**3.3 Materials and methods**

This study uses an on-the-go ECa sensor for producing ECa map and to use it for soil nutrients assessment. The field soil salinity readings can be obtained through this soil-toinstrument contact device that permits rapid soil ECa measurement without requiring a permanently buried detector. The study was conducted in paddy fields at Tanjung Karang, Selangor, Malaysia. The study site has 118 plots covering 144 ha with an average plot size of about 1.2 ha (Figs. 10 and 11). The EC sensor was pulled by a tractor at a speed of about 15 km h-1 in a U-shape pattern 15 m apart. The data was later transferred to a notebook computer for generation of ECa maps using Surfer 7.0 software and ArcGIS 8.3 with Spatial

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Fig. 12. Kriged Map for the Shallow ECa (mS m-1) Classified by Smart Quantiles

Fig. 13. Kriged Map for the Deep ECa (mS m-1) classified by Smart Quantiles

and 3D Analyst extensions. A total of 63,578 data points were obtained. For comparison, a total of 236 soil samples were collected and analyzed in the laboratory for their chemical and physical properties.

Fig. 10. Map of the Soil Chemical and Physical Sampling Points in Block C

Fig. 11. (a) The EC sensor pulled by a tractor installed with DGPS in a paddy field, (b) results of 4 passes spaced 15m apart in a typical 1.2 ha plot, and (c) krigged map of ECa.

### **3.4 Results and discussion**

Soil ECa could provide a measure of the spatial differences associated with soil physical and chemical properties, which for paddy soil may be a measure of soil suitability for crop growth, its water demand and its productivity. The ECa maps indicated that it is similar to some soil nutrient maps. It was found that the technique could identify the zone of a former river located within the study area while detailed soil series map alone could not have found it. The relation of ECa to soil P, K, Mg and CEC in the paddy fields indicates that their

and 3D Analyst extensions. A total of 63,578 data points were obtained. For comparison, a total of 236 soil samples were collected and analyzed in the laboratory for their chemical and

Fig. 10. Map of the Soil Chemical and Physical Sampling Points in Block C

(a) (b) (c)

Fig. 11. (a) The EC sensor pulled by a tractor installed with DGPS in a paddy field, (b) results of 4 passes spaced 15m apart in a typical 1.2 ha plot, and (c) krigged map of ECa.

Soil ECa could provide a measure of the spatial differences associated with soil physical and chemical properties, which for paddy soil may be a measure of soil suitability for crop growth, its water demand and its productivity. The ECa maps indicated that it is similar to some soil nutrient maps. It was found that the technique could identify the zone of a former river located within the study area while detailed soil series map alone could not have found it. The relation of ECa to soil P, K, Mg and CEC in the paddy fields indicates that their

physical properties.

**3.4 Results and discussion** 

Fig. 12. Kriged Map for the Shallow ECa (mS m-1) Classified by Smart Quantiles

Fig. 13. Kriged Map for the Deep ECa (mS m-1) classified by Smart Quantiles

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total S (r = -0.93) at 95% level. This indicates that soil pH and total S decreased when shallow ECa increased. Hence a good water management is to apply more irrigation water to

The stratification of total N, BS and coarse sand by deep ECa were homogenous between the zones when their mean values within the zone were not significantly different at 0.05. Mean soil OM, C and total S in zone 3 were significantly higher than those in other zones and they were different to shallow ECa zone where it indicated that zone 1 has significantly high OM, C and total S. Mean soil pH, EC, P, Mg, K, Fe, total cation and clay within zone 1 were significantly low as compared to other zones, but significantly high Al, fine sand and sand. Deep ECa has significantly positive correlation to soil pH and Fe at 0.01 and significantly

Soil Properties Function R2 b0 b1 b2 b3

pH Quadratic 0.18\*\*\* 4.8999 -0.0042 1.0 x 10 -4

K Quadratic 0.05\*\* 0.1689 0.0028 -1.0 x 10-5

Total Cation Quadratic 0.13\*\*\* 2.3696 0.0874 -4.0 x 10-4

Fine Sand Quadratic 0.14\*\*\* 38.4191 -0.3710 1.4 x 10-3

Na Cubic 0.07\*\* 0.0868 0.0093 -7.0 x 10-5 1.5 x 10-7

Al Cubic 0.14\*\*\* 1.0148 0.0903 -1.0 x 10-3 2.8 x 10-6

Moisture Content Cubic 0.04\* 72.0409 -0.6757 8.2 x 10-3 -3.0 x 10-5 Clay Cubic 0.18\*\*\* 32.8506 0.0487 2.6 x 10-3 -1.0 x 10-5

Sand Cubic 0.15\*\*\* 30.6979 -0.0402 -2.5 x 10-3 1.3 x 10-5

Table 3. Significant Relationship of Soil Properties to ECa for the Study Area (n = 236)

EC S 0.07\*\*\* -2.0959 -5.7515 N Compound 0.02\* 0.1301 0.9826 CEC S 0.06\*\*\* 3.0187 -5.1707 ESP Exponential 0.02\* 2.7937 -0.0053

OM Inverse 0.02\* 8.2653 140.3670 C Inverse 0.02\* 4.7938 81.4321 S Inverse 0.02\* -0.0907 16.1845 P Logarithm 0.04\*\* 1.2987 2.0881 Ca Inverse 0.02\* 4.5322 -42.3070 Mg Power 0.28\*\*\* 0.1362 0.6004

Fe Exponential 0.20\*\*\* 0.2478 0.0054 BS S 0.04\*\* 3.7573 -11.1340

increase the soil pH in zones 4 and 5.

**3.7 Deep ECa zoning characteristics** 

negative correlation to coarse sand at 0.05.

**Predictor: shallow ECa**

**Predictor: deep ECa**

concentration can be estimated. Hence, quick nutrients determination can be done through the ECa sensor detection. The average values of ECa are significantly different between shallow (0-30 cm) and deep depths (0-90 cm) signifying differences in soil structure and nutrient status. The sensor can measure the soil ECa through the field quickly for detailed features of the paddy soil, and can be operated by just one worker.

The study area was divided into 5 manageable zones by smart quantiles method (ESRI, 2001). Fig. 12 shows the shallow ECa and Fig. 13 shows the deep ECa. The map for the deep ECa shows the distribution clearly, especially for very low and low ECa levels. Fig. 13 shows the pattern of a former river clearly as a continuous line about 45 m wide at the northern and central regions of the study area.

The on-the-go EC sensor can be used to replace the traditional way of acquiring soil data by intensive sampling technique and laboratory analysis, which is usually time consuming and laborious. The resulting ECa maps are useful in showing the management zones for improving crop productivity with minimum inputs. The delineation by ECa showed that some soil properties significantly differ from zone to zone. A total of 21 parameters were significantly predicted by using ECa which shows that the EC probe can predict multivariables, hence reduces time for sampling and analyses.
