Groundwater Geochemistry of Neyveli Lignite Mine-Industrial Complex, Tamil Nadu, India and Its Suitability for Irrigation

This study was undertaken to assess the quality of groundwater for irrigation and level of trace metal concentration in the surface and groundwater bodies from Neyveli lignite mine-industrial complex which is located in Cuddalore district, Tamil Nadu, India. The hydrogeology of the Neyveli groundwater basin is extremely complex, consisting of a series of productive, confined aquifers below the lignite seam in both Mine I and II areas, while a semi-confined aquifer lies above the seam and occurs only in the Mine II area. The suitability of groundwater quality for agricultural purposes in and around Neyveli lignite mine-industrial complex was assessed by measuring physicochemical parameters, including major cation and anion compositions, pH, total dissolved solids, electrical conductivity, and trace metals. The results of the chemical analysis of the groundwater showed that concentrations of ions vary widely and the most prevalent water type is mixed CaNaHCO3, followed by other water types: mixed CaMgCl types and NaCl which is in relation with their interactions with the geological formations of the basin, dissolution of feldspars and chloride and bicarbonate minerals, and anthropogenic activities. The most dominant class is C1-S1, C2-S1 (85% PRM and 74% POM) in the study area, indicating that sodicity is very low and salinity is medium, and that these waters are suitable for irrigation in almost all soils.Based on sodium absorption ratio the groundwater of the study area is suitable for all types of crops and soil except for those crops sensitive to Na and based RSC values of the groundwater, considered safe. Based on the parameters such as TDS, EC, SO4, Cl and Wilcox diagram about 99% of samples are suitable for irrigation. The average concentration of trace metals (Fe, Mn, Cr, Zn, Pb, and Cu) in groundwater samples fall within the permissible limit, with the exception of Ni which is recorded higher than the permissible limit which may retard growth and metabolic activities while the groundwater used for irrigation

Evaluation of tuberose genotype IIHR 17-23SP-08 (IC0642158) for flower yield, quality and response to biotic stress

Tuberose (Agave amica, family Asparagaceae) is an important commercial flower crop valued for its spectacular fragrant flowers. An experiment was conducted to evaluate the single petalled tuberose genotypes for growth, flowering, flower yield, concrete yield and response to biotic stress for two consecutive years from 2020 to 2022. Tuberose genotype IIHR 17-23SP-08 was found to be superior with highest plant height (55.53 cm), early flowering (94.93 days), highest number of spikes/plant (8.47), longest spikes (114.61cm) and rachis (32.11cm) and maximum number of florets/spike (54.87). The matured bud weight of IIHR 17-23SP-08 was 1.29 g, which is preferable in the medium segment range with higher number of flower buds (725 buds per kg). It is a high yielder producing the highest number of spikes/m2 (76.20) and loose flower yield 18.88 t/ha/year among the genotypes evaluated. The genotype IIHR 17-23SP-08 was also found to be a good multiplier with the maximum bulb production of 8.94 bulbs per clump. It was found to be resistant to root knot nematode (Meloidogyne incognita) and tolerant to leaf burn disease (Alternaria polianthi) under field conditions. It was found suitable as loose flower for garland preparation with the shelf life of 2 days under ambient conditions and for concrete extraction with the concrete yield of 0.095%. It produces white buds (RHS colour: NNI55D, white group, Fan 4) with green tinge on the tip. Thus, the genotype IIHR 17 23SP 08 was found promising and novel among the single types with better flower and bulb yield parameters

Effect of growth regulators and micronutrients on quality parameters in cashew (Anacardium occidentale L.)

Cashew (Anacardium occidentale L.) is an important tropical nut crop of social and economic importance worldwide. However, the crop is threatened with the low yield. In the present study, an attempt was made to test the effects of plant growth hormones as well as micronutrients on nut and apple quality of cashew var. Bhaskara. Significant differences in kernel weight, shelling percentage, carbohydrates and starch content of cashew kernel and juice content of cashew apple were observed with the foliar application of growth hormones and micronutrients. The foliar application of ethrel @ 50 ppm increased shelling percentage (35.8%), carbohydrate content (21.63%), sugar content (6.26%), protein content (32.4%), starch content (31.42%), juice content (78.3%) and total soluble solids (120 Brix). Further, the foliar spray of zinc sulphate (0.5%) + borax (0.1%) increased shelling (36.13%), protein content (32.15%), starch content (32.03%) among all the treatments tested. Furthermore, higher cashew apple juice content (78%) and total soluble solids (120Brix) was also recorded with the foliar spray of zinc sulphate (0.5%) + borax (0.1%)

Rootstocks EFFECT ON CASHEW CULTIVARS

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Strategies for classifying water quality in the Cauvery River using a federated learning technique

Artificial intelligence methods are emerging techniques used in the field of environmental protection, especially in the analysis of air, water, and soil quality. AI analyzes vast amounts of environmental data to predict pollution and provide decision-makers with the information they need to develop efficient policies. One of the most important problems in environmental analysis is data security, and many organizations are actively working to ensure the secure collection, storage, and utilization of sensitive environmental data. In addition, organizations are focusing on developing strategies to protect their data from malicious attacks, such as cyber-attacks, as well as from accidental misuses, like unauthorized access. For this purpose, we have introduced a novel water quality prediction using the Federated Learning Technique. Federated learning enables multiple parties to collaborate and train a model on their local data without sharing it with others, thereby preserving data privacy. The proposed method is applied to a Cauvery River dataset of water quality parameters, and the results demonstrate that the PSO-optimized federated learning process achieves better prediction accuracy of 87%, a precision of 85%, a recall of 93%, and an 89% F1 score

Mineral nutrient composition in leaf and root tissues of fifteen polyembryonic mango genotypes, grown under varying levels of salinity

Mango (Mangifera indica L.) is a salt sensitive crop and its cultivation in salt affected area is declining day by day. There is a need to find out the rootstocks to sustain under saline conditions which can be used for commercial cultivation of superior cultivars through grafting. To achieve this, the present study was carried out to understand the salt tolerance and sensitive nature of fifteen polyembryonic mango rootstock seedlings (EC-95862, Bappakkai, Vellaikolamban, Nekkare, Turpentine, Muvandan, Kurukkan, Kensington, Olour, Manipur, Deorakhio, Vattam, Mylepelian, Sabre and Kitchener) which were exposed to 0 mM, 25 mM, 50 mM and 100 mM concentration of NaCl+CaCl2 (1:1) salt. The outcome of this study revealed that there was reduction in K+, Ca++, Mg++, Fe++ and Zn++ while the content of Cu++ and Mn++ in both leaf and root tissues were found to increase with gradual increase in salt concentration from 0 to 100 mM. The overall results of this study revealed that the salinity stress caused the alterations in mineral nutrient composition of polyembryonic mango genotypes. Among the fifteen genotypes the seedlings of Turpentine, Deorakhio, Olour and Bappakkai respond better in maintaining the mineral nutrient status in leaf and root tissues under higher level of salinity