631 5.583E × 10−7 0.94 0.56

Notes. Lag distance was set at 900 m with the uniform lag class distance interval of 120 m.


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samples is increased. However, high C0 in some situations may be due to high variation within the minimum sampling space (Davis, 2002). Shallow ECa was found to have the lower C0 , which meant low sampling error. The C0 + C represented spatially-independent variance, where the data locations were separated by a distance beyond which semivariance did not change. It showed that deep ECa had lower C0 + C values. Deep ECa had lower Ao which meant it was dependent within a shorter distance as compared to the shallow ECa . The proportion of C and C0 + C (partial sill per sill) defines the best variogram model. This was the opposition to C0 to C0 + C ratio. They were found to have moderate spatial dependence. Soil Properties. Geostatistical analyses of soil chemical properties and plant nutrients in soil were also presented according to their semivariograms (Table 4). The C0 for available P was higher than the other variables which show higher error in sampling error. Soil pH was found to have the lowest C0 . The C/(C0 + C) indicated that the models were acceptable. OC, CEC, and pH had strong spatial dependence but higher R2 was for K and CEC as compared to the other variables. The lowest amount of R2 with relatively high RSS was for OC which indicated the weakness of the model (Figure 3). Spatial Variability Soil ECa , soil chemical properties, and plant nutrients in soil were interpolated geostatistically by kriging technique. The purpose of this interpolation was to produce a surface of predicted value in order to identify the surface coverage or spatial distribution of the soil chemical properties and plant nutrients in soil. Bulk Soil Electrical Conductivity. Maps for ECa were produced. Deep ECa values mostly fell into classes 2 and 3, with class 2 occupying a bigger area than class 3 (Figure 4a). Class 5 was the minimum area with the lowest percentage of occupied area (less than 10%). This indicates that after spatial interpolation, there were just about 10% deep ECa values that were higher than 1 dS m−1 . The map for deep ECa showed that most of high ECa levels were distributed in the southern regions. Shallow ECa kriged map (Figure 4b) showed that about 32.91% area was occupied by low level ECa of 0.25 to 0.30 dS m−1 (it was categorized in class 2). It was followed by class 1 of 0.20 to 0.25 dS m−1 (22.67%). Class 5 was found to cover a very small area of less than 4%. According to the map, the areas were mostly occupied by low shallow ECa levels. Class 1 which was the lowest average was distributed more to the west and middle of the study area, and it seemed to be concentrated on a few plots, while class 2 scattered all over the area. The scatter of moderate shallow ECa level may be due to human activities or farm practices such as fertilization, land preparation, and leveling on the top layer. Soil Chemical Properties and Plant Nutrients in Soil. Total N map was produced in this study and it was classified to 5 classes (Figure 5a). Most of total N data fell into class 3 and the other classes occupied small areas; for example class 5 occupied a very small area (about 2%). The pattern of the spatial variability for total N and ECa were approximately similar in terms of moderate amounts, while the highest total N values, in spite of ECa values, were found in the upper right of the field and the lowest amount of this nutrient could be observed in the middle.

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Spherical 0.0007 0.01 0.0093 423 3.144E-06 0.93 0.95

Spherical 111 933 822 273 74553 0.19 0.88

P Exponenitial 0.005 0.03 0.025 651 6.78E-06 0.95 0.81

K Spherical 0.01 4.56 4.55 118 6.94 0 0.99

OC

Notes. ∗ Lag distance was set at 900 m with the uniform lag class distance interval of 120 m.

Model type Nugget variance (Co ) Structural variance sill (C0 + C) Partial sill Range (Ao ) Residual sum of square (RSS) Coefficient (R2 ) Proportion (C/[C0 + C])

N

Exponenitial 0.1 59.29 59.19 633 55.9 0.95 0.99

CEC

Exponenitial 0.0001 0.06 0.0599 144 2.255E-04 0.07 0.99

pH

Table 4 Summary of isotropic semivariogram variables for soil chemical properties and plant nutrients in soil


Exponenitial 0.0003 0.003 0.0027 198 5.152E-07 0.12 0.88

EC

ECa Relationship with Paddy Field Soil Properties Soil N Variogram

Soil P Variogram 1113 Semivariance

Semivariance

0.0129 0.0097 0.0064 0.0032 0.0000 0.00

835 556 278 0 0.00

300.00 600.00 900.00 Separation Distance (h) (a)

Soil Total C Variogram

Semivariance

Semivariance

0.0147 0.0074

4.46 2.97 1.49 0.00 0.00

300.00 600.00 900.00 Separation Distance (h) (c) Soil CEC Variogram

300.00 600.00 900.00 Separation Distance (h) (d) Soil pH Variogram

61.8 Semivariance

0.0682

46.3 30.9 15.4 0.0 0.00

302.33 604.67 907.00 Separation Distance (h) (b)

5.94

0.0221

300.00 600.00 900.00 Separation Distance (h) (e)

0.0512 0.0341 0.0171 0.0000 0.00

300.00 600.00 900.00 Separation Distance (h) (f)

Soil EC Variogram 31.586E-04 Semivariance

Semivariance


Soil K Variogram 0.0294

0.0000 0.00

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23.689E-04 15.793E-04 78.964E-05 00.000E+00 0.00

300.00 600.00 900.00 Separation Distance (h) (g)

Figure 3. Isotropic semivariogram of soil chemical properties and plant nutrients in soil.


