Relationship Between Soil Apparent Electrical Conductivity and Selected Soil Properties, and Oil Palm Yield

The ultimate goal of precision farming is to manage the farm on a site specific basis. In realizing this goal, knowledge of the soil and crop characteristics on a practical fine grid basis is necessary. However, to obtain soil and crop parameters on a greater intensity in oil palm plantation using the traditional soil and crop sampling and analysis methods are very expensive, tedious, and time consuming. This problem could be addressed using sensors that can gather information on-the-go. A sensor that is currently available with the above feature is Veris® 3100 soil EC sensor (Electrical Conductivity sensor), a product of Technologies and Probe Systems, Kansas, USA. An evaluation of the sensor was conducted in a mature oil palm plantation at Dusun Durian Estate, Banting, Selangor with the following objectives : (i) To determine the relationships between Veris soil EC and selected soil chemical and physical properties, and oil palm yield, (ii) To investigate the spatial variability of soil EC, selected soil chemical and physical properties, and oil palm yield, and (iii) To define spatial classes of the variables based on interpretation of geostatistical parameters. The evaluation conducted proved that EC sensor can be used to develop spatially dense datasets desirable for describing within-field spatial soil variability for precision agriculture. The data obtained using this sensor were very intensive and can rapidly determine soil EC in-situ. The electrical conductivity mapping is a fast means of characterizing spatial patterns in soil profiles. Mapping with an EC sensor provides information at two levels of soil profile, i.e. shallow (<30 cm) and deep profiles (<90 cm) which allow calibration of soil parameters at different depths. It was also noted that the patterns of soil EC within a field tend to remain the same from season to season. Therefore, once a map is generated it could be used for several years. The results showed that significant positive relationship (p<0.01) existed between shallow EC and pH, K, Ca, Mg but there was negative relationship with fine sand content of the soil. A significant correlation at p<0.05 was also observed between shallow EC and total P, CEC, silt, clay and percentage of coarse sand content in the soil. The relationships observed between EC and certain nutrient concentrations were also influenced by the relationship between EC and other soil properties, and it was not really a direct measure of the nutrient itself. Electrical conductivity measured at the experimental site was significantly explained by the soil Potassium, sand content, and Nitrogen levels. This relationship was linear, i.e EC shallow = 9.1212 + 1.0271 Potassium – 0.0308 fine sand – 4.669 Nitrogen. The use of EC in measuring nutrient concentrations depends on the relationship between EC and key soil properties (i.e. K, fine sand and N). There were also significant correlations between the soil physical properties and measured EC. However, the coefficients of determination were weak (i.e. less than 25%). Kriged map produced based on soil physical properties showed relatively uniform silt, clay, and fine sand distribution. The pattern was attributed to the homogeneous soil type and flat topography of the study area. The overall results showed that EC data did not correlate with FFB yield despite significant correlation between EC shallow and FFB yield in 2000/01 and EC deep and FFB yield in 1999/00 at P=0.05 level. Thus it can be concluded that Soil EC cannot directly predict crop growth or yield. Kriging analysis of yield data from 1999 to 2003 showed that the Q-value varied from 0.53 to 0.72 indicating a relatively spatial structure. However, the range of spatial dependence varied between 3243 m and 7681 m. This long spatial dependence is reflective of the uniform soil type and relatively flat topography of the study area that resulted in low variation of FFB yields production. Principal Component Analysis (PCA) analysis using these latent variables revealed that the yield variability could be explained by principal components such as available P, and CEC although their influence was not always consistent. It appears that soil management zones cannot be strictly determined using any one soil parameter across the field. Management zones represent the integration of detailed information regarding soil types, depth, texture, moisture, conductivity and crop type found in the field. Consequently, a database of soil characteristics becomes fundamental for the identification of the soil management zones. For the study area, available P and CEC are the factors that influence soil EC and thus, these parameters can be used to delineate the management zone. Thus, PCA is a useful statistical tool in delineating management zones. From the experience and results obtained, it is strongly believed that soil EC map can provide information for precision agriculture. It can be used to guide soil sampling, conduct crop yield map analysis, and help to decide whether or not to vary the amounts of agricultural inputs such as fertilizers across the field. Soil EC is one of the simplest and least expensive soil measurements available for precision farming at the moment. Veris EC sensor is useful in assessing the spatial variation that affects productivity of oil palm field but site specific calibration is required because several characteristics of soils can influence EC simultaneously. It has also the potential to provide estimates of within-field variations of some soil properties. However, care must be taken to understand the effects of the other, non-estimated properties on the conductivity measurement.

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