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ThesisItem Open Access Assessment of soil quality in the post flood scenario of AEU 13 in Palakkad district of Kerala and mapping using GIS techniques(Department of Soil Science and Agricultural Chemistry, College of Horticulture, Vellanikkara, 2020) Gadha, V P; KAU; Thulasi, VSoil quality is the capacity of the soil to function within its ecosystem boundaries to sustain biological productivity, maintain environmental quality and promote plant and animal health. It primarily depends on its dynamic properties which significantly change under environmental disturbances. The flood of August 2018 witnessed by Kerala not only caused havoc to life and properties but also triggered alarming changes in soil quality. Two types of flood damages were noticed throughout the state either due to river overflow and water logging or by caustic landslides. The parts of AEU 13 (Northern foothills) in Palakkad district consisting of low hills with undulating topography was affected both by river overflow and landslides. The study area in AEU 13 comprises of ten panchayats belonging to Mannarkkad and Sreekrishnapuram block panchayats. Heavy overflow of Nellippuzha, Kunthippuzha and Kanjirappuzha rivers in the area caused destruction of field crops and sand and silt deposition on their banks. Landslides from Kalladikkodan and Anangan hills resulted in complete demolishment of the nearby areas in Karimba, Kottopadam and Kanjirappuzha panchayats. The present study was undertaken to assess the soil quality in the flood affected areas of AEU 13 in Palakkad district and to develop maps on soil characters and quality using GIS techniques. The soils of AEU 13 are poor in organic matter, strongly acidic, dominated by low activity clays and sesqui-oxides and suffer from multi-nutrient deficiencies. One hundred and one georeferenced soil samples were collected from the flooded and landslide affected areas, processed and analyzed for different chemical, physical and biological properties. The results showed variation in all soil attributes except in available B, exchangeable acidity and electrical conductivity. The bulk density ranged from 1.11 Mgm-3 to 1.69Mgm-3 with 54 percentage of samples coming above 1.4 Mgm-3. Regarding particle density, seventy five percentage of the samples had values greater than 2.4 Mgm-3, whereas porosity and water holding capacity were in an optimum range. All the samples were acidic with pH ranging from 3.9 (Alanallur) to 6.8 (Mannarkkad), but with low exchangeable acidity. Soil organic carbon varied from 0.38 (Mannarkkad) to 1.78 percent (Kottopadam) with40percentage of samples coming under low category. Seventy five percentage of the samples were low in available N with an average value of 238.2 kg ha-1 for the area. The available P and K were high in the area with 67 and 74 percentage of samples coming under high category for available P and K respectively. Available Ca was sufficient (>300 mg kg-1) in 70 percentage of samples while available Mg was deficient (2.33) with respect to available P and K. Pearsons correlation matrix showed a strong positive correlation between organic C and available N and negative correlation between OC and bulk density. Soil pH is negatively correlated with exchangeable acidity and positively correlated with available Ca, Mg and K. When compared with the pre-flood analytical data collected from District Soil Testing Laboratory (DSTL), Pattambi, proportion of soil samples coming under medium and high category of soil organic carbon increased after flood, which may be due to organic matter deposition. There was a reduction in available Ca and Mg after flood which might be due to leaching and infiltration loss. The pre-flood data collected as well as the analytical results of the present study indicated deficiency of B and sufficiency of cationic micronutrients like Fe, Cu, Mn and Zn in AEU 13. Assessment of the present status of the land slide affected soils indicated higher bulk density and particle density than that of flood affected soils of the study area which may be due to the addition of heavier minerals during land slide from subsurface areas to topsoil. Available S, Fe and Mn were also higher in soil samples collected from landslide affected areas. For developing minimum data set (MDS), principal component analysis (PCA) was performed for 22 attributes and resulted in seven principle component groups. Soil quality index (SQI) was worked out using non linear scoring method. The MDS comprised of eight attributes with available Ca and bulk density having highest contribution to SQI. Soil quality index ranged from 0.408 (Karimba) to 0.539 (Kumaramputhur). The average relative soil quality index (RSQI) of flood affected soils of AEU 13 in Palakkad district was 43.92 percent which is rated as low. Only 20 % of the soil samples collected from the area had medium RSQI values. When averaged over different panchayats, Alanallur (48.67 percent) had highest and Karimba had lowest RSQI (37.36 percent). High bulk density and particle density and low available