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There exist a huge uncertainty cascade in projecting the future climate and in-turn using the same in assessing the impact on crop yields. To address the problem of projecting the future climate over Tamil Nadu in Southern India, 29 Global Circulation Models defined under coupled model inter comparison project (CMIP5) of IPCC were employed for understanding the range of deviations in future climate from current condition. Outputs of representative concentration pathway (RCP) 4.5 and 8.5 scenarios for mid-century were processed for impact assessment. Selected models output was forced in dynamic crop simulation model DSSAT for assessing the impact on spatial variability in maize productivity over Tamil Nadu and possible adaptation options were tailored forreducing impacts. During the Northeast Monsoon (NEM), the maximum temperature is expected to rise by 2.0 °C for RCP 4.5 and 3.0 °C for RCP 8.5, while the minimum temperature is expected to rise by 1.8 °C for RCP 4.5 and 2.4 °C for RCP 8.5 in the mid-century. Rainfall is anticipated to vary from -22.8 to 21.1% for RCP 4.5 and -31.3 to 32.6% for RCP 8.5. The magnitude of decline in yield would be more in RCP 8.5 (30.7%) over RCP 4.5 (10.6%) scenario. Impacts of future climate change could be reduced by altering the date of sowing (early sowing) to September 1st against September 15th (normal sowing) under RCP 4.5 and RCP 8.5 scenarios. Additional dose of (25%) fertilizer had positive response with yield increase up to 15% under future climatic conditions.

Climate change, as well as an increase in extreme weather, are posing a serious threat to socioeconomic and livelihood security. For this study, the Cauvery delta zone (CDZ) was chosen to examine current and anticipated climate variability and trend. The four largest districts of Tamil Nadu's Cauvery delta region are Thanjavur, Thiruvarur, Mayiladuthurai, and Nagapattinam. The Indian meteorological agency provided the baseline data sets (IMD). Using IPCC AR5 RCP4.5 and RCP8.5 scenarios, the maximum and minimum temperatures, as well as precipitation, were investigated. The maximum temperature could rise to 1.1oC to 3.6oC, while the minimum temperature could rise to 2.8 to 4.5oC, according to the findings. It was also concluded that there will be 15-16% rise in the rainfall during end of the century.

Increased concentration of greenhouse gases is expected to alter the radiative balance of atmosphere, causing increase in temperature and changes in precipitation patterns. Climate variability has been principal source of fluctuations in Indian food production. Even though there is no long-term trend, inter - annual variability of Indian monsoon rainfall leading to frequent droughts and floods has profound influence on agriculture and intern, national economy. It is well recognized that El Niño / Southern Oscillation (ENSO) is the dominant mode of climate variability on seasonal to inter-annual scales and its impacts are felt worldwide. ENSO often affects seasonal temperature, precipitation and thus crop yields in many regions, however, the overall impacts of ENSO on global yields are uncertain Maximum and minimum average temperature was calculated for different seasons such as Cold Weather Period (CWP), Hot Weather Period (HWP), South West Monsoon (SWM), North East Monsoon (NEM) for each El Niño, La Niña and neutral years at various districts of Tamil Nadu. Relation between ENSO and temperature was analyzed by computing the anomaly in temperature at different districts of Tamil Nadu. The temperature deviation between the ENSO and neutral phases was not significant as the deviation was within + 0.3oC in all the seasons.

Drought is the major precipitation anomaly that has greater impact on water resource management, ground water table and agricultural production. The WASP index (Weighted Anomaly of Standardized Precipitation Index) was estimated for a period 1901-2019 (119 years) – four time scales (12 months, 9 months, 6 months and 3 months) based on the IMD precipitation dataset to analyse the dry events. Analysis over different time scales is necessary to find out the impact of the slowly evolving drought on the agricultural production as well as water resources. The numbers of moderate dry events were 276, 256, 188 and 100 for WASP12, WASP9, WASP6, and WASP3 respectively for the whole of 119 years data. The number of extreme dry events with WASP12, WASP9, WASP6, and WASP3 were 0, 4, 29 and 59, respectively, for 119 years. There was no specific trend in number of dry events over the study region and 1991-2019 was found to have higher number of dry events when compared with the other tri-decadal periods.

Drought happen aimlessly and there is no periodicity in its event and can't be anticipated ahead of time. In semiarid stations, the event of precipitation is occasional and is known something else for its changeability regarding reality. Meteorological drought is characterized as the time of drawn-out dry weather pattern because of underneath ordinary precipitation. Drought is an extensive stretch of incredibly low precipitation that essentially affects crop result and individuals' prosperity. Recent study has made substantial progress in understanding drought in Tamil Nadu, as well as the implications of climate change, but more research is needed because regional climatic responses remain unknown. The Meteorological Drought Indicator was also a useful and appropriate index for drought monitoring in the research area since it provided drought analysis on a variety of timeframes. This study might help us better understand the features of drought.

