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Articles by S.K. Balasundram
Total Records ( 12 ) for S.K. Balasundram
  S. Liaghat and S.K. Balasundram
  Precision agriculture is an emerging farm management strategy that is changing the way people farm. At present, there is an increasing commitment to reduce reliance on excessive chemical inputs in agriculture. Numerous technologies have been applied to make agricultural products safer and to lower their adverse impacts on the environment, a goal that is consistent with sustainable agriculture. Precision agriculture has emerged as a valuable component of the framework to achieve this goal. This review highlights on remote sensing technology and describes how it can be used as an effective tool in precision agriculture.
  S.K. Balasundram , P.C. Robert , D.J. Mulla and D.L. Allan
  This study reports on the spatial variability of soil fertility variables influencing oil palm yield in small-scale plots situated at varying topographic positions. For each topographic position, Yield-influencing Variables (YIVs) were determined and subjected to spatial data analyses involving variography and interpolation (inverse distance weighting). Results showed that the spatial structure of YIVs differed across topographic positions. The optimum sampling strategy was found to depend on the type of variable being investigated and its topographic position. A management zone concept with topography as the delineation factor seemed appropriate for fertility management. Only potassium (K) showed a clear demarcation of zones with high, moderate or low values and hence the need for variable rate management.
  S.K. Balasundram , D.J. Mulla and P.C. Robert
  Accounting for spatial variability of soil properties commonly requires intensive soil sampling, which inevitably involves a high cost. Geo-spatial statistical tools enable characterization of spatial variability and development of sampling strategies from limited data. This study outlines a simple approach of using classical and geo-spatial statistics to understand the spatial variability of soil Phosphorus (P) and discusses its relevance to sampling strategy and variable rate P application. The Bray (I) extractable-P data, obtained from a previous study, was first explored using descriptive statistics, box plot and normal quantile plot analyses. Spatial description of the data was performed using qualitative (data posting) and quantitative (variography) methods. Information derived from the fitted semivariogram was used to perform data interpolation (kriging). A management zone concept was used to delineate the Bray P test values. Results showed that Bray P exhibited a strong spatial dependence with 94% of its variability explained. The spatial correlation length was 177 m. Spatial attributes of the data appeared to justify the sampling design employed with regard to sample size, spacing and arrangement. To facilitate variable rate P application, three management zones were established so as to receive low, moderate and high P rates, respectively.
  M.C. Law , S.K. Balasundram , M.H.A. Husni , O.H. Ahmed and Mohd. Haniff Harun
  This study aimed at quantifying the spatial variability of Soil Organic Carbon (SOC), estimating SOC at unsampled locations and comparing the spatial variability of SOC between young and mature oil palm stands. Two study sites were chosen to represent two different palm age groups, i.e., 5 Years after Planting (YAP) and 17 YAP. A systematic sampling design was employed for soil sampling at the 0-20 cm depth based on a cluster of four palms that comprised three operational zones: Weeded Circle (WC), Frond Heap (FH) and Harvesting Path (HP). A total of 60 sampling clusters were obtained for each site. Soil samples were analyzed for SOC by dry combustion method. All measurement points were geo-referenced by differential Global Positioning System (dGPS). The SOC data were first explored using descriptive statistics, normality check, outlier detection and data transformation, followed by variography and interpolation. Spatial variability of SOC was mapped based on measured and kriged values. Results showed that all operational zones exhibited a definable spatial structure, which were described by either spherical or exponential models. All operational zones exhibited strong spatial dependence. Operational zones of 5-year old palms exhibited a shorter effective range than those of 17 year old palms. Additionally, SOC heterogeneity was evident among operational zones at both sites, where FH registered the highest SOC, followed by WC and HP. SOC concentration at 17 year old palms was found to be more stable than that from 5 year old palms. This study suggests spatial variability assessment appears to be a feasible technique to quantify the variability of SOC in oil palm cultivation.