N and B might be the reason for low soil quality observed throughout the area. The soil quality of the post flooded soils in the AEU 13 can be improved by adopting appropriate soil health management strategies with major thrust on site specific and integrated nutrient management practices.ThesisItem Open Access Assessment of soil quality in the post flood scenario of AEU 15 (northern high hills) in Thrissur district of Kerala and mapping using GIS techniques(Department of Soil Science and Agricultural Chemistry, College of Horticulture, Vellanikkara, 2020) Mili, M; KAU; Betty, BastianAgro-ecological unit 15 (AEU 15) represents the Northern High Hills which is characterised by long dry spells (4 months in a year), a tropical humid monsoon type climate with an average annual precipitation of 3459.5 mm and a mean annual temperature of 26.20 C. The hilly terrains have deep, well drained clayey soils rich in organic matter, strongly acidic and low in bases whereas the valleys have deep, imperfectly drained acid clayey soils. The August floods of 2018 had caused great havoc to life, property and agriculture of the state causing drastic changes in soil properties thereby affecting soil quality and fertility and thereby its productivity. The study entitled ‘Assessment of soil quality in the post flood scenario of AEU 15 (Northern High Hills) in Thrissur district of Kerala and mapping using GIS techniques’ was therefore conducted with an objective to assess soil quality in the designated AEU and prepare thematic maps using GIS. A total of one hundred and four geo-referenced soil samples were collected from five grama panchayats namely Pazhayannur, Pananchery, Puthur, Varantharappilly and Mattathur, which were affected by floods. These soils were characterized for physical, chemical and biological properties. The bulk density values ranged from 0.83 to 1.74 Mg m-3 and 80.77% of the soils had a bulk density greater than 1.20 Mg m-3. Porosity ranged between 30 to 60% in 99.04 per cent of the samples and 83.65 per cent of the samples had maximum water holding capacity in the range of 30-50 %. Among the soil samples, 53.84 per cent belonged to moderately acidic /slightly acidic/neutral category (pH ≥ 5.6). All the soils had electrical conductivity less than 1.0 dS m-1. Exchangeable acidity was greater than 1 cmol kg-1 in 81.73% of the samples. In case of organic carbon, 39.40 per cent of the samples had low (25.0 kg ha-1) in all the samples. In the case of available potassium, 58.65 per cent of the samples had medium (116.0 -275.0 kg ha-1) and 31.73 per cent had high (>275.0 kg ha-1) contents. Available calcium content was sufficient (>300 mg kg-1) in 99.04 per cent of the samples. Deficiency of available magnesium (2.33) with respect to available phosphorus. Using principal component analysis (PCA), seven principal components with eigen values greater than one were extracted and eight soil parameters were identified as the key indicators determining the soil quality of the area. The key indicators formed the minimum data set (MDS) viz., porosity, water holding capacity, pH , available nitrogen, potassium, magnesium, manganese and boron. Non linear scoring method was adopted to assess soil quality. The products of score and weightage factor of the MDS parameters were summed up to obtain a soil quality index (SQI) of that particular site. Soil quality indices were rated using relative soil quality index (RSQI). It was found that 79.81 per cent of the soil samples had a medium relative soil quality index . Organic carbon showed a significant positive correlation with available nitrogen, zinc, magnesium, copper, maximum water holding capacity and dehydrogenase activity. In comparison with preflood data (GoK, 2013) where all samples were found to be acidic (pH 4.50- 6.50), 8.65% of the post flood soils exhibited neutral range of pH. Organic carbon, available nitrogen, sulphur and boron became more deficient after the floods. But there was an increase in the content of nutrients like available phosphorus, potassium, calcium, magnesium, copper and zinc. Bulk density of soils also increased after the floods. The soil quality of post flood soils in the AEU have to be improved by adopting site specific and integrated nutrient management practices in a comprehensive manner including fertilizers, organic sources and biofertilizers.ThesisItem Open Access Anionic equilibria in major soil types of Kerala(Department of Soil Science and Agricultural Chemistry, College of Horticulture, Vellanikkara, 2020) Reshma M R, M R; KAU; Sureshkumar, PSoils formed under tropical humid climate of Kerala are one of the best to study about chemistry of anions. Twenty two representative soil samples were collected from 7 different agro-ecological units of Kerala with wide variation in organic matter content and texture. The study aimed to understand the relative adsorption of selected anions on soil solid phase and to know the competitive interaction of fractions of these anions with respect to their adsorption behavior and bio-availability and the relative intensities of