Land, water, and food resources are essential for human survival, economic development, and social stability. Water and land are the basic resources in irrigated agricultural systems. Sustainable agricultural development entails effective management of the land-water-food nexus. The complex relationship in land-water-food nexus, with large uncertainties encompassed therein. Approaches that interconnect Land-Water-Food have grown significantly in scope and intricacy. In evaluating solutions to accomplish Sustainable Development Goals (SDGs) under the contexts of rising demands, resource paucity, and climate change, nexus techniques are helpful. The nexus analysis includes the important interlinkages that could be addressed. In the water-food-land nexus system, optimization approach can increase irrigation water production and optimise the allocation of scarce resources. Model optimisation considers the uncertainties in the systems to help decision-makers devise effective strategies for allocating water and land resources effectively. Integrated modelling with efficient optimization methods aid in solving real-world nexus management issues and provides the results that could serve as the basis for effective management of land-water-food nexus and formulation of agricultural policies.

The complex and multi-dimensional nature of droughts in Tamil Nadu state and its impact on agricultural sector and farming community requires a thorough analysis for developing short and long– term adaptation and mitigation measures with institutional arrangement and policy support. In the present investigation, drought analysis was made for Tamil Nadu state using 148 years (1871 – 2018) monthly rainfall data obtained at district scale from India Meteorological Department (IMD) and Tamil Nadu Agricultural University by employing various drought indices. Larger co-efficient of variation (139 and 149 %) during the major growing seasons (Southwest and Northeast monsoon)evidenced high undependability of rainfall over Tamil Nadu. Drought Index (DI) analysis indicated a 51 per cent drought years in SWM and 48 per cent in NEM season, while Standardized Precipitation Index (SPI) exhibited 43 and 48 per cent of drought years in SWM and NEM respectively, indicating occurrence of drought once in every four years. More number of Consecutive Drought years (CDY) posed serious impacts on food production and livelihoods. Drought management framework as well as input for designing drought policy for Tamil Nadu.

he project entitled as “Rainfall Prediction using Machine Learning & Deep Learning Algorithms” is a research project which is developed in Python Language and dataset is stored in Microsoft Excel. This prediction uses various machine learning and deep learning algorithms to find which algorithm predicts with most accurately. Rainfall prediction can be achieved by using binary classification under Data Mining. Predicting the rainfall is very important in several aspects of one’s country and can help from preventing serious natural disasters. For this prediction, Artificial Neural Network using Forward and Backward Propagation, Ada Boost, Gradient Boosting and XGBoost algorithms are used in this model for predicting the rainfall. There are totally five modules used in this project. The Data Analysis Module will analyse the datasets and finding the missing values in the dataset. The Data Preprocessing includes Data Cleaning which is the process of filling the missing values in the dataset. The Feature Transformation Module is used to modify the features of the dataset. The Data Mining Module is used to train the dataset to models using any algorithm for learning the pattern. The Model Evaluation Module is used to measure the performance of the model and finalize the overall best accuracy for the prediction. Dataset used in this prediction is for the country Australia. This main aim of the project is to compare the various boosting algorithms with the neural network and find the best algorithm among them. This prediction can be major advantage to the farmers in order to plant the types of crops according to the needy of water. Overall, we analyse the algorithm which is feasible for qualitatively predicting the rainfall.

Over the last century, mean annual temperatures increased by ~1°C. UNFCCC has proposed to limit warming below 1.5°C relative to pre-industrial levels. A study was conducted on rice (C3 pathway) and maize (C4 pathway) over Tamil Nadu using DSSAT to understand the climate change impacts with projected temperature increase of 1.5°C.The future climate under RCP 4.5 and RCP 8.5 indicated 1.5°Cincrease in temperature to happen by 2053 and 2035, respectively over Tamil Nadu.Annual rainfall deviations in RCP4.5 showed drier than current condition and RCP8.5 projected wetter SWM and drier NEM (90 % of current rainfall).Impact of 1.5°C warming on crop phenology indicated 8 days reduction in duration for rice and maize. The W UE of rice would decrease by 17 per cent at current CO2 whereas, enrichment (430 ppm) would reduce by12 per cent and rice yield is reduced by 21 per cent with 360 ppm CO2 and 430 ppm reducedby 17 per cent. There is no considerable varaition (- 5 to 1 %) in maize productivity with 1.5 ºC warming. The above results indicated that 1.5 ºC warming has more negative impacts on plants with C3 compared to C4 pathway

