Saturday, 15 July 2006

Site-Specific Soil Management for Pineapple Production in a Tropical Peat.

Siva Balasundram, Ahmad Husni, and Osmanu Ahmed. Universiti Putra Malaysia, Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Selangor, 43400, Malaysia

Quantification of spatial variability is a vital prerequisite for precision agriculture. This study was aimed at quantifying the spatial variability of pineapple yield and selected soil (tropical peat) properties. A 1-ha study plot was demarcated based on crop variety (i.e. ‘Gandul'). Pineapple yields were recorded based on a rectangular grid scheme. Each grid had a dimension of 0.6 x 25 m and represented a single yield record. Corresponding topsoil samples were obtained and measured for total C, extractable P, K, Cu, Zn and B. A total of 60 yield and soil records were made. Recording points were spaced 8 m in the x direction (inter-bed) and 18 m in the y direction (intra-bed). Yield and soil data were analyzed using spatial continuity (variography) and interpolation (kriging) techniques. An isotropic semivariogram was constructed to determine the spatial structure and quantify spatial attributes such as nugget, sill and effective range. These attributes were used to perform point kriging. Results showed that pineapple yields were significantly variable across space within a 1-ha field. Pineapple yield variability fitted an exponential model with a nugget of 49.3, a sill of 137.6 and an effective range of 38.1 m. 64% of the total variation in pineapple yields was attributable to spatial variability. In terms of spatial distribution, 31% of the study area had yields close to the plot average, while 36% had yields above the average, and 33% with yields below the average. Results also showed a high degree of spatial variability in the majority of soil properties, which exhibited non-normal distributions with CVs ranging from 12 to 54%. All properties exhibited a definable spatial structure, which were described by either spherical or exponential models. Carbon, P and B showed strong spatial dependence. The majority of properties had a short effective range. Surface maps of chemical properties clearly showed spatial clustering of test values. Excepting K, all other properties showed acceptable accuracy of interpolated values. These combined data suggest the need for a site-specific approach in managing pineapple production on tropical peat, particularly with regard to nutrient inputs.

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