© 2008 Plant Management Network.
Spatial Variability of Pineapple Yields on a Tropical Peat
S. K. Balasundram, Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia; M. H. A. Husni, Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia; and O. H. Ahmed, Department of Crop Science, Faculty of Agriculture and Food Sciences, Universiti Putra Malaysia, 97008 Bintulu, Sarawak, Malaysia
Corresponding author: S. K. Balasundram. firstname.lastname@example.org
Balasundram, S. K., Husni, M. H. A., and Ahmed, O. H. 2008. Spatial variability of pineapple yields on a tropical peat. Online. Crop Management doi:10.1094/CM-2008-0418-01-RS.
The spatial variability of pineapple yields from a one-hectare field located on a tropical peat was quantified. In situ yield measurements were recorded based on 0.6 × 25-m rectangular grids. A total of 60 geo-referenced yield records were obtained. Recording points were spaced 8 × 18 m. Yield data were subjected to semivariogram and kriging analyses. The average pineapple yield was 93.3 kg per grid with a CV of 13.7%. The spatial structure of pineapple yields was fitted using an exponential function with a total variation (sill) of 137.6, a random variation (nugget) of 49.3, and an effective range of 38.1 m. Based on the nugget to sill ratio, pineapple yields showed a moderate spatial dependence. A map comprising measured and interpolated yield values showed that 31% of the field had yields close to the field average, 36% had yields above the average, and 33% with yields below the average. These results suggest that site-specific management of pineapple is necessary.
Despite being Malaysia’s oldest export-oriented crop, pineapple has remained a subsidiary crop due to increasing production cost, stagnant if not declining acreage and competition from cheaper producers. The export value of canned pineapple dropped from USD18.3 million in 1997 to USD12.1 million in 2006 (5). It is high time for the pineapple industry to move towards production technologies that have the capacity to enhance long-term crop productivity and/or quality in a cost-effective and environmentally-friendly manner. One such production technology that closely matches this call is precision agriculture (PA), also known as site-specific crop management or prescription farming.
Operationally, PA involves mapping and analyzing spatial and/or temporal variability of a field, and linking such variability to management actions. In recent years, the concept of PA has been considered for a wide range of common and specialty crops worldwide (6). PA solutions as a function of farm size, profitability, and the scale of the problem to be addressed usually differ among crops and cropping systems. However, the principles underlying PA implementation for different crops are universal. In Malaysia, PA applications have been explored mainly on oil palm and rice. There is now a keen interest to develop PA strategies for aquaculture as well as for livestock and fruit crops farming, including pineapple. Seemingly, growing interest in PA is attributable to its exciting suite of technologies, potential for increased farm profitability and environmental protection, and the need for product traceability to ensure food safety.
The first step in PA is to quantify the spatial variability of important production factors that affect crop growth efficiency (3). Such variability is typically manifested in or correlates with crop yields and/or soil fertility.
This study is part of an ongoing research program to diagnose site-specific strategies suitable for pineapple management on tropical peat. The objective of this study was to quantify the spatial variability of pineapple yields within a one-hectare field.
Quantifying Spatial Dependence of Pineapple Yield
This study was conducted in a commercial pineapple plantation located in Simpang Rengam, Johor, Malaysia. Pineapple in this plantation is cultivated on deep peat (classified as ‘Saprist’) and its cultivation is based on a biennial cropping cycle, which includes a 6-month fallow period. Selected fertility characteristics of the peat are given in Table 1.
Table 1. Selected peat fertility characteristics at a depth
A 1-ha field, with fairly flat topography, was demarcated based on crop variety (i.e., ‘Gandul’). The field comprised 95 planting beds with each bed measuring 0.6 × 100 m and an inter-bed spacing of 0.9 m. The field had a plant spacing of 0.3 m and a net stand density of 55,000 plants/ha. Pineapple yields were recorded based on a rectangular grid scheme. Each grid had a dimension of 0.6 × 25 m representing a single yield record. For each bed, there were 4 yield grids. A total of 60 geo-referenced yield records were obtained. Recording points were spaced 8 m in the x direction (inter-bed) and 18 m in the y direction (intra-bed).
Yield 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. The most appropriate semivariogram model was fitted based on goodness of fit (i.e., R² value) and Residual Sum Squares (RSS). The spatial attributes were used to perform point kriging. Variography and kriging were computed using GS+ Version 5.1.1 (Gamma Software Design, Plainwell, MI). Measured and kriged values were mapped using Surfer Version 7.0 (Golden Software Co., Golden, CO).