BACKGROUND Neonates presenting with seizures require long term stay in hospital and have higher chances of neuro-developmental delay later. Seizures in neonates are mostly subtle and difficult to diagnose. We wanted to study the clinical profile and short-term outcome on term and later pre-term neonates presenting with seizures. METHODS The study was a retrospective observational study done from November to December 2020 at PES Hospital, Kuppam. Retrospective data of neonatal seizures from May 2019 to April 2020 was considered for study. Details from the case records of neonates with seizures was collected. RESULTS Neonatal seizures (NS) were most common in females (53.8 %, 72/134). Subtle seizures were most common form of seizures in neonates which was seen in 64.2 % (86/134) babies followed by tonic seizures in 22.4 % (30/134). Neonatal seizures were most commonly seen in babies with hypoxic ischemic encephalopathy in 63.4 % (85/134) followed by metabolic disturbances in 15.6 % (21/134) and meningitis in 13.5 % (18/134). In babies with hypoxic ischemic encephalopathy, metabolic and meningitis subtle seizures were observed to be more common. Seizures were seen most commonly in first 72 hours of life (82.85 %, 111/134). Seizures in babies with hypoxic ischemic encephalopathy, metabolic causes most commonly occurred within first 24 hours of birth. Seizures in babies with meningitis most commonly occurred after 7 days of birth. Babies with neonatal seizures with sequelae was seen in 17.96 % (24/134) and 11.94 % babies died (16/134). CONCLUSIONS Subtle seizures were most common form and hypoxic ischemic encephalopathy was most common risk factor. Most neonatal seizures present within 72 hours of birth. Babies with meningitis presented with seizures most commonly after 72 hours of birth. KEYWORDS Hypoxic Ischemic Encephalopathy, Neonatal Seizures, Newborn, Aetiology, Outcome

The intelligent personal assistant system is designed to support the individual researchers to enhance their quality of the research through the natural language interface. Specifically, this system automatically provides intrinsic details about the importance of the topic of discussion using the timeline analysis. The results generated by the system help the researchers to understand the preference of the global researchers in the specific research field. This system primarily identifies the core topic of the discussion from the user's presentation. Further, the importance of the topic is calculated based on the research articles published over three decades in the related field. The experimental results confirm that the proposed method accurately identifies whether the research topic the user presented is HOT.

Water hyacinth is a perennial aquatic weed that can be utilized effectively rather than spending energy in eradicating them. It is the source of N, P, K which is essential for the growth of plants. In India, locally available horticulture wastes and grasses are fed to cattle predominantly. Water hyacinth in the dried form or as ensilage may be given as cattle feed due to high protein content in it. The amino acid profile of water hyacinth makes it suitable to be used as pig feed. When Water hyacinth is used as poultry feed, it increases the egg laying ratio and boosts cell mediated immunity of the poultry. Water hyacinth can be used as an alternative of soy bean meal in fish feed which reduces the feed cost. Water hyacinth is also used to make paper, cardboards, disposable cups, plates and variety of products for human use. Water hyacinth thus becoming potential raw material for making valuable products to meet the sustainable solution for water hyacinth management.

The FAO Penman-Monteith equation is used by the ETo calculator software to calculate reference evapotranspiration – ETo from meteorological data. FAO chose this technique as the standard because it is physically based, roughly approximates grass ETo in the studied region, and explicitly considers both physiological and aerodynamic aspects. The application can handle climate data on a daily, ten-day, and monthly basis. The data may be supplied in a variety of units, and it can be analyzed using regularly used meteorological parameters. In this study, ETo calculator version 3.2 is used to estimate the ETo in Bhavanisagar. The results revealed that, it is possible to estimate ETo by using the ETo calculator. The concept of the reference evapotranspiration was introduced in this study and the result obtained are used further to estimate the evaporative demand of the atmosphere independently of crop type, crop development and management practices.

Weather forecasting is an important subject in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. The present paper describes an empirical study for modeling and forecasting the time series of monthly rainfall patterns for Coimbatore, Tamil Nadu. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The best SARIMA models were selected based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) and the minimum values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The study has shown that the SARIMA (0,0,0)(2,0,0)12 model was appropriate for analysing and forecasting the future rainfall patterns. The Root Means Square Error (RMSE) values were found to be 52.37 and proved that the above model was the best model for further forecasting the rainfall.