We investigated the effect of practically realizable doses of silicate on arsenic (As) uptake by differential-As-accumulating rice cultivars grown on geogenically As-polluted soil. The possible health risk from the dietary ingestion of As through rice was also assessed. In addition, a solution culture experiment was conducted to examine the role of root-secreted weak acids in differential As acquisition by rice cultivars. When grown without silicate, Badshabhog accumulated a much smaller amount of As in grain (0.11 mg kg⁻¹) when compared to the other three varieties. Satabdi, IR-36, and Khitish accumulated As in grain beyond the permissible limit (0.2 mg kg⁻¹) for human consumption. The application of silicate effectively reduced the As content in the grain, husk, and straw of all of the cultivars. The grain As content fell to 17.2 and 27.6% with the addition of sodium metasilicate at the rates of 250 and 500 mg kg⁻¹, respectively. In the case of Khitish, the grain As content was brought down within permissible limits by the applied silicate (500 mg kg⁻¹). The integrated use of low-As-accumulating cultivars and silicate has great potential to reduce the public health risks associated with As. A positive correlation between root-secreted total weak acid and grain As content could explain the different rice cultivars’ differential As acquisition capacity.

Artificial intelligence (AI) is highlighted as a crucial enabler of a circular agriculture economy. Utilising advanced data analytics and predictive modeling, AI allows precise monitoring, analysis, and optimisation of various agricultural processes. AI-driven predictive analytics further optimise resource allocation, reducing the overuse of fertilisers, pesticides, and water while maximising yields and profitability. Furthermore, AI-powered supply chain management enhances traceability and transparency, enabling stakeholders to identify inefficiencies, mitigate risks, and minimise food loss and waste. Additionally, AI facilitates the valorisation of agricultural by-products and waste through innovative recycling and upcycling initiatives, transforming organic waste into valuable inputs such as biofuels, fertilisers, and animal feed. Overall, this chapter delves into the transformative potential of AI-driven circular economy approaches within the agricultural sector, emphasising the critical need for waste reduction, resource optimisation, and enhanced efficiency.

West Bengal is the largest rice (Oryza sativa L) producing state of India. In spite of leading the world in rice production, India lags behind in terms of rice productivity. In view of limited information, a pot experiment was conducted at ICAR-IARI, New Delhi during kharif, 2018-19 to evaluate the response of important rice cultivars to applied silicate. The experiment was conducted with four rice cultivars (one indigenous aromatic and rest three HYVs) and three doses of sodium metasilicate (0, 250 and 500 mg/kg) using an alluvial Inceptisol collected from West Bengal. Results indicated that the grain and straw yield was increased up to 40.7 and 18.1%, respectively, due to application of silicate. Rice cultivars, on the basis of grain yield, can be arranged in the order: Khitish (20.8 g/ pot)> Satabdi (17.6 g/pot) > IR-36 (15.4 g/pot) >Badshabhog (3.72 g/pot). The highest and the lowest straw yield was recorded with Badshabhog (70.1 g/pot) and IR-36 (14.6 g/pot), respectively. Rice cultivars showed differential responses in terms of yield to applied silicate. It can be concluded that application of sodium metasilicate can be one of the effective options for enhancing rice yield in the alluvial Inceptisol of West Bengal.

Aims: To model the concentration variation of PM_2.5 and PM₁₀ in selected locations of Delhi. Study Design: ARFIMA-GARCH model. Place and Duration of Study: The study was conducted by using daily (24 hour interval) data of PM_2.5 and PM₁₀ concentration from three air quality monitoring stations of Delhi namely, Narela, Okhla Phase II and Pusa. Methodology: The ARFIMA model is applied as the mean model and the GARCH model as the variance model. Results: The selected series are stationary and exhibit the presence of long memory in the mean structure. Due to the presence of long memory in mean, the ARFIMA model is applied. The residual series have conditional heteroscedasticity. Hence, the GARCH model is applied as a variance model. The fitted models are validated using RMSE, MAE and MAPE. Conclusion: The concentration variation of PM_2.5 and PM₁₀ followed long memory process in mean structure. ARFIMA-GARCH model satisfactorily explained the variation of concentration.