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.

Rainfall is critical to agricultural and drinking water supply in the Thamirabharani river basin. The upper catchment areas of the Thamirabharani basin are located in high-elevated forest regions, and rainfall variability affects dam inflow and outflow. The well-known methods for rainfall analysis such as the coefficient of variation (CV), the precipitation concentration index (PCI), and trend analysis by Mann-Kendall and Sen’s slope test, as well as the Sen’s graphical innovative trend method (ITA) recently reported in several studies, were used. Rainfall data from gauge stations and the satellite-gridded Multisource Weighted Ensemble Precipitation (MSWEP) dataset were chosen for analysis at the annual and four-season time scales, namely, the Southwest Monsoon, Northeast Monsoon, winter, and summer seasons from 1991 to 2020. The mean annual PCI value reflects irregular monthly rainfall distribution (PCI > 20) in all gauge stations. The spatial monthly rainfall distribution of PCI values remarkedly shows a moderate distribution in the western and an anomalous distribution in the eastern part of the basin. The annual mean rainfall ranges from 718.4 to 2268.6 mm/year, decreasing from the high altitude zone in the west to the low plains and coastal regions in the east. Seasonal rainfall contributes about 42% from the NEM, 30.6% from the SWM, 22.8% from summer, and 3.9% from winter, with moderate variability (CV less than 30%). Ground stations experienced extremely high interannual variability in rainfall (more than 60%). Trend analysis by the MK, TFPW-MK, and ITA methods shows increasing annual rainfall in the plains and coastal regions of the basin; particularly, more variations among the seasons were observed in the Lower Thamirabharani sub-basin. The NEM and summer season rainfall are statistically significant and contribute to the increasing trend in annual rainfall. The ITA method performed better in the annual and seasonal scale for detecting the rainfall trend than the MK and TFPW-MK test. The Lower Thamirabharani sub-basin in the eastern part of the basin receives more rain during the NEM than in other areas. To summarize, the low plains in the central and coastal regions in the southeast part experience an increase in rainfall with irregular monthly distribution. This study helps farmers, governments, and policymakers in effective agricultural crop planning and water management.

Climate change is the most serious problem of the last two centuries. It is being observed as a silent threat to global food production, leading to food insecurity for the burgeoning population. Rice is an important food crop and has also been identified as sensitive and highly vulnerable to climate change. Global rice production is affected by climate change and will soon be seen as a food security threat. Climatic factors like temperature, rainfall, wind speed, relative humidity, and solar radiation significantly impact physiological, biochemical and morphological traits, eventually resulting in a decline in yield. The whole process is discussed in this review. Several previous studies focused more on the impact of climate change on the productivity and production of crops without paying any or less attention to the vulnerability of value chain actors to climate risks and climate-related losses in the value chain. The climate risk management by value chain approach establishes connections between input suppliers, farmers, processors, retailers and consumers, identifying risks and formulating adaptation and mitigation strategies at every stage across the value chain. The identified appropriate strategy from the review, including climate-resilient rice varieties, conservation agricultural practices, climate-smart cultivation and water management techniques, could reduce the impact of climate change and enhance food security.

The impact of climate change on methane (CH₄) emissions from rice production systems in the Coimbatore region (Tamil Nadu, India) was studied by leveraging field experiments across two main treatments and four sub-treatments in a split-plot design. Utilizing the closed-chamber method for gas collection and gas chromatography analysis, this study identified significant differences in CH₄ emissions between conventional cultivation methods and the system of rice intensification (henceforth SRI). Over two growing seasons, conventional cultivation methods reported higher CH₄ emissions (range: from 36.9 to 59.3 kg CH₄ ha⁻¹ season⁻¹) compared with SRI (range: from 2.2 to 12.8 kg CH₄ ha⁻¹ season⁻¹). Experimental data were subsequently used to guide parametrization and validation of the DeNitrification–DeComposition (DNDC) model. The validation of the model showed good agreement between the measured and modeled data, as denoted by the statistical tests performed, which included CRM (0.09), D-index (0.99), RMSE (7.16), EF (0.96), and R² (0.92). The validated model was then used to develop future CH₄ emissions projections under various shared socio-economic pathways (henceforth SSPs) for the mid- (2021–2050) and late (2051–2080) century. The analysis revealed a potential increase in CH₄ emissions for the simulated scenarios, which was dependent on specific soil and irrigation management practices. Conventional cultivation produced the highest CH₄ emissions, but it was shown that they could be reduced if the current practice was replaced by minimal flooding or through irrigation with alternating wetting and drying cycles. Emissions were predicted to rise until SSP 370, with a marginal increase in SSP 585 thereafter. The findings of this work underscored an urgency to develop climate-smart location-specific mitigation strategies focused on simultaneously improving current water and nutrient management practices. The use of methanotrophs to reduce CH₄ production from rice systems should be considered in future work. This research also highlighted the critical interaction that exists between agricultural practices and climate change, and emphasized the need to implement adaptive crop management strategies that can sustain productivity and mitigate the environmental impacts of rice-based systems in southern India.

In this study, an extensive exploration survey of wild progeny was conducted which yielded 18 candidate plus trees (CPTs) of Terminalia bellerica. Seeds of these CPTs were collected from diverse locations between 10°54′ and 28°07′ E longitude, and 76°27′ and 95°32′ N latitude, covering 18 different locations across 5 states of the Indian subcontinent. The objective of the progeny trial was to assess genetic associations and variability in growth and physio-chemical characteristics. Significant variations (p < 0.05) were observed among the growth traits, encompassing plant height, basal diameter, girth at breast height and volume, as well as physio-chemical characteristics such as leaf length, width, area and chlorophyll content, carotenoids, and protein in the progeny trial. Broad-sense heritability (h²_b) estimates were consistently high, exceeding 80% for all growth and physiological related traits under investigation except for plant height, leaf length, and girth at breast height. A correlation study revealed that selecting based on plant height, leaf area, and girth at breast height effectively enhances T. bellerica volume. A moderate genetic advance in percent of the mean (GAM) was observed for most traits, except leaf length, leaf width, girth at breast height, and plant height. Across all 13 traits, phenotypic coefficient of variation (PCV) surpassed genotypic coefficient of variation (GCV). Utilizing principal component analysis (PCA) and dendrogram construction categorized the genotypes into seven distinct groups. In conclusion, the study has demonstrated that targeting girth at breast height and plant height would be a highly effective strategy for the establishment of elite seedling nurseries and clonal seed nurseries for varietal and hybridization programs in the future.