Spatial variability among experimental units is a common problem in field experiments on tree crops such as tea (Camellia sinensis). Spatial variability is partly accounted for by blocks, but a substantial amount remains unaccounted for and this may lead to erroneous conclusions. In order to capture spatial variability in field experiments on tea, six commonly used spatial analysis techniques were investigated: Covariate method with pre-treatment yield as the covariate, Papadakis and the Modified Papadakis nearest neighbour adjustments, Moving means and Modified moving means methods, and Autoregressive method. The data from long-term fertilizer experiments and cultivar evaluation trials, conducted at different locations by the Tea Research Institute of Sri Lanka, were used in the study. Spatial techniques were evaluated by means of their relative efficiency at each location and year. Evaluation of the four neighbour methods analysed in conjunction with the pre-treatment yield, revealed that spatial variability due to both past and current conditions are operative, especially in experiments with large blocks, and could be captured simultaneously. Relative efficiencies averaging 141% clearly indicated that the neighbour techniques in combination with pre-treatment yield would be effective in controlling the experimental error in tea experiments with large blocks (nine plots per block or more). Experiments with small blocks were not affected by spatial variability due to past conditions and only that due to current conditions need to be addressed. Neighbour techniques, on their own, were found to be adequate to capture spatial variability due to current conditions. The modified Papadakis technique was found to be the best with an average relative efficiency of 145%. The techniques investigated in the study can easily be implemented using standard statistical software. The precision of tea experiments could be increased by using covariate analysis with pre-treatment yield and any one of the four nearest neighbour adjustments tested, when the block size is large; and modified Papadakis technique, on its own, when the block size is small.

No abstract

The changes in the global rainfall have raised concerns over the present and possible future use of coastal areas in many Asian countries including Sri Lanka. However, no in-depth analysis of rainfall of these coastal lines of Sri Lanka has been performed yet to understand any changes in the pattern in general. This study was carried out to fulfil the above need, especially focusing on rainfall of North-Western and Eastern coastal lines of Sri Lanka. The rainfall data for a period of 30 years were used in the study and analysis was performed using the tests, Mann Kendall, Sen's slope estimator, and sequential Mann Kendall. Mann-Kendall's test indicated positive trend in annual rainfall of most parts of North-Western coastal line but negative monotonic decrease at 4 stations, namely, Chilaw, Horakele, Lunuwila and Palaviya. In the Eastern coastal line, significant positive trend in annual rainfall was observed at 4 stations, namely, Kantale, Batticaloa, Pottuvil and Mylambaveli. The monthly rainfall analysis of North-Western coastal line revealed significant positive trends at Kottukachchiya and Anamaduwa stations in March and December respectively and in the Eastern costal line, positive trends were observed at Kantalae, Mylambaveli and Navatkiri Aru for February. The stations except Anamaduwa in North-Western coastal line recorded a significant negative trend in rainfall in the month of May. No station showed a significant positive trend during Southwest Monsoon season. During the Northeast Monsoon season, all the nineteen stations showed an increase in rainfall amounts while two locations in North-Western region and six locations in Eastern showed a significant upward trend. With these dynamic rainfall trends, it can be reiterated that it is vital to advocate climate mitigating actions to reduce impacts of changes in rainfall trends.

Designated food products are considered the best on the basis of their authenticity. These products have a much higher market price and therefore, subjected to frauds. Developing a scientific mechanism to ensure authenticity is a timely requirement. The objective of this study was to establish a base to classify geographical origins of Sri Lankan black tea using chemical compounds of tea coupled with discriminant function analysis. Thirteen different geographical origins which have a distinct character unique to the region were considered and eleven chemical parameters were analysed in the study. The highest rate of correctly classified cases was observed in Nuwara Eliya tea followed by Mid Country, Uva Medium, Matara/Akuressa and Maskeliya. Total colour was sufficient to discriminate Nuwaraeliya tea. Thearubugins was the most important chemical compound to discriminate between high grown, low grown and mid grown tea. This study gives an insight on the potential to classify Sri Lankan black tea using the profile of chemical constituents and to ensure the authenticity of Ceylon speciality tea in order to upkeep the market share.

Inconsistency in sensory evaluation is a serious problem and it often leads to loss of large revenue. Screening tasters before the sensory evaluation is the only remedy to overcome this inconsistency. This paper aims at showing how the above requirement can be achieved by using Multivariate Analysis of Variance (MANOVA) and follow-up Canonical Variate Analysis (CVA). The approach was illustrated using sensory profiles of Sri Lankan tea. The principle behind the approach is that any discrepancy between assessors on several attributes is detected simultaneously using MANOVA, and the discrepancy interacts with the factors such as products or region is detected using CVA. Data used for the study consisted of sensory scores given by 8 tea tasters for 13 tea growing regions on 6 attributes; colour, brightness, strength, flavour, aroma, quality. Samples from four factories represented a region, and data were collected for a one-year period on a monthly basis. Data from each month were analyzed separately. The Wilk's Lambda statistics of MANOVA revealed assessor effect as well as assessor × region interaction effect (P < 0.05) in every month indicating the inconsistency among assessors. The CVA for each region, specifically the 95% confidence regions of CV bi-plots, clearly identified clusters of assessors. Based on the location of these clusters in bi-plots, assessors who are suitable for different attributes were also identified. MANOVA followed by CVA, can effectively be used to identify discrepancies between assessors, discrepancy interacts with factors such as geographical region, and selecting consistent assessors depending on product or region and season.