PG Thesis
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Item Spatio-temporal analysis of groundwater quality in central Kerala(Department of Agricultural Statistics College of Agriculture,vellayani, 2022-02-20) Ardra, K Y; Brigit JosephThe research work titled “Spatio-temporal analysis of groundwater quality in central Kerala” was carried out at the College of Agriculture, Vellayani during the period 2019 to 2021. The objectives set for the study were to analyze the inter-regional variation in groundwater quality parameters over ten years using various multivariate and geostatistical techniques in Alappuzha and Kottayam districts. Also to analyze the interrelationship between the water quality parameters and groundwater levels and to map the locations using the GIS package. 26 location in the Alappuzha district and 22 locations in the Kottayam district was selected for the study. Secondary data on fourteen groundwater quality parameters (Electrical conductivity (EC), pH, Total Hardness (TH), Total alkalinity (TA), anions; CO3, HCO3, SO4, NO3, Cl, F, and cations; Na, K, Ca, and Mg) and groundwater levels were collected from Central Ground Water Board for ten years (2010-2019). On the secondary data of fourteen quality parameters, descriptive statistics such as mean, standard deviation, and coefficient of variation were investigated. The parameters were compared with drinking and agricultural irrigation guidelines defined by the Bureau of Indian Standards (BIS) and the World Health Organization (WHO). All parameters in the Alappuzha district, with the exception of fluoride (F-) in the location Thaikkattusseri (F-: 2.86 mg/L), were confirmed to be within BIS and WHO limits. Two parameters, total alkalinity (TA) and potassium (K+) were found to be over the standard limits in the Kottayam District. In Vaikkom, the TA was higher than the norm (BIS: 300 mg/L), while in three locations, Changanasserry, Ezhinjillam, and Thiruvarpu, the K+ was higher than the limit (WHO: 10 mg/L). All of the other parameters were determined to be within the BIS and WHO guidelines. Principal component analysis (PCA) was carried out for the construction of the Principal component weighted Water Quality Index (WQI). In the Alappuzha district, four principal components contributed 82.71 percent of the total variation. In the Kottayam district, three principal components accounted for 86.06 percent of the total variation. Based on the WQI, the locales in Alappuzha districts are grouped into five: very low, low, medium, high, and very high. The Index is found to be increasing with the parameters EC, TH, HCO3, and TA, and the ‘very low’ category had mean value for the parameters 58.8 μS/cm, 15.64 mg/L, 11.28 mg/L, and 7.39 mg/L respectively, the ‘low’ category recorded the mean values 159.95 μS/cm, 44.5 mg/L, 45.52 mg/L, and 35.95 mg/L respectively. The ‘medium category had the mean values 200.17 μS/cm, 69.45 mg/L, 78.52 mg/L, and 55.56 mg/L parameter levels respectively. For the category, ‘high’, mean values were 252.65 μS/cm, 110 mg/L, 164.11 mg/L, and 182.23 mg/L respectively for the parameters. In the category ‘very high’, recorded mean values for these parameters were 297.39 μS/cm, 98.21 mg/L, 116.82 mg/L, and 89.36 mg/L respectively. In the Kottayam district with a CV of 41.38 percent and a mean of 95.09, the estimated WQI ranged from 17.88 in Kattanam to 161.14 in Cherthalai. WQI was found to be below the mean value in 16 of the 26 locations. In the Kottayam district, the estimated WQI varied from 22.04 in Narianganam to 300.48 in Vaikom, with a CV of 83.65 percent and a mean of 94.78. WQI was found to be below the mean value in 15 of the 22 locations. The Index is found to be increasing with the parameters EC, TH, HCO3, and TA, and the ‘very low’ category had mean value for the parameters 94.09 μS/cm, 21.21 mg/L, 17.73 mg/L, and 13.57 mg/L respectively, the ‘low’ category recorded the mean values 241.21 μS/cm, 55.27 mg/L, 36.28 mg/L, and 31.53 mg/L respectively. The ‘medium category had the mean values of 374.02 μS/cm, 106.62 mg/L, 125.79 mg/L, and 89.99 mg/L parameter levels respectively. For the category, ‘high’, mean values were 594.89 μS/cm, 109.34 mg/L, 41.6 mg/L, and 31.61 mg/L respectively for parameters. In the category ‘very high’, recorded mean values for these parameters were 592.78 μS/cm, 211.88 mg/L, 297.5 mg/L, and 201.39 mg/L respectively. The interrelationship between water quality parameters and groundwater level was studied using Pearson's correlation coefficient. In the Alappuzha district, the parameters EC (-0.45), pH (-0.65), TH (-0.5), TA (-0.42), HCO3 (-0.42), and Ca (-0.51) had a significant negative correlation with groundwater level. NO3 (0.33, p-value=0.1) was the only parameter with a positive correlation but was found to be non-significant. In the Kottayam district, a positive-significant correlation was present in parameters pH (0.75), TA (0.22), SO4 (0.59), and Mg (0.6), and a negative significant correlation was present in EC (-0.61), TH (-0.52), HCO3 (-0.48), Na (-0.54) and Ca (-0.46). The remaining parameters were found to have a non-significant correlation. The temporal change was analyzed employing the relative change in the level of each parameter for ten years in selected districts. In the Alappuzha district, a positive temporal increase was observed in all the parameters except pH over ten years. In the Kottayam district, a considerable increase