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Figure 4. Kriged map for deep ECa and shallow ECa (dS m−1 ) classified by smart quantile method (color figure available online).

In available P map class 1 was the dominant class in this study (60.46%), whereas class 5 which is the maximum amount of available P occupied only 0.4% of total area. Available P map can be seen in Figure 5b. Soil K map was classified by the smart quantile method such as the other variables (Figure 5c). Most of the values were in class 2 (0.28 to 0.38 cmol kg−1 ) and followed by class 3. Class 5 for the values between 0.58 and 0.68 cmol kg−1 was found in the lowest coverage area (less than 3%). K map had low K values crossing from the southwest to the west and covering some smaller area on the other parts of the field. Maps for K and ECa showed little similar patterns in terms of moderate amounts. The OC was also classified in to 5 classes (Figure 5d). Class 2 was found to cover the largest area (about 50%) while, Class 5 was the smallest covering area (less than 0.5%). The spatial variability of OC showed that most of the high values could be found in the northern region of the study area. Low OC clustered in the south, but some parts in the south were occupied by class 2. In this study area, CEC mostly found in class 3 (22.5–27.5 cmol kg−1 ) and covered the area of about 28.83% of the total area and class 5 (32.5–37.5 cmol kg−1 ) was the smallest area with 3.73% of the field (Figure 5e). The map shows a similar pattern to ECa maps, where low CEC values were almost in the area of low ECa of the former water canals. Aimrun et al. (2007) also reported same result. For the study area, the standard classification of soil pH showed most of the areas was found to be in class 2 (Figure 5f). High pH value was found in the north and middle of the area and moderate value was in the southern region, while low pH was distributed in the lower east of the study area. The ECa and pH maps both were found in the lower east.

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Figure 5. Kriged map for soil chemical properties and plant nutrients in soil classified by smart quantile method (color figure available online).



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Figure 5. (Continued)

Based on Figure 5g, most of the EC was in class 2. Class 5 was the least and its pattern was similar to ECa maps where there are former canal routes in the south crossing from the east to the west. This shows that the spatial variability for Lab EC and ECa may be close to each other.


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Table 5 Pearson correlation coefficient of ECa and soil properties Deep EC

Shallow EC

N

P

K

OC

CEC

pH

EC

Deep 1 0.839∗∗ 0.330∗ −0.058 0.251 0.250 0.367∗∗ 0.324∗ 0.508∗∗ EC Shallow 0.839∗∗ 1 0.411∗∗ 0.118 0.311∗ 0.354∗∗ 0.425∗∗ 0.347∗∗ 0.699∗∗ EC


Notes. ∗∗ Correlation is significant at the 0.01 level (2-tailed). ∗ Correlation is significant at the 0.05 level (2-tailed).

Correlation of ECa and Soil Properties Pearson’s 2-tailed correlation has been displayed in Table 5. It shows that soil deep ECa had significant positive correlation with shallow ECa, CEC, and EC (P = 0.01) and also with total N and pH (P = 0.05). Table 5 also shows the positive correlation of shallow ECa with deep ECa , total N, OC, CEC, pH, and EC at a = 0.01 and with K at a = 0.05. Strongest correlation can be seen between shallow ECa and EC (0.699) after shallow ECa and deep ECa . In general, shallow ECa had stronger correlation with different soil properties over the study duration than deep EC.

Conclusion The use of VerisEC sensor in a paddy field produced a very dense soil ECa dataset with less time as compared to normal grid sampling and it can be used as a useful variable to estimate soil chemical properties and plant nutrients in soil. Available P had the highest (83%) variation coefficient and pH had the lowest (4%). There is a high correlation between shallow and deep ECa. Deep ECa had significant correlation with total N, CEC, pH, and EC while shallow ECa had significant correlation with total N, K, OC, CEC, pH, and EC. Most measured variables could be estimated by shallow ECa. The study was able to show some links between ECa , soil chemical properties, and plant nutrients in soil according to precision farming technique. The spatial variability map displayed the zone of high and low soil characteristics indicating land productivity suggestion that low nutrient areas may need special treatment.

Acknowledgment This study has been financed by University Putra Malaysia and Institute of Advanced Technology. The authors are grateful and also thank the Institute of Advanced Technology staff for their help and support which highly improved this paper.

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