each of these anions. Out of the 22 samples collected, 5 samples were in near neutral pH, all others were acidic. Low lands of Pokkali, Kole and Kuttanad showed high organic carbon status. In general, sandy soils from northern coastal plain and Onattukara sandy plain were low in fertility and low land soils of Pokkali, Kole and Kuttanad were high in fertility. XRD data revealed the dominance of kaolinite mineral in all the representative soils except the soil from Palakkad eastern plain. Fractionation of phosphorous, sulphur, boron and silicon was carried out to know the major fractions and its contribution to the available pool. Dominance of P fractions was different in different types of soil. Saloid - bound phosphorous is contributing least to the total P content. All the S fractions were high in Pokkali soil. The percentage contribution of different fractions of Si to the total Si were in the order; residual Si > amorphous Si > occluded Si ≈ organic Si > adsorbed Si> mobile Si. The percentage contribution of different fractions of B to the total B were in the order; residual B > organically bound B > oxide bound B> readily soluble B > specifically adsorbed B. Among all the fractions, readily soluble and mobile fractions are the major contributor to the available pool. Single anion adsorption experiments were conducted for nitrate, P, S, B, Mo and Si at 250C and 400C. Quantity - intensity relations and thermodynamic parameters were worked out based on the adsorption data. Freundlich, Langmuir and Tempkin isotherms were fitted using the adsorption data. Dominance of desorption was observed in case of Si and S, with added concentration of these elements. Whether it is adsorption or desorption, the process was spontaneous in most of the soil for all anions under study. Q-I curve of these elements showed reduction in desorption after reaching a maximum desorption, indicating the possibility of adsorption on further addition of higher concentration of these elements. Adsorption of B was less because only lower concentration of B was used in adsorption study and the existence of non-ionised forms of B at the acidic equilibrium solution pH. Due to these reasons adsorption of B, S and Si was found less than nitrate adsorption. P and Mo showed very high similarity in adsorption behavior with high affinity of these elements to the adsorption sites, which is due to the inner-sphere complex formation by these elements. Increase in buffer power and maximum quantity adsorbed with added concentration of P and Mo was observed especially in low land soils of Pokkali, Kole and Kuttanad indicating the chemical nature of adsorption. Freundlich adsorption isotherm was the best to explain adsorption of anions in soil followed by Tempkin and Langmuir adsorption isotherm, which implies that the bonding energy of the adsorbate anion on the soil surface decreases with the fractional coverage of the adsorbent surface. Among P and Mo, the constants related to strength of adsorption were high for Mo adsorption than P adsorption. Kaolinite, hematite, goethite, other oxides and hydrous oxides of Fe and Al are the major sites for anion adsorption. Preferential adsorption of phosphorous over boron was observed in all soils in adsorption study conducted with binary system of P and B. Using P, for estimation of AEC can lead to the overestimation due to the specific adsorption behavior of P. A general trend in silicon extractability of different extractants was in the order; Bray I reagent > CBD > 0.5M acetic acid > 0.1M HCl. The possibility of over estimation of P due to the interference of Si in molybdenum blue colorimetric estimation was observed at high concentration of Si. Greater than 10 per cent over estimation was observed if Si:P ratio is greater than 80.ThesisItem Open Access Assessment of soil quality in the post floods scenario of AEU 5 and AEU 9 of Ernakulam district of Kerala and Mapping using GIS Techniques(Department of Soil Science and Agricultural Chemistry, College of Horticulture, Vellanikkara, 2020) Neha, Unni; KAU; Sreelatha, A KKerala state witnessed large scale devastating flood in 2018 due to excess rainfall, causing significant damage to agricultural sector and human life. One of the most affected districts was Ernakulam, especially AEU 5 and AEU 9. The AEU 5 - Pokkali lands, represent the lowlands, often below sea level, in coastal areas of Ernakulam district and extending to parts of Thrissur and Alappuzha districts. The soils are hydromorphic, often underlain by potential acid-sulphate sediments with unique hydrological conditions. Seawater inundation is not controlled and hence soils are acid-saline. The AEU 9 - south central laterites represent midland laterite terrain with typical laterite soils. The study aimed at the assessment of soil quality in the post flood scenario of AEU 5 and AEU 9 in Ernakulam district and to develop maps on soil characters and quality using GIS techniques and to workout soil quality index (SQI). For this purpose 100 geo-referenced soil samples were collected from different panchayats of AEU 5 and AEU 9 in