Increase in global warming poses a severe threat on agricultural production thereby affecting food security. A drastic reduction in yield at elevated temperature is a resultant of several agro-morphological, physiological and biochemical modifications in plants. Heat tolerance is a complex mechanism under polygenic inheritance. Development of tolerant genotypes suited to heat extremes will be more advantageous to tropical and sub tropical regimes. A clear understanding on heat tolerance mechanism is needed for bringing trait based improvement in a crop species. Heat tolerance is often correlated with undesirable traits which limits the economic yield. In addition, high environmental interactions coupled with poor phenotyping techniques limit the progress of breeding programme. Recent advances in molecular technique led to precise introgression of thermo-tolerant genes into elite genetic background which has been reviewed briefly in this chapter.

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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.

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.

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.