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.

Intercropping is a sustainable, eco-friendly, and economically beneficial cropping system. Elephant foot yam (EFY), a multifarious long-duration vegetable, takes 60 days or more to spread its canopy. Hence, this research assessed the impact of intercropping short duration vegetables, viz., cluster bean, radish, Amaranthus, and fenugreek, in elephant foot yam for two seasons (2021 and 2021/22). It included the analysis of parameters such as carbon accumulation, soil chemical properties, nutrient, enzyme, and microbial activities. The findings revealed that for both the seasons there was a significant (p < 0.01) rise in all the parameters examined in the intercropping patterns. Cluster bean (legume) outperformed the other intercrops utilised. Overall, carbon accumulation was improved by 54.40% when cluster beans were intercropped in EFY. Cluster bean intercropping increased the microbial and enzyme activities in the soil rhizosphere and improved soil organic carbon, microbial biomass carbon, nitrogen, phosphorus, and potassium by 31, 42, 28, 37, and 11%, respectively, compared to the sole crop. A positive correlation was observed between the soil microbes and enzyme activity with the soil chemical properties. As a result, the research concludes that intercropping cluster bean in EFY promotes carbon accumulation, soil nutrients, enzymes, and microbial community, which, in turn, favour the productivity of the elephant foot yam.

The change in air temperature will influence the insect behavior, physiology and population dynamics of insects as they are poikilothermic. Global temperature has increased by 1.09°C during 2001-2020 when compared to 1850-1900. It is projected that the temperature will further increase by 5.7°C under high emission scenario, if mitigation strategies are not adopted. Increase in temperature would affect the physiology and population dynamics of the insects. A study was undertaken to understand the effect of different temperature regimes on rice leaffolder. Survival fraction decreased with increasing temperatures as more successful development was at lower temperature regimes. Apparent mortality increased towards the higher temperature regimes as the insects cannot tolerate high temperature stress. The Mortality Survivor Ratio (MSR) revealed that the population increase would be more at higher temperature regimes as the MSR remained higher at higher temperature regimes. The Indispensable Mortality was lesser under higher temperature regimes as the number of adults emerged in the high temperature regime was less. Generally, K - values increased with increasing temperature. It indicates that the insects which happened to live under higher temperature regimes were reproduction oriented as most of the energy was spent in reproduction rather than for living longer time.

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.