Turmeric, Curcuma longa (family Zingiberaceae) is a herbaceous perennial plant with an underground rhizome which is valuable as a spice, coloring agent, and medicinal plant. The turmeric seed rhizome production in Sri Lanka was not sufficient for the increasing demand in the food sector and Ayurveda and traditional medical systems, and there was a scarcity of turmeric planting materials for the commercial production of turmeric. Due to the scarcity of planting materials for turmeric production, smaller pieces, 10-12 g of seed rhizomes were recommended by the Department of Export Agriculture instead of the recommended 30-35 g sized seed rhizomes. Therefore, this study was carried out to enhance the growth of such small pieces of turmeric by incorporating inorganic nutrient sources. As treatments, three different compound fertilizers such as Yara complex (N: P: K, 12:11:18), Yara grower (N:P:K, 21:7:14), and Hayleys balance (N:P:K, 15:15:15) were added to the potting mixture (Top soil: sand: cow dung, 1:1:1) in three different levels of each fertilizer as 2 g per pot, 4 g per pot, and 6 g per pot (Black polythene bag, size 6″×5″). The same potting mixture (Top soil: sand: cow dung, 1:1:1) was used as the control without adding any compound fertilizer. Each treatment contained three replicates and was laid in a Complete Randomized Block Design (RCBD). Growth parameters per pot such as percentage of sprouting (%), shoot height (cm), number of leaves, number of shoots, dry weights of uprooted plants, and root volume were measured after seven weeks from establishment. Soil pH and EC were taken as soil parameters, and leaf tissue analysis was also done for N%, P%, and K% at the end of the experiment. All data were analyzed according to Analysis of Variance (ANOVA) using the analytical software Minitab version 19. Tukey Pairwise Comparisons was performed to compare the differences among treatment means, and a probability of 5% or less was considered statistically significant. No significant differences were (P≥0.05) observed in sprouting % and number of shoots among treatments. Significantly the highest (P ≤0.05) shoot height (63.8 cm) and number of leaves (5.4) were recorded in plants treated with 6 g of Yara complex fertilizer (N: P: K, 12:11:18). Plant tissues of all fertilizer treatments had significantly high (P<0.05) total N content than the control. Therefore, the results revealed that there is positive growth in 10 g sized turmeric rhizomes with 6 g of Yara complex fertilizer (N:P:K, 12:11:18) per pot and enhanced growth in 6″×5″ sized pot filled with the recommended potting mixture of the Department of Export Agriculture.

Keywords :  Compound fertilizer, Potting mixture, Rhizome, Turmeric

The Department of Irrigation in North Central Province of Sri Lanka has implemented a land consolidation project at Nachchaduwa tank irrigation scheme with the objectives of making easy access for farm mechanization, improving water management efficiency, and reducing the cost of production in paddy farming. As a parallel activity, a study was carried out to assess soil physiochemical parameters of land blocks to come up with some recommendations to correct nutrient imbalances before handing them over to landowners. Soil samples were collected randomly from 0-30 cm depth from each land block. The locations of sampling points were recorded using a handheld GPS receiver. Collected soil samples were analyzed to determine soil pH, electrical conductivity (EC), soil organic matter (OM), total nitrogen (N), available phosphorus (P), exchangeable potassium (K), soil texture, and the bulk density. The most appropriate interpolation technique in ArcGIS was selected based on accuracy assessment, and the selected technique was employed to map soil parameters. Results revealed that soil pH varied from 6.3 to 10.1 across the land blocks, indicating moderately acidic to strongly alkaline conditions. Soil EC values were within the range from 0.02 to 0.27 mSm^-1, showing no potential for salinity development. The soil OM content was very low and varied from 0.2% to 0.8%. Total N content was in the range of 0.007% to 0.089%. The available P and exchangeable K contents were within the range of 2.2 to 45.6 mg kg^-1 and 32 – 166 mg kg^-1, respectively. The soil texture showed spatial variation from loamy sand to sandy loam. The bulk density varied from 1.5 – 1.8 g cm^-3, indicating a somewhat compacted nature of the topsoil. The results revealed the necessity of enriching soil nutrient levels, especially soil N level and organic matter content, by supplementary sources. The prepared spatial variability maps can be used as base material to develop site-specific nutrient management plans for each land allotment.

Keywords: Compound fertilizer, Potting mixture, Rhizome, Turmeric.

Journal Title : Journal of Technology and Value Addition

Volume : 6

Issue : 1

Page  : 31– 50

Correspondence :   raveenaudari@gmail.com

The perishable and seasonal nature of vegetables causes price fluctuations, and their volatility is particularly impactful. This study aims to analyze the price behaviour and forecast the future prices of selected vegetables. The average monthly price data from 2012 to 2022 was obtained from the Hector Kobbekaduwa Agrarian Research and Training Institute, and the Colombo Consumer Price Index served as the price deflator. The coefficient of variation was used to assess price variability, while median and frequency analyses of prices were employed to determine the span of commercialization opportunities. The time series of bean and tomato prices were predicted using Auto-Regressive Integrated Moving Average and Seasonal Auto-Regressive Integrated Moving Average models. The suggested models were validated using the Box-Jenkins methodology in R version 4.3.0. The results showed that vegetables exhibit significant price variability. The variability in wholesale prices is greater than that in the retail prices. Beans, carrots, capsicum, and bitter gourds offer excellent opportunities for commercialization. There is an increasing trend in nominal prices but not in real prices. The ARIMA (2,1,1) [12] model was identified as the best-fitting model for predicting wholesale prices of beans and tomatoes with accuracies of 76% and 54%, respectively. It is recommended to expand the analysis and incorporate seasonal factors into the model to enhance forecasting results.

Keywords: ARIMA, Box-Jenkins methodology, Coefficient of Variation, Price Forecasting, Vegetable Price