Ernakulam district and were characterized for physical, chemical and biological properties. The Pokkali soils recorded low bulk density whereas porosity, water holding capacity and soil moisture were found high. Available N content was medium to high, available phosphorus and potassium was high in the soil. Among the secondary nutrients, available Ca and S were found sufficient for majority of the samples, while a deficiency of available Mg was noticed in Pokkali soils. In AEU 9, the soil pH varied from 5.01 to 7.69 and all the soils had an electrical conductivity less than 1.0 dS m-1. Organic carbon was noticed low to medium in the soils. Available N content was medium for 87 per cent of the samples, whereas all the samples were high in available P content. Available K was recorded low to medium values in AEU 9. Soil quality index was calculated using principal component analysis (PCA). There are three main steps involved in the soil quality index method which includes (i) selection of a minimum data set (MDS) of indicators (ii) formation of the MDS indicators and scoring of each indicators (iii) computation of index of soil quality. For developing minimum data set, principal component analysis (PCA) was performed for 23 soil attributes and resulted in 7 PCs. The indicators with high loading factors in each PCs were selected to develop minimum data set (MDS). MDS constituted 8 attributes for AEU 5 and 9 attributes for AEU 9 respectively. After the development of MDS, the soil indicators were converted to unit-less scores ranging from 0 to 1 using non-linear scoring function methods. Three types of scoring curves were used: i) more is better, ii) less is better, iii) optimum curve. Soil quality index ranged from 0.42 in Nayarambalam to 0.76 in Vadakkekkara in AEU 5, and 0.39 in Edathala to 0.92 in Aluva in AEU 9. The highest RSQI value was recorded in Aluva (69.7%) and the lowest in Edathala (29.55%) under AEU 9. In AEU 5 the highest RSQI was obtained in Vadakkekkara (71.58%) and the lowest in Nayarambalam (37.06%). Nutrient indices of flood affected areas in AEU 9 were low with respect to organic carbon and available potassium, medium with respect to available nitrogen and high with respect to available phosphorus. Nutrient index was high for nitrogen, phosphorus, potassium and organic carbon in AEU 5. Significant positive correlations were observed between organic carbon and available nitrogen, organic carbon and soil moisture content. Negative correlation existed between bulk density and porosity, organic carbon and bulk density in both AEUs. The present study revealed that soil fertility and productivity have been disturbed after the floods. In AEU 9 available potassium was found decreased after the flood. Prior to flood Kottuvally, Elamkunnapuzha, Edavanakkad and Kuzhuppilly panchayats in AEU 5 were medium in relative soil quality index (Joseph, 2014) and post flood assessment showed that these panchayats shifted to poor relative soil quality index.ThesisItem Open Access Assessment of soil quality in the post flood scenario of AEU 4 in Kottayam district of Kerala and generation of GIS maps(Department of Soil Science and Agricultural Chemistry, Vellayani, 2020) Anusha, B; KAU; Sailajakumari, M SThe study entitled ‘Assessment of soil quality in the post-flood scenario of AEU 4 in Kottayam district of Kerala and generation of GIS map’ was conducted with the objective to assess the soil quality of post-flood soils, to work out soil quality index (SQI) and to develop GIS maps based on soil characters and quality. Preliminary survey was conducted in four different blocks of AEU 4 in Kottayam district viz. Vaikom, Kaduthuruthy, Ettumanoor and Madapally. Seventy-five geo-referenced surface soil samples were collected from eighteen panchayats selected based on the survey. Paddy, banana, vegetables, coconut and nutmeg were found to be the major crops cultivated in the study area. Ninety-four percentage of farmers in the surveyed area were small and marginal mostly following conventional method of nutrient management. The soil samples collected from the eighteen panchayats were analysed for various physical, chemical and biological attributes. The physical attributes included bulk density, particle density, porosity, water holding capacity, soil moisture, soil texture, depth of sand/silt/clay deposition, aggregate analysis. Soil texture for majority of the samples (68.8 percent) was sandy clay loam with water holding capacity ranging from 20.6 to 68.8 per cent. Bulk density of 50.7 per cent of samples recorded a value less than 1.2 Mg m-3 with a mean value of 1.2 Mg m-3. Particle density of 73.3 per cent samples were less than 2.2 Mg m-3. Depth of sand/silt/clay deposition was not much significant in the study area. The chemical parameters analysed were pH, EC, organic carbon, available macronutrients and boron (micronutrient). More than 90 per cent of samples were in the acidic range with 6.67 per cent as ultra-acidic, 17.30 per cent as extremely acidic, 20 per cent as very strongly acidic, 14.70 per cent as strongly acidic, 14.6 per cent