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Articles by P.C. Robert
Total Records ( 2 ) for P.C. Robert
  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.
  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.
 
 
 
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