Rice is a water-guzzling crop cultivated mostly through inefficient irrigation methods which leads to low water use efficiency and many environmental problems. Additionally, the export of virtual water through rice trading and the looming water crisis poses significant threats to the sustainability of rice production and food security. There are several alternative rice production methods to improve water use efficiency. These include aerobic rice, direct-seeded rice (DSR), alternate wetting and drying (AWD), saturated soil culture (SSC), drip-irrigated rice, a system of rice intensification (SRI), and smart irrigation with sensors and the Internet of Things (IoT). However, each method has its own advantages and disadvantages. For example, drip-irrigated rice and IoT-based automated irrigation are not feasible for poor farmers due to the high production costs associated with specialized machinery and tools. Similarly, aerobic rice, drip-irrigated rice, and the SRI are labor-intensive, making them unsuitable for areas with a shortage of labor. On the other hand, DSR is suitable for labor-scarce areas, provided herbicides are used to control weeds. In this article, the suitability of different water-saving rice production methods is reviewed based on factors such as climate, soil type, labor, energy, and greenhouse gas emissions, and their prospects and challenges are evaluated. Additionally, the article examines how cultural practices, such as seed treatment, weed control, and nutrition management, contribute to enhancing water use efficiency in rice production.

Adopting sustainable agricultural practices that enhance productivity while preserving ecosystem services is essential to ensure food security for a growing global population and address environmental challenges. This review examines the impact of legume intercropping on nitrogen (N) fixation, soil physio-chemical properties, water retention, pest and disease control, and crop yield across diverse agro-climatic zones and cropping systems. The findings consistently demonstrate that integrating legumes into the cropping system improves soil health by reducing bulk density, breaking up hardpan layers, reducing erosion, increasing soil organic matter, and fixing atmospheric nitrogen (~125 kg N/ha/season) reducing the need for inorganic N fertilizers. It boosts crop yields by 30–35% (in terms of main crop equivalent yield) and land productivity per unit area and time, mitigates total crop loss, and promotes biodiversity. It also improves water use efficiency by 20–25% and enhances nutrient use efficiency by 25–30%. Additionally, legume intercropping reduces yield losses from pests and diseases by 20–25% compared to sole cropping systems. The practice bolsters crop resilience through ecological processes like bio-littering, bio-ploughing, bio-irrigation, and bio-pumping (the “4Bs”), which are valuable for adapting to climate variability. However, research gaps remain, particularly in the optimal selection of legume species for specific regions, suitable agronomic practice for each system, and addressing socio-economic barriers to widespread adoption.

Land degradation and climate change, two intricately intertwined phenomena, demand appropriate management solutions to effectively tackle the escalating issues of food and nutritional security. In this context, the realm of agriculture confronts formidable challenges in its pursuit of soil resource reclamation, improving water quality, mitigating climate change, and maintaining soil and natural resources for posterity. Central to these aspirations is the preservation of an optimum organic matter, serving as a linchpin threshold is crucial for protecting the physical, chemical, and biological integrity of the soil, while simultaneously sustaining agricultural productivity. To address these multifaceted challenges, the introduction of diverse organic amendments has emerged as a crucial strategy. Noteworthy among these is the application of biochar, which functions as a soil conditioner capable of bolstering soil health, mitigating the impact of climate change, and securing global food security. Biochar is a carbon-enriched substance produced through pyrolysis of assorted biomass waste. It has a larger surface area, higher cation exchange capacity, and an extended carbon storage capability. The strategic integration of biochar production and subsequent soil application engenders an array of benefits, encompassing the amelioration of soil physical properties, augmented retention and the availability of nutrients, and the enhancement of biological activity, resulting in higher agricultural yields and societal benefits through the curtailment of soil to atmosphere greenhouse gas emissions. Additionally, biochar demonstrates its efficacy in the realm of environmental restoration by serving as a medium for extraction and elimination of heavy metals, which often pervade aquatic ecosystems and soil matrices. This review addressed the need for biochar production, characterization, soil health, the possibility for environmental restoration, and crop yield fluctuations owing to climate change.

The growing demand for food grains amidst resource constraints necessitates advancements in crop management. Artificial intelligence, particularly machine learning and deep learning, is revolutionizing agricultural practices by enabling data-driven, precise, and sustainable solutions. This review synthesizes advancements in artificial intelligence applications across key domains, including crop yield prediction, precision irrigation, soil fertility mapping, insect pest and disease forecasting, and foodgrain quality assessment. Artificial intelligence algorithms efficiently process vast datasets from unmanned aerial vehicles, ground vehicles, and satellites, enabling precise and timely interventions. Artificial intelligence-driven tools automate pest detection and classification, optimize irrigation with minimal human input, generate high-resolution soil fertility maps, and enhance foodgrain quality assessment through rapid defect and contaminant detection. Artificial intelligence-powered precision irrigation integrates real-time soil moisture data and weather predictions for optimized water usage. Similarly, artificial intelligence-driven soil fertility mapping not only enables high-resolution assessments but also facilitates real-time monitoring of nutrient dynamics, supporting sustainable land management. In pest and disease detection, artificial intelligence systems combining image processing and real-time analytics demonstrate promise for early intervention. Artificial intelligence integration into foodgrain quality assessment leverages hyperspectral imaging and predictive models to enhance grading, adulteration detection, and contaminant screening, contributing to food safety and market competitiveness. Furthermore, advancements in transfer learning and data augmentation have improved artificial intelligence adoption in regions with limited datasets. While artificial intelligence technologies promise to boost agricultural productivity and sustainability, their efficacy and scalability hinges on data quality, diversity, and availability. Graphical Abstract