as moderately acidic and 7.61 as slightly acidic. EC value was less than 1 dS m-1 for 89.3 per cent of the samples. Organic carbon was high in 58.7 per cent samples analysed. Availability of nitrogen was found to be low in 78.7 per cent of samples, phosphorus and potassium was high in 54.7 per cent and 40 per cent samples respectively. Among the secondary nutrients, available calcium was adequate in 88 % of samples while available magnesium was sufficient in 58.7 % samples. Sulphur availability was found to be adequate in 81.3 per cent samples and boron was deficient in 78.7 per cent samples. Activity of acid phosphatase was also analysed as a biological attribute. Activity of 41.3 percentage sample were in the range of 10 to 25 μg p-nitrophenol g-1 soil h-1 Nutrient indices were calculated from the analysed data. The analysed data was also used to set up a minimum dataset (MDS) by employing principal component analysis (PCA). Principal component analysis of 20 attributes resulted in a MDS containing seven attributes (organic carbon, available N, P, K, Ca, per cent sand and per cent silt). By giving scores and weightage to each component in the MDS, soil quality index (SQI) was worked out. The relative value for soil quality index (RSQI) was used to categorize the soil into low, medium and good quality. GIS techniques were used to prepare thematic maps of various soil parameters and soil quality indices. Simple correlations were also worked out among various analysed parameters. Nutrient index was high for organic carbon, low for available nitrogen while it was medium for available phosphorus and potassium. Compared to the pre flood data (KSPB,2013) soil acidity was increased as there was an increase in percentage samples in ultra-acidic, moderately acidic and strongly acidic range, an increase in organic carbon, available potassium, calcium and magnesium were observed. Even though the availability of phosphorus and sulphur were high in the AEU, percentage of samples in low fertility class was increased compared to pre-flood data. However, availability of boron was decreased and the per cent deficient soil samples considerably increased in the post-flood scenario The study indicated that RSQI in the majority of soils of AEU 4 of Kottayam district was medium and land quality index was very low to low. The study recommends the site specific adoption of soil management strategies for the control of soil acidity, applications of soil ameliorants, micronutrients such as B for maintaining soil health and quality in the AEU 4 regions of Kerala.ThesisItem Open Access Assessment of soil quality in the post flood scenario of AEU 16 in Idukki district of Kerala and generation of GIS maps(Department of soil science and agricultural chemistry, College of Agriculture, Vellayani, 2020) Sreekutty, M R.; KAU; Visveswaran, SThe present study entitled “Assessment of soil quality in the post- flood scenario of AEU 16 in Idukki district of Kerala and generation of GIS maps” was conducted during 2018-2020 with the objective of assessing the soil quality of post-flood soils of AEU 16, formulation of Soil Quality Index (SQI) and generation of GIS maps of soil characters and land quality. The study was initiated with the survey, collection followed by characterization of soil. Seventy-six representative geo referenced surface soil samples were collected from eight flood affected panchayaths viz., Rajakumari, Santhanpara, Senapathy, Udumbanchola, Pampadumpara, Karunapuram, Nedumkandam and Vandiperiyar. Cardamom, pepper, nutmeg, clove, ginger, paddy, cocoa, banana, cassava, vegetables etc. were the major crops grown. Farmers commonly use dolomite and organic nutrient sources like fresh and dried cow dung, goat manure, vermi compost etc. The soil samples were characterized for physical, chemical and biological attributes. The data was interpreted and Minimum Data Set (MDS) was developed using Principal Component Analysis (PCA). Eight principal components were extracted from which ten indicators that highly influenced the soil quality (eigen value >1) were identified, viz., bulk density, clay per cent, silt per cent, soil moisture content, pH, electrical conductivity, organic carbon, available N, K and B. SQI for each sampling site was generated by aggregating the scores following standard methods (Kundu et al., 2012). The relative soil quality index of the soils was also calculated and 77.6 per cent of soil samples had medium soil quality index. Correlation between the analysed parameters were worked out. Deposition of sediments with varying depth and texture was found in Rajakumari, Udumbanchola, Karunapuram and Vandiperiyar panchayaths, of which the sand deposition in Rajakumari panchayath was prominent. Most of the soils had a BD <1.2 Mg m-3 (80.3%), PD <2.2 Mg m-3 (88.2%), porosity between 30 and 50 % (68.4%), soil moisture content less than 10% (29.0%), WHC between 30 and 50% (79.0%), WSA between 50 and 70% (59.2%) and MWD < 1 mm (72.4%). Soil pH was found to be moderately acidic for 30.3 per cent of the soil samples. All the soil samples had low electrical