was observed in parameters TH, TA, HCO3, SO4, F, and Mg and a decline in the levels of EC, pH, NO3, Na, K, and Ca. Thematic map based on WQI and groundwater level (GWL) was prepared using ArcGIS software version 10.4 for Alappuzha and Kottayam districts. Based on the thematic maps, spatial distribution and variation of groundwater quality parameters and groundwater level were drawn. In the Alappuzha district, both WQI and GWL maps were categorized into ten classes. Nearing the various water bodies, WQI was recorded higher. The locations bordered by the Pathanamthitta district had comparatively lower WQI. From the GWL map, it was inferred that as the elevation of the district increased, the depth to the water table was increasing. The change observed was according to the topography of the district. In the Kottayam district, locations bordered by Alappuzha showed high WQI value and the locations bordered by the Idukki district had comparatively less WQI. In Kottayam also, depth to the water table showed an increasing trend with the elevation. Spatial and temporal variations in the Alappuzha and Kottayam districts were studied with the help of multivariate and geostatistical techniques. Both methods confirmed the inter-regional and timely disparity of fourteen groundwater quality parameters and groundwater level.Item Application of response surface methodology for optimal yield of transplanted rice Oryza sativa L(Department of Agricultural Statistics, College of Agriculture,Vellayani, 2022-02-05) Anjana R Pillai; Brigit JosephThe research work entitled “Application of response surface methodology for optimal yield of transplanted rice (Oryza Sativa L.)” was carried out at Cheruniyoor panchayat of Varkala in Tiruvananthapuram district and College of Agriculture, Vellayani during 2019-2021. The objective was to the development of response surface model using Central Composite Design (CCD) to optimize N, P and K of transplanted rice and to develop a web based application for Response Surface Methodology (RSM). A CCD experiment was conducted with 15 treatment combinations in 3 replications using the rice variety (Uma (MO16)) during the mundakan season as second crop. The grain yield and straw yield data were as the source of primary data which was used for analysis. The analysis was initiated by finding the high and low levels of the central value taken for the studies which was 90:45:45 NPK kg ha-1. The levels were selected on the basis of previous studies and soil tests conducted. Since three factors were chosen for the experiment, α value of ±1.682 was considered. The experiment was conducted using these and observations were found for grain yield and straw yield. The average grain yield, straw yield and harvest index were found to be 5792 kg ha-1, 7578 kg ha-1 and 0.43 respectively with a standard deviation of 934.63 for N, 749.98 for P and 0.026 for K. From analysis, it was found that the multiple R2 for grain yield was 0.89 which was closer to 1 indicating that there is a high correlation between the response variable and the predictor variables while the adjusted R2 was 0.80 representing that the model fitting was good. The predicted R2 was 0.69 i.e., it can predict 69% of new observations for the regression model. The predicted R² of was in reasonable agreement with the adjusted R²; i.e., the difference is less than 0.2. In the case of straw yield (Y2), it was found that there were no significant and the lack of fit value of 0.1046 implied that the model fitting was not good enough. The response model was estimated using R for grain yield. The best model Y was selected based on the lack of fit and R2 values. 𝑌̂ = 6281.62 + 462.271N*+ 546.27P* + 636.12K* + 66.75N*P* + 98.25N*K* + 285.75P*K* -109.08N*2 -331.20P*2-281.88K*2 Where N*, P* and K* are the coded variables and N*= (N-90)/18 115 P* = (P-45)/18 K* = (K-45)/18 From the model it was found that N*, P*, K*, P*², K*² were significant model terms. The model in overall is a significant one with a lack of fit of 0.59 (>0.05). The stationary points for grain yield were determined which is the point of determination of maximum, minimum or saddle points. In this experiment, the optimum levels were found to be beyond our range of interest i.e., outside the fixed domain of -α and +α. Hence, the ridge analysis was performed where the optimal solutions are found within a particular radius of the stationary points. They were 94 N, 62 P and 65 K; 101 N, 65 P and 69 K and 109 N, 69 P and 73 K for 7295 kg ha-1, 7630 kg ha-1 and 7930 kg ha-1 respectively. Among these the most feasible solution both physically and economically were 109 N, 63 P and 73 K i.e., in terms of maximisation of yield and cost. In order to develop the RSM for CCD, a web application for experimental runs required for Circumscribed CCD and Inscribed CCD with 2, 3 and 4 factors were developed initially. The application was developed using the R codes and the server used was R Shiny. In addition, the method of estimating the Response surface models and optimum levels of factors were involved. The stationary points are also generated for the factors and ridge analysis when required. The results of the RSM analysis using CCD concluded that the optimum doses of N, P and K for transplanted rice for variety Uma (MO16) was 93.690, 62.388 and 65.178 Kg ha-1 respectively. A web application for RSM with CCD for factors ranging from 2 to 4 was also developed using R.