Assessment of soil fertility is essential to help identify strategies for sustainable agricultural production systems that decrease the negative environmental impact. The objective of this research study is to carry out a preliminary assessment of soil fertility status to adopt climate-smart agriculture to address the climate change challenges that adversely affect crop productivity and livelihoods of the farming community. The research was carried out in Mambattu village, Maduranthakam block of Kanchipuram district, Tamil Nadu. A systematic set of twenty geo-referenced soil samples were collected from the study village using GPS (Global Positioning System) and analysed for pH, EC, available macro, secondary and micronutrients to develop a credible soil fertility index (SFI). The preliminary fertility data of Mambattu village revealed that the pH of soil samples varied from acidic to alkaline with about 40% as neutral while the electrical conductivity showed non-saline and medium status of Organic Carbon (OC). The soil samples were predominantly sufficient in N and some micro nutrients (Fe, Mn), while medium in S and B and deficient in P, K, Ca, Mg, Zn and Cu. Results from initial studies indicate that practices like site specific nutrient management, green manuring, use of organic inputs, use of integrated pest management, seed treatment etc., have a high potential for implementing climate-smart agricultural technologies. Soil fertility evaluation can be an efficient tool to improve soil health which can positively impact crop productivity and be one of the important climate-smart technologies practices adopted by the farmers.

The burning of crop residues (traditionally called ‘Parali’) has recently become a hot topic in India because it is presumed to be one of the reasons for abnormally high levels of air pollution in New Delhi, the capital city of India, after harvesting of Kharif crops during winter months. During the process of finding out a feasible solution for quick disposal of agricultural waste in a safer way, a novel method has been developed by the authors in which crop residue is converted into a useful product, biochar, which can be applied back to the fields for amendment of soil. It not only reduces the introduction of harmful gases into the environment but also improves the physical and chemical properties of the soil. This method is very simple and can be adopted by an individual farmer without much investment and technical skills. Many studies have been conducted on the factors involved in the production and use of biochar as a soil amendment; but in India, not much work has been carried out yet, as it is relatively a new concept in terms of using biomass for biochar production and application. Although biochar is not a new product, it has drawn the attention of researchers and other stakeholders in the near past because of its usefulness in improving the physical and chemical properties of the soil and at the same time reducing greenhouse gas emission, which is one of the biggest challenges for the modern world to protect the environment.

Due to the very coarse spatial resolution of the different global circulation model (GCM), we cannot use them in their natural form to study the various impacts of climate change. For matching this spatial inequality between the GCMs output (predictor) and historical precipitation data (predictands), we need to establish a relation between them which is known as downscaling. In the present study, we tried to examine the efficiency of the Artificial Neural Network (ANN) with Principal Component Analysis (PCA) for downscaling the rainfall for 3 districts of Andhra Pradesh of India. Firstly, for all the regions, the downscaling was performed by using ANN. Then seasonal and annual analysis was performed based on the R2 and RMSE. The results show that the ANN worked adequately based on the statistical parameters. The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, re-analysis from the National Centre for Environmental Prediction (NCEP) as GCM model, and observed rainfall data as the observed rainfall. The analysis was performed for the three RCPs scenario, RCP 2.6, 4.5 and 8.5. Finally, the ANN model is applied to downscale the precipitation.