Spatial Distribution of Pineapple Yield
The average pineapple yield was 93.3 kg per grid with a CV of 13.7%. Effectively, this average translates to 35.5 mt/ha, which is classified as medium yield for commercial production on peat. Exploratory data analysis revealed that yield was normally distributed and without outliers. This allowed for the yield data to be subjected to spatial continuity analysis. The semivariogram fitted for pineapple yield, based on an active lag distance of 56 m and a lag class interval of 7 m, is given in Figure 1.
Spatial structure of the yield data was best modeled using an
exponential function (Table 2) with a total variation (sill) of 137.6, a random
variation (nugget) of 49.3, and an effective range of 38.1 m. Spatial dependence
was defined using the nugget to sill ratio convention (2), whereby:
Table 2. Comparison of semivariogram models describing the spatial structure of pineapple yields.
The pineapple yield data had a nugget to sill ratio of 0.36, inferring moderate spatial dependence. This means that 64% of the total variation in pineapple yield can be explained by spatial variation while the remaining 36% is attributable to random or unexplained sources of variation. The effective range, also commonly referred to as the correlation length, derived from semivariogram analysis was 38.1 m. The practical significance of this value is that sampling points separated at distances greater than 38.1 m will no longer exhibit spatial correlation. At this juncture, it is worth noting that the semivariogram does not provide any information for distances shorter than the minimum spacing between samples. Sampling designs aimed at delineating spatial structures usually employ separation distances that are lesser than the effective range. Flatman and Yfantis (4) recommended that samples be spaced between 0.25 and 0.5 of the effective range.
The spatial distribution of pineapple yield (based on measured and predicted values) is given in Figure 2. Figure 2 suggests that pineapple yields are spatially clustered with 31% of the field registering yields close to the average value (i.e., 93 kg per grid), 36% with yields above the average, and 33% with yields below the average. The minimum continuity stretch (as denoted by the dotted line in Figure 2) indicates short-range variability. Interpolation of pineapple yield showed a reasonable accuracy (Fig. 3).
Based on the spatial distribution of pineapple yield, it seems justifiable to carry out site-specific application of production inputs (e.g., fertilizers, chemical/organic amendments). However, there is a need to investigate the cause of pineapple yield variability. Possible factors that could induce yield variability include variations in soil physical and chemical properties, planting material, and crop-environment interaction (e.g., water table, heat tolerance). There is also a need to evaluate yield variability across planting season, i.e., temporal variability. Information derived from such investigations can be used to formulate crop management zones, a flagship application of precision agriculture. Essentially, crop management zoning refers to quantitative grouping of areas with similar production potential/limitation in order to facilitate optimum crop and soil management as a function of local variability. Crop management zones, if designed accurately and efficiently, can potentially lead to increased crop productivity and quality.
This study 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. Sixty-four percent of the total variation in pineapple yields was attributable to spatial variability. In terms of spatial distribution, 31% of the field had yields close to the field average, while 36% had yields above the average, and 33% with yields below the average. This study provides reasonable justification that pineapple production on peat requires site-specific management.
The authors are grateful to Peninsular Plantations Sdn. Bhd., Simpang Rengam, Johor for logistical support and technical collaboration. Appreciation is also extended to Mr. Junaidi Jaafar for assisting in field work.
1. Ahmed, O. H. 1999. Nutrients uptake and cycling of pineapple planted on tropical peat. MA Agric. Sci. diss., Faculty of Agriculture, Universiti Putra Malaysia.
2. Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco R. F., and Konopka A. E. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Sci. Soc. Am. J. 58:1501-1511.
3. Earl, R., Wheeler, P. N., Blackmore, B. S., and Godwin, R. J. 1996. Precision farming: The management of variability. Landwards 51:18-23.
4. Flatman, G. T., and Yfantis, A. A. 1984. Geostatistical strategy for soil sampling: The survey and the census. Env. Monit. Assess. 4:335-349.
6. Robert, P. C., Rust, R. H., and Larson, W. E., eds. 2000. Proc. of the 5th Intn'l Precision Agric. Conf., Minneapolis, MN, July 16-19, 2000. ASA, CSSA, and SSSA, Madison, WI.