  I.F. Ibrahim , S.K. Balasundram , N.A.P. Abdullah , M.S. Alias and M. Mardan
  Apis dorsata is one of the important honeybee species in tropical and subtropical regions that forage on various plants including herbs, grasses, forest trees and plantation trees. However, information on the favored bee plants in terms of identity and quantity is lacking. The objectives of this study were: (1) to identify the pollen sources of Apis dorsata and (2) to develop a pollen atlas for selected plants foraged by Apis dorsata. Pollen cell samples from twenty one different colonies of Apis dorsata combs were collected, identified and quantified based on several reference materials. A total of twelve different pollen sources were identified in the samples. Pollen sizes were 8-9x38-40 μm, comprising five different shape classes. Inaperturate granulum pollen grains were observed in Ceiba pentandra and Garcinia hombroniana while rugulate grains were found in Mangifera indica. Pantoporate, syncolpate and pericolpate pollen grains with reticulum to microreticulate exine patterns occurred in Acacia auriculiformis, Melaleuca cajuputi and Ixora congesta. Elaeis guineensis showed trichotomosulcate pollen grains with a microreticulate sexine. Pantocolpate areola pollen was found in Mimosa pudica while granulum pollen was observed in Cocos nucifera. Anacardium occidentale showed a disulcate grain with a striate sexine pattern. Pollen grains of Averrhoa carambola and Dimocarpus longan were tricolpate and fossulate perforate to striate perforate. This work shows that Elaeis guineensis and Mimosa pudica were the most commonly found pollen sources. A pollen atlas of selected plants foraged by Apis dorsata in the tropical rainforest of Marang, Terengganu was developed.
  S.K. Balasundram , D.J. Mulla , P.C. Robert and D.L. Allan
  Spatial variations in soil fertility can obscure treatment effects and hence lead to incorrect fertilizer recommendations. This study was aimed at evaluating oil palm growth response to K application. The response variable in this study was plant growth, expressed as plant height and leaf length. Treatment effects on plant height and leaf length were investigated using Analysis Of Variance (AOV). Both growth variables were assessed for spatial structure using variography. This was followed by Nearest-Neighbor Analysis (NNA) to derive adjusted growth data. The NNA involved a 3-step procedure carried out in an iterative fashion. Treatment effects on the NNA-adjusted growth data were examined using AOV and compared with those obtained using the original growth measurements. Results showed that before removing spatial trends, the effect of treatments on plant growth were not significant. Growth variables exhibited a significant spatial trend. A corresponding observation was found for growth residuals. The NNA technique was found to substantially reduce structural variance present in the growth data sets, which enabled the assessment of true treatment effects. Following the NNA adjustment, growth variables varied significantly among treatments with the untreated control giving the highest increase in plant growth. The NNA adjustment also rendered improved precision to the linear model, computed using AOV.
  M.C. Law , S.K. Balasundram , M.H.A. Husni , O.H. Ahmed and Mohd. Hanif Harun
  This study aimed at quantifying the spatial variability of SOC and estimating SOC concentration in oil palm. This study was carried out in a commercial oil palm plantation bearing 27 year old palms. A systematic design was employed for soil sampling at the 0-20 cm depth based on a cluster of 4 palms that included three operational areas Weeded Circle (WC), Frond Heap (FH) and Harvesting Path (HP). A total of 60 sampling clusters were established. SOC was analyzed using dry combustion method. All measurement points were geo-referenced by a differential Global Positioning System (dGPS). The SOC data were first explored using descriptive statistics, normality check and outlier detection. This followed by variography and interpolation techniques to quantify the spatial variability of SOC. Results showed that all three operational areas exhibited a definable spatial structure and were described by either spherical or exponential models. SOC from WC and HP showed moderate spatial dependence while that from FH showed a strong spatial dependence. The FH had a shorter effective range than other operational areas. Contour maps for WC, FH and HP clearly showed spatial clustering of SOC values. All three operational areas fulfilled the interpolation accuracy criteria. This study suggests that site-specific management could be considered as a strategy to increase SOC sequestration in oil palm.
  M.C. Law , S.K. Balasundram , M.H.A. Husni , O.H. Ahmed and Mohd. Hanif Harun
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  S.K. Balasundram , M.H.A. Husni and O.H. Ahmed
  Quantification of spatial variability is a vital prerequisite for precision agriculture. This study was aimed at quantifying the spatial variability of selected chemical properties in a tropical peat cultivated with pineapple. A 1-ha study plot was established in a commercial pineapple plantation in Simpang Rengam, Johor. Georeferenced topsoil samples (n = 60) were obtained systematically from 8x18 m spacings in the x and y direction, respectively. These samples were tested for total C, extractable P, K, Cu, Zn and B. Soil data were first explored using univariate statistics, including normality check, non-spatial outlier detection and data transformation. This was followed by variography and kriging analyses to quantify the spatial variability of chemical properties. Results revealed a high degree of spatial variability in the majority of chemical 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 tropical peat cultivated with pineapple, particularly with regard to nutrient management.
