Spatial Apparent Electrical Conductivity of Paddy Soil as an Indicator of Rice Productivity

Paddy soils are naturally heterogeneous in terms of their physico-chemical properties which influence rice productivity. Currently, uniform application of agricultural fertilizers for the entire field is not efficient and could result in either insufficient or excess nutrient supply. Good agricultural practices can be achieved if soil and nutrient variations within a farm are considered, and a soil-yield interrelationship is established. Simple, rapid and accurate methods to characterize variation in soil properties are needed. This study was conducted on two different plots within Malaysia Agricultural Development and Research Institute (MARDI) Research Station located at the northern part of Peninsular Malaysia. One of the plots is a single large contiguous plot of 9-ha, free of farm encumbrances and the other is equipped with subsurface drainage facilities. Soil samples were collected at regular grid spacing from the upper (0-20 cm) and lower (30-50 cm) soil layers respectively. These samples were analyzed for their soil texture and chemical properties. Crop cutting test yields were taken at the same soil sampling locations. Geo-referenced apparent electrical conductivity (ECa) measurements were obtained by using Veris 3100 cart equipped with a data logger and a differential global positioning system. Soil ECa mapping is a simple and rapid tool that can be used to provide estimate of the within field soil differences associated with soil properties which is a measure of field conditions and soil suitability for crop growth and yield. The significant correlations of soil ECa and mapping date showed that the patterns of soil ECa within a field do not tend to change significantly over time. Generally, once an ECa map has been made, it will remain relatively accurate unless significant soil movements occur. The correlations between shallow and deep soil ECa were found to be significant too. And significant relationships between potential grain yield and ECa were found using a form of boundary line analysis in scatter plots with r2 > 0.58 in all the six investigations in three crop-seasons. The log-normal function chosen to fit the boundary datasets was flexible in representing various responses combination to ECa values and could correctly indicated significant higher yield can be obtained from areas with high predicted potential yield. Comparison of Ypo and Yob can delineate farm areas into different management zones and allows for discriminate management practices particularly to low yield areas due to less then ideal field conditions, thus resulting good agricultural practices. These practices could result in less wastage of applied inputs, less pollution, lower input costs and most important higher return.

Preview PDF 1600467.pdf Download (92kB)

Actions (login required)

View Item