This study was performed to further understanding of the variations in hydrology and rice crop productivity during different El Niño/Southern Oscillation (ENSO) events in the Cauvery River Basin of Tamil Nadu, India using the Soil and Water Assessment Tool (SWAT). The entire Cauvery Basin was divided into 301 sub-basins and further subdivided into 3,601 Hydrological Response Units (HRU). Based on the National Oceanic and Atmospheric Administration (NOAA) official website, information on El Niño (1972, 1982, 1987, 1991, 1997, 2002 and 2004) and La Niña (1970, 1971, 1973, 1974, 1975, 1988, 1998, 1999 and 2000) years were obtained. The SWAT model was continuously run from 1970 to 2008, and a composite for El Niño, La Niña and normal years was constructed to understand their influence on hydrology and rice crop productivity in the study area. From the analysis, it was clear that an El Niño episode is correlated with rainfall, hydrology and rice productivity in the Cauvery river basin. The validation of the SWAT model indicated the capability of SWAT to accurately predict stream flow and rice productivity. It was evident from the investigation that the quantum of rainfall was more during El Niño years with high inter-annual rainfall variability (809.3mm to 2,366mm) compared with La Niña and normal years. As a result, the soil water recharge, including percolation and soil water availability in the surface layers, was increased in the El Niño years. Simulated rice productivity over 39 years in the Cauvery Basin ranged between 1,137 and 7,865kgha−1 with a mean productivity of 3,955kgha−1. The coefficient of variation in rice productivity was higher during La Niña (21.4%) years compared with El Niño (14.7%) and normal years (14.6%). The mean rice productivity was increased in both El Niño and normal years, indicating the possibility of higher yields than those in La Niña years. An analysis of the hydrological data and rice productivity showed that the risk of failure was low during El Niño years compared with normal or La Niña years. This behavior could be utilized for forecasting rice crop productivity under different ENSO conditions and can provide information for policy makers when deciding on water allocation and import / export policies

An efficient analytical method was developed and validated using a modified QuEChERS method and LC-MS/MS for the detection and quantification of neonicotinoid insecticide residues in rice whole grain and rice straw. The samples were extracted using acetonitrile and cleaned up by dispersive solid-phase extraction. Validation based on five fortification levels showed good recoveries of neonicotinoids ranging from 75% to 116 % and 60% to 105 % for rice whole grain and straw, respectively. The precision ranged between 3% and 17 %, and 2% and 10 % for grain and straw, respectively. The limit of detection was from 0.007 to 0.0084 mg kg-1 and 0.005 to 0.15 mg kg-1 and the limit of quantification was in the range of 0.024-0.028 mg kg-1 and 0.016-0.051 mg kg-1 for rice whole grain and rice straw, respectively. Monitoring of farm gate samples indicated that, out of 24 samples, 1 rice whole grain sample was contaminated with thiamethoxam residues (0.07 mg kg-1).

Tamil Nadu in particular is a key rice-producing region in peninsular India. Hydrochemistry, viz., redox potential (Rh), soil temperature and dissolved oxygen (DO), of rice soils can determine the production of greenhouse gas methane (CH₄). In recent decades, the cultivation of crops organically became a viable option for mitigating climate change. Hence, this study aimed to investigate the effects of different organic amendments on CH₄ emission, Rh, DO, and soil and water temperature (T) in relation to the yield of paddy. The treatments composed of viz., control, blue-green algae (BGA), Azolla, farm yard manure (FYM), green leaf manure (GLM), blue-green algae + Azolla, FYM + GLM, BGA + Azolla + FYM + GLM, vermicompost and decomposed livestock manure. With the addition of BGA + Azolla, the highest reduction in CH₄ emission was 37.9% over the control followed by BGA. However, the same treatment had a 50% and 43% increase in Rh and DO, respectively, over the control. Established Pearson correlation analyses showed that the CH₄ emission had a positive correlation with soil (r = 0.880 **) and water T (r = 0.888 **) and negative correlations with Rh (r = −0.987 **) and DO (r = −0.963 **). The higher grain yield of 26.5% was associated with BGA + Azolla + FYM + GLM application. Our findings showed that there are significant differences in CH₄ emissions between different organic amendments and that hydro-parameters may be a more important controlling factor for methane emissions than temperature. The conclusion has been drawn based on valid research findings that bio-fertilization using BGA and Azolla is an efficient and feasible approach to combat climate change, as it assists in reducing methane emissions while simultaneously boosting crop yield by fixing nitrogen into the soil in the studied agro-climatic zone.