conductivity in a range less than 1.0 dS m-1. Organic carbon was high for 85.5 per cent of samples. Available N was low for 77.6 per cent of samples. Available P and available K was high for 54.0 and 80.3 per cent of the samples respectively. Available Ca, Mg and available S were sufficient whereas available B was deficient for most of the samples. Acid phosphatase activity was between 10 and 25 μg PNP produced g soil-1 h-1 for 42.1 per cent of samples. The mean of relative soil quality index was found to be highest in Pampadumpara panchayath (60.9% - medium) and lowest in Senapathy panchayath (55.2% - low). Nutrient index for organic carbon was high for all the panchayaths except for Nedumkandam panchayath (medium). Nutrient index for available N was low in all panchayaths. Nutrient index for available P was high in Karunapuram, Rajakumari, Santhanpara and Vandiperiyar panchayaths, medium in Udumbanchola and Pampadumpara panchayaths and low in Nedumkandam panchayath. Nutrient index for available K was high in all panchayaths except in Senapathi panchayath (medium). Land quality was low for 65.8 per cent of samples. In comparison with the pre-flood data of GOK (2013), there is an increase in pH from strongly acidic to moderately acidic, warranting lower requirement of lime. The previous values of organic carbon, available P and available K were also high similar to the post-flood status, indicating that there is no shift in the status of these nutrients. Per cent of samples with adequate levels of available Ca and available B were similar in the pre-flood and post-flood study whereas per cent of samples with adequate available Mg and available S increased. Establishment of soil quality index is very important as far as soil health is concerned. It is advisable to analyse the physcio chemical characteristics of soil and derive soil quality index every year, in order to have an effective alternate site-specific management of crops especially in the events of natural calamities. Thus, the present study shows a need for the revision of soil management practices, as there is an improvement of major nutritional factor viz., organic carbon, available P and K requiring only lesser nutrient requirement for maintaining the crops with same level of productivity compared to pre-flooded condition in the AEU 16.ThesisItem Open Access Assessment of soil quality in the post flood scenario of AEU 6 in Thrissur and Malappuram districts of Kerala and mapping using GIS techniques(Department of Soil Science and Agricultural Chemistry, College of Horticulture, Vellanikkara, 2020) Safnathmol, P.; KAU; Rajalekshmi, KKerala State experienced a devastating flood in 2018, causing significant damage to agricultural sector and human life. Major crop systems in the State have been negatively impacted, with more than 80 per cent of paddy fields including in kole lands. Kole land (AEU 6) is a low lying area situated 0.5m to 1m below mean sea level, which spread over an area of 13,632 ha in coastal parts of Thrissur and Malappuram districts of Kerala. Considering the damage caused by the flooding to the kole lands, the present study was carried out in the flood affected areas of kole lands in order to put forward post flood management strategies. A survey was carried out to identify the flood affected locations in kole lands. Hundred georeferenced composite soil samples were collected from seven block panchayats viz., Mullassery, Anthikkad, Cherpu, Irinjalakkuda, Puzhakkal, Perumpadappu and Ponnani of Thrissur and Malappuram districts and analysed for different physical, chemical and biological soil quality indicators. The results showed that the soils of kole lands were low in bulk density and high in porosity while particle density varied from 2.05 to 2.67 Mg m-3. Maximum water holding capacity and soil moisture content of the soil samples ranged from 18.11 to 73.49 per cent and from 12.00 to 41.60 per cent respectively. High mean weight diameter of soil was also noticed in the study. The soils were acidic in reaction and the exchangeable acidity varied from 0.05 to 2.2 cmol (+) kg-1. Electrical conductivity was below toxic level. The organic carbon was shifted towards medium to high level from low to medium after the flood. Available nitrogen content was high with a mean of 704.59 kg ha-1. Availability of phosphorus and potassium were in the medium status within 55 and 44 per cent of samples respectively. Among the secondary nutrients, available calcium was sufficient in 64 per cent of soil sample while available magnesium was deficient in 72 per cent of soil samples and available sulphur was sufficient in 89 per cent of soil samples. The micro nutrients like available Fe, Mn and Zn were high in AEU 6. Available copper was sufficient in 83 per cent of soil samples, whereas available boron was deficient in all the soil samples. Effective cation exchange capacity of soil in AEU 6 varied from 0.62 to 9.00 cmol (+) kg-1. Among the biological attributes, kole lands showed high dehydrogenase activity while microbial biomass carbon was found medium in 58 per cent of soil samples. Available Ca, S, N, porosity, exchangeable acidity, available Fe, Zn, particle density and available B formed the minimum data set for soil quality index. The highest mean soil quality index was recorded in Mullassery block panchayat and the lowest mean was in Cherpu block panchayat. Relative soil quality index varied from 25.93 to 72.22 per cent with 63 per cent of soils showing poor soil quality and 3 per cent showing high soil quality. Nutrient index was high for nitrogen and medium for phosphorus, potassium and organic carbon in kole lands. Significant positive correlations were observed between organic carbon and available nitrogen, organic carbon and soil moisture content. Negative correlation existed between bulk density and porosity, organic carbon and bulk density. The post flood study in kole lands revealed that drastic changes in soil environment had occurred with more than 50 per cent of soil samples falling in low soil quality range. Hence, proper adoptions of site specific soil management practices are essential to improve the soil fertility in kole lands.ThesisItem Open Access Assesment of soil quality in the post flood scenario of AEU 14 in Idukki district of Kerala and generation of GIS maps(Department of Soil Science and Agricultural Chemistry, College of Agriculture, Vellayani, 2020) Sreejitha M, Babu; KAU; Naveen, LenoA study entitled ‘Assessment of soil quality in the post-flood scenario of AEU 14 in Idukki district in Kerala and generation of GIS maps’ was carried out with objectives to evaluate the soil quality of the flood affected areas of AEU 14 in Idukki district, to work out the soil quality index and to map the various soil attributes and quality using GIS techniques. On the basis of the survey conducted, seventy eight georeferenced soil samples were collected from the severely flood affected panchayaths viz. Vazhathope, Kamakshy, Konnathady, Rajakkad, Mariyapuram, and Kanjikuzhy. Pepper, cardamom, coffee, cocoa, coconut, nutmeg and vegetables were the major crops in the area. Major- ity (91 %) of the farmers were small and marginal, adopting organic practices and lim- ing integrated with conventional fertilisers. The soil samples, collected from a depth of 0-20 cm, were characterized for phys- ical (bulk density, particle density, porosity, texture, water holding capacity, depth of sand/ silt/ clay deposition, soil moisture and aggregate analysis), chemical (pH, EC, OC, available N, P, K, Ca, Mg, S, B) and biological (acid phosphatase) attributes. The min- imum data set of indicators for computing the soil quality index was selected by princi- pal component analysis. Six principal components were extracted from which ten indi- cators that highly influenced the soil quality were identified, viz. available calcium, organic carbon, available magnesium, acid phosphatase activity, clay per cent, silt per cent, pH, electrical conductivity, water stable aggregates and available boron. Scores and weights were assigned to each indicator, and they were aggregated to compute the soil quality index. The relative soil quality indices of the soils were computed. Thematic maps of the analysed soil parameters were generated in ArcGIS software and interpo- lated by Inverse distance weighting method. Correlations were worked out between physical, chemical and biological parameters. Bulk density of 67 per cent sam- ples were in a range of 1.0 to 1.2 Mg m-3 and particle density of 72 per cent samples were in the range 2.0 to 2.4Mg m-3. Mean value of water holding capacity ranged be- tween 40-50 per cent. Soil porosity of 58 per cent samples was between 40 and 50 percent. 118 Soil textural classes of the samples were clay (42 %), clay loam (27 %), sandy clay loam (17 %), sandy clay (9 %) and loamy (5 %). Based on soil pH, the samples belonged to moderately acid (37 %), strongly acid (28 %), slightly acid (25 %) and very strongly acid (9 %) classes. Electrical conductivity was < 2 dSm-1. Organic carbon content was high in 70 per cent of the soils. Available nitrogen was medium in 67 per cent of the samples and available phosphorus was high in 65 per cent of the soil samples. Available K status was high in 54 per cent and medium in 32 per cent of the soils. Available Ca was adequate in 89 per cent whereas Mg and S were deficient in 49 per cent of the samples and B was deficient in 100 per cent of the samples. Significant correlation was observed between clay content and water holding capacity of the soil, and acid phosphatase activity and organic carbon content of the soil. Nutrient index of organic carbon was high in all panchayaths except Vazhathope which was medium. Nutrient index of available K was low in Vazhathope and high in other panchayaths. Land quality index of the soil samples were very low (35 %), low (60 %) and medium (5 %). Based on the relative soil quality index value, soils were categorized as medium (59 %), good (37 %) and poor (4 %). Relative soil quality index was the highest in Rajakkad and lowest in Vazhathope panchayath. Vazhathope pan- chayath was comparatively vulnerable to floods in terms of soil physical, chemical and biological attributes. Rajakkad panchayath exhibited better tolerance to floods and was resilient. Compared to 2013 data, a moderation in soil reaction, an increase in the organic carbon content, available P, K, Ca, and alleviation in deficiency status of Mg and S was observed in the post- flood soils, boron being an exception. An enhancement of soil quality parameters has been facilitated in the post-flood soils of AEU 14. Liming of acid soils, regular application of recommended doses of nitrogenous fertilisers, application of potassium fertilisers in splits, application of magnesium sul- phate in Mg and S deficient area, application of borax for crop plants are the suggested interventions for the soils of AEU 14 in Idukki district of Kerala.ThesisItem Open Access Assessment of soil quality in the post floods scenario of AEU 5 and AEU 9 of Ernakulam district of Kerala and Mapping using GIS Techniques(Department of Soil Science and Agricultural Chemistry, College of Horticulture, Vellanikkara, 2020) Neha, Unni; KAU; Sreelatha, A KKerala state witnessed large scale devastating flood in 2018 due to excess rainfall, causing significant damage to agricultural sector and human life. One of the most affected districts was Ernakulam, especially AEU 5 and AEU 9. The AEU 5 - Pokkali lands, represent the lowlands, often below sea level, in coastal areas of Ernakulam district and extending to parts of Thrissur and Alappuzha districts. The soils are hydromorphic, often underlain by potential acid-sulphate sediments with unique hydrological conditions. Seawater inundation is not controlled and hence soils are acid-saline. The AEU 9 - south central laterites represent midland laterite terrain with typical laterite soils. The study aimed at the assessment of soil quality in the post flood scenario of AEU 5 and AEU 9 in Ernakulam district and to develop maps on soil characters and quality using GIS techniques and to workout soil quality index (SQI). For this purpose 100 geo-referenced soil samples were collected from different panchayats of AEU 5 and AEU 9 in Ernakulam district and were characterized for physical, chemical and biological properties. The Pokkali soils recorded low bulk density whereas porosity, water holding capacity and soil moisture were found high. Available N content was medium to high, available phosphorus and potassium was high in the soil. Among the secondary nutrients, available Ca and S were found sufficient for majority of the samples, while a deficiency of available Mg was noticed in Pokkali soils. In AEU 9, the soil pH varied from 5.01 to 7.69 and all the soils had an electrical conductivity less than 1.0 dS m-1. Organic carbon was noticed low to medium in the soils. Available N content was medium for 87 per cent of the samples, whereas all the samples were high in available P content. Available K was recorded low to medium values in AEU 9. Soil quality index was calculated using principal component analysis (PCA). There are three main steps involved in the soil quality index method which includes (i) selection of a minimum data set (MDS) of indicators (ii) formation of the MDS indicators and scoring of each indicators (iii) computation of index of soil quality. For developing minimum data set, principal component analysis (PCA) was performed for 23 soil attributes and resulted in 7 PCs. The indicators with high loading factors in each PCs were selected to develop minimum data set (MDS). MDS constituted 8 attributes for AEU 5 and 9 attributes for AEU 9 respectively. After the development of MDS, the soil indicators were converted to unit-less scores ranging from 0 to 1 using non-linear scoring function methods. Three types of scoring curves were used: i) more is better, ii) less is better, iii) optimum curve. Soil quality index ranged from 0.42 in Nayarambalam to 0.76 in Vadakkekkara in AEU 5, and 0.39 in Edathala to 0.92 in Aluva in AEU 9. The highest RSQI value was recorded in Aluva (69.7%) and the lowest in Edathala (29.55%) under AEU 9. In AEU 5 the highest RSQI was obtained in Vadakkekkara (71.58%) and the lowest in Nayarambalam (37.06%). Nutrient indices of flood affected areas in AEU 9 were low with respect to organic carbon and available potassium, medium with respect to available nitrogen and high with respect to available phosphorus. Nutrient index was high for nitrogen, phosphorus, potassium and organic carbon in AEU 5. Significant positive correlations were observed between organic carbon and available nitrogen, organic carbon and soil moisture content. Negative correlation existed between bulk density and porosity, organic carbon and bulk density in both AEUs. The present study revealed that soil fertility and productivity have been disturbed after the floods. In AEU 9 available potassium was found decreased after the flood. Prior to flood Kottuvally, Elamkunnapuzha, Edavanakkad and Kuzhuppilly panchayats in AEU 5 were medium in relative soil quality index (Joseph, 2014) and post flood assessment showed that these panchayats shifted to poor relative soil quality index.