  R.F. A. ElSheikh , N. Ahmad , A.R.M. Shariff , S.K. Balasundram and S. Yahaya
  The study aimed to produce an investment classification map, which shows the potential areas of investment in agriculture in Sinnar, Sudan. The spatial multi-criteria analysis was used to rank and display potential locations, while the analytical hierarchy process method was used to compute the priority weights of each criterion. The study attempted to explore the utilization of Geographic Information System (GIS) to map the potential investment areas, therefore, it did not cover a comprehensive analysis of all factors that influence investment in agriculture. In addition, the analysis was limited to criteria that had spatial reference. The investment criteria for spatial analysis were defined from the guidelines provided by the Ministry of Investment, Sudan. Even with the shortcomings of the data, it was found that the results obtained were very encouraging and provided clear indicative areas for agricultural investment in Sinnar. Government agencies can use GIS to access information regarding the potential areas of investment, and minimize investment risks. On the other hand, the economic development organizations will now have the ability to benefit from the Geographic Information System (GIS) solutions by leveraging on this technology to attract and retain business from worldwide sources. Thus, the model will serve as a decision support tool for investors and decision makers at various levels.
  S. Selvaraja , S.K. Balasundram , G. Vadamalai and M.H.A. Husni
  Orange Spotting (OS) disease which is caused by Cadang-Cadang Coconut Viroid (CCCVd) is an emerging problem in oil palm. This study was aimed at quantifying the spatial variability of OS disease severity as an effort to augment the effectiveness of OS phytopathometry appraisal. A 4.2 ha study plot was established in a commercial oil palm plantation at Sungai Buloh, Selangor. A total of 587 geo-referenced trees were visually observed for OS disease symptoms. OS disease severity data were first subjected to exploratory analysis and followed by variography and interpolation analyses to assess spatial variability. The incidence OS disease in the study area was 74.3%. Measured OS disease severity ranged from 0-92.3%. The spatial structure of OS disease severity was described by an exponential model with an effective range of 29.1 m. OS disease severity exhibited a strong spatial dependence with a nugget to sill ratio of 0.15. The spatial variability map of OS disease severity revealed spatial clustering of kriged values, where 73% of the study area showed low severity (1-30%), 25% showed moderate severity (30-60%) and approximately 2% showed high severity (> 60%). This study demonstrates the utility of geo-spatial information in understanding the OS disease severity scale which could assist in site-specific disease monitoring and intervention.
  S.K. Balasundram , P.C. Robert and D.J. Mulla
  This study was aimed at investigating the relationship between oil content in oil palm fruit and its surface color distribution. A total of 80 fruit samples were randomly collected from the field; each sample consisted of two individual fruits. Fruit samples were photographed digitally under room temperature and controlled lighting and then subjected to total oil analysis using soxhlet extraction techniques. The digital images were first rectified using Adobe Photoshop®. Rectified images were clustered and subjected to unsupervised classification using MultiSpec Application®. Quantification of surface color distribution was performed in Arc View®. Relationship between oil content and color distribution was determined using multiple linear regression. Results showed that total oil content ranged between 35.2 and 86.4%. Significant correlation was found between total oil and all color components, with black yielding the strongest correlation (r = -0.85), followed by red (r = 0.81), orange (r = 0.62) and yellow (r = 0.48). The relationship between total oil content and color components was best explained with the following regression models: (1) %Total oil = 88.08-0.52 (%Black)+1.30 log (%Yellow), and (2) %Total oil = 36.84+0.63 (%Red)+1.52 log (%Yellow). Both these models explained 80-81% of the variation in fruit color with 76-78% accuracy. When validated on a separate data set, these models showed 55-56% accuracy. The benefit of harvesting oil palm fruits based on the relationship between surface color distribution and total oil was estimated as USD 0.15 per tree per year.
 
 
 
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