Browsing by Author "Waidyarathne, K. P."

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    Developing regression models to estimate leaf area of split/ partially split fronds of coconut seedlings
    (Faculty of Science, University of Kelaniya, Sri Lanka, 2021) Gunarathe, K. M. U.; Waidyarathne, K. P.; Jayasundara, D. D. M.
    Leaf area (LA) is an important parameter measuring plant growth as it is highly responsive to the environment. Evaluation of leaf area is essential in plant research as it also helps in estimating plant productivity with net assimilation rate and total photosynthetic area of leaves. Coconut is a major plantation crop widely grown in Sri Lanka. To date, there is no non-destructive method of measuring leaf area of partially/fully split leaves of coconut seedlings. This is a drawback in coconut research as measuring LA is highly time consuming in the field. Therefore, the aim of this study was to determine an easy, accurate, cost-effective, and non-destructive formula to estimate leaf area of split/partially split leaves in 1 - 3 years old seedlings of three commonly grown coconut hybrids; Tall*Tall (TT), Dwarf Green*Tall (DT), and Dwarf Yellow*Tall (DY). Sixty leaf samples were randomly collected from each hybrid from the nurseries of Coconut Research Institute of Sri Lanka. Leaf parameters including maximum length (A), distance between two tips (B), midrib height (C), average length of first two leaflets (D), average length of last two leaflets (E), average length of middle three leaflets (F), average width of middle three leaflets (G), width between middle two leaflets (H), width between first two leaflets (I), and number of leaflets ((J) were collected from each frond. Actual leaf area was measured by LI-COR 3000 electronic leaf area meter. Linear polynomial model and multiple linear regression (MLR) analysis was used to define leaf area estimation models using different variable selection techniques such as the best subset method. Data were normalized (for TT and DY) and log- transformed (for DT) to satisfy the model assumptions. The lowest MSE and the highest R2 values were considered to evaluate the results of the polynomial model and MLR approach. Models with better combinations of variables were developed for both TT and DY varieties by the best subset method. The polynomial model was carried out with the product of F, G, and H variables as an independent variable for DY variety as it did not produce satisfactory results with MLR analysis. Accordingly, the study revealed that the leaf area of Tall*Tall variety was best represented by the equation, Area (TT) = 0.46* (A) - 0.23*(E) + 0.54*(G) with 86% R2 and 0.15 MSE. The best regression model for DY variety acted for; Area (DY) = -0.96 + 1.09*(A) – 0.59*(E) + 0.14*(B) + 1.22*(G) + 0.72*(F). This model had 94.3% R2 as accuracy and 0.01 MSE. The adjustment with product of F, G and J represented 80.63% R2 value, and 0.006 MSE for leaf area of DT hybrid. The model was ln (area) = 2.10 + 0.52*(ln (FGJ)). Neural network approaches with the same parameters will be evaluated to further improve the accuracy of the formula estimating leaf area.
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    Estimating the optimum plot size for coconut field experiment
    (Faculty of Science, University of Kelaniya Sri Lanka, 2023) Fernando, K. M. T. L.; Waidyarathne, K. P.; Jayasundara, D. D. M.
    Coconut stands as a prominent plantation crop in Sri Lanka, contributing to around 12% of the country's total agricultural output, as reported by the Sri Lanka Export Development Board (2021). A significant focus should be drawn towards designing the field research with coconut palms because coconut is a highly heterogeneous perennial crop. The efficient testing of treatment effects in field studies depends on experimental precision. On the other hand, coconut crops show considerable vulnerability to weather and spatial fluctuations. Weather fluctuation affects experimental units depending on the degree of severity, enhancing the yield variability within experimental plots. This causes a high experimental error, masking true treatment effects. Therefore, a proper plot size should be used to treat and handle this uncertainty and improve the coconut experimentation. Remarkably, prior to this research, there was no predetermined optimal plot size for agricultural coconut experiments. Thus, this study bridges this need by carrying out extensive research into the optimal plot size for these experiments. Using optimum plot size helps minimize the yield variation between the individual coconut palms inside a plot. The aim of minimizing yield variance among individual coconut palms is to detect the treatment effects in a precise way. Two methods are available to determine the optimum plot size: The Maximum Curvature Method and Fairfield-Smith’s variance law. The Maximum Curvature Method was selected to determine the optimal plot size for coconut experiments, as it has been frequently used for plot size determination in various field crops. The study analysed 26 years of coconut yield data from 1975 to 2000. The method was illustrated using a data set consisting of annual coconut yield from a design-free area at the Coconut Research Institute, Sri Lanka. The coconut palms were 16 years old and belonged to a “tall by tall” coconut cultivar. The obtained optimum plot sizes from the Maximum Curvature Method for coconut vary between four and ten palms per plot for 26 different years. According to the post Runs test, the sequence of optimal plot sizes stable over the years at a significance level of 5%. The results showed that the optimum plot size in coconut field experiments for a huge acreage of agroecological regions is six palms per plot. Thus, the disclosed finding can be defined as the optimum plot size for the Randomized Complete Block Design (RCBD). The practical implications of the result are for resource management, precision agriculture, sustainability, and adaptation to changing conditions. It will also contribute to the existing knowledge base by refining agricultural practices and enabling the integration of technology for improved coconut farming. Result consistency will be enhanced by analyzing additional similar datasets and employing variograms to examine spatial fluctuations in addition to the statistical analysis.