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.

Climate change is often linked with record-breaking heavy or poor rainfall events, unprecedented storms, extreme day and night time temperatures, etc. It may have a marked impact on climate-sensitive sectors and associated livelihoods. Block-level weather forecasting is a new-fangled dimension of agrometeorological services (AAS) in the country and is getting popularized as a climate-smart farming strategy. Studies on the economic impact of these microlevel advisories are uncommon. Agromet advisory services (AAS) play a critical role as an early warning service and preparedness among the maize farmers in the Parambikulam–Aliyar Basin, as this area still needs to widen and deepen its AWS network to reach the village level. In this article, the responses of the maize farmers of Parambikulam–Aliyar Basin on AAS were analyzed. AAS were provided to early and late Rabi farmers during the year 2020–2022. An automatic weather station was installed at the farmers’ field to understand the real-time weather. Forecast data from the India Meteorological Department (IMD) were used to provide agromet advisory services. Therefore, the present study deserves special focus. Social media and other ICT tools were used for AAS dissemination purposes. A crop simulation model (CSM), DSSAT4.7cereal maize, was used for assessing maize yield in the present scenario and under the elevated GHGs scenario under climate change. Our findings suggest that the AAS significantly supported the farmers in sustaining production. The AAS were helpful for the farmers during the dry spells in the late samba (2021–2022) to provide critical irrigation and during heavy rainfall events at the events of harvest during early and late Rabi (2021–22). Published research articles on the verification of weather forecasts from South India are scanty. This article also tries to understand the reliability of forecasts. Findings from the verification suggest that rainfall represented a fairly good forecast for the season, though erratic, with an accuracy score or HI score of 0.77 and an HK score of 0.60, and the probability of detection (PoD) of hits was found to be 0.91. Verification shows that the forecasted relative humidity observed showed a fairly good correlation, with an R² value of 0.52. These findings suggest that enhancing model forecast accuracy can enhance the reliability and utility of AAS as a climate-smart adaptation option. This study recommends that AAS can act as a valuable input to alleviate the impacts of hydrometeorological disasters on maize crop production in the basin. There is a huge demand for quality weather forecasts with respect to accuracy, resolution, and lead time, which is increasing across the country. Externally funded research studies such as ours are an added advantage to bridge the gap in AAS dissemination to a great extent.

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.

In order to accomplish economic, social, and environmental objectives, farm diversification is a significant feature in India’s agricultural systems and rural development. It is crucial to examine factors impacting farm productivity, such as socio, structural, and Circular Economy (CE) factors, in order to ensure the sustainability of varied farms. A decision to follow a farm circularity approach is required to increase agricultural production, return on investment, and achieve sustainability, so the current study was designed to move forward in identifying farm efficiency and their influencing drivers in production systems for the successful execution of circularity in future farms. The objective of this paper is to find the existing operational efficiency of farms, variables that impact a farm’s efficiency, the disparities in farm efficiency, and variables dominating farm efficiency. The efficiency behaviours of Indian farms operating in 2020–2022 were studied and regressed against the factors influencing farm performance. A two-step technique and survey data were employed. To assess variations in the relevance of drivers, an input-oriented Data Envelopment Analysis was employed; in the second stage, efficiency ratings were used as a dependent variable in a Tobit regression analysis. Explanatory variables for enhancing farm performance included social, structural, and Circular Economy (CE) drivers. The age of the farmer, total farm area, and dryland area as socio-structural variables, and reduce, recycle, and redesign as CE variables, have proven to affect the farm’s efficiencies significantly. Managerial implications in terms of technical, allocative, cost efficiency, and their drivers have been drawn from study findings. CE has been confirmed to be the strategy over which the farmer has complete control and has been found to have a more significant impact on farm efficiency. Social structural determents are second in terms of their impact. Farmers in the region have cushy access to inputs; however, they are not utilized to their maximum effect. Therefore, the study indicates that inefficient dryland farmers could increase their production by concentrating on a circular economy strategy.

Standard unit operations/equipment have not evolved for the traditional rice varieties of the Cauvery Deltaic region of Tamil Nadu. The fame of traditional rice is increasing nowadays owing to its health benefits. Non-standard unit operations may cause rice grains to crack during milling, accumulating more broken rice and yields in products of inferior quality. As a result, research into the physical properties of rice is crucial for the development of rice processing equipment that minimizes post-harvest losses during milling. Hence, an assessment was made to evaluate 30 traditional rice cultivars on their Physical (grain length, width, thickness, shape, and size), gravimetric (bulk, true, tapped density, porosity, Carr’s index, and Hausner ratio), and engineering characteristics (equivalent, arithmetic, square mean, and geometric mean diameter) using standard protocols, with the goal of reviving and preserving older varieties. The results from the analysis showed significant variations (p < 0.05) between all properties that were evaluated. According to length, a substantial amount of traditional rice varieties were long grain (76.7%), whereas (16.7%) belonged to the medium type and (3.3%) were short-grain types, respectively. There were variations among the three different categories of local rice grains when it comes to size, ranging from 3.26 to 4.69 mm for arithmetic mean diameter, 2.84 to 4.00 mm for geometric mean diameter, and 3.02 to 4.28 mm for square mean diameter, respectively. Sphericity, aspect ratio, and surface area measurements of the samples varied from 37.7% to 81.2%, 0.26 to 1.00, and 25.4 to 50.1 mm², respectively. Of the 30 varieties, 28 were under the high amylose category, and 2 belonged to the intermediate type. The Pearson correlation was established to study the interrelationships between the dimensions and engineering properties. Principal component analysis (PCA) reduced the dimensionality of 540 data into five principal components (PC), which explained 95.7% of the total variance. These findings suggest that it is possible to revive old landraces through careful selection and analysis of these properties. The superior characteristics of these traditional varieties can be further evaluated for breeding programs in order to improve the cultivation of these cherished rice landraces to